co capture by aqueous absorption summary of 4th quarterly progress reports...

CO2 Capture by Aqueous Absorption

Summary of 4th Quarterly Progress Reports 2007

Supported by the Luminant Carbon Management Program and the

Industrial Associates Program for CO2 Capture by Aqueous Absorption

by Gary T. Rochelle Department of Chemical Engineering

The University of Texas at Austin January 31, 2008

1. Introduction This research program is focused on the technical obstacles to the deployment of CO2 capture and sequestration from flue gas by alkanolamine absorption/stripping and on integrating the design of the capture process with the aquifer storage/enhanced oil recovery process. The objective is to develop and demonstrate evolutionary improvements to monoethanolamine (MEA) absorption/stripping for CO2 capture from coal-fired flue gas. The Luminant Carbon Management Program and the Industrial Associates Program for CO2 Capture by Aqueous Absorption support 12 graduate students. Eleven of these students have prepared detailed quarterly progress reports for the period October 1, 2007 to December 31, 2007.

2. Conclusions 3.1 With the treatment of samples by 2.5 M NaOH for 24 hours, we have shown that the MEA amide of formate is 1 to 3 times the concentration of formate and the MEA amide of oxalate is as much as 10 times greater that oxalate itself. 3.2 Piperazine is resistant to oxidative degradation with catalysis by Fe++. 4.1 Piperazine does not degrade in 8 weeks at temperatures up to 150oC. 4.2 MEA readily degrades at elevated temperature, as a function of CO2 loading, temperature, and amine concentration. Its degradation can be mostly avoided by stripper operation at 100-110oC. 4.3 Piperazine readily degrades in MEA/PZ at elevated temperatures. 4.4 MDEA, ethylenediamine, diethylenetriamine, and AMP degrade at 135oC in loaded solutions. 5.1 10 m PZ does not oxidize significantly with catalysis by 10 ppm Fe++, 30 ppm Cr++, and 10 ppm Ni++. 5.2 10 m PZ oxidizes at rates comparable to MEA with 250 ppm Cu++. 5.3 The viscosity of loaded PZ is an exponential function of amine concentration. At 25oC the viscosity of loaded, 8 m PZ is about 20 cP. 5.4 Loaded PZ is resistant to thermal degradation at 150oC with up to 15 m PZ in continuing experiments at 5 weeks.

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6.1 The solubility of K2SO4 in loaded, aqueous MEA has bee correlated with the electrolyte-NRTL model in AspenPlus®. 7.1 The apparent activity coefficient of PZ in MDEA/PZ varies from decreases from 0.008 to 0.0015 as the CO2 increases to form 0.0 to 1 mole/mole PZ. 7.2 The apparent activity coefficient of MDEA in lean solutions is 0.12 to 0.27. 8.1 The gas film coefficient of the wetted wall column was measured by CO2 absorption in unloaded 2 m PZ. 9.1 An increase in viscosity from 1 cP to 5 cP had little effect on pressure drop or liquid holdup with Sulzer Mellapak 250Y structured packing.

3. Oxidative Degradation of Amines by Andrew Sexton

During the past quarter, the presence of previously undetected amides in degraded MEA solutions is a key finding. In both the low and high gas flow degradation apparatus, formate concentration increased by anywhere from a factor of 2 to 4, while oxalate concentration typically increased by a factor of 10. This means that N-formyl MEA is present in solution in similar or greater quantities than formic acid, and oxalic acid amide concentration is an order of magnitude greater than oxalic acid concentration in the analyzed degraded amine solutions. Acetic acid amide and glycolic acid amide are present as well, but in extremely low concentrations.

Long-term experiments performed in the high gas flow degradation apparatus are inconclusive at this point. Copper catalyzed solutions favor the production of NOx, while iron catalyzed solutions favored the production of CO. The average NH3 production rate was similar for both experiments, although steady-state concentrations differed. Furthermore, the high formate production rate (in the form of formate and N-formyl MEA) obtained from IC analysis from the MEA/Fe/Cu experiment suggests that the material balance might be closer than previously thought.

The aqueous piperazine experiment performed in the low gas flow degradation apparatus suggests that iron has a negligent effect on catalyzing oxidative degradation. Therefore, it may be more advisable to minimize iron concentration in PZ solutions to inhibit corrosion, rather than adding Cu to inhibit corrosion and Inhibitor A to inhibit degradation.

4. Thermal Degradation by Jason Davis

Piperazine has been shown to have little, if any thermal degradation by cation chromatography, HPLC, pH titration, and NMR analysis at standard stripper conditions, 120oC - 135oC, over an 8-week time span. As a comparison, an 11 m MEA solution with a loading of 0.5 mol CO2/mol amine exhibits a loss of more than 60 wt % MEA at 135oC after 8 weeks.

5. Degradation of ROC20 by Stephanie Freeman

An investigation into the oxidative and thermal degradation of ROC20 (10 m PZ) solutions has been undertaken this quarter. Preliminary results suggest that thermal degradation of ROC20

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solutions is negligible at 135 and 150°C. In addition, oxidative degradation of ROC20 is negligible in the presence of trace amounts of chromium (30 ppm), nickel (10 ppm), and iron (10 ppm). Copper actively catalyzes the oxidative degradation of ROC20 solutions producing formate and ethylenediamine as the main detected degradation products. The increased viscosity of ROC20 solutions has also been initially investigated and will continue to be a research topic of interest through the next quarter.

6. Reclaiming by Crystallization of Potassium Sulfate by Qing Xu

One side reaction in CO2 capture when using MEA/PZ is the generation of sulfate from SO2. This sulfate has to be removed so that the MEA/PZ solution can be reused for CO2 capture. Potassium compounds can be used in the removal of sulfate. In order to determine how best to accomplish this, the solubility of potassium sulfate was measured with variable MEA/PZ concentration and CO2 loading. Solubility measurement was conducted at temperatures from 25ºC to 80ºC. A model predicting experimental Ksp was developed, with equivalent amine concentration, temperature, and ionic strength as the variables. Based on the previous work of Hilliard (2005), new interaction parameters were regressed in this work to match Söhnel’s data (1985) in water and the experiment data by Xu. The regression was done using Data Regression System in Aspen Plus®. An interaction parameter set for CO2-MEA-H2O-K+-SO4

= system in electrolyte-NRTL model was developed. The parameter set was tested by a series of flash simulations. The parameters can simulate the interactions between ion pairs and molecules within certain condition ranges, but still needs further modification.

7. Thermodynamics of MDEA/PZ by Bich-Thu Nguyen

This work explores the volatility of MDEA and PZ in blends of varying amine concentrations at 40ºC and 60ºC. Amine volatilities are reported in terms of partial pressures and are further analyzed in terms of their apparent activity coefficients as a function of loading, temperature, and amine concentration. PZ partial pressure is found to decrease steadily as CO2 loading, as mol CO2/mol PZ, is increased. Furthermore, as expected, this partial pressure increases with temperature and PZ concentration. The apparent PZ activity coefficient behaves the same as the partial pressure with respect to all the experimental variables mentioned. Conversely, the apparent MDEA activity coefficient is seen to be higher with respect to the lower experimental temperature (40ºC) for reasons that need further investigation.

8. Rate Measurements of 7 m MEA and a Preliminary Gas Film Mass Transfer Correlation for the Wetted Wall Column by Ross E. Dugas

A variety of modifications and fixes were made to the wetted wall column system since the previous reporting period. Some of the previously obtained rate data may be inaccurate due to a leaky needle valve. Rate data from Aboudheir (2002) using highly concentrated, highly loaded monoethanolamine (MEA) was matched to literature data during my work with IFP in France. Recently obtained rate data agrees with Aboudheir for 7 m MEA at high loading. Wetted wall column obtained rates at low CO2 loading are still significantly higher than rates reported by

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Aboudheir with his laminar jet absorber. A new regression for a gas film mass transfer coefficient correlation for the wetted wall column did produce a slightly different correlation than produced by Pacheco (1998). The new correlation does not explain the rate discrepancy at low CO2 loading. The rate discrepancy remains unexplained.

9. Influence of Liquid Properties on Effective Mass Transfer Area of Structured Packing by Robert Tsai (Also supported by the Separations Research Program}

The hydraulic performance (pressure drop and hold-up) of Sulzer Mellapak 250Y structured packing was compared under baseline (i.e., water) and moderately viscous conditions (approximately 5.5 mPa·s, achieved using high molecular weight poly(ethylene oxide) (POLYOXTM WSR N750). Pressure drop was nearly the same for the majority of tested liquid loads, and hold-up values appeared to be consistently higher (albeit only by ~1%) for the viscous solution. The effective mass transfer area of Mellapak 250Y was measured as a function of liquid load via CO2 absorption into dilute caustic solution. Experiments were conducted at enhanced viscosities (approximately 2.5 and 5 mPa·s). The data at 5 mPa·s showed the mass transfer areas to be surprisingly low, with fractional areas ranging from 0.26–0.36—around 3 times lower than in the base case. The experiment at 2.5 mPa·s was subsequently conducted, and oddly, the measured areas were practically identical to the 5 mPa·s data. This suggested that the results were being influenced by a systematic error. Extensive troubleshooting revealed that the vacuum pump at the outlet sample line was leaking in ambient air, thereby causing the apparent CO2 removal—and hence, mass transfer performance—to be falsely low.

10. CO2 Absorption Modeling Using Aqueous Amines by Jorge M. Plaza

Work in this period has focused on the development of an absorber model that includes the new thermodynamic model by Hilliard (2007), recent observations on properties of this system by Dugas (2007), and a simpler set of reactions to represent MEA-CO2 absorption kinetics. Forward rates have been extracted from Aboudheir’s (2002) kinetic data by modeling his laminar jet apparatus using Aspen Plus® RateSep™. Results show considerable differences between hand calculated values and the Aspen Plus® values. Further work needs to be carried out to determine source of differences and to assure proper modeling of the laminar jet.

11. Modeling Stripper Performance for CO2 Removal by David Van Wagener This quarter focused on two tasks. The first was creating an Aspen Plus® simulation to verify a recent pilot plant run with 35% MEA using a new thermodynamic model developed by Hilliard. The initial simulation using pilot plant data yielded a close prediction of performance, but the temperatures were not accurate. The new VLE model should be able to accurately predict temperatures in the system, so it is likely that some of the measured values were inaccurate. Regressions are being investigated to reconcile the differences. One regression was done focusing on the reboiler section, and it was able to narrow the gap for the temperatures in that section. However, the other predictions were consequently more incorrect, including the

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performance. The second stripper task was to investigate flashing stripping as a means of using solar energy to provide the heat for liberating CO2. The selected solvent was 7 m MEA, and three flashes were used to adiabatically decrease the temperature and pressure and release vapor. The simulation was debugged, and it is now capable of simulating the configuration. By varying the temperature step between each flash, the lean loading result could be matched to the lean loading input of the absorber model for the same solvent system. In the future, the maximum temperature in the heater and the lean loading will be varied to minimize the total equivalent work.

12. Dynamic Operation of CO2 Capture by Sepideh Ziaii Fashami

Coal-fired power plants generate electricity at base load and could run CO2 capture continuously at a constant level of CO2 removal. The peak load of electricity demand during the day is largely met by natural gas power plants at a much higher price relative to non-peak load time. To run CO2 capture at a lower energy cost and to meet a portion of peak load by coal-fired power plants, we propose to turn off CO2 capture or run it at a percentage of its full capacity during the peak load. In this case, either the entire capture plant or part of it, depending on the implemented dynamic strategy, is expected to operate at different modes and transient operations during a day. In this work, at first the strategy of electricity supply in Texas is presented and secondly different dynamic strategies of CO2 capture operation and possible control configurations for controlling the stripper combined with the letdown steam turbine and the compressor are proposed.

13. Analysis of CO2 Capture Systems in the Dynamic Electric Grid by Stuart Cohen

A review of techno-economic studies of CCS revealed that most work done thus far consists of single plant studies or macro-scale energy analyses. Thus, an intermediate approach will be taken that incorporates a more realistic grid level analysis that considers variation in electricity supply and demand. Investigation will focus on the effects of variable CO2 capture efficiency or on/off operation on overall electric system performance, economics, and environmental effects. Varied or on/off operation of CO2 capture may eliminate the need for makeup generation capacity for the energy requirement of CCS. If CO2 capture is turned off when electricity prices are high, the revenue acquired during these times may decrease the implementation costs of CCS. Using varied CO2 capture operation will be analyzed in several scenarios of varying electricity demand and supply, where dynamic supply can result from renewable resources which offer limited controllability. Further study will examine the technical limitations of variable CO2 capture operation at the system level, possibly considering effects on other generation sources, transmission hardware, and a CO2 transport and storage network.

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Oxidative Degradation of Amines Quarterly Progress Report

October 1, 2007 – December 31, 2007

by

Andrew Sexton

Supported by the Industrial Associates Program in CO2 Capture

and the

Luminant Carbon Management Progman

Department of Chemical Engineering

The University of Texas at Austin

January 31, 2008

Introduction This effort is an extension of work by George Goff on the oxidative degradation of MEA. Goff showed that oxidative degradation, under high catalyst conditions, is mass-transfer limited by the physical absorption of O2 into the amine and not by reaction kinetics. Goff also theorized that the oxidative degradation of MEA produced volatile ammonia as well as a host of other degradation products. The major liquid-phase degradation products among these include the heat stable salts of carboxylic acids, nitrite, and nitrate.

The oxygen stoichiometry necessary to produce these degradation products varies for each individual component; overall, it varies anywhere from 0.5 to 2.5 (Goff, 2004). It is believed that the particular degradation products are specific to certain metal catalysts present in the absorption/stripping system – specifically iron and copper. For example, the following balanced reactions illustrate the differences in oxygen consumption based upon the end products:

MEA + 1.5 O2 2 Formate + Ammonia

MEA+ 3.5 O2 2 Formate + Nitrate + Water

MEA + O2 Glycolate + Ammonia

Goff’s work on MEA degradation was limited to analyzing MEA degradation rates via the evolution of NH3. The ammonia evolution rates were measured using a Fourier Transform Infrared (FTIR) analyzer.

This effort extends Goff’s gas-phase analysis by applying various methods of liquid-phase analysis, specifically ion chromatography. These analytical methods will be used to quantify the rate of amine degradation as well as the rate of degradation product formation for amine systems.

Since most gas treating processes using alkanolamines for CO2 removal are performed in the absence of oxygen, oxidative degradation is a source of solvent degradation that has

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not been properly quantified. Oxidative degradation is important because it can impact the environment, process economics, and equipment lifespan due to corrosion.

The environmental effects refer to the degradation products themselves: what is being produced, how much of it is being produced, and how can it be disposed of without doing significant damage to the environment? Process economics being impacted are the solvent make-up rate and design of the reclaiming operation. If amine is continually being degraded, then fresh amine must be continually added to the process at a significant cost. In addition, CO2 loaded amine solutions corrode carbon steel equipment, which catalyzes oxidative degradation even further. It is imperative to quantify how much of this solvent make-up rate is due to oxidative degradation.

Experimental Ion chromatography is the most extensively used liquid-phase analytical method. Anion chromatography utilizes an AS15 (a low-capacity column designed to separate low-molecular weight anions, specifically acetate, glycolate, and formate) IonPac column and an ASRS 4-mm self-regenerating suppressor made by Dionex, while cation analysis uses a CS17 and a CSRS 4-mm self-regenerating suppressor. Anion analysis employs a linear gradient of NaOH eluent, while cation analysis uses a constant concentration methanesulfonic acid (MSA) eluent. Refer to the June 2007 Quarterly Report for detailed descriptions of the Dionex ICS2500 and ICS3000 analytical equipment.

Analytical methods are currently being developed for the detection of aldehyde, amino acid, and amide compounds. While it is believed that anionic products make up the majority of the amine oxidative degradation products, concentrations for these classes of compounds are needed to further close the material balance.

Aldehydes (formaldehyde, acetaldehyde, glyoxal, and hydroxyacetaldehyde) can be detected using high pressure liquid chromatography (HPLC) with UV detection. Aldehyde compounds absorb at a UV wavelength of 200 nm; unfortunately, most classes of compounds absorb at this wavelength as well. In order to separate aldehydes so they can be identified properly, they must be derivatized with 2,4-dinitrophenylhydrazine (DNPH), also known as Brady’s reagent. Aldehydes react with DNPH in a condensation reaction to form water and a structure known as a 2,4-dinitrophenylhydrazone. The 2,4-dinitrophenylhydrazone absorbs at a UV wavelength of 365 nm, which does not interfere with any other substances in solution.

Calibration standards are derivatized with DNPH and dissolved in acetonitrile. Experimental samples are derivatized with DNPH and perchloric acid. All samples are diluted using a solution of 45% EtOH/55% H2O (by volume). Aldehyde analysis is performed on a Waters HPLC (with autosampler) using a standard C-18 column. The UV lamp is set to detect at a wavelength of 365 nm. A gradient of methanol/water eluent (initially set at 65% methanol/35% water) set at 1.0 mL/min is used to separate the derivatized aldehydes.

Amino acids (specifically glycine, bicine, and diglycine) are detected using an ED electrochemical detector installed on the Dionex ICS3000. The method employs a series of AminoPac PA10 columns with a gradient profile of water/sodium hydroxide/sodium

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acetate at flowrate of 0.25 mL/min. The cell in the electrochemical detector is a conventional fixed gold electrode.

Amides are a class of compounds that were detected in an HPLC-MS screening performed by Huntsman Chemical’s analytical department. Amides are formed from the condensation reaction of an amine with a carboxylic acid. Since both of these species are known to exist in appreciable quantities in degraded amine solutions, it is conceivable that amides are present. Amides can be detected using HPLC with UV detection, but amides absorb at 200 nm, thus making it difficult to separate them from other species in the solution.

Koike et al (1987) concluded that the addition of excess NaOH was successful at reversing the amide formation reaction to amine and carboxylic acid. This hypothesis was tested using various concentrations of sodium hydroxide reacted with formamide and N-(hydroxymethyl)acetamide. It was concluded that reacting 1g of sample with 1g of 5 M NaOH at 25oC and allowing the solution to sit for twenty-four hours was sufficient to reverse the amide reaction. Using the anion IC procedure detailed previously, all experimental samples are analyzed pre- and post-addition of NaOH to determine carboxylic acid and amide concentrations. All amide concentrations (as calculated by the difference between the carboxylic acid concentrations pre- and post-NaOH addition) will still be reported as carboxylic acids.

Amine solutions in the low gas flow degradation apparatus are oxidized for 12 to 14 days in a low-gas flow jacketed reactor at 55oC. The solutions are agitated at 1400 RPM to produce a high level of gas/liquid mass transfer by vortexing. 98% O2/2% CO2 at 100 ml/min is introduced across the vortexed surface of 350 ml of aqueous amine. Samples were taken from the reactor at regular intervals in order to determine how degradation products formed over the course of the experiment. Prior quarterly reports provide a detailed explanation of the low gas flow degradation apparatus.

Two low gas flow apparatus are now operating in parallel. One system operates via the original configuration, which uses an inlet gas of 98% O2/2% CO2 premixed in a cylinder provided by Matheson Tri-Gas. A Cole-Parmer rotameter is used to control the flowrate at 100 mL/min. The second apparatus is set up for the modified configuration, which operates with two separate cylinders provided by Matheson Tri-Gas – a pure oxygen cylinder and a pure CO2 cylinder. The 98% O2/2% CO2 mixture is achieved using a 4 channel Brose box and two model 5850E mass flow controllers, both manufactured by Brooks. Oxygen flowrate is controlled by a 100 cc flow controller, while carbon dioxide is controlled by a 20 cc flow controller. The control box displays a digital readout corresponding to the % open of the valve on the mass flow controller. The valve % open corresponds to a gas flowrate, which is determined from the calibration curve constructed for each flowmeter.

The original setup is limited by the fact that the partial pressure of CO2 over the top of the amine solution entering low gas flow apparatus is fixed. Therefore, the loading of each amine solution is fixed and corresponds to 2% CO2 concentration in the vapor space above the amine solution. The recently constructed apparatus allows for variations in the CO2 concentration such that a particular loading in the amine solution can be achieved by

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adjusting the incoming CO2 concentration via the flow controllers. Amine loading has become a variable, whereas in the past it was constrained.

Experiments are performed in the high gas flow degradation apparatus by sparging gas through an agitated amine solution in a temperature controlled semi-batch reactor. Reaction gas, consisting of a mixture of house air, nitrogen, and CO2 is bubbled through water to presaturate the gas before it is sparged through the amine solution in order to minimize water losses in the reactor.

A Parr 1108 Oxygen Combustion Bomb served as the water presaturator. A 1/8” stainless steel tube on the inside of the presaturator carries the gas mixture into the water reservoir ¼” above the bottom of the presaturator bomb. The gas bubbles through the heated water and out the presaturator bomb. The bomb and its contents are kept at 55oC in a water bath.

Water level in the presaturator is controlled using a series of Masterflex peristaltic pumps. The inlet pump is a ColeParmer Masterflex Model 7520-50 (range 1-100 RPM). Affixed to the pump is a Masterflex Model 7013-20 pump head. Distilled, deionized water from the Millipore Direct-Q 3 system is contained in an atmospheric reservoir located on top of the inlet pump. The water is pumped into the presaturator through Masterflex 6409-13 Tygon tubing (0.03” ID) at a flowrate of 1 mL/min. This exceeds the rate at which water evaporates from the presaturator.

A ColeParmer Masterflex Model 7521-40 (range 6-600 RPM) with an Easy-Load II variable speed drive (Model 77200-50) serves as the outlet pump motor. Affixed to the pump is a Masterflex Model 7016-20 pump head threaded with Masterflex Model 6409-16 Tygon tubing (0.123” ID). The outlet pump is set at a flowrate of 2 mL/min; the outlet flowrate is set at twice the inlet flowrate to ensure that the presaturator does not flood and send water directly to the reactor.

A ¼” stainless steel tube extends 1” down from the top of the presaturator into the reservoir. If the water level in the presaturator is below the bottom of the tube, the outlet pump will only pull the reaction gas mixture at 2 mL/min out of the bomb. Once the water level reaches the bottom of the tube, the outlet pump will begin to pull water out of the reservoir and keep the level in the presaturator bomb constant.

The outlet pump carries the gas/water mixture into a 500 cc flash tank (16 cm OD, 30.5 cm height). Any entrained water drops to the bottom of the tank through a U-tube (1/4” ID) and a gate valve cracked open. Static pressure from the water level slowly forces the water through the U-tube and out the valve, where the water falls back into the DDI reservoir. Reaction gas exits the top of the tank and flows through ¼” PE tubing (max 150oF, 120 psig) to a Swagelok tee, where it recombines with saturated gas exiting the presaturator on its way to the reactor.

Temperature is continuously monitored throughout each high gas flow experiment, and the temperature of the heat baths were adjusted to keep the reactor at a constant temperature of 55oC. Temperature in the jacketed reactor is kept constant using a temperature bath. The heat transfer fluid is dimethyl silicone oil (50 cSt viscosity). Temperature was controlled within ± 1oC by monitoring the temperature with a PT-100 immersion probe (Class B, 4x150 mm) connected to PicoLog Recorder software through

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a PT-104 converter. For this system, in order to maintain a reactor temperature of 55oC, the temperature bath and presaturator bath are set at a temperature of approximately 63oC (depending on ambient conditions).

Results Using the analytical methods described in the prior section, analysis was completed on low gas flow experiments conducted during the prior quarter. All experiments were conducted at 55oC and 1400 RPM and kept at a loading (α) of 0.40 using 100 mL/min of a saturated 98% O2/2% CO2 gas mixture (α is defined as mol CO2/mol amine).

1. AQ PZ / 5mM Fe

2. 7 m MEA / 1mM Fe

3. 7 m MEA / 1mM Fe / 0.5 M Formaldehyde

4. 7 m MEA / 1mM Fe / 0.5 M Formic Acid

For the four proceeding experiments, anion chromatography analysis was conducted (as well as cation chromatography analysis in the case of the piperazine experiment) on all raw samples taken from the reactor during the course of each experiment. In addition, 1g of 5 M NaOH was added to approximately 1g of each raw sample and reanalyzed via anion chromatography to account for amide concentrations.

In addition, the following two high gas flow degradation experiments were performed and analyzed using anion chromatography and FTIR analysis. All experiments were carried out at a temperature of 55oC and an agitation rate of 1400 RPM. A loading of 0.4 was achieved using 7.5 L/min of an air/N2/CO2 mixture. The MEA/Fe/Cu experimental samples were also treated with NaOH and reanalyzed via anion chromatography to account for amide concentrations.

5. 7 m MEA / 1mM Fe

6. 7 m MEA / 0.1mM Fe / 5mM Cu

Figures 1 through 4 detail concentration versus time graphs for the four low gas flow degradation experiments performed during the previous quarter. All solid lines represent anion concentrations as determined from analysis the diluted raw samples. All dashed lines represent concentrations calculated after the sample was reacted with NaOH and subsequently analyzed.

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Figure 1: Oxidative degradation of AQ PZ, 5mM Fe, 55oC, 1400 RPM

Figure 2: Oxidative Degradation of 7 m MEA, 1mM Fe, 55oC, 1400 RPM

EDA

Formate

Oxalate

Oxalate

Formate

Nitrite

Nitrate

Acetate, Glycolate

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Figure 3: Oxidative Degradation of 7 m MEA, 1mM Fe, 0.5 M Formaldehyde, 55oC,

1400 RPM

Figure 4: Oxidative degradation of 7 m MEA, 1mM Fe, 0.5M Formic Acid, 55oC,

1400 RPM

Formate

Nitrate Oxalate

Nitrite

Acetate, Glycolate

Formate

Nitrate

Acetate, Glycolate

Oxalate

Nitrite

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Figures 1 through 4 illustrate several points – some of which have been stated before, while others were discovered during this quarter. Figure 1, which details the degradation of aqueous piperazine in the presence of high iron catalyst concentration (5mM), reveals that ethylenediamine is present in quantities on the same level as formate. However, all final concentrations are present at quantities less than 6mM at time = 290 hours. It can be concluded that Fe has minimal catalytic effect on aqueous piperazine solutions, even at high concentrations.

After NaOH was added to all samples taken during this experiment, re-analysis revealed an increase in formate and oxalate concentration, represented by amides formed from the reaction of MEA with formic and oxalic acid. It is believed acetate, glycolate, nitrite and nitrate were present in concentrations below the detection limit. Average degradation product formation rates are listed in Table 1 for the degradation of aqueous piperazine solutions.

The first two columns show that the addition of 100mM Inhibitor A reduces degradation by an order of magnitude. Moreover, the third column shows that if Cu is absent from the solution, oxidative degradation remains minimal, even at high concentrations of iron. From this data, one can conclude that Fe poses only a corrosion problem for aqueous piperazine solutions and does not impact oxidative degradation. In the fourth column, treating degraded samples with NaOH revealed that N-formyl MEA is present in concentrations almost equal to formate.

Table 1: Aqueous PZ Rate Summary (mM/hr) – Effect of NaOH Addition

Figures 2 through 4 confirm that the addition of NaOH increases the concentration of all carboxylic acid degradation products, especially formate and oxalate. In Figure 2, nitrite concentration appears to be reaching a steady state value at approximately 100mM. Moreover, formate + N-formyl MEA concentration (represented by the dashed line) appears to be leveling off as well at an experiment time past the equilibration of nitrite concentration. Another MEA experiment needs to be conducted for a longer period of time to confirm this.

Table 2 confirms that N-formyl MEA and the oxalic acid amide are both present in significant quantities in the degradation of 7 m MEA in the presence of 1mM Fe++. N-

Iron Conc. (mM) 0.1 0.1Copper Conc. (mM) 5 5

Inhibitor A Conc. (mM) - 100NaOH Addition No No No Yes

Formate 0.22 0.004 0.006 0.011EDA 0.25 0.03 0.02 0.02

Carbon 0.76 0.06 0.046 0.053Nitrogen 0.52 0.06 0.04 0.04

5--

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formyl MEA appears to exist in a concentration similar to formic acid, while oxalic acid amide is present in concentrations 4X to 5X greater than oxalic acid.

Table 2: 7 m MEA Rate Summary (mM/hr) – Effect of NaOH Addition

Experiments represented by Figures 3 and 4 differ from the prior two experiments in that compounds which are known intermediates (formaldehyde and formic acid) were added initially to 7 m MEA/1mM Fe solutions. The hypothesis is that the intermediates would react faster with the available oxygen and be consumed quicker than the MEA, thereby protecting it from oxidative degradation. In the formaldehyde added experiment, rates of formate and nitrate/nitrite production are similar to formation rates in the MEA/Fe experiment. In the formic acid added experiment, the same conclusion can be reached regarding nitrite/nitrate production; formic acid production cannot be quantified because formic acid is being depleted at the same time it is being formed.

Other notable observations can be taken from Figures 3 and 4. In Figure 3, it appears that nitrite is reaching a steady-state concentration of approximately 80 mM. Likewise, formate production (as well as formate + N-formyl production) appears to be slowing and reaching some unknown steady-state concentration. Figure 4 illustrates some unexplained fluctuations in formic acid concentration through the course of the experiment. Formic acid + N-formyl MEA concentration shows that an appreciable amount of amide was made at time = 75 h and persisted throughout the experiment. The exact concentration of formaldehyde in solution, as well as further analysis of water loss, would be beneficial in understanding the behavior of these two experiments.

The last four columns of Table 2 verify the importance of amide concentrations in the oxidatively degraded monoethanolamine samples. In the case of the formaldehyde added experiment, formate concentration increases by a factor of four after the amide reaction is reversed, while oxalate concentration increases by a factor of ten. Likewise, for the formic acid added experiment, oxalate concentration increases by an entire order of magnitude after NaOH is added to reverse the amide formation reactions.

Figures 5 and 6 illustrate concentration versus time graphs for experiments performed in the high gas flow degradation apparatus. In the case of the MEA/Fe experiment, only one set of curves exist because NaOH addition has not yet been performed on any of the

Iron Conc. (mM)Copper Conc. (mM)

Formaldehyde Conc. (Molarity)

Formic Acid Conc. (Molarity)

NaOH Addition No Yes No Yes No Yes

Formate 0.289 0.641 N/A N/A 0.223 0.916Oxalate 0.020 0.110 0.012 0.11 0.007 0.075

Nitrite/Nitrate 0.265 0.307 0.285 0.298 0.285 0.296Carbon 0.335 0.872 N/A N/A 0.241 1.086

0.1-

0.5

-

1-

-

-

0.1-

-

0.5

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samples. For the MEA/Fe/Cu experiment, formate appeared to be the only significant anionic degradation product, even after NaOH addition.

Figure 5: Oxidative Degradation of 7 m MEA, 0.1mM Fe, 5mM Cu, 55oC, 1400 RPM, High Gas Flow Apparatus

Formate

Other Anionic Products

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Figure 6: Oxidative Degradation of 7 m MEA, 1mM Fe, 55oC, 1400 RPM, High Gas Flow Degradation Apparatus

Table 3 shows that in that high gas flow degradation apparatus formate and carbon production are approximately 10 times higher in a system with low iron/high copper than in a 7 m MEA system with iron only. Nitrate/nitrite production for both experiments is minimal because most of the NOx is stripped out of the reactor, preventing its accumulation in solution. Treating the MEA/Fe/Cu experimental samples with NaOH revealed that amide formation is significant in the high gas flow apparatus as well. Calculations show that formate concentration tripled after the amides were reverted to carboxylic acids.

Table 3: High Gas 7 m MEA Rate Summary – IC Analysis (mM/hr)

Table 4 lists average formation rates for volatile degradation products as detected by the FTIR for the high gas flow experiments. The FTIR calculates volatile product concentrations continuously in units of ppmv. Concentration was plotted as a function of

Iron Conc. (mM) 1Copper Conc. (mM) -

NaOH Addition No No Yes

Formate 0.049 0.455 1.237Carbon 0.064 0.513 1.417

Nitrogen 0.028 0.051 0.043

0.15

Glycolate

Oxalate

Formate

Nitrite

Nitrate

Acetate

16

experiment time for each component, and the area under the curve was approximated. This area was normalized by total experiment time and converted from ppmv/hr to mM/hr to produce the rates listed in Table 4.

Table 4 shows that average ammonia production is similar for the MEA/Fe and MEA/Cu/Fe experiments. NOx and N2O production is much greater in the presence of copper, while aldehydes and carbon monoxide are formed in greater quantities for the Fe only experiment.

Table 4: High Gas 7 m MEA Rate Summary – FTIR Analysis (mM/hr)

Conclusions and Future Work

During this past quarter, the presence of previously undetected amides in degraded MEA solutions is a key finding. In both the low and high gas flow degradation apparatus, formate concentration increased by anywhere from a factor of 2 to 4, while oxalate concentration typically increased by a factor of 10. This means that N-formyl MEA is present in solution in similar or greater quantities than formic acid, and oxalic acid amide concentration is an order of magnitude greater than oxalic acid concentration in the analyzed degraded amine solutions. Acetic acid amide and glycolic acid amide are present as well, but in extremely low concentrations. I plan on pulling final experimental samples taken for all prior low gas flow degradation experiments and analyzing them for amide concentration.

Long-term experiments performed in the high gas flow degradation apparatus are inconclusive at this point. Copper catalyzed solutions favor the production of NOx, while iron catalyzed solutions favored the production of CO. The average NH3 production rate was similar for both experiments, although steady-state concentrations differed. Furthermore, the high formate production rate (in the form of formate and N-formyl MEA) obtained from IC analysis from the MEA/Fe/Cu experiment suggests that the material balance might be closer than previously thought.

The aqueous piperazine experiment performed in the low gas flow degradation apparatus suggests that iron has a negligent effect on catalyzing oxidative degradation. Therefore, it may be more advisable to minimize iron concentration in PZ solutions to inhibit

Iron Conc. (mM) 1 0.1Copper Conc. (mM) ‐ 5

Ammonia 1.793 1.750NO 0.132 0.126

NO2 0.076 0.284

N2O 0.001 0.165

Formaldehyde 0.081 0.034Acetaldehyde 0.139 0.076

Carbon Monoxide 0.273 0.001

17

corrosion, rather than adding Cu to inhibit corrosion and Inhibitor A to inhibit degradation.

In the next quarter, I plan to continue work on the oxidative degradation of various amine systems (under varying catalyst and inhibitor conditions) using both the low and gas flow degradation apparatus. I will perform prolonged experiments to determine total amine losses on the cation IC and pair it with degradation product concentrations (carboxylic acid, nitrite/nitrate, amide, amino acid and aldehyde). In order to achieve this, experimental methods need to be adjusted for amino acid and aldehyde detection. In addition, the preliminary HPLC-MS screening revealed the presence of 2-hydroxyethyl(imidazole) in degraded MEA solution. Analytical methods for quantifying imidazole concentration need to be researched.

Inhibited oxidation conditions will be extensively researched during the next two quarters. Sulfite, EDTA, and other oxygen scavengers/chelating agents will be compared to Inhibitor A as a means of inhibiting degradation.

Water balance issues can be resolved by analyzing degraded amine solutions for sulfate concentration using anion chromatography analysis. All copper and iron are added to the solutions in the form of inorganic sulfate salts. Since no sulfur is added during the course of the experiment, sulfate concentration should remain the same throughout the experiment and serve as a tracer. Therefore, any fluctuations in sulfate concentration can be attributed to changes in water content.

References Goff, GS & GT Rochelle, “Monoethanolamine Degradation: O2 Mass Transfer Effects

under CO2 Capture Conditions.” Ind. & Eng. Chem. Res. 2004, 43(20), 6400-6408.

Koike, L et al. “N-Formyldiethanolamine: a new artifact in diethanolamine solutions.” Chem. & Ind. 1987, 626-627.

Nascimento, RF, et al. “Qualitative and quantitative high-performance liquid chromatographic analysis of aldehydes in Brazilian sugar cane spirits and other distilled alcoholic beverages.” J. Chrom. A 1997, 782(1), 13-23.

18

1

Thermal Degradation Progress Report for October – December, 2007

by Jason Davis

Supported by the Luminant Carbon Management Program

and the




January 31, 2008

Abstract Piperazine has been shown to have little, if any thermal degradation by cation chromatography, HPLC, pH titration, and NMR analysis at standard stripper conditions, 120oC - 135oC, over an 8-week time span. As a comparison, an 11 m MEA solution with a loading of 0.5 mol CO2/mol amine exhibits a loss of more than 60 wt % MEA at 135oC after 8 weeks.

Introduction This section will cover thermal degradation in an amine absorber/stripper system. As a base case monoethanolamine (MEA) will be tested at varying concentrations, CO2 loadings, and temperatures in order to develop a model to define degradation at stripper conditions. Piperazine (PZ) and MEA/PZ blended systems will also be tested followed by a general screening of a wide variety of amines.

Theory Monoethanolamine

Traditional thermal degradation in amine systems is characterized by a carbamate polymerization reaction. The first defined system was for monoethanolamine. Polderman, Dillon and Steele describe the mechanism for thermal degradation of MEA by carbamate polymerization. In CO2 capture, MEA associates with CO2 in the absorber to form MEA carbamate as illustrated below.

This reaction is normally reversed in the stripper, but in some cases the MEA carbamate will cyclize to form 2-oxazolidone, which is also a reversible reaction, as shown below.

NH2OH

NHOH CO2-++ CCOO22

MMEEAA CCaarrbbaammaattee MMEEAA

2

MEA carbamate can also irreversibly dehydrolize to form N,N’-di(2-hydroxyethyl)urea (Yazvikova et al, 1975).

The former product, 2-Oxazolidone, can then react with another molecule of MEA to form 1-(2-hydroxyethyl)-2-imidazolidone which is sometimes referred to as HEIA.

HEIA can then be hydrolyzed to form N-(2-hydroxyethyl)-ethylenediamine or HEEDA.

These four species (2-oxazolidone, dihydroxyethylurea, HEIA and HEEDA) plus further polymerization products are believed to be the main products of thermal degradation. The rate of formation of these products is a function of temperature (faster kinetics), CO2 loading (more carbamate present) and MEA concentration. Piperazine

Piperazine (PZ) is a cyclic diamine whose structure is shown below.

NHO

O

22--OOxxaazzoolliiddoonnee

++ HH22OO

MMEEAA CCaarrbbaammaattee

NHOH CO2-

OHNHNH

OH

O

++ HH22OO

NN,,NN ‘‘--ddii((22--hhyyddrrooxxyyeetthhyyll))uurreeaa

NHOH CO2-

MMEEAA CCaarrbbaammaattee

NH2OH+

MEA

NNH

O

OH ++ HH22OO

11--((22--hhyyddrrooxxyyeetthhyyll))--22--iimmiiddaazzoolliiddoonnee

((HHEEIIAA))

NHO

O

22--OOxxaazzoolliiddoonnee

++ NH2OH

NHOH NH2 ++ CCOO22

NN--((22--hhyyddrrooxxyyeetthhyyll))--eetthhyylleenneeddiiaammiinnee

((HHEEEEDDAA))

NNH

O

OH ++ HH22OO

11--((22--hhyyddrrooxxyyeetthhyyll))--22--iimmiiddaazzoolliiddoonnee

((HHEEIIAA))

20

3

NH NH

Piperazine

Since PZ does not have an alcohol group present as MEA does, thermal degradation by the pathway shown for MEA above should be minimized since the initial reaction step will be eliminated. Other reaction pathways could exist like the one listed below in which the carbamate of one piperazine and the protonated amine of another piperazine molecule react to form a urea.

NCOO-NH NHNH2++ NNH NHN

O

+ H2O

Methods 10mL high pressure sample containers were filled with varying concentrations of amine and CO2. Five containers of each solution were then placed in a temperature controlled forced convection oven and removed at weeks 1, 2, 4, 6, and 8. The samples were cooled on removal and tested with Cation IC to determine the final concentration of amine. HPLC was then used to test for nonionic products.

Results and Discussion MEA Experimental Results

Over 140 MEA samples were run and tested to form an empirical model to describe MEA losses at stripper conditions. Temperatures of 100oC, 120oC, 135oC, and 150oC were used in order to encompass the full range of possible stripper conditions. The following empirical model was regressed from this data.

where K is the temperature dependent rate constant given by:

MEAf = final MEA concentration (molality) MEAo = initial MEA concentration (molality) α = loading (mol CO2/mol amine) t = time (weeks) A graph showing the effect of loading on 11 m MEA at a temperature of 135oC is shown below. The solid lines are the models’ predicted values while the individual data points represent the actual data.

tMEAKof

oeMEAMEA *** 5.045.1α−∗=

)987.1*/(289004.33 TeK −=

21

4

Figure 1: 11 m MEA concentration over time at 135oC with loadings of 0.2(blue), 0.4(green) and 0.5(purple)

MEA losses are slightly more than first order in loading. The model does a reasonable job of describing the data. The next graph shows the effect of temperature on a 7 m MEA system with a CO2 loading of 0.4 mol CO2/mol MEA.

Figure 2: 7 m MEA with a loading of 0.4 at temperatures of 100oC (light blue), 120oC (green), 135oC (dark blue), and 150oC (brown)

Temperature is more than 1st order with a model predicted activation energy of 29kcal/mol. An increase of 15oC roughly quadruples the rate of thermal degradation which roughly corresponds to a doubling of the stripper pressure. The next graph shows the effect of amine concentration on thermal degradation. These samples have a CO2 loading of 0.4 at a temperature of 135oC

4

5

6

7

8

9

10

11

0 2 4 6 8

Time (wks)

MEA

(mol

ality

)

0

1

2

3

4

5

6

7

0 2 4 6 8Time (wks)

MEA

(mol

ality

)

22

5

Figure 3: Percent MEA remaining over time at a temperature of 135oC and a loading of 0.4 for 3.5 m MEA (light blue), 7 m MEA (green), and 11 m MEA (dark blue)

Thermal degradation is slightly more than first order in amine concentration. If it were first order, then all three concentrations would fall on the same line. The next graph shows the concentration of HEEDA over time in 11 m MEA at 135oC.

Figure 4: HEEDA concentration over time for 11 m MEA at 135oC with loadings of 0.2 (light blue), 0.4 (royal blue) and 0.5 (dark blue)

50

55

60

65

70

75

80

85

90

95

100

0 1 2 3 4 5 6 7 8Time (wks)

MEA

Rem

aini

ng (%

)

00.05

0.1

0.15

0.2

0.25

0.30.35

0.4

0.45

0.5

0 2 4 6 8

Time (weeks)

HEE

DA

(m

)

23

6

The HEEDA concentration reaches equilibrium very quickly in the highly loaded samples and decreases proportionally to the MEA concentration. The lower loaded sample takes a while to establish an equilibrium, but it can be seen that the equilibrium concentration is inversely proportional to the loading. MEA Discussion

MEA has been shown to thermally degrade under stripper conditions and a reasonable empirical model has been developed to describe this mechanism. This degradation is more than first order in amine concentration and CO2 loading, but the largest effect is temperature. An increase of 15oC results in a quadrupling of the thermal degradation rate, which corresponds to a doubling of the stripper pressure. Concentration actually has multiple effects, since in practice an increase in the concentration will increase the boiling point of solution. For instance, increasing the concentration from 3.5 m to 11 m will increase the BP of solutions by 4oC which corresponds to a 40% increase on top of the simple concentration increase effect described in the empirical model. In ASPEN models of test stripper conditions, the vast majority of the degradation occurred in the reboiler since it is the hottest section of the stripper and also has relatively long hold-up times compared to the structured packing. In these test runs it has been shown that thermal degradation in a MEA system can be significant, but can be controlled in the engineering design of the plant. PZ and MEA/PZ Blended Sytem Experimental Results

A set of PZ samples at concentrations of 5 and 2.5 m were run in much the same way as the MEA samples. Comparisons of the initial sample to the degraded samples using IC showed little if any loss of piperazine. Below is a set of IC chromatograms of aqueous PZ incubated at 135oC with a CO2 loading of 1 mol CO2/mol PZ at time 0, 1, 2, 4, 6, and 8 weeks.

Figure 5: Overlayed IC chromatograms of aqueous PZ after 0, 1, 2, 4, 6, and 8 weeks at 135oC

Piperazine

These two peaks are found in the initial sample as well as the degraded samples.

0.0 1.3 2.5 3.8 5.0 6.3 7.5 8.8 10.0 11.3 12.5 13.8 15.0 16.3 17.5 18.8 20.0-0.50

1.00

2.00

3.00

4.00

5.00

1 - 10292007 5mPZ 135 Autosampler #2 [modified by TEXAS UNIVERSITY OF] 5m PZ a=0.5 T=135C t=1wks ECD_12 - 10292007 5mPZ 135 Autosampler #1 [normalized] 5m PZ a=0.5 t=0wks ECD_13 - 10292007 5mPZ 135 Autosampler #3 [normalized] 5m PZ a=0.5 T=135C t=2wks ECD_14 - 10292007 5mPZ 135 Autosampler #4 [normalized] 5m PZ a=0.5 T=135C t=4wks ECD_15 - 10292007 5mPZ 135 Autosampler #5 [modified by TEXAS UNIVERSITY OF, normalized] ECD_16 - 10292007 5mPZ 135 Autosampler #6 [modified by TEXAS UNIVERSITY OF, normalized] ECD_1µS

min

654321

1 - PZ - 12.883

24

7

There is very little change in the size and shape of the PZ peak over the 8-week time span with a relative standard deviation of less than 3% for all samples. There are also no new peaks in the week 8 sample compared to the initial sample. This indicates that there is very little change in the PZ concentration, and that no new amine structures are being formed.

As an additional test, an initial sample and a degraded sample of the PZ were submitted for NMR analysis. This analysis showed nearly identical scans of both samples by C13 and proton NMR indicating that no large contaminants were present in the degraded sample.

A set of MEA/PZ blended systems was run as well. The table below quantifies the amount of amine loss in a pure 11 m MEA system, a pure 5 m PZ system, and a 7 m MEA/2 m PZ blended system with a loading of 0.4 mol CO2/mol amine functional group.

Table 1: MEA, PZ, and MEA/PZ blended system amine losses

Solvent Temp (oC) MEA Loss (%) PZ Loss (%) Amine Loss (%)

Pure MEA 120 4 - 4

Pure PZ 120 - <2 <2

MEA/PZ Blend 120 5 18 6

Pure MEA 135 18 - 18

Pure PZ 135 - <2 <2

MEA/PZ Blend 135 12 32 19

This table shows that while PZ does not degrade by itself, it does interact with MEA in the MEA/PZ blended system and shows significant losses. This is unfortunate considering PZ is the more expensive solvent. It is believed that the PZ reacts this way due to the fact that it is a stronger nucleophile and will interact with the MEA oxazolidone structure more readily than another MEA molecule would.

Amine Screening Experiments

A set of amines were screened in the same manner as the MEA tests. They were all formulated to have roughly the same CO2 absorption capacity and were loaded to 0.4 mol CO2/mol functional amine group. The table below summarizes the results from this experiment after 4 weeks at 135oC.

Table 2: Amine losses after 4 weeks for a variety of amines at a loading of 0.4

Amine Concenctration (molality)

Remaining Amine Peak (%)

Total Area Retention (%)

PZ 3.5 100 100

DGA 7 93 98

MDEA 50 wt % 71 97

AMP 3 97 96

EDA 3.5 64 91

25

8

MEA 7 76 80

DETA 2.3 9 71

HEEDA 3.5 3 17

These amines were ranked based on their total area retention of the IC chromatogram which should give some indication as to whether the amine degraded to a useful degradation product or to a non-CO2 absorbing species. PZ, DGA, MDEA, and AMP all had very little degradation after the given time frame. MDEA was interesting in that the MDEA peak on an IC chromatogram decreased significantly, but it seemed to shift to other monoamine species. EDA, and AMP both had similar degradation rates, while DETA and HEEDA were both consumed in significant quantities after just 4 weeks. The HEEDA consumption rate is important in the context of the MEA degradation system since it is one of the main degradation products.

Conclusions Thermal degradation can be a significant cost in the amine absorber/stripper system, but can be controlled as long as it is taken into account in the design phase of the stripper and reclaimer system. The majority of this degradation in an MEA system seems to occur in the reboiler of the stripper as this is the hottest section and temperature has a more dramatic effect than loading does. Further study of reclaiming systems needs to be performed in order to more accurately predict the full extent of thermal degradation since natural gas treating experience says that roughly 50% of thermal degradation occurs in the thermal reclaiming system.

Piperazine does not degrade by itself, but is preferentially destroyed in the MEA/PZ blended system. This should be considered in any blended system – that additional side reactions may occur and even though a pure system might not degrade, the combined system could have unforeseen consequences.

A wide variety of amines were also tested and all but piperazine were shown to have some form of thermal degradation at a temperature of 135oC which is a slightly elevated stripper temperature. According to operating experience, MDEA does not thermally degrade, but in this experiment, MDEA did degrade in measurable quantities. AMP had the second highest amine peak retention behind piperazine while DETA and HEEDA were both consumed in appreciable quantities after the four week period.

References Polderman, LD, CP Dillon, and AB Steele, "Why monoethanolamine solution breaks down in

gas-treating service." Oil Gas J., 1955. 54(No. 2): 180-3. Yazvikova, NV, LG Zelenskaya, and LV Balyasnikova, "Mechanism of Side reactions During

removal of Carbon Dioxide from Gases by Treatment with Monoethanolamine." Zhurnal Prikladnoi Khimii, 1975. 48(3): 674-676.

26

Degradation of ROC20 Progress Report for October – December, 2007

by Stephanie Freeman


and the




January 31, 2008

Abstract An investigation into the oxidative and thermal degradation of ROC20 (10 m PZ) solutions has been undertaken this quarter. Preliminary results suggest that thermal degradation of ROC20 solutions is negligible at 135 and 150°C. In addition, oxidative degradation of ROC20 is negligible in the presence of trace amounts of chromium (30 ppm), nickel (10 ppm), and iron (10 ppm). Copper actively catalyzes the oxidative degradation of ROC20 solutions producing formate and ethylenediamine as the main detected degradation products. The increased viscosity of ROC20 solutions has also been initially investigated and will continue to be a research topic of interest through the next quarter.

Introduction The novel amine solvent ROC20 is being investigated as a possible alternative to the standard 30 wt % MEA in absorber/stripper systems removing CO2 from coal-fired power plant flue gas. ROC20 is a proprietary name for the solvent, which is 10 m piperazine (PZ). Additional concentrations are also being investigated, especially ROC16 (8 m PZ).

Both oxidative and thermal degradation of ROC20 have been preliminarily investigated this quarter. Oxidative degradation of amine solvents in these systems can occur in the absorber where oxygen is present from the flue gas. It can also occur in the sump of the absorber or in the lines of the cross exchanger before the rich solvent enters the stripper. Thermal degradation primarily occurs in the stripper where high pressure and temperature steam is used to strip the rich solvent. Temperatures in the stripper are usually up to 120°C, but degradation of ROC20 was investigated at 135 and 150°C.

Materials and Methods

Experimental Methods Low Gas Flow Apparatus: The low gas flow apparatus is used to measure oxidative degradation through the exposure of an amine solution to an oxygen rich environment. The apparatus has

27

been detailed previously in the progress reports of Andrew Sexton. No major modifications have been made. Overall, the apparatus consists of a glass vessel that is maintained at 55°C using a recirculating, heated water bath. The amine solution is mechanically agitated at 1400 RPM and the vessel is loosely sealed with a large rubber stopper. A gas flow of 100 mL/min of 98%/2% O2/CO2 is introduced at the top of the gas liquid interface during agitation. The agitation vortex creates mass transfer by significant gas entrainment.

Thermal Degradation: Thermal degradation experiments are performed as detailed previously in the progress reports of Jason Davis. No major modifications have been made. In summary, 2 mL of amine solution is loaded into an airtight, stainless steel bomb made of swagelock connections. The bombs are placed in forced convection ovens for extended periods of time and removed for analysis.

Analytical Methods Anion IC: The anion IC was used to determine the concentration of glycolate, acetate, formate, nitrite, oxalate, and nitrate in experimental samples. A Dionex ICS3000 instrument with AS15 IonPac column and ASRS 4mm self-regenerating suppressor was used as previously described by Andrew Sexton using a linear NaOH eluent concentration. No major modifications have been made to the method in this quarter.

Cation IC: The cation IC was used to determine the concentration of PZ and EDA in experimental samples. A Dionex ICS2500 instrument with CS17 IonPac column with CSRS 4-mm self-regenerating suppressor was used as previously described by Jason Davis with methanesulfonic acid (MSA) eluent. No major modifications have been made to the method in this quarter.

NaOH Treatment for Amides: An analytical test for the formation of amides has been developed by Andrew Sexton during the last quarter and has been included in the results shown here. Experimental samples are treated with 5 N NaOH (in equimolar amounts) and allowed to sit over night. Then, the anion IC analytical method is used to quantify increases in the concentrations of analytes as compared to the original samples. In most cases, the main increases are shown in the production of formate and oxalate following NaOH treatment.

Acid pH Titration: Titration with 0.2 N H2SO4 was used to determine the concentration of amines in experimental samples. The automated Titrando apparatus is used for this method. A known mass of sample is diluted with water and the autotitration method is then used. The Titrando titrates the sample with acid while monitoring the pH. The equivalence points are recorded. The equivalence point around a pH of 3.9 corresponds to basic amine species in solution. The test is not sensitive to the type of amine, so if PZ has degradation to EDA, the titration test will detect the sum of contributions from the species.

Viscosity Measurements: Viscosity of solutions was measured using a Physica MCR 300 cone and plate rheometer (Anton Paar, Graz, Austria). The apparatus allows for precise temperature control for measuring viscosity at temperatures ranging from 25 to 70°C. To take a measurement, 700 microL of solution is loaded onto the measurement disk. The instrument accelerated the top disk and a predetermined angular speed and measures the shear stress over time. The program is used that increased the angular speed from 100 to 1000 over a period of 100 seconds, measuring shear stress every 10 seconds. Viscosity is calculated for each sampling

28

instance and an average and standard deviation are calculated from the 10 individual measurements.

Results Multiple experiments on both oxidative and thermal degradation of ROC solutions were performed this quarter. The results of two oxidative and three thermal degradation experiments are reported. The loading, designated as α, is defined as mol CO2/2 mol ROC16.

Oxidation Degradation Experiments:

1. ROC20 with 30 ppm of chromium (Cr2+), 10 ppm of nickel (Ni2+), and 10 ppm iron (Fe2+) (loading of 0.4, 55°C, 1400 RPM agitation) 2. ROC20 with 250 ppm copper (Cu2+) (loading of 0.4, 55°C, 1400 RPM agitation)

Thermal Degradation Experiments:

1. ROC20 at 135°C (loadings of 0.3 and 0.4, 2 mL bombs) 2. ROC20 at 150°C (loadings of 0.3 and 0.4, 2 mL bombs) 3. ROC30 and ROC40 at 150°C (loading of 0.3, 2 mL bombs)

Results of Oxidative Degradation

The first oxidative degradation experiment was run for 604 hours and showed negligible degradation overall. The concentration profiles for the various analytes are shown in Figure 1. Notably absent are ethylenediamine (EDA), glycolate, acetate, and nitrite which were not detected in any experimental samples. Small concentrations of glycolate, acetate, and nitrite were detected upon NaOH treatment, but were all less than 0.2 mM. Only a very low concentration of formate was produced throughout the experiment, indicating negligible degradation occurred.

29

Figure 1: Concentration Profiles for Oxidation Degradation Experiment 01 (30 ppm Cr2+,

10 ppm Ni2+, 10 ppm Fe2+, 55°C, α=0.4) The second oxidative degradation experiment was continued for 495 hours and showed significant degradation of ROC20. Figure 2 shows the concentration profiles for the experiment, including EDA and the formate after NaOH treatment for amides. Figure 3 shows a close-up view of Figure 2 in order to see the other analytes. Glycolate was only detected after 450 hours of operation and after NaOH treatment and is not included in the figures.

30

Figure 2: Concentration Profiles for Oxidative Degradation Experiment 02 (250 ppm Cu2+,

55°C, α=0.4)

Figure 3: Close-up View of Concentration Profiles of Oxidative Degradation Experiment

02 (250 ppm Fe2+, 55°C, α=0.4)

31

Results of Thermal Degradation The first experiment tested the degradation of ROC20 solutions with loadings 0.3 and 0.4 mol CO2/2 mol ROC20 at 135°C. This experiment is slated to continue for 15 weeks and is currently in week 8. The results of this experiment will be included in the next progress report.

The second experiment tested the degradation of ROC20 solutions with loadings of 0.3 and 0.4 mol CO2/2 mol ROC20 at the elevated temperature of 150°C. The experiment was conducted for 5 weeks and the concentration profiles of degradation products detected are shown in Figure 4 and Figure 5 below. The experiment with α=0.3 is designated Experiment 02-A (Figure 4) while the second with α=0.2 is Experiment 02-B (Figure 5).

The overall behavior of the solution with loadings of 0.3 and 0.4 is quite similar. Very low levels of glycolate and oxalate were consistently observed. Nitrite and nitrate levels were zero for all samples except for one in each Expt 02-A and 02-B. As with the oxidative degradation experiments, formate was the primary degradation product detected. The final formate concentration in the solution with a loading of 0.4 was higher than that of the solution with the lower loading of 0.3. EDA was only detected in the sample at 3.8 weeks for both solutions. Since the EDA was not found to be present before or after this sample for either solution, the presence of EDA in those samples appears to be an artifact. The samples will be analyzed again to confirm this and reported in the next quarterly report.

The detection of amides, as indicated by the “Post NaOH Formate” line, has not yet been completed on the final two (Expt 02-A) or three (Expt 02-B) samples and will be included in the next progress report.

Figure 4: Concentration Profiles for Thermal Degradation Experiment 02-A (ROC20,

α=0.3, 150°C, 2mL bombs)

32

Figure 5: Concentration Profiles for Thermal Degradation Experiment 02-B (ROC20,

α=0.4, 150°C, 2mL bombs) The third thermal degradation experiment tested elevated concentrations of PZ at 150°C. Solutions of ROC30 and ROC40, corresponding to concentrations of 15 m and 20 m PZ, respectively, were degraded for 5 weeks. The concentration profiles of the degradation products detected for the ROC 30 (Expt 03-A) and ROC40 (Expt 03-B) solutions are shown in Figure 6 and Figure 7 below.

The overall behavior of both solutions is similar within the 5 week time span. As with thermal degradation experiment 02, very low levels of glycolate and oxalate were detected. No nitrite, nitrate, or EDA was detected in this experiment. The final formate concentrations were similar between the two solutions, with the ROC40 solution producing a slightly higher final formate concentration.

The detection of amides, as indicated by the “Post NaOH Formate” line, has not yet been completed on the final three samples and will be included in the next progress report.

33

Figure 6: Concentration Profiles for Thermal Degradation Experiment 03-A (ROC30,

α=0.3, 150°C, 2mL bombs)

Figure 7: Concentration Profiles for Thermal Degradation Experiment 03-B (ROC40,

α=0.3, 150°C, 2mL bombs)

34

Viscosity Measurements Viscosity measurements of ROC20 and ROC30 solutions were taken at 25°C. Dilution series were made for each of the solutions and viscosity data was obtained at varying molalities of PZ. The data collected are shown in Figure 8.

The primary result was that the viscosities of the two solutions were very similar. The loading did not seem to affect the viscosity greatly, especially at low molalities. At molalities above 5, a small difference in viscosity is seen where the solution with a loading of 0.4 having a slightly higher viscosity, as expected.

Figure 8: Viscosity at 25oC of Dilution of ROC20 and ROC30

Discussion

Oxidative Degradation Experiments This initial oxidative degradation experiment was performed to simulate degradation of ROC20 that may occur in stainless steel reactors where chromium, nickel, and iron are present in trace amounts. Overall, very little degradation of ROC20 occurred in this environment. The final levels of formate, the degradation product in highest abundance, were only slightly above the background noise in the anion IC method. Therefore, stainless steel vessels can be used with ROC20 solutions without enhancing the degradation of the amine.

In the second oxidative degradation experiment, copper was added to simulate the metal level present when copper based corrosion inhibitors are used. There was a significant amount of degradation compared to the experiment with stainless steel metals. The concentration profiles

35

of EDA and total formate (Figure 2) follow each other closely , suggesting that the production of EDA, formamide, and formate are closely linked. Additionally, the total formate found after NaOH treatment is more than double that of the formate of the experimental sample, indicating that more formate is present as formamide than formate. The method for detection of amides was recently developed in our group and it shows that previous experiments might have missed a significant portion of degradation products that were present as amides and not detected by the anion IC.

Thermal Degradation Experiments The two thermal degradation experiments that have been completed this quarter, Expt 02 and 03, show little thermal degradation overall. PZ is known to degrade to EDA and formate as confirmed by the oxidative degradation experiments. EDA was not detected in either experiments. Also, final formate levels were low overall, only a fraction of the formate found in the oxidative experiment with copper.

Initial testing of ROC20, ROC30, and ROC40 solutions show that PZ has little tendency to degrade thermally under the experimental conditions. This will be confirmed during the next quarter as the first thermal degradation experiment is completed and amide detection for all three experiments is concluded.

Conclusions and Future Work Initial experiments on the degradation of ROC20 show some promising results. Oxidative degradation in the presence of chromium, nickel, and iron is minimal. Thermal degradation of ROC20 is negligible in the experiments that have been run to date. This confirms earlier results of Jason Davis on ROC10 (5 m PZ) solutions.

My work on the degradation of ROC solutions will continue in the next quarter. Experiments on the degradation of ROC16 and ROC14 in the presence of metals will be performed. The thermal degradation experiment that is ongoing will be completed and analyzed. The final NaOH-treated samples from the experiments completed so far will be analyzed and reported in the next quarterly report.

36

Reclaiming by Crystallization of Potassium Sulfate

Progress Report for October – December, 2007 by Qing Xu


Industrial Associates Program for CO2 Capture by Aqueous Absorption Department of Chemical Engineering


Abstract

One side reaction in CO2 capture when using MEA/PZ is the generation of sulfate from SO2. This sulfate has to be removed so that the MEA/PZ solution can be reused for CO2 capture. Potassium compounds can be used in the removal of sulfate. In order to determine how best to accomplish this, the solubility of potassium sulfate was measured with variable MEA/PZ concentration and CO2 loading. Solubility measurement was conducted at temperatures from 25ºC to 80ºC. A model predicting experimental Ksp was developed, with the same amine concentration, temperature, and ionic strength as the variables. Based on the previous work of Hilliard (2005), new interaction parameters were regressed in this work to match Söhnel’s data (1985) in water and the experiment data by Xu. The regression was done using Data Regression System in Aspen Plus®. An interaction parameter set for CO2-MEA-H2O-K+-SO4

= system in electrolyte-NRTL model was developed. The parameter set was tested by a series of flash simulations. The parameters can simulate the interactions between ion pairs and molecules within certain condition ranges, but still need further modification.

Introduction

One side reaction in CO2 capture when using MEA/PZ is the generation of sulfate from SO2. This sulfate has to be removed so that the MEA/PZ solution can be reused for CO2 capture. Potassium compounds can be used in the removal of sulfate. In order to determine how best to accomplish this, the solubility of potassium sulfate was measured with variable MEA/PZ concentration and CO2 loading.

In recent work more experiments were conducted at 80ºC and varied MEA/PZ concentrations and CO2 loading. TIC and pH titration were used to analyze the CO2 and amine concentration changes before and after the experiments.

Based on previous work by Hilliard (2005), new interaction parameters in electrolyte-NRTL model for CO2-MEA-H2O-K+-SO4

= system were regressed using Söhnel’s data (1985) in

37

water and the experiment data by Xu. The regressed result was tested by a series of flash simulations.

Ksp error, which is a representative of the accuracy of the regression result for each experimental data, is dependent on MEA and CO2 concentrations, and is independent on temperature. The regression is applicable within certain range of MEA and CO2

concentrations, but still needs modification.

Experimental CO2 loading

A bubbler was used to add CO2 to stock amine solutions (7 m MEA, 11 m MEA, 7 m MEA/2 m PZ, and 4 m PZ). The amounts of CO2 added into the solutions were weighed with a balance. CO2 loading is defined as follows:

eq.amine of molesCO of moles 2=α

For these experiments, PZ) of moles(2MEA) of moles(eq.amine of moles ⋅+=

Method 1 50 g of the loaded solution was agitated by a magnetic stir bar through the following process. 0.1-0.4 g K2SO4 was sequentially added to the system and conductivity was measured with each addition until the solution was saturated. Then an excess of K2SO4 was added to the solution and the final conductivity was recorded. Conductivity was correlated with K2SO4 concentration and extrapolated to obtain the K2SO4 saturation concentration. The conductivity is not corrected for temperature.

In modifications of this procedure, KOH or H2SO4 was added to the solution before the additions of K2SO4. The loading is still defined as moles of CO2 per mole of equivalent amine.

These experiments were conducted at room temperature and 40ºC. A water bath was used to conduct the experiments at 40ºC.

Method 2 This method was used to measure high CO2 loading solutions at relatively high temperatures.

First, a measured amount of K2SO4 was added into a jacketed beaker, and a certain volume (about 40 mL) of the loaded solution was added and weighed. Then the beaker was sealed by a plug with 2 holes, one for a conductivity meter and the other for liquid entrance. The solution was agitated by a magnetic stir bar through the following process. 2.0-3.5 mL loaded

solution was sequentially added to the system through a Brinkmann® bottle top and

conductivity was measured with each addition. In the beginning the solution was over-saturated with solids, and then became diluted. Conductivity was correlated with

38

K2SO4 concentration and extrapolated to obtain the K2SO4 saturation concentration.

In modifications of this procedure, KOH or H2SO4 was added to the stock solution.

These experiments were conducted at 80ºC. A water bath was used to maintain temperatures.

For the last 14 experiments, an Eppendorf® pipette, instead of the Brinkmann® bottle top,

was used to add solution.

The experiment apparatus is shown in Figure 1.

Figure 1: Experiment Apparatus for Method 2

TIC was used to analyze CO2 concentration change before and after experiments. The result shows that the volume of CO2 lost during experiments was negligible.

pH titration was conducted to analyze amine concentration change before and after experiments. The result shows that in the reduction in amine concentration during experiments was negligible.

Examples

Following are experimental graph examples. The intersections of the curves are the saturation points, and solubility of K2SO4 is calculated from the two equations of the curves. Similar graphs were created from method 1 and method 2. Method 2 can effectively avoid CO2 loss, because there is no bend in the curve which represents a composition change in the loaded stock solution.

With increasing solubility after the saturation point:

BottleTop

Water Bath

Conductivity Meter

39

y = -133.42x2 + 31.003x + 0.3986

y = 0.7979x + 1.4548

0.3

0.5

0.7

0.9

1.1

1.3

1.5

0.00 0.02 0.04 0.06 0.08 0.10 0.12

[K2SO4](m)

cond

(mS/

cm)

Figure 2: Conductivity dependence on concentration -1 11 m MEA, [CO2]t=0 m

With decreasing solubility after the saturation point:

y = -65.741x2 + 253.83x + 0.3015

y = -3.9263x + 178.72

0

20

40

60

80

100

120

140

160

180

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4

[K2SO4](m)

cond

(mS/

cm)

Figure 3: Conductivity dependence on concentration -2

0 m MEA, [CO2]t=0 m

Data tables (including previous data):

40

Table 1: K2SO4 solubility in water

Concentration(m) Ksp T(°C)

K+ SO4= Ia

exp calcc Kspcalc/Ksp

20 1.268 0.634 1.902 1.020 0.778 0.763 25 1.375 0.688 2.063 1.300 1.003 0.771 30 1.477 0.738 2.215 1.610 1.269 0.788 40 1.700 0.850 2.550 2.456 2.011 0.819 50 1.899 0.950 2.849 3.427 2.997 0.875 60 2.105 1.053 3.158 4.665 4.362 0.935 70 2.301 1.150 3.451 6.091 6.137 1.008 80 2.468 1.234 3.703 7.519 8.268 1.099

Table 2: K2SO4 solubility in 0 CO2 loading solution


K+ SO4= MEAIa


22 1.337 0.668 0 2.005 1.194 0.892 0.747 45.05 1.796 0.898 0 2.693 2.895 2.457 0.849 40.05 0.234 0.117 3.5 0.352 0.008 0.027 3.127 24.15 0.205 0.103 7 0.308 0.004 0.005 1.052

40 0.245 0.122 7 0.367 0.010 0.008 0.807 80 0.436 0.218 7 0.653 0.041 0.036 0.869

24.05 0.086 0.043 11 0.130 3.23E-04 3.855E-04 1.194 24.6 0.119 0.060 11 0.179 0.001 0.001 0.660

80.05 0.195 0.098 11 0.293 0.004 0.003 0.644 40.2 0.102 0.051 11.4 0.154 0.001 0.001 1.057

Table 3: K2SO4 solubility in 7 m MEA with CO2 loading


K+ SO4= CO2Ia


23.8 0.836 0.218 2.8 3.455 0.153 0.203 1.329 40 0.618 0.309 1.4 2.327 0.004 0.005 1.052

39.95 0.910 0.455 2.8 4.165 0.001 0.001 0.660 39.95 0.735 0.193 1.4 1.978 0.161 0.175 1.090

40 0.614 0.457 1.4 2.771 0.172 0.189 1.094 39.9 0.432 0.366 2.2 3.297 0.068 0.062 0.907 39.85 0.695 0.173 1.4 1.918 0.083 0.097 1.165 41.5 0.925 0.463 2.8 4.188 0.396 0.429 1.082 40 0.441 0.220 0.7 1.361 0.054 0.055 1.019 40 0.432 0.216 0.35 0.998 0.023 0.033 1.459

79.9 1.351 0.676 2.8 4.827 1.233 1.125 0.912 80 0.936 0.618 1.4 3.255 0.542 0.519 0.958

41

Table 4: K2SO4 solubility in 11 m MEA with CO2 loading


K+ SO4= CO2Ia


23.95 0.685 0.343 5.5 6.528 0.161 0.175 1.090 23.95 0.756 0.378 5.5 6.634 0.216 0.181 0.839 40.2 0.887 0.444 5.5 6.831 0.349 0.272 0.780 39.9 0.419 0.210 2.2 2.829 0.037 0.046 1.256 40 0.949 0.300 4.4 5.299 0.270 0.159 0.588 40 0.330 0.165 1.1 1.595 0.024 0.017 0.703 40 0.309 0.155 0.55 1.014 0.021 0.008 0.392

39.85 0.678 0.489 4.4 5.868 0.225 0.196 0.869 79.85 1.245 0.622 5.5 7.367 0.964 0.649 0.674

80 1.196 0.423 4.4 5.668 0.604 0.372 0.615 79.95 0.992 0.646 4.4 6.338 0.636 0.470 0.740

Table 5: K2SO4 solubility in aqueous PZ solution


K+ SO4= CO2 PZIa


24.8 0.539 0.270 2 4 2.809 0.078 0.098 1.250 22.85 0.719 0.359 4 4 5.078 0.186 0.298 1.603 39.95 0.435 0.218 1.88 5 2.533 0.041 0.054 1.318

40 0.780 0.390 4.17 5 5.338 0.237 0.231 0.974 40.05 0.344 0.172 8 10 8.515 0.020 0.017 0.851

40 0.340 0.1702 4.8 8 5.311 0.039 0.026 0.679 40.1 0.318 0.1591 6.4 8 6.877 0.026 0.046 1.773 40 0.420 0.210 0.8 4 1.430 0.028 0.041 1.473 80 0.508 0.254 4.8 8 5.562 0.044 0.059 1.344 80 0.552 0.276 6.4 8 7.228 0.064 0.103 1.613

Table 6: K2SO4 solubility in aqueous MEA/PZ solution


K+ SO4= CO2 MEA PZIa


22.9 0.766 0.383 5.5 7 2 6.649 0.225 0.178 0.793 24.1 0.346 0.173 2.2 7 2 2.718 0.021 0.031 1.493 40.1 0.831 0.415 5.5 7 2 6.746 0.287 0.265 0.923

39.95 0.742 0.371 2.2 3.7 0.8 3.313 0.204 0.486 2.379 40 0.594 0.122 2.2 7 2 2.566 0.043 0.039 0.902

39.9 0.432 0.366 2.2 7 2 3.297 0.068 0.062 0.907 39.95 0.255 0.127 1.1 7 2 1.482 0.031 0.015 0.486

42


K+ SO4= CO2 MEA PZIa


40 0.644 0.322 3.85 7 2 4.816 0.196 0.130 0.667 79.95 1.036 0.518 5.5 7 1 7.053 0.555 0.592 1.065 79.85 0.944 0.472 3.85 7 2 5.266 0.287 0.318 1.110

80 0.670 0.335 2.2 7 2 3.205 0.057 0.119 2.090

a. I: ionic strength: ∑=

=n

1i

2iizc

21I

b. )m],SO([)m],K([Ksp 24

2exp

−+ ⋅=

c. Based on experimental data and water data from Söhnel, 1985, the correlation of ln(Ksp) was

modified: 3.75-T

1966.4ine]0.36[eq.amI9.15lnKsp 0.17 −−⋅= . Kspcalc was calculated from this equation.

†: from Söhnel, 1985.

Regression Theory

The Data Regression System in Aspen Plus® was used to regress for interaction parameters in the electrolyte-NRTL model from all of the experimental data.

The electrolyte-NRTL model is originally for aqueous electrolyte systems, and extended to mixed solvent electrolyte systems. It is based on two assumptions: the like-ion repulsion assumption and the local electroneutrality assumption. τ is the energy parameter, one of the electrolyte-NRTL parameters, for molecule-molecule, molecule-electrolyte, and electrolyte-electrolyte pairs. It is among the adjustable parameters for the electrolyte-NRTL model. The values of τ are used in the activity coefficient calculation in the electrolyte-NRTL activity coefficient model.

For electrolyte-molecule pair parameters, the temperature dependency relations are as follows:

]TTln

TTT[E

TD

C ref

ref

B,caB,ca

B,caB,ca +−

++=τ

]TTln

TTT[E

TD

C ref

ref

ca B,ca B,

ca B,ca,B +−

++=τ

Where:

15K.298Ta and cpair eelectrolytca

moleculesolvent B

ref =

——

43

GMELCC, GMELCD and GMELCE are referred to as C, D and E, respectively. Aspen Plus® has default values for these parameters:

Table 7: Default values of GMELCC

Component i Component j Value (SI units)H2O Salt 8 Salt H2O -4

MEA Salt 15 Salt MEA -8

Ksp is the solubility product:

∏= i)x(Ksp iiυγ

For K2SO4,

)SO()x(SO)K()K(xKsp 24

24

22 −−++ ⋅⋅⋅= γγ

K-SALT is defined as follows:

)T(ln*CT/BAln(Ksp) ++=

A: K-SALT/1

B: K-SALT/2

C: K-SALT/3

T is in Kelvin.

Regression

K-SALT of K2SO4 was regressed from K2SO4 solubility data in water (Söhnel, 1985).

The sum of squares of this regression result is 0.313.

Residual root mean square error is 0.250.

Table 8: Regression Result — K-SALT of K2SO4

Parameter Value (SI units) Standard deviation K-SALT/1 235.0 2.5 K-SALT/2 -13227 118 K-SALT/3 -36.2 0.4

Then with these K-SALT values, the regression was done based on Hilliard’s template in 2005 for MEA-H2O-CO2 system, which contains regressed values for 30 GMELCC, GMELCD, GMELCE, and parameters for pure component and binary components. The parameters for τ are listed below.

44

Table 9: τ Parameters in Hilliard’s template

Component i Component j GMELCC GMELCD GMELCE MEA (MEA+, MEACOO-) 16.9 -2810 22.4

(MEA+, MEACOO-) MEA -13.6 1865 16.5 H2O (MEA+, HCO3-) 12.8 3129 66.0

(MEA+, HCO3-) H2O -3.8 6982 440.4 MEA (MEA+, HCO3-) 1.8 430 2262.8

(MEA+, HCO3-) MEA -30.8 14445 659.2 CO2 (MEA+, HCO3-) 49.2 156 24.6

(MEA+, HCO3-) CO2 -5.9 -215 -5.9 H2O (MEA+, MEACOO-) 8.2 -790 -19.7

(MEA+, MEACOO-) H2O -7.4 432 1.8

Regression was started from the parameters set in Table 10.

Table 10: Target parameters in this work

Component i Component j GMELCC GMELCD (K+,MEACOO-) H2O χ χ

H2O (K+,MEACOO-) χ χ (K+,MEACOO-) MEA χ χ

MEA (K+,MEACOO-) χ χ (MEA+,SO4--) H2O χ χ

H2O (MEA+,SO4--) χ χ (MEA+,SO4--) MEA χ χ

MEA (MEA+,SO4--) χ χ (K+,SO4--) MEA χ χ

MEA (K+,SO4--) χ χ (K+,HCO3-) H2O χ χ

H2O (K+,HCO3-) χ χ (K+,HCO3-) MEA χ χ

MEA (K+,HCO3-) χ χ

All the experiment data were regressed, but with different weights in order to get better simulation results. The weight was 1 for data without CO2 loading and 5 for MEA data with CO2 loading. The weights were varied because most parameters regressed are affected by CO2, therefore data with CO2 loading has a higher weight.

The parameters that were selected had little correlation with the others in the same regression. Other parameters were excluded as much as possible to get small standard deviations and small sum of squares.

The sum of squares of this regression result is 8474.

Residual root mean square error is 13.2.

The final regression result is given in Table 11.

45

Table 11: Regression result — GMELCC

Parameter Component i Component j Value (SI units) Standard deviation GMELCC H2O (K+,MEACOO-) 15.67 2.80 GMELCD H2O (K+,MEACOO-) -1985.3 926.3 GMELCC (K+,MEACOO-) MEA -8.01 2.50 GMELCD (K+,MEACOO-) MEA 608.4 795.0 GMELCC (MEA+,SO4--) H2O -4.06 0.49 GMELCD (MEA+,SO4--) H2O 11.02 153.13 GMELCC (MEA+,SO4--) MEA 13.71 20.72 GMELCC MEA (MEA+,SO4--) 112.4 3382.5 GMELCC (K+,SO4--) MEA -0.35 0.76 GMELCD (K+,SO4--) MEA -862.5 242.9 GMELCC (K+,HCO3-) MEA 8.10 10.66 GMELCD (K+,HCO3-) MEA -306.4 3238.8

The other τ parameters were automatically set to be default values as in Table 7.

Therefore the parameter set of 12 regressed values and default values was developed; this set is expected to simulate the interaction between ion pairs and molecules within certain condition ranges.

Test for regression

The electrolyte-NRTL model in Aspen Plus®, with the developed parameter set above, was input to a series of flash simulations, which was also developed from Hilliard’s template. Each of the experimental conditions was used to get the activity coefficients and mole fractions of K+ and SO4

=, as well as the Ksp error of K2SO4. The regressed value of GMELCC for the electrolyte pair MEA/(MEA+, SO4

--) has a large standard deviation; in order to fit the data, it was corrected to Aspen default value 15. The Ksp error is calculated from the equation below:

)T(Ksp)SO()K(

)T(Ksp)SO()SO(x)K()K(x

SALT)-K Ksp(fromt)coefficienactivity andfraction Ksp(from

Ksp(T)Ksp(a)error Ksp

-24

224

24

22 ααγγ ⋅=

⋅⋅⋅=

==

+−−++

The Ksp error for each experimental case fluctuates within a range (0.1, 5.1), which illustrates that the regression is not accurate and still needs modification.

Discussion The dependence of the Ksp error on temperature, MEA concentration, and CO2 loading is studied. The prediction of Ksp using K-SALT is tested by experiment data.

46

0.1

1

10

20 40 60 80Temperature(C)

Ksp

(a)/K

sp(T

)

Figure 4: Dependence of Ksp error on Temperature

There is no obvious effect of temperature on Ksp error. Therefore GMELCD parameters adequately represent the temperature effect.

0.1

1

10

0 5 10[MEA](m)

Ksp

(a)/K

sp(T

)

Figure 5: Dependence of Ksp error on MEA concentration

For 0 m MEA from water experiment data, Ksp error is close to 1; for 11 m MEA, there are fluctuations of Ksp error, but these may come from effects other than MEA. For 7 m MEA solution, Ksp error tends to be smaller than 1; that illustrates there is a systematic error for 7 m MEA data in regression. The parameter set is not good enough to represent the interaction of MEA related molecule and ions, especially for 7 m MEA solution.

47

0.1

1

10

0 2 4 6[CO2](m)

Ksp

(a)/K

sp(T

)

Figure 6: Dependence of Ksp error on [CO2]t

At high CO2 concentration, the Ksp error tends to be higher than 1; while at lower CO2 concentration, the Ksp error tends to be smaller than 1; 0 loading of CO2 experiment data, is well represented. This may be because not enough parameters related to CO2 were adjusted.

Conclusions

More experiments were conducted and verified the correlation equation for apparent Ksp:

3.75-T

1966.4ine]0.36[eq.amI9.15lnKsp 0.17 −−⋅= .

A new parameter set based upon Hilliard’s CO2-MEA-H2O template using electrolyte-NRTL for the CO2-MEA-H2O-K+-SO4

= system was developed. It simulates the interactions between ion pairs and molecules within certain condition ranges, but has systematic errors.

Ksp error, which represents the accuracy of the regression result for each experimental data, is dependent on MEA concentration and CO2 loading, and is independent on temperature. The regression still needs modification.

The regression error may result from the omission of important parameters, and experiments.

Future work

Modify the regression of MEA experiment data; get more accurate GMELCC/GMELCD/GMELCE parameter set for the energy parameter τ.

Conduct experiments of K2SO4 continuous crystallization. Find out:

-Slurry characteristics: settling rates, filterability, drying rates and final moisture contents.

-Crystal characteristics: composition, form, habit, shape factors, and solid density.

48

-Crystallization kinetics: crystal nucleation, growth.

Modify Aspen reclaiming process model with regressed parameters, optimize conditions, and add crystallization data into the model.

References

Aspen Plus® help: Electrolyte-NRTL activity coefficient model, etc. Jones AG, Crystallization Process Systems, 2002. CRC Handbook of Chemistry and Physics, 87th edition, online, section 8-114,

http://www.hbcpnetbase.com/articles/08_21_86.pdf Hilliard M., Aspen Plus® template for MEA-CO2-H2O system Söhnel O & P Novotny. Densities of Aqueous Solutions of Inorganic Substances. Elsevier,

Amsterdam, 1985.

49

Amine Volatility over MDEA/Piperazine. Progress Report for October – December, 2007

by Bich-Thu Nguyen


and the




January 31, 2008

Abstract This work explores the volatility of MDEA and PZ in blends of varying amine concentrations at 40ºC and 60ºC. Amine volatilities are reported in terms of partial pressures and are further analyzed in terms of their apparent activity coefficients as a function of loading, temperature, and amine concentration. PZ partial pressure is found to decrease steadily as CO2 loading, reported on a basis of mol CO2/mol PZ, is increased. Furthermore, as expected this partial pressure is higher with respect to the higher experimental temperature and concentration. As for the apparent PZ activity coefficient, it behaves the same as the partial pressure with respect to all the experimental variables mentioned. Conversely, the apparent MDEA activity coefficient is seen to be higher with respect to the lower experimental temperature (40ºC) for reasons that need further investigation.

Introduction The objective of this work is to determine the volatility of amine species (methyldiethanolamine and piperazine) in blends of varying amine concentrations at 40ºC and 60ºC. More specifically, amine volatilities are explored in terms of activity coefficients as a function of loading, system temperature, and amine concentration

Data and Experimental Methods The equilibrium partial pressures of CO2, PZ, MDEA, and water were measured in a stirred reactor coupled with FTIR analysis (Figure 1).

50

Figure 1: Schematic of Vapor-Liquid Equilibrium FTIR Apparatus This VLE apparatus allows simultaneous measurements of CO2 solubility and amine volatility. The heated sample is introduced at 180ºC to a hot gas FTIR at the same temperature. The elevated operating temperature serves to eliminate condensation and adsorption of vapor amine to any apparatus surface; thus, low vapor concentrations of amine can be analyzed more accurately.

The FTIR equipment has the capability of performing a multi-component analysis by combining complete calibration spectra of each component to make up the measured spectrum of the unknown gas. Once FTIR analysis is complete, gas is recycled back to the reactor at a temperature that is ~55ºC higher than the reactor temperature to preserve low amine concentrations. Note that it takes roughly 1.5-2.0 hours for the system to reach equilibrium before a data point can be taken.

Solution loading is prepared gravimetrically by weighing the total contents of the system before and after sparging with CO2. CO2 content of the solution is also determined by acidification and evolution into an IR analyzer. The total amine concentration in solution is determined by acid titration to an inflection point.

51

The table below captures all of the experimental data collected up to this point.

Table 1: Experimental Amine Volatility Data

Theory PZ vapor pressure is calculated from the below equation as provided in DIPPR database:

Pvap (Pa) = exp [A + B/T + C(ln T) + DTE] where T is in degrees Kelvin

Table 1 displays the values of the equation constants along with a temperature bracket applicable to the equation.

Table 2: Constants for DIPPR Model of Liquid PZ Vapor Pressure

A B C D E Min T (K)

Max T (K)

PZ 70.503 -7914.5 -6.6461 5.21E-18 6 379.15 638

While the temperatures explored in this experiment are outside the applicable range of this equation, it has been confirmed that this model performs satisfactorily at predicting vapor pressures in the lower temperature regime (which includes the experimental temperatures 40ºC and 60ºC). Figure 2 below is a log scale plot of the predicted PZ vapor pressure with respect to inverse temperature. Given the relative linearity of the plot in the low temperature regime, one can reasonably conclude that the model is fairly robust and consistent at predicting vapor pressure of pure PZ from 10ºC up to 450ºC (283K to 785K respectively).

52

100

1000

10000

100000

1000000

10000000

0.0015 0.002 0.0025 0.003 0.0035

1/Temperature (1/K)

Pred

icte

d PZ

Vap

or P

ress

ure

(Pa)

40C, 1142.6

60C, 3460.2

100C, 20.9 kPa

Figure 2: Predicted Vapor Pressures of Liquid PZ Using DIPPR Model Similarly, MDEA vapor pressure can be calculated using the equation above by substituting in appropriate constants specific to this compound.

Table 3: Constants for DIPPR Model of Liquid MDEA Vapor Pressure

A B C D E Min T (K) Max T (K)

MDEA 253.07 -18378.00 -33.97 2.33E-05 2.0 252.15 540

The modified Raoult’s law is then used to calculate the activity coefficient for PZ:

γi = (yi P) / (xi Po)

where γi is the activity coefficient of species i, yi is the mole fraction of species i in vapor phase, P is the total pressure, xi is the mole fraction of species i in liquid phase, and Po is the pure component vapor pressure of i. Note that the activity coefficients calculated in this report are apparent values that serve as an approximation of actual activity coefficient values. The reason that only apparent values can be calculated is because xi, the liquid mole fraction of a given amine species, reflects the total amount of that species present in the system instead of the amount of that species that is free or unreacted.

Results Figure 3 below illustrates PZ volatility in terms of its partial pressure plotted with respect to CO2 concentration.

53

0.1

1

10

0 0.2 0.4 0.6 0.8 1

CO2 Loading (mol CO2/mol PZ)

PZ P

artia

l Pre

ssur

e (P

a) 60ºC

40ºC

4M MDEA / 1.2M PZ

4M MDEA / 0.6M PZ

Figure 3: PZ Volatility as a Function of Loading, Temperature, and Amine Concentration

It can be seen from above that PZ partial pressure decreases steadily as CO2 loading increases. For a given PZ concentration, it is believed that more PZ is consumed at a higher CO2 loading as compared to a lower loading; therefore, the partial pressure of the former is expected to be lower than the latter. In addition, PZ partial pressure is primarily a function of temperature. Note from above that the two series with the highest PZ partial pressures correspond to the higher experimental temperature (60ºC). Furthermore, for the same two series mentioned, the one having a higher PZ concentration (1.2 M PZ) gives rise to higher partial pressures relative to the other with the lower PZ concentration.

The following figure plots the apparent PZ activity coefficient with respect to CO2 loading.

54

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0 0.2 0.4 0.6 0.8 1

CO2 Loading (mol CO2/mol PZ)

App

aren

t PZ

Act

ivity

Coe

ffici

ent

4M MDEA_0.6M PZ_40C

4M MDEA_1.2M PZ_60C

4M MDEA_1.2M PZ_40C

4M MDEA_0.6M PZ_60C

Figure 4: Apparent PZ Activity Coefficient as a Function of Loading, Temperature, and

Amine Concentration The apparent PZ activity coefficient trend with respect to CO2 loading is similar to the partial pressure trend shown previously. As the loading increases, the apparent activity coefficient decreases steadily due to higher consumption of piperazine by CO2. Furthermore, it is worthwhile to note that the two experimental series run at 60ºC both show higher coefficient than those run at 40ºC. Finally, the extremely small magnitude of the coefficient values indicates the highly non-ideal nature of PZ solution in reaction with CO2.

A plot is also prepared to illustrate MDEA volatility as a function of CO2 molar concentration.

55

0

0.5

1

1.5

2

2.5

3

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16

CO2 Concentration (mol/L)

MD

EA P

artia

l Pre

ssur

e (P

a) 60ºC

40ºC

4M MDEA / 0.6M PZ2M MDEA / 0.6M PZ

Figure 5: MDEA Volatility as a Function of Temperature, CO2, and Amine Concentration It is worthwhile to make a note of MDEA partial pressure being primarily a function of temperature. Note from above that the two series with the highest MDEA partial pressures correspond to the higher experimental temperature (60ºC). Also, it is not apparent how MDEA partial pressure varies with amine concentration. At the given temperatures, one notes conflicting trends of partial pressure vs. amine concentration due to possible experimental inconsistency that needs to be verified.

The following plot depicts the apparent behavior of MDEA activity coefficient.

56

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 0.05 0.1 0.15 0.2 0.25 0.3

CO2 Concentration (mol/L)

MD

EA A

ctiv

ity C

oeffi

cien

t 40ºC

60ºC

Figure 6: Activity Coefficient as a Function of Temperature, CO2, and Amine

Concentration In regard to the apparent MDEA activity coefficient, it can be seen that the activity coefficients at 40ºC are higher than those at 60ºC. This trend is rather counterintuitive as one would expect the opposite given that the heat of solution of MDEA in water is exothermic. Provided that the solvation of MDEA in water releases heat, it naturally follows that at higher temperatures the hydrated MDEA molecules would tend to come apart giving rise to more free MDEA than at lower temperatures; thus one should logically expect to see greater activity coefficient at the higher temperature as there is more free amine present to contribute to this phenomenon.

Future Work In the near future, effort will be made to continue collecting additional data for blends of other amine concentrations. Additionally, the focus will be on exploring amine volatility in the absorber lean and wash water streams along with stripper unit. Subsequently, AspenPlus® will be used to model experimental data in order to arrive at thermodynamic models for PZ and MDEA activity coefficients. Aside from exploring amine volatility, a separate effort will be made to conduct heat capacity measurements.

57

Rate Measurements of 7 m MEA and a Preliminary Gas Film Mass Transfer Correlation for the Wetted Wall Column

Progress Report for October – December, 2007

by Ross Dugas


and the




January 31, 2008

Abstract A variety of modifications and fixes were made to the wetted wall column system since the previous reporting period. Some of the previously obtained rate data may be inaccurate due to a leaky needle valve. Rate data from Aboudheir (2002) using highly concentrated, highly loaded monoethanolamine (MEA) was matched to literature data during my work with IFP in France. Recently obtained rate data agrees with Aboudheir for 7 m MEA at high loading. Wetted wall column obtained rates at low CO2 loading are still significantly higher than rates reported by Aboudheir with his laminar jet absorber. A new regression for a gas film mass transfer coefficient correlation for the wetted wall column did produce a slightly different correlation than that produced by Pacheco (1998). The new correlation does not explain the rate discrepancy at low CO2 loading. The rate discrepancy remains unexplained.

Introduction This report contains updated carbon dioxide (CO2) absorption/desorption rate data for 7 m monoethanolamine (MEA). Results were obtained by a wetted wall column at 40 and 60˚C. Many of the methods and procedures used in obtaining this data have been discussed in earlier reports and will not be duplicated here. However, essential information will be duplicated to preserve the continuity of the report.

As in previous work, solution CO2 loadings are determined on an alkalinity basis. The alkalinity is essentially the number of nitrogen atoms on the amine; 1 for MEA but 2 for PZ. The CO2 loading definition is shown in Equation 1.

PZMEA

CO

nnn

LoadingCO22

2 += (1)

58

Methods The 100 and 500 SCCM flow controllers were repaired and reinserted into the wetted wall column system. A gas leak was discovered through the valve stem of a needle valve for the CO2 flow. Since this leak occurred after the measured CO2 flow rate and before the mixing point, previous results may be inaccurate due to incorrect CO2 partial pressures. The leaking needle valve and the other needle valves were replaced with globe valves. The valves are used strictly for on/off operation and the application is more suited for globe valves.

The extent of errors due to leaking CO2 is difficult to quantify and is likely a function of gas flow rate, CO2 concentration and the experimental pressure. Some experiments will need to be reproduced to determine whether the previously obtained rate data is still relevant.

The wetted wall column was disassembled prior to this reporting period. When the cell was reassembled, the glass piece separating the gas flow from the thermal fluid was cracked. The piece could not be repaired and a near identical replacement part was built by a glassblower. Measurements of the new piece are very similar to the older piece.

Results and Discussion A significant amount of work concerning the kinetics of Aboudheir (2002) was performed during my work (May-Sept 2007) with IFP in France. Accounting for activity coefficients and correcting the diffusion coefficient of CO2 for the viscosity effects of loaded MEA solutions allowed the Aboudheir rate data to match literature sources and a correlation produced by Versteeg (1996). Therefore, the data collected by Aboudheir is considered very high quality data which I will use to compare results. My work with IFP also led me to further consider the effect of diffusion on the rate measurements. An analysis of the previously obtained rate data via equations presented by Danckwerts (1970) led to the conclusion that the wetted wall column will operate in the instantaneous reaction regime under some conditions. In the instantaneous reaction regime, kinetics no longer affect the flux of CO2. The solution becomes diffusion controlled and fluxes are determined by the ability of reactants and products to diffuse near the interface. In this regime kinetics cannot accurately be extracted from experimental data.

Since diffusional resistances of the MEA-CO2-H2O system are difficult to quantify, kinetics can only be accurately extrapolated when the Hatta (Ha) number is equivalent to the enhancement factor (E). This case must be satisfied to verify that the experiment is not operating in the instantaneous reaction regime. The E=Ha condition was verified to apply to all of the data collected by Aboudheir (2002) with the laminar jet absorber. The E=Ha case did not apply to all the wetted wall column data previously collected. Data points collected near 0.50 loading were determined to be operating in the instantaneous regime. This explains why wetted wall column measurements in this range showed lower mass transfer coefficients than Aboudheir. The controlling variable which makes some wetted wall column measurements susceptible to this special case is the resonance time of the solvent. Wetted wall column experiments have a liquid resonance time on the order of 25 times larger than the laminar jet experiments by Aboudheir.

Five new data points were run for 7 m MEA at absorber conditions. A kg’ rate comparison versus the Aboudheir data is shown in Figure 1.

59

6x10-8

7x10-8

8x10-89x10-81x10-7

2x10-7

3x10-7

4x10-7

5x10-7

0.1 0.2 0.3 0.4 0.5

Aboudheir 40Aboudheir 60Dugas 40CDugas 60C

k g' (m

ol/c

m2 Pa

. s)

Loading (mol/mol)

7m MEA

40C

60C

Figure 1: Recent Wetted Wall Column Rate Data Compared to Laminar Jet Absorber

Data by Aboudheir (2002) Rate data with a 0.42 loading shows good agreement with the data by Aboudheir. The three data points at a CO2 loading of 0.21 show significantly higher rates than literature data. This trend was also observed in previous experiments with low loadings. The reason for the discrepancy is still being investigated. One possible explanation which was examined is the estimation of the gas film mass transfer coefficient. The duplicate data point for 60ºC with a 0.21 loading represents two experiments performed at varying pressures. The upper point was tested at 25 psig while the lower was tested at 15 psig.

The gas film mass transfer coefficient correlation for the wetted wall column was originally determined by Pacheco (1998) by absorbing CO2 into 2 M MEA. Bishnoi (2000) later verified the correlation using a more gas film controlled system: SO2 absorption into 0.1 M NaOH. However, since the wetted wall column was disassembled and a new glass tube was installed, the correlation may have significantly changed. My gas film mass transfer experiments utilized CO2 absorption into unloaded 2 m piperazine.

The advantage of using piperazine rather than monoethanolamine to quantify the gas film mass transfer coefficient is that piperazine is a faster reacting amine than MEA. It also has more amine functional groups which would reduce the diffusion requirements of the solution. Therefore piperazine systems, at least at low amine concentrations, should be more gas film controlled and should give a more accurate gas film correlation. However, the rate constant of piperazine is not known with as much certainty as the rate constant of MEA. Piperazine rate constant data was obtained from Bishnoi (2000) but was assumed to be underreported by 30% due to an odd relationship between the Reynolds and Sherwood numbers.

60

Results from the gas film mass transfer coefficient experiments are shown in Tables 1 and 2.

Table 1: Overall Mass Transfer Coefficients Obtained with Unloaded 2 m PZ at 40ºC

KG @ 40ºC Gas Flow (SLPM) 3 4 5

15 2.37E-10 2.75E-10 3.07E-10 25 2.05E-10 - 2.69E-10 45 1.39E-10 1.87E-10 2.05E-10 P

(psi

g)

70 1.12E-10 - 1.49E-10

Table 2: Overall Mass Transfer Coefficients Obtained with Unloaded 2 m PZ at 60ºC

KG @ 60ºC Gas Flow (SLPM) 3 4 5

15 2.79E-10 3.26E-10 3.72E-10 25 2.04E-10 - 3.03E-10 45 1.54E-10 1.99E-10 2.125E-10 P

(psi

g)

60 1.23E-10 - 1.78E-10

Preliminary results seem to suggest the gas film mass transfer correlation can be represented as Equation 2. The previously used correlation developed by Pacheco is shown in Equation 3. The Sherwood number relates to the gas film mass transfer coefficient via Equation 4.

89.0

Re083.1 ⎟⎠⎞

⎜⎝⎛ ⋅⋅=

hdScSh (2)

85.0

Re075.1 ⎟⎠⎞

⎜⎝⎛ ⋅⋅=

hdScSh (3)

2CO

g

DdkTR

Sh⋅⋅⋅

= (4)

The preliminary gas film mass transfer correlation (Equation 2) is slightly different from the correlation developed by Pacheco. The newer correlation does somewhat converge the two 60ºC data points which were operated at different pressures. The correlation also reduces the calculated kg’ and moves the data points at 0.21 loading a bit closer to the results of Aboudheir. However, a large discrepancy is still present. The data at 0.42 loading shows a slightly worse fit with the literature data using the new correlation. Calculated kg’ values using the Pacheco and the current “Dugas” gas film mass transfer correlations are shown in Table 3.

61

Table 3: Calculated kg’ values for Recent Data Using the Pacheco (1998) and Dugas Gas Film Mass Transfer Correlations Compared to Aboudheir (2002) Trends

kg' (mol/cm2Pa.s) Loading

Temp (ºC) Pacheco Dugas Aboudheir (Estimation)

0.21 40 3.25E-10 3.08E-10 2.10E-10 0.21 60 4.19E-10 3.83E-10 2.88E-10 0.21 60 3.97E-10 3.73E-10 2.88E-10 0.42 40 9.14E-11 8.88E-11 9.31E-11 0.42 60 1.20E-10 1.14E-10 1.31E-10

At this point it is not certain whether the Dugas gas film correlation is more accurate than the Pacheco correlation. Future data will decide which correlation is ultimately used.

References Aboudheir, AA. "Kinetics, Modeling, and Simulation of CO2 Absorption into Highly

Concentrated and Loaded Monoethanolamine Solutions." PhD Dissertation, University of Regina, 2002. 364.

Bishnoi, S. "CO2 Absorption and solution equilibrium in piperazine activated methyldiethanolamine." PhD Dissertation, University of Texas at Austin, 2000. 270.

Danckwerts, PV. Gas-Liquid Reactions, McGraw-Hill, Inc. 1970. Pacheco, MA. "Mass Transfer, Kinetics and Rate-Based Modeling of Reactive Absorption." PhD

Dissertation, University of Texas at Austin, 1998. 291. Versteeg, GF, LAJ Van Dijck, et al. "On the Kinetics Between CO2 and Alkanolamines Both in

Aqueous and Non-aqueous Solutions. An Overview." Chem. Engr. Comm 144: 113-158. 1996.

62

Influence of Liquid Properties on Effective Mass Transfer Area of Structured Packing

Quarterly Progress Report

October 1, 2007 – December 31, 2007 by

Robert Tsai Supported by the Industrial Associates Program in CO2 Capture,

the Luminant Carbon Management Program and the

Separations Research Program

Department of Chemical Engineering The University of Texas at Austin

January 31, 2008

Abstract

The hydraulic performance (pressure drop and hold-up) of Sulzer Mellapak 250Y structured packing was compared under baseline (i.e., water) and moderately viscous conditions (approximately 5.5 mPa·s, achieved using high molecular weight poly(ethylene oxide) – POLYOXTM WSR N750). Pressure drop was nearly the same for the majority of tested liquid loads, and hold-up values appeared to be consistently higher (albeit only by ~1%) for the viscous solution. The effective mass transfer area of Mellapak 250Y was measured as a function of liquid load via CO2 absorption into dilute caustic solution. Experiments were conducted at enhanced viscosities (approximately 2.5 and 5 mPa·s). The data at 5 mPa·s showed the mass transfer areas to be surprisingly low, with fractional areas ranging from 0.26-0.36 – around 3 times lower than in the base case. The experiment at 2.5 mPa·s was subsequently conducted, and oddly, the measured areas were practically identical to the 5 mPa·s data. This suggested that the results were being influenced by a systematic error. Extensive troubleshooting revealed that the vacuum pump at the outlet sample line was leaking in ambient air, thereby causing the apparent CO2 removal – and hence, mass transfer performance – to be falsely low.

Introduction Packing is commonly used in industrial processes as a means of promoting efficient gas-liquid contact. One important application for which packed columns are being considered is treating flue gas for CO2 capture. The conventional method consists of an aqueous amine solvent such as monoethanolamine (MEA) contacting the gas, resulting in the absorption of CO2 (Kohl and Nielsen, 1997). The enriched solvent is sent to a stripper for regeneration and is then recycled back to the absorber. Gas-liquid contact in both the absorber and stripper is enhanced through the use of packing.

63

2

Reliable mass transfer models are necessary for design and analysis purposes. A critical factor involved in modeling is the prediction of the effective interfacial area of packing (ae), which can be considered as the total gas-liquid contact area that is actively available for mass transfer. The current research effort is focused on this parameter. Characterization of effective areas is vital to amine-based CO2 capture at the industrial level, because absorption rates actually become independent of conventional mass transfer coefficients (kG or k˚L) but remain directly proportional to the effective area. Thus, it is especially desirable to have an accurate model for the latter.

Numerous empirical or semiempirical packing area correlations have been presented in the literature, but none has been shown to be truly predictive over a wide range of conditions. Wang et al. (2005) performed a comprehensive review of the available models. The various correlations predict different and sometimes even contradictory effects of liquid viscosity and surface tension, properties that would be expected to fundamentally influence the wetted area of packing. It is evident that their role is not well understood, and there is a definite need for work in this subject matter.

The Separations Research Program (SRP) at the University of Texas at Austin has the capability of measuring packing mass transfer areas. Measurements are performed by absorbing CO2 from air with 0.1 M NaOH in a 430 mm (16.8 in) ID column. Unfortunately, physical parameters are limited to those of water, making it potentially inaccurate to extend these results to other fluids of interest, such as amine solvents, due to the differences in viscosity and surface tension.

Limited understanding of the fluid mechanics and mass transfer phenomena in packed columns has been noted, and the need for experiments over a broader range of conditions has been identified (Wang et al., 2005). The goal of this research is to address these shortcomings and ultimately develop an improved effective area model for structured packing in particular. The general objectives are to:

• Develop a fundamental understanding of the fluid mechanics associated with structured packing operation;

• Determine suitable chemical reagents to modify the surface tension and viscosity of the aqueous caustic solutions employed to make packing area measurements, and characterize potential impacts of such additives on the CO2-NaOH reaction kinetics;

• Expand the SRP database by measuring the mass transfer areas of several different structured packings over a range of liquid viscosities and surface tensions;

• Combine the data and theory into a semiempirical model that captures the features of the tested systems and adequately represents effective area as a function of viscosity, surface tension, and liquid load.

Experimental

430 mm ID Packed Column The packed column had an outside diameter of 460 mm (18 in), inside diameter of 430 mm (16.8 in), and a 3 m (10 ft) packed height. For details regarding the apparatus and procedure for mass transfer tests, the Q4 2006 report may be consulted.

64

3

Liquid hold-up tests required a slight alteration to be made to the valve setup of the system. The column was isolated from the storage tank, and the lines in this closed loop were primed with liquid. By measuring the steady-state liquid level in the column sump (continuously monitored via a level transmitter) during operation and knowing the sump/column dimensions, the approximate hold-up within the packing could be calculated.

Wetted-Wall Column (WWC) The wetted-wall column (WWC) is a vapor-liquid contactor with an interfacial area of 38.52 cm2 and is the same apparatus that Mshewa (1995), Bishnoi (2000), and Cullinane (2006) employed to measure the kinetics of various CO2-amine systems. The auxiliary equipment and protocol associated with a standard WWC experiment are archived in earlier quarterly reports (e.g. Q3 2006).

Goniometer The goniometer (ramé-hart Inc., Model #100-00) was equipped with an adjustable stage; the overall setup included a computer-linked camera for live image display and a light source (see Q3 2006 report). The apparatus was utilized in conjunction with FTA32 Video 2.0 software (First Ten Angstroms, Inc.) to make surface tension measurements via the pendant drop method.

Rheometer The rheometer employed for viscosity measurements was first described in the Q4 2006 report. The apparatus (Physica MCR 300) was manufactured by Anton Paar USA. Temperature was regulated with a Peltier TEK 150P-C unit and a Julabo F25 water bath unit (for counter-cooling). Measurement profiles consisted of a linearly or logarithmically increased or decreased shear rate (100-2000 s-1), with 10-20 data points recorded at 15 second intervals. Viscosity was determined from a plot of shear stress (measured) vs. shear rate.

Materials 0.1 M NaOH solution for WWC experiments was purchased from Fisher Scientific (certified grade). The solid NaOH pellets (ACS grade) used in packed tower experiments were ordered through Capitol Scientific, Inc. POLYOXTM WSR N750 (pharmaceutical (NF) grade) was procured from Dow Chemical. Antifoam agent (Dow Corning® Q2-3183A) was supplied by Dow Corning®.

Results and Discussion

Theoretical Analysis of Data The fundamental equation used to interpret both the packed column and WWC results is presented in eq 1. The overall mass transfer resistance is expressed as a series relationship of the gas and liquid contributions. KG, kG, and kg′ respectively represent the overall, gas-side, and liquid-side mass transfer coefficients.

'k1

k1

K1

gGG+= (1)

65

4

For the WWC, the overall mass transfer coefficient is calculated from the CO2 flux and the partial pressure driving force.

⎟⎟⎠

⎞⎜⎜⎝

⎛

−==

outCO

inCO

outCOinCO

CO

LMCO

COG

2

2

22

2

2

2

yy

ln)yy(P

NPN

K (2)

A gas-side mass transfer coefficient correlation for the WWC was developed by absorption of SO2 into 0.1 M NaOH, an entirely gas-film controlled process (Bishnoi, 2000). Equation 3 is a rearrangement of this correlation, which involved the Sherwood, Reynolds, and Schmidt numbers and the physical dimensions of the system.

⎟⎟⎠

⎞⎜⎜⎝

⎛⎟⎟⎠

⎞⎜⎜⎝

⎛=

RTdD

DLdu

075.1k G,CO85.0

G,CO

2G

G2

2

(3)

Equations 1-3 are used to calculate kg′, which has been defined as a liquid-side mass transfer coefficient expressed in terms of a CO2 partial pressure driving force.

2

2

CO

L,COOH'g H

D]OH[kk

−−

= (4)

For comparison, eq 4 can be evaluated using literature correlations for the diffusivity of CO2 in electrolyte solutions (DCO2,L), the Henry’s constant of CO2 in electrolyte solutions (HCO2), and the rate constant (kOH-) (Pohorecki and Moniuk, 1988).

Equation 1 is also central to the analysis of data gathered using the 430 mm ID packed column. The gas-side resistance was intentionally limited by using dilute caustic solution (0.1 M) and operating at high superficial air velocities (1 or 1.5 m/s). Even under the worst circumstances, this resistance (estimated using the correlation for kG proposed by Rocha et al. (1996)) should have been accountable for no more than 1.5% of the overall mass transfer resistance. Therefore, gas-side resistance was ignored in the analysis, and KG was assumed to be equal to kg′. This approximation enabled the effective packing area (ae) to be determined, by separating it from the volumetric mass transfer coefficient, KGae, as shown in eq 5.

RTkZ

yy

lnu

RTKZ

yy

lnu

a 'g

outCO

inCOG

G

outCO

inCOG

e2

2

2

2

⎟⎟⎠

⎞⎜⎜⎝

⎛

≈⎟⎟⎠

⎞⎜⎜⎝

⎛

= (5)

WWC Enhanced Viscosity Studies POLYOXTM WSR N750 (NF grade) was used as a viscosity enhancer for several reasons:

• Only small concentrations (~1 wt %) were required to achieve a considerable increase in viscosity.

• Literature studies (Lohse et al. (1981), Komiyama and Fuoss (1972), and Rischbieter et al. (1996)) suggested that even for an appreciable viscosity increase, a minor impact on CO2 diffusivity and a negligible impact on CO2 solubility could be expected. We

66

5

obviously would prefer to minimize any additive-related adjustments to these parameters, since drastic corrections could confound the interpretation of the results.

• It was not anticipated that the POLYOX would interfere as a reactive species.

WWC tests with 10-fold viscosity-enhanced solutions (see Q3 2007) were encouraging. The data exhibited marginally lower kg′ values compared to the “pure” 0.1 M NaOH base case (which has been shown in previous quarterly reports to correspond favorably with the correlation of Pohorecki and Moniuk). This was in agreement with the literature and clearly contrasted with the significant reduction in kg′ that would be expected under “normal” circumstances.

Hydraulics The hydraulic behavior of Sulzer Mellapak 250Y structured packing was measured under baseline (water: μL ~ 1 mPa·s, σ ~ 72 mN/m) and moderately viscous (water + POLYOX: μL ~ 5.5 mPa·s, σ ~ 60 mN/m) conditions. The pressure drop data are shown in Figures 1 and 2. At higher liquid loads (i.e., 36.6, 48.8 m3/m2-h), the pressure drop for the more viscous solution was somewhat higher, but aside from that, there seemed to be little difference between the two liquid systems.

1 0

1 0 0

1 0 0 0

0 .4 0 .6 0 .8 1 3 5

D ry

2 .4 m 3/m 2-h

6 .1 m 3/m 2-h

1 2 .2 m 3/m 2-h

1 8 .6 m 3/m 2-h

2 4 .4 m 3/m 2-h

3 6 .6 m 3/m 2-h

4 8 .8 m 3/m 2-h

7 3 .4 m 3/m 2-h

Pres

sure

dro

p (P

a / m

pac

king

)

F -fa c to r (m /s )(k g /m 3)0 .5

Figure 1: Mellapak 250Y pressure drop data (baseline).

67

6

10

100

1000

0.4 0.6 0.8 1 3 5

Dry

2.4 m3/m2-h

6.1 m3/m2-h

12.2 m3/m2-h

18.6 m3/m2-h

24.4 m3/m2-h

36.6 m3/m2-h

48.8 m3/m2-h

73.4 m3/m2-h

Pres

sure

dro

p (P

a / m

pac

king

)

F-factor (m/s)(kg/m3)0.5

Figure 2: Mellapak 250Y pressure drop data (μL ~ 5.5 mPa·s). POLYOX solution

contained small quantity of antifoam (~10 ppmw/v).

In Figure 3, the hold-up data obtained at an F-factor of around 0.7 (m/s)(kg/m3)0.5 (i.e., well below the loading point) are plotted against the model of Suess and Spiegel (1992). Suess and Spiegel used a gamma ray absorption technique to measure hold-up in various Mellapak packings (including Mellapak 250Y) and correlated their results into the form shown below.

( )25.0

0,L

LxL

83.0pL μ

μuca%h ⎟

⎟⎠

⎞⎜⎜⎝

⎛= (applies below loading point) (6)

c = 0.0169, x = 0.37 for uL < 40 m3/m2-h c = 0.00075, x = 0.59 for uL > 40 m3/m2-h

Equation 6 was slightly under-predictive for the base case and over-predictive for the viscous system but overall, matched the data reasonably well. The enhanced viscosity generally seemed to result in higher hold-ups, which was expected, although the effect was not as emphatic as the one predicted by eq 6.

68

7

1

10

1 10

h L (%)

Liquid load (m3/m2-h)

Suess and Spiegel (1 mPa s)

Expt (1 mPa s)

Expt (5.5 mPa s)

Suess and Spiegel (5.5 mPa s)

Figure 3: Comparison of Mellapak 250Y hold-up data with predicted values from eq 6.

POLYOX solution contained small quantity of antifoam (~10 ppmw/v).

Mass Transfer The effective mass transfer area of Mellapak 250Y (ap = 250 m2/m3) was measured at approximately 5 mPa·s and 60 mN/m (Figure 4). (Note: the slightly reduced viscosity compared to the hydraulic tests was due to day-to-day temperature variation.) The fractional areas (ae/ap) were unexpectedly low, increasing from 0.26 to 0.36 over the range of liquid loads. Several additional data sets are shown for comparison. Bringmann was a visiting German scholar who worked for the SRP from 2004-2005. He measured the mass transfer area of Mellapak 250Y at several different viscosities, modified using sucrose (Bringmann, 2005). His base case matched up quite nicely with our more recent baseline measurements, which gives us confidence in relating our findings. As can be seen, his data at 5 mPa·s showed a moderate decrease in performance. However, in his analysis, he assumed that the sucrose had no impact on the CO2-NaOH kinetics. Since then, a survey of the literature (Vázquez et al., 1997), as well as WWC studies conducted by Rocha (2005) and Tam (2005), have led us to believe otherwise. Rocha and Tam both found the reaction rate constant to be enhanced by the presence of sucrose (i.e., kOH-, norm > 1). The reproducibility between their investigations was somewhat questionable, but based on their findings, one could certainly make the argument that Bringmann’s “adjusted” data should be roughly on par with the POLYOX results.

69

8

It is important to point out that in their original reports, Bringmann, Rocha, and Tam applied different exponents (ranging from 0.45 to 1) in their treatment of the diffusivity-viscosity relation. Their results have all been standardized here with an exponent of 1 (the value that Pohorecki and Moniuk used in their analysis).

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 10 20 30 40 50 60 70 80

Liquid load (m3/m2-h)

Frac

tiona

l are

a

Baseline (Bringmann): μ ~ 0.75 mPa·s

Sucrose (Bringmann): μ ~ 5 mPa·s

"Adjusted" sucrose (Bringmann-Tam)(kOH-,norm ~ 10.2)

POLYOX: μ ~ 5 mPa·s, σ ~ 60 mN/m

"Adjusted" sucrose (Bringmann-Rocha)(kOH-,norm ~ 6.4)

Figure 4: Fractional area measurements for Mellapak 250Y packing compared at baseline

and enhanced viscosity (5 mPa·s) conditions. uG = 1.5 m/s for all data sets. POLYOX solution contained small quantity of antifoam (~10 ppmw/v).

Figures 5 and 6 respectively compare the POLYOX data from Figure 4 with several random packing models (Onda (1968), Bravo-Fair (1982), and Billet-Schultes (1993)) and structured packing models (Rocha-Bravo-Fair (1996), Henriques de Brito (1994), and Olujic (1997)). The measured values were well below those predicted by the correlations (with the exception of Onda and perhaps Henriques de Brito) and also exhibited a weaker dependence on liquid load.

70

9

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

0 5 10 15 20 25 30 35 40Liquid load (m3/m2-h)

Frac

tiona

l are

a Bravo-Fair

Billet-SchultesOnda

Experimental

Figure 5: Comparison of Mellapak 250Y data (μL ~ 5 mPa·s, uG = 1.5 m/s) with random

packing models.

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0 5 10 15 20 25 30 35 40Liquid load (m3/m2-h)

Frac

tiona

l are

a

Henriques de Brito

Olujic

Rocha-Bravo-Fair

Experimental

Figure 6: Comparison of Mellapak 250Y data (μL ~ 5 mPa·s, uG = 1.5 m/s) with structured

packing models.

71

10

Despite the apparent agreement between Bringmann and our data sets at 5 mPa·s, skepticism remained regarding the rather extreme effect of viscosity. For this reason, a POLYOX experiment at 2.5 mPa·s was conducted (Figure 7). Bringmann’s adjusted data at 2.3 mPa·s suggested that fractional areas on the order of 0.5-0.6 should be anticipated. Chen performed a pilot-plant campaign at the SRP facility with 5 m K+ / 2.5 m PZ (Chen, 2007) and regressed the interfacial area of the absorber packing in his analysis of the data. One of the tested packings was Flexipac AQ Style 20 (ap = 213 m2/m3), which is comparable to Mellapak 250Y in terms of capacity/surface area. As can be seen, Chen’s overall regressed value for Flexipac AQ was exactly where Bringmann’s data would predict it to be on the basis of viscosity (2.8 mPa·s).

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1.1

1.2

0 10 20 30 40 50 60 70 80Liquid load (m3/m2-h)

Frac

tiona

l are

a

Baseline (Bringmann): μ ~ 0.75 mPa·s

Sucrose (Bringmann): μ ~ 2.3 mPa·s

"Adjusted" sucrose (Bringmann-Tam) (kOH-,norm ~ 4.4)

POLYOX: μ ~ 2.5 mPa·s, σ ~ 60 mN/m

"Adjusted" sucrose (Bringmann-Rocha)(kOH-,norm ~ 2.7)

Chen (Flexipac AQ, 5 m K+ / 2.5 m PZ): 2.8 cP, 40 mN/m)

Figure 7: Fractional area measurements for Mellapak 250Y packing compared at baseline and enhanced viscosity (2.3-2.8 mPa·s) conditions. uG = 1.5 m/s for all data sets. POLYOX

solution contained small quantity of antifoam (~20 ppmw/v).

Unfortunately, the measured areas at 2.5 mPa·s were lower than expected and were, in fact, essentially the same as in the 5 mPa·s case. This indicated that there was possibly a systematic problem with the experiment.

The system was cleaned out, and a few baseline points were run. Measured fractional areas were around 0.25, confirming our suspicions. A N2 test with the vacuum pump at the outlet sample line revealed that the pump was leaking in ambient air. It is strongly suspected that this issue affected both POLYOX mass transfer experiments in this report (Figures 4 and 7), causing the apparent CO2 removal – and hence, mass transfer performance – to be falsely low. Therefore, little stock should be placed in the obtained data. (As an aside, the Flexipac 1Y results presented in the Q3 2007 report are thought to still be credible, as the pump was believed to have broken down sometime afterward.)

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11

Even though the recent measurements (5 mPa·s, 2.5 mPa·s, and baseline) were obtained under faulty circumstances, it is perhaps worthwhile to note that they all appeared to overlap. Consequently, we might expect there to be little or no effect of viscosity – the same result obtained with Flexipac 1Y. If this indeed turns out to be true, then it will raise questions regarding the reliability of the sucrose data. Such a scenario would not be very surprising, given that the applied “corrections” to the rate constant, diffusivity, and Henry’s constant are all expected to be substantial when sucrose is used as a viscosity enhancer. As was emphasized earlier, the greater the adjustments that need to be made to these parameters, the less reliable the end result will likely be. Obviously, though, this all remains speculation until the viscosity experiments can be re-run.

Conclusions The effect of viscosity (increased to ~5.5 mPa·s) on the hydraulic performance of Mellapak 250Y was not particularly significant. It would appear that the model of Suess and Spiegel is satisfactory for the prediction of liquid hold-up in Mellapak packings.

At the moment, no definitive conclusions can be made regarding the impact of viscosity on effective mass transfer area. The literature models are unable to provide much assistance in the matter; one can see that there is considerable disagreement among them (Figures 5 and 6).

Nomenclature ae = effective area of packing, m2/m3 ap = specific (geometric) area of packing, m2/m3 DCO2 = diffusivity of CO2, m2/s d = estimated hydraulic diameter of WWC reaction chamber, m HCO2 = Henry’s constant of CO2, m3·Pa/kmol hL = (total) liquid hold-up, dimensionless KG = overall gas-side mass transfer coefficient, kmol/(m2·Pa·s) kG = gas-side mass transfer coefficient, kmol/(m2·Pa·s) kg′ = liquid-side mass transfer coefficient, kmol/(m2·Pa·s) k˚L = physical liquid-side mass transfer coefficient, m/s kOH- = second-order reaction rate constant, m3/(kmol·s) L = exposed length of WWC, m NCO2 = molar flux of CO2, kmol/(m2·s) P = pressure, Pa R = ideal gas constant, (m3·Pa)/(kmol·K) T = absolute temperature, K u = superficial velocity, m/s yCO2 in/out = mole fraction of CO2 at inlet/outlet Z = packed height, m

Greek Symbols μ = dynamic viscosity, Pa·s μL,0 = dynamic viscosity of water at 20˚C, Pa·s σ = surface tension, N/m

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12

Subscripts G = gas phase L = liquid phase

Dimensionless Groups af = fractional area of packing, ae/ap

References Billet, R & M Schultes. "Predicting Mass Transfer in Packed Columns." Chem. Eng. Technol.

1993, 16 (1), 1-9.

Bishnoi, S. "Absorption of Carbon Dioxide into Aqueous Piperazine: Reaction Kinetics, Mass Transfer, and Solubility." Chem. Eng. Sci. 2000, 55 (22), 5531-5543.

Bravo, JL, & JR Fair. "Generalized Correlation for Mass Transfer in Packed Distillation Columns." Ind. Eng. Chem. Process Des. Dev. 1982, 21 (1), 162-170.

Bringmann, P. "Experiments on the Influence of Liquid Phase Viscosity on the Effective Interfacial Area of the Chemical Absorption of CO2 in Aqueous Sodium Hydroxide Solutions." Semester Thesis. University of Texas at Austin. 2005.

Chen, E. "Carbon Dioxide Absorption into Piperazine Promoted Potassium Carbonate using Structured Packing." Ph.D. Dissertation, University of Texas at Austin. 2007.

Cullinane, JT. "Kinetics of Carbon Dioxide Absorption into Aqueous Potassium Carbonate and Piperazine" Ind. Eng. Chem. Res. 2006, 45 (8), 2531-2545.

Henriques de Brito, M, U von Stockar, AM Bangerter, P Bomio, M Laso. "Effective Mass-Transfer Area in a Pilot Plant Column Equipped with Structured Packings and with Ceramic Rings." Ind. Eng. Chem. Res. 1994, 33 (3), 647-656.

Kohl, A & R Nielsen. Gas Purification; Gulf Publishing Co.: Houston, 1997.

Komiyama, J & RM Fuoss. "Conductance in Water-Poly(vinyl alcohol) Mixtures." Proc. Natl. Acad. Sci. U. S. A. 1972, 69 (4), 829-833.

Lohse, M, E Alper, G Quicker, WD Deckwer. "Diffusivity and Solubility of Carbon Dioxide in Diluted Polymer Solutions." AIChE J. 1981, 27 (4), 626-631.

Mshewa, MM. "Carbon Dioxide Desorption/Absorption with Aqueous Mixtures of Methyldiethanolamine and Diethanolamine at 40 to 120oC." Ph.D. Dissertation, University of Texas at Austin. 1995.

Olujic, Z. "Development of a Complete Simulation Model for Predicting the Hydraulic and Separation Performance of Distillation Columns Equipped with Structured Packings." Chem. Biochem. Eng. Q. 1997, 11 (1), 31-46.

Onda, K, H Takeuchi, Y Okumoto. "Mass Transfer Coefficients Between Gas and Liquid Phases in Packed Columns." J. Chem. Eng. Jpn. 1968, 1 (1), 56-62.

Pohorecki, R & W Moniuk. "Kinetics of Reaction between Carbon Dioxide and Hydroxyl Ions in Aqueous Electrolyte Solutions." Chem. Eng. Sci. 1988, 43 (7), 1677-1684.

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13

Rischbieter, E, A Schumpe, V Wunder. "Gas Solubilities in Aqueous Solutions of Organic Substances." J. Chem. Eng. Data 1996, 41 (4), 809-812.

Rocha, JA. "Effect of Viscosity and Surface Tension on Packing Gas/Liquid Contact Area." Summer Research Thesis. University of Texas at Austin, Austin. 2005.

Rocha, JA, JL Bravo,JR Fair. "Distillation Columns Containing Structured Packings: A Comprehensive Model for Their Performance. 2. Mass-Transfer Model." Ind. Eng. Chem. Res. 1996, 35 (5), 1660-1667.

Suess, P & L Spiegel. "Hold-up of Mellapak Structured Packings." Chem. Eng. Process. 1992, 31 (2), 119-124.

Tam, J. "The Effects of Viscosity on the Kinetics of the Absorption Reaction of Carbon Dioxide into Sodium Hydroxide Solution." Summer Research Thesis. University of Texas at Austin. 2005.

Vázquez, G, F Chenlo, G Pereira. "Enhancement of the Absorption of CO2 in Alkaline Buffers by Organic Solutes: Relation with Degree of Dissociation and Molecular OH Density." Ind. Eng. Chem. Res. 1997, 36 (6), 2353-2358.

Wang, GQ, XG Yuan, KT Yu. "Review of Mass-Transfer Correlations for Packed Columns." Ind. Eng. Chem. Res. 2005, 44 (23), 8715-8729.

75

CO2 Absorption Modeling Using Aqueous Amines Progress Report for October – December, 2007

by Jorge M. Plaza


and the




January 31, 2008

Abstract Work in this period has focused on the development of an absorber model that includes the new thermodynamic model by Hilliard (2007), recent observations on properties of this system by Dugas (2007), and a simpler set of reactions to represent MEA-CO2 absorption kinetics. Forward rates have been extracted from Aboudheir’s (2002) kinetic data by modeling his laminar jet apparatus using Aspen Plus® RateSep™. Results show considerable differences between hand calculated values and the Aspen Plus® values. Further work needs to be carried out to determine the source of differences and to ensure proper modeling of the laminar jet.

Description Work is being conducted on a new CO2 absorber model for MEA-Water. It includes the new thermodynamic model developed by Hilliard (2007) and recent observations on properties of this system by Dugas (2007). Kinetics are represented using the following set of reactions:

2 MEA + CO2 → MEAH+ + MEACOO- (1)

MEAH+ + MEACOO- → 2 MEA + CO2 (2)

MEA + CO2 → HCO3- + MEAH+ (3)

MEAH+ + HCO3- → MEA + CO2 (4)

Rate data generated by Aboudheir (2002) was used to evaluate the forward rate constants for this set of equations. An absorber was set up in Aspen Plus® RateSep™ with the variables presented in Table 1 to evaluate the forward kinetics for carbamate formation in the laminar jet system. The reverse rate constant will be obtained using the equilibrium constants determined by Hilliard. Carbonate formation reactions were not included as a first approach. Dimensions of the laminar jet reported by Aboudheir were scaled for modeling purposes. The diameter was multiplied by 35 and the height by 20. The solvent flow rate was increased by 100.

76

The liquid mass transfer coefficient (kL) was determined using a FORTRAN subroutine included in the Aspen model to bypass the correlation corresponding to the CMR packing. The calculation uses the following formula:

kL = (4/(π*d)) * (D*L/h)1/2 (5)

Where: kL is the liquid mass transfer coefficient (no reaction) – m/s

d is the jet diameter – m

D is the diffusivity of CO2 in the solvent – m2/s

h is the length of the jet - m

L is the liquid flow rate. – m3/s

Aspen calculates kL using the form k = A*Dn where k is in kgmol/s and D is the diffusivity. The mass transfer FORTRAN subroutine calculated the A parameter as:

A = 4/(π*d)) * (L/h)1/2 (6)

The exponent term (n) was set to 0.5. The resulting kL was then multiplied by the liquid density and the interfacial area of the jet. The latter was calculated by an interfacial area FORTRAN subroutine and is equal to the surface area of a cylinder with the height and diameter of the jet.

Table 1: Absorber design conditions for all modeling cases

Variable Value

Number of stages 2

Liquid hold up (%) 5

Packing Characteristics

Type CMR

Vendor MTL

Material Metal

Dimension NO-1.5P

The gas mass transfer coefficient (kG) was fixed at 10-6 kgmol*m-2*Pa-1s-1. The mass transfer subroutine converted this value to kgmol/s by multiplying kG by the interfacial area and the pressure of the system. The value of the gas mass transfer coefficient was defined in conversations with Dr. Rochelle and it is based in a system with minimum gas phase resistance.

77

Work by Dugas (2007) concluded the need to correct parameters in ASPEN to approach values of the density of mixture to those reported by Weiland (1998). These corrections were incorporated into the absorber model.

ASPEN Plus uses a power law to represent the forward kinetics for carbamate formation. The equation has the following form for activity based kinetics.

r = k*(T/To)n exp(-E/R*(1/T – 1/To))*(xi*γi)αi (7)

Where: r is the reaction rate – kgmol/s

k is the pre-exponential factor – kgmol*Kn/s

T is the temperature of the system - K

To is the reference temperature (298 K) – K

E is the activation energy

R is the universal gas law constant

X is the mole fraction

γ is the activity coefficient

Aboudheir’s data was regressed in Aspen Plus® to determine the values of k, E and n in the power law equation. However, due to convergence difficulties it was decided to set n and E to zero and evaluate k as the only regressed parameter. The gas inlet flow was set to 8.00E-05 kmol/s and the CO2 outlet flow was calculated based on the flux reported by Aboudheir. It was set as the design specification to match while varying k in the power law equation.

The calculated k in Aspen Plus® was compared to the value found by using the pseudo-first order approximation that assumes that the liquid reactant concentration is constant throughout the boundary layer. The resulting formula for the carbamate forward rate constant is as follows:

k = (NCO2*HCO2)2*(PCO2i - PCO2*)-1*(DCO2*[MEA])-1*(γCO2* γMEA)-1 (8)

Where: NCO2 is the flux of CO2 – mol/s m2 – It is calculated by dividing Aboudheir’s reported CO2 absorption rate by the interfacial area (radius * π* height)

HCO2 is Henry’s constant for CO2 - kPa-m3/kmol

PCO2i is the partial pressure of CO2 at the interface – kPa

PCO2* is the equilibrium pressure of CO2 at the loading and temperature of the

system.

DCO2 is the diffusion coefficient for CO2

[MEA] is the concentration of MEA – mol/m3

γ is the activity coefficient.

78

Using this equation two k values can be calculated. One is found by using Aspen properties for HCO2, DCO2 and density. The second calculation method uses literature correlations for DCO2 (Ko. 2001), HCO2 (Tsai, 2000) and density (Weiland, 1998). Table 2 shows a comparison between the Aspen properties obtained and the literature calculated properties for 293 K and 18 wt % MEA. Results show a good correlation for H and density (<5% deviation). Diffusivity shows a deviation of 5.3% but data analyzed has shown deviation as high as 35% for different MEA concentrations at the same temperature.

Table 2: Property comparison for 18 wt % MEA T= 293 K ldg =0.2699

Property Aspen Value Literature

Correlation Deviation

HCO2 (kPa-m3/kmol) 2.9080 2.7905 4.2%

DCO2 (m2/s) 1.24E-09 1.31E-09 5.3%

ρ (kmol/m3) 48.37 49.50 2.29%

Table 3 shows the values of k calculated for the same point in Table 2. Three types of k values are presented. Aspen calculated refers to a value calculated using properties extracted from Aspen in equation (8). Literature calculated uses properties obtained by evaluating literature correlations. Aspen regressed is the value that results from the design specification calculation set up in Aspen.

Table 3: Forward carbamate formation rate constants for 18 wt % MEA T= 293 K ldg =0.2699 laminar jet height = 1.854 cm.

ASPEN Calculated Literature Calculated Aspen Regressed

k (kmol/(s-m3) 106,900,000 95,700,000 467,700,000

Results show that there is a considerable difference between the values calculated using equation (8) and the regressed Aspen value. Using the MEA concentration at the interface in equation (8) the following results are obtained:

Table 4: Forward carbamate formation rate constants for 18 wt % MEA T= 293 K ldg =0.2699 laminar jet height = 1.854 cm. Using interface MEA concentration

Aspen Calculated Literature Calculated Aspen Regressed

k (kmol/(s-m3) 281,371,000,000 240,493,000,000 467,700,000

Calculations using the interface MEA concentration result in an accentuation of the difference between the regressed value and the values calculated using equation (8).

79

Conclusions Aspen values for density and Henry’s constant are in good correlation with the available literature data. However, further analysis is required for the diffusion constant for CO2.

Differences in property values (D, H, ρ) generate a disparity in the calculated rate constants as high as 26%. Differences between the values of k calculated using equation (8) and the Aspen regressed value require more analysis to determine the source of discrepancy. This is vital in order to proceed with any further modeling of this system.

Values obtained for the liquid and gas mass transfer coefficients, and the interfacial area proved adequate implementation of the FORTRAN subroutines to calculate these variables. The maximum observed deviation was 1%.

Future Work Discrepancies in the values of k using equation (8) and the Aspen regressed data suggest the need to review the setup of the laminar jet model in Aspen Plus®. Work will be conducted to evaluate the effect of property values and mass transfer coefficients. Approximation to the laminar jet using CMR packing needs to be revised to establish any additional packing parameters that need to be adapted.

Further analysis of diffusivity constants will be conducted to minimize disagreement between literature data and Aspen generated values.

There is still a need to review the kinetics for formation of bicarbonate to evaluate the importance of bicarbonate formation in CO2 absorption modeling.

Once the kinetic model has been set up, it will be used to model the available MEA Pilot Plant data. This will serve as a validation of the resulting absorption model.

Finally, work will also be conducted to assess and compare the performance of 35-40 wt % MEA to the commonly used 30 wt %.

References Aboudheir, A. “Kinetics, Modeling and Simulation of CO2 Absorption into Highly Concentrated

and Loaded MEA Solutions.” Ph.D. Dissertation. University of Regina. 2002.

Dugas, R. Personal communication on October 8, 2007.

Hilliard, M, "A Predictive Thermodynamic Model for an Aqueous Blend of Potassium Carbonate, Piperazine, and Monoethanolamine for Carbon Dioxide Capture from Flue Gas," Ph.D. Dissertation, University of Texas at Austin (2008).

Ko J.; Tsai, T.; Lin, C; Wang, H.; Li, M. “Diffusivity of Nitrous Oxide in Aqueous Alkanolamine Solution.s” J.Chem. Eng. Data. 2001. 46, 160-165

Tsai T.; K0, J.; Wang, H.; Lin, C.; Li, M. “Solubility of Nitrous Oxide in Alkanolamine Aqueous Solutions.” J. Chem. Eng. Data. 2000, 45, 341-347.

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Weiland, R.; Dingman, J.; Cronin, B.; Browning G. “Density and Viscosity of Some Partially Carbonated Aqueous Alkanolamine Solutions and Their Blends.” J. Chem. Eng. Data 1998, 43, 378-382

81

Modeling Stripper Performance for CO2 Removal

Quarterly Progress Report October 1, 2007 – December 31, 2007

by David Van Wagener

Supported by the Industrial Associates Program in CO2 Capture and the

Luminant Carbon Management Progman Department of Chemical Engineering


Abstract This quarter focused on two tasks. The first was creating an Aspen Plus® simulation to verify a recent pilot plant run with 35% MEA using a new thermodynamic model developed by Hilliard. The initial simulation using pilot plant data yielded a close prediction of performance, but the temperatures were not accurate. The new VLE model should be able to accurately predict temperatures in the system, so it is likely that some of the measured values were inaccurate. Regressions are being investigated to reconcile the differences. One regression was done focusing on the reboiler section, and it was possible to narrow the gap for the temperatures in that section. However, the other predictions were consequently more incorrect, including the performance. The second stripper task was to investigate flashing stripping as a means of using solar energy to provide the heat for liberating CO2. The selected solvent was 7 m MEA, and three flashes were used to the adiabatically decrease the temperature and pressure and release vapor. The simulation was debugged, and it is now capable of simulating the configuration. By varying the temperature step between each flash, the lean loading result could be matched to the lean loading input of the absorber model for the same solvent system. In the future, the maximum temperature in the heater and the lean loading will be varied to minimize the total equivalent work.

Introduction The pilot plant facility at the J. J. Pickle Research Campus has been used in the past to investigate the feasibility of proposed solvents and verify the accuracy of solvent models in Aspen (Oyenekan (2007), Chen (2007)). Generally, agreement between the pilot plant data and Aspen predictions has proved to be difficult to accomplish, especially in the stripper section. Previous thermodynamic models developed for the CO2 removing amines have only been valid within the range of absorber operating conditions because the data from which they were regressed were in that range.

Recently, however, Hilliard has developed a new thermodynamic model for H2O-MEA-CO2. This model was made using a large collection of data points from a wide range of conditions. The electrolyte-NRTL model was used to predict the behavior of the solvent. This model is novel because it relates the speciation of the solvent to its heat properties (ΔHabs, Cp) in the hope

82

of making it more accurate (Hilliard, 2008). This new model will be used to simulate a recent pilot plant run using 35 wt % MEA.

In this quarter, work on flashing strippers as a method of solvent regeneration has started. Flashing strippers operate by sending the rich solvent through the typical heat exchanger and then an additional preheater. The solvent reaches a higher temperature compared to that in a simple stripper configuration. The solvent is then passed through sequential adiabatic flash tanks, each reducing the temperature and pressure of the liquid. Carbon dioxide and water vapor are released in each flash, and the liquid decreases in loading. In this work, a three-step flash is used.

The benefit of the flashing stripper configuration is that continuous heating from solar energy can be used. Steam heating in traditional reboilers is provided at one temperature level, the condensation temperature of water at the source pressure. When converting the heat to an equivalent work, Carnot efficiency is calculated with a 10° approach at the reboiler:

∑=

⎟⎟⎠

⎞⎜⎜⎝

⎛+

−+=

reboilersn

i i

iieq KT

TKTQW

1

sink

1010

*75.0 (1)

where Tsink is 313K, the sink temperature for the turbines converting the coal-generated steam into electricity in the power plant. When a heating medium besides steam is used, its temperature is not constant throughout heating, so the temperature driving force is decreased. The smaller temperature driving force reduces the lost work and equivalent work for heating. The new equivalent work can be calculated by integrating eq. 1 from the initial to final temperature in the case where there is only one heater:

⎟⎟⎟⎟⎟

⎠

⎞

⎜⎜⎜⎜⎜

⎝

⎛

−

⎟⎟⎠

⎞⎜⎜⎝

⎛−−

=of

o

fof

eq TTTT

TTTQW

ln*75.0

sink

(2)

Flashing is not a common approach for stripping of solvents. One reason it is generally avoided is that the CO2/H2O selectivity can be very low. However, by using three flashes, some of the stripping is done at high pressure, and high-pressure stripping has been shown to be beneficial for CO2 selectivity in the multi-pressure and double matrix stripper configurations (Oyenekan, 2007).

Methods and Results

Pilot Plant Stripper Simulation A run was performed at the pilot plant using 35% MEA for CO2 removal on October 5, 2007. The stripper was operated at approximately 1 atm. A thermodynamic model for H2O-MEA-CO2 was recently completed by Hilliard, and this pilot plant run is an excellent opportunity to compare simulation results using the new model with physical data. Table 1 summarizes important data and calculations from the process. There were six thermocouples in the column at various heights, each indicated by Temperature i in Table 1.

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Table 1: Pilot Plant Run Results

Lean stream Column data

Temperature (°F) 112.8 Temperature 1 (°F) 189.6Flow (GPM) 17.4 Temperature 2 (°F) 187.3

Density (lb/ft3) 69.35 Temperature 3 (°F) 190.2Loading (mol/mol) 0.36 Temperature 4 (°F) 194.7

Rich stream Temperature 5 (°F) 195.8

Temperature (°F) 122.3 Temperature 6 (°F) 203.5Flow (GPM) 17.1 Reboiler vapor T (°F) 216.8

Density (lb/ft3) 67.97 Reboiler duty (MMBTU/h) 0.488Loading (mol/mol) 0.48 Heat loss (BTU/h) 76366Heat cross exchanger temperatures Sump T (°F) 208.8

Lean in (°F) 112.8 Column pressure, bot (psia) 15.23

Lean out (°F) 196.9 Pressure drop, top (inH2O) 0.57

Rich in (°F) 209.5 Pressure drop, bot (inH2O) 0.6Rich out (°F) 122.3 Outlet vapor T (°F) 189.3

Not only was the CO2 removal low, but the equivalent work was also very high. The equivalent work was calculated to be 41.2 kJ/mol CO2. The pilot plant equivalent work calculation does not include solvent pumping or vapor compression contributions, but the value is substantially higher than expected based on previous stripper simulations, which required 30-35 kJ/mol CO2 including pumping and compression.

A simulation was built in Aspen Plus® to reflect the pilot plant operation, shown in Figure 1. During the run, selected data were collected from the stripping column, and complete data were collected for the rich solvent entering the section and the lean solvent leading to the section. Unfortunately, the feed and product are not directly connected to the stripper, so additional process units must be simulated. The simulation also includes a cross exchanger which cools the stripper vapor to condense water from the CO2 product. An equilibrium flash is linked to the cross exchanger to send the condensed water back to the stripper. The solvent cross heat exchanger is also included to reproduce the streams for which data were collected in the run. Finally, the reboiler is not what would normally be encountered in a separation column. The pilot plant was set up to route only a portion of the column sump to the reboiler, where only a fraction of that liquid was vaporized and circulated back to the column. For this reason, an alternate reboiler section was designed, depicted in the bottom right of Figure 1.

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Figure 1: Aspen PFD of Pilot Plant Stripper Section

This Aspen simulation uses the recently completed H2O-MEA-CO2 thermodynamic model by Hilliard. The simulation assumes equilibrium reactions in the stripping column: only the equilibrium based reactions are entered, but RateSep™ is activated within RadFrac to calculate accurate mass transfer rates.

The simulation was first run using the recorded data from the pilot plant run. The fraction of the sump drawoff was not recorded in the run, so it was estimated to be 75%. Additionally, the heat loss was assumed to be distributed evenly throughout the column. The performance of the column was predicted with adequate accuracy, giving a lean loading and a lean mass flow rate close to their measured values. However, the temperatures predicted in the simulation were 4°F-10°F above the measured values, with the exception of the stripper vapor temperature which was 4°F lower than the measured value.

In an attempt to reduce the difference between the calculated and measured values, a regression was performed on the reboiler section using the Data Fit package in Aspen. The reboiler duty and the fraction of sump directed to the reboiler were varied so the temperatures in the reboiler section would closely match the measured values. The reboiler duty was changed to 0.387 MMBTU/h and the sump split fraction was changed to 15%. The regression was successful at

Rich solvent

Reboiler

Vapor cooling

Water knockout

Product CO2

Lean solvent

85

reducing the gap between calculated and measured values in the reboiler section, but all other calculated temperatures were 1°F-18°F higher than the measured values. In addition, the calculated lean loading changed from being accurate at 0.36 to being too high at 0.41. The change in loading can easily be explained by the 20% decrease in reboiler duty. The reduction in the reboiler duty lowered the amount of CO2 liberated from the solvent; therefore, the final lean solvent contained more CO2 and had a higher loading.

In the future, different regressions will be explored to narrow the gap between the calculated and measured values of more variables. The range of possible reboiler duties will be decreased to drive the calculated lean loading value closer to the measured value. However, it is possible that there was a systematic error in loading measurements, in which case the individual loading values would be inaccurate, but the measured difference should be true. Therefore, a constant loading difference of 0.12 should be maintained.

Solar Stripping A new stripper configuration was developed to utilize solar energy as a source for heating. A diagram is shown in Figure 2. Following the cross heat exchanger, rich solvent enters a solar heater where it is heated to a maximum temperature, To. The liquid proceeds through three sequential adiabatic flashes, each with a temperature change of dT. In each step CO2 and water vapor are flashed from the liquid, and the loading of the liquid is decreased. The liquid leaving the third flash is the lean stream to be recycled back to the absorber. The three vapor streams are sent to a compression train where most of the water is knocked out and the CO2 is compressed to pipeline specs.

Figure 2: Flashing Stripper PFD

The model was run using 7 m MEA. Based on previous simulations, typical optimized lean streams seemed to have a loading of around 0.4, so this was the initial loading selected. An absorber model by Plaza was used to determine the rich stream which could be achieved by a 15 m absorber. This rich stream was then used as the base specification for the stripper section. The available variables were To and dT, and using those variables the lean loading should be satisfied.

Work in this area is still in progress, but initial investigation has begun. Figure 3 displays some of the initial findings.

Rich

Lean

Water knockout

CO2

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Figure 3: Lean Loading Response to Temperature Step (flashing stripping, 7 m MEA, 5° approach)

While attempting to reach the lean loading of 0.4, it was apparent that it would not be able to be reached for every initial heating temperature. The 100°C curve in Figure 3 demonstrates that using too low a temperature will result in a final lean loading higher than 0.4, no matter the magnitude of the temperature step. On the other hand, the 120°C exhibits the opposite problem: all lean loadings with this initial temperature are too low. The curve should continue upward at smaller temperature steps to intersect the y-axis at approximately 0.5 (the initial rich loading), but Aspen would not converge at a lower temperature step. The curves in the middle, 110°C and 115°C, were able to produce a final lean loading of 0.4. The continuation of this work will establish which initial temperature minimizes the equivalent work while producing a lean stream with the correct loading. Following this optimization, the lean loading itself will be optimized by running the absorber model at different lean loadings to find the accompanying rich loadings and performing the same stripper optimization process.

Conclusions Aspen Plus® was used to create a simulation of a recent pilot plant run using 35% MEA. Since data were provided for only the rich stream, lean stream, and column values, process units in addition to the stripping column were added to make the simulation complete. The stripping column uses equilibrium reactions with RateSep™ to model mass transfer. The recently completed Hilliard Aspen VLE model for MEA-H2O-CO2 was implemented. The initial run using the collected data predicted correct performance in the column, but the temperatures were not accurate. Since the new Hilliard model should accurately predict the VLE of this system, the Data Fit package in Aspen will be used to run regressions and determine which collected data points are inaccurate. The first regression focused on the reboiler section and changed the reboiler duty and fraction of bottoms sent to the reboiler. The temperatures in the reboiler section were then more accurate, but the predicted performance was no longer correct and the other predicted temperatures were even more off than initially.

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A new flashing stripper configuration was developed to utilize solar energy for heating in the stripper section. Initial testing of this configuration using MEA has begun in Aspen Plus®. After numerous problems with design specifications, the simulation is functioning properly. It was found that the rich solvent can only be heated to a certain range of temperatures to achieve the correct lean loading.

Future Work First, the current tasks will be completed. More regressions will be run in the pilot plant simulation to determine the source of the error in measurements of the data at the pilot plant. There are a number of possible variables to change, including the height of packing, heat loss, and rich loading. It will be important to determine which variables it is necessary to include in the regression. Including too many variables could cause the simulation to wander off and produce bad results. Additionally, the flashing stripper configuration will be optimized for the current lean loading of 0.4, and then the lean loading will be optimized to find an overall minimum equivalent work. This configuration will then be compared to the double matrix to see if stripping by flashing is a beneficial method of stripping.

New thermodynamic models will be available for K+/PZ and ROC16 imminently. These models will be used in any new work. Additionally, it will be determined whether rate based simulations should be used in the stripper. The kinetic reactions using the new thermodynamic model are still under development, but current simulations can be upgraded to kinetic models when they are finished.

References Chen, E, "Carbon Dioxide Absorption into Piperazine Promoting Potassium Carbonate Using

Structured Packing," PhD Dissertation, University of Texas at Austin, 2007.

Hilliard, M, "A Predictive Thermodynamic Model for an Aqueous Blend of Potassium Carbonate, Piperazine, and Monoethanolamine for CO2 Capture from Flue Gas," PhD Dissertation, University of Texas at Austin, 2008.

Oyenekan, B, "Modeling of Strippers for CO2 Capture by Aqueous Amines," PhD Dissertation, University of Texas at Austin, 2007.

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Dynamic Operation of CO2 Capture

Progress Report for October – December, 2007 by Sepideh Ziaii Fashami




Abstract Coal-fired power plants generally generate electricity at the base load and could run CO2 capture continuously at a constant level of CO2 removal. The peak load of electricity demand during a day is basically met by natural gas power plants at much higher price of energy relative to non-peak load time. To run CO2 capture at a lower cost of energy and to meet a portion of peak load by coal-fired power plants, I propose to turn off CO2 capture or run it at a percentage of its full capacity in the peak load. In this condition, either entire the capture plant or part of it, depending on the implemented dynamic strategy, is expected to operate at different modes and transient operations along a day. In this work, at first the policy of electricity supply in Texas is presented and secondly different dynamic strategies of CO2 capture operation and possible control configurations for controlling the stripper combined with the letdown steam turbine and the compressor are proposed.

Motivation The motivation for this work is to run CO2 capture at a lower energy cost and also meet the peak load of electricity demand by coal-fired power plants by implementing on/off operation of CO2 capture during a day.

Texas Electricity Supply At present, several power plants using different fuels provide the electrical energy of Texas. The highest percentage of electricity is generated from natural gas. Coal fired and nuclear plants share significant portions of electricity generation and other sources of energy such as water and wind have the lowest contribution in electricity generation in Texas.

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Figure 1: The percentage of energy sources used in power generation in Texas in 2005 (Reports on Energy in Texas, 2007)

Power demands vary over time and the general strategy used in supplying the demands is to use some units for the base load and others for the peak load. Typically, coal-fired and nuclear plants run 24 hours/day and are base load generators while natural gas units meet peak power. Figure 2 illustrates this concept graphically using the load curve for Texas on August 23, 2005, extracted from ERCOT (Electricity Reliability Council of Texas).

Figure 2: Load curve in Texas on August 23, 2005 (Reports on Energy in Texas, 2007)

Different power units generate electricity at different prices for two reasons. Firstly, the prices of energy sources used in these units are different. Secondly, they generate at different efficiencies. Estimation for cost of electricity generated by natural gas and coal-fired plants is done based on the operation cost and efficiencies for these plants (Table 1).

Coal

Nuclear

Natural Gas

Wind

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Table 1: Estimation of electricity generation prices for two types of power plants

Type Operation Cost (¢/KWh) Efficiency Electricity Price

(¢/KWh) Simple Gas Turbines 8 0.2 40

Boilers 8 0.4 20 Natural Gas Combined Cycles 8 0.5 18

Coal Fired 1.5 0.3 5

As Table 1 shows coal fired plants generates electricity at lower price relative to natural gas plants.

Typically, simple gas turbines are used to meet the peak load because they can work in offline/online operations, but due to low efficiency they generate electricity at the highest price. Therefore, by the policy usually used in supplying power demand, the electricity generated costs too much at the peak load. If the CO2 capture is turned off or working at a lower percent of capacity during peak load, the coal-fired power plant would be able to produce more electricity and meet part of the peak load. The energy price data for August 9, 2006 are extracted and plotted versus time in Figure 3. This graph would be an economic reference for our dynamic operation studies.

Figure 3: Energy price versus time (ERCOT)

Interaction of the Power Plant and CO2 Capture In a coal-fired power plant, the steam is produced at high pressure in the boilers. Its thermal energy is converted to electricity by going through multi-stage steam turbines (high-pressure, medium-pressure, and low-pressure stages). Steam is finally condensed and recycled as feed water for the boilers. If the power plant is running CO2 capture, a stream of medium pressure steam should be extracted from MP steam turbine and conducted to a let down turbine in order to supply the required heat in the reboiler of the stripper as shown in figure 4. (DOE/NETL, 2006).

August 9,2006

0

5

10

15

20

25

30

35

12:00 AM 2:24 AM 4:48 AM 7:12 AM 9:36 AM 12:00 PM 2:24 PM 4:48 PM 7:12 PM 9:36 PM 12:00 AM

Time

Energy Price (¢/KWh)

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Figure 4: Power generation and CO2 capture The vapor off the top of the stripper and the condenser is compressed in a multistage compressor. If one or more stages of the compressor are driven by a letdown steam turbine instead of using electrical motors or other types of drivers, higher efficiency is achieved, however the stripper, let-down steam turbine, and compressor require more complicated control design.

Dynamic Options to Run CO2 Capture As mentioned before, by implementing dynamic operating options for CO2 capture, we are able to run it at lower cost of energy. In this section, three different options for dynamic operation and their advantages and disadvantages are discussed. Detailed study of one or all of these options is a part of the scope of this research.

Option 1: In this case, the absorber is running at a constant level of CO2 removal but the stripper is turned off or working at a reduced flow rate. The features of this option are:

1. Steam flow rate is reduced. 2. Sufficient storage is needed for rich and lean solutions. 3. Larger stripper, compressor, and letdown steam turbine should be used for stripping and

compression higher flow rates during non-peak load period. 4. Dynamic behaviors of the stripper and the absorber are independent, so the stripper can

be isolated to study and further control design. (Dynamic and control problems have less complexity).

Option 2: In this case, both the absorber and stripper are either turned off or running at a reduced level of CO2 removal and flow rate. The features of this option are:

1. Steam flow rate is reduced. 2. Solvent flow rate is reduced but lean loading is constant. 3. No storage is needed for the solvent. 4. More CO2 released to the atmosphere. (We lose the value of CO2). 5. Dynamic behaviors of the stripper and the absorber are not independent. (Dynamic and

control problems have high complexity).

Boiler HP MP LP

Generator

Power Steam Turbines

Letdown Steam Turbine

Condensate

Stripper Water

Flue Gas From FGD

AbsorberSteam

Gas Out

Lean Rich

CO2 92

Option 3: In this case, both the absorber and stripper are either turned off or running at variable levels of CO2 removal and lean loading. The features of this option are:

1. Steam flow rate varies with time. 2. Solvent flow is constant but lean loading is varying with time. 3. No storage is needed for the solvent. 4. More CO2 released to the atmosphere. (We loose the value of CO2) 5. Dynamic behaviors of the stripper and the absorber are not independent. (Dynamic and

control problems have high complexity).

Control Configurations In a CO2 capture plant, the stripper, the letdown steam turbine, and the CO2 compressor interact with each other with respect to dynamic behavior and control issues.

In this section, two control configurations are presented for the stripper combined with the steam turbine and the compressor. I plan to study these control configurations in detail and design an optimum model based control for the best one by solving a multivariable control problem. The control objectives that I will follow up in my work are:

1. Minimize time of transition operations; 2. Minimize energy lost during transition operations: - Drive at least one stage of the compressor by letdown steam turbine

- Minimize energy lost through control valves; 3. Minimize fluctuations of process variables for equipment protection.

Control Configuration 1 As shown in figure 5 there are two main control variables.

The pressure of the column is controlled by speed control in the letdown steam turbine and the compressor. The load (steam flow rate) in the turbine controls the speed (Liptak, 2003). In the other control loop, the temperature in the stripper, representative of the lean loading, is controlled by steam flow rate in the bypass steam line.

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Figure 5: Control configuration 1 for the stripper

Control Configuration 2

In this case, the only main controlled variable is lean loading or temperature and the manipulated variable is steam flow rate (Figure 6).

Obviously, we have a smaller number of control valves in this configuration and lower energy loss through control valves compared to configuration 1. However, the pressure of the column is not under control. This configuration is not necessarily applicable with respect to safety and equipment protection. Specifically, during transition conditions the column may be exposed to the large variation of pressure and vapor flow rate. Therefore, I am looking for a dynamic strategy that satisfies safety criteria in this case.

HPS

to 2nd stage

Lean Solution

MP LP

Stripper

LPS

FT

TT

PT

1st

Rich Solution

ST

MVC

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Figure 6: Control configuration 2 for the stripper

Future work For future work, the rate-based dynamic model of the stripper, letdown steam turbine, and CO2 compressor are created in a flow sheet in Aspen Custom Modeler to study the dynamic behavior of the system and its performance in different proposed strategies. In addition, the performance of control configurations in each dynamic option will be evaluated.

References DOE-NETL401/120106, Carbon Dioxide Capture from Existing Coal-Fired Power Plants.

December 2006.

Liptak BG. “Steam Turbine Controls, Process Control and Optimization” (Instrument Engineers’ Handbook), Fourth Edition, 2123-2137,(2003.

Lean Solution

MP LP

HPS

LPS

TC TT

to 2nd stage

1st stage

Rich Solution

Stripper

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Analysis of CO2 Capture Systems in the Dynamic Electric Grid

Progress Report for October – December, 2007 by Stuart Cohen




Abstract A review of techno-economic studies of CCS revealed that most work done thus far consists of single plant studies or macro-scale energy analyses. Thus, an intermediate approach will be taken that incorporates a more realistic grid level analysis that considers variation in electricity supply and demand. Investigation will focus on the effects of variable CO2 capture efficiency or on/off operation on overall electric system performance, economics, and environmental effects. Varied or on/off operation of CO2 capture may eliminate the need for makeup generation capacity for the energy requirement of CCS. If CO2 capture is turned off when electricity prices are high, the revenue acquired during these times may decrease the implementation costs of CCS. Using varied CO2 capture operation will be analyzed in several scenarios of varying electricity demand and supply, where dynamic supply can result from renewable resources which offer limited controllability. Further study will examine the technical limitations of variable CO2 capture operation at the system level, possibly considering effects on other generation sources, transmission hardware, and a CO2 transport and storage network.

Description of Current Techno-Economic Analysis of CCS

There is a large body of work directed towards examining the tradeoffs between several CO2 capture technologies, which consistently point to amine absorption as the current leading CO2 capture technology (Davidson, 2007; Aaron & Tsouris, 2005). Any discrepancy in this conclusion recognizes that amine absorption is the leading technology choice given current technological development, and techniques such as membrane separation, while promising, require a greater amount of development time before implementation is possible (Aaron & Tsouris, 2005). Because of decades of experience using monoethanolamine (MEA) for CO2 removal in the natural gas purification and ammonia production industries, there is much more known about the associated costs of an amine absorption system than for other CO2 capture technologies. Thus, this process is most often the subject of any analysis that incorporates the performance of CO2 capture into an economic analysis, commonly termed a techno-economic analysis.

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Bottom-Up Approach – Single Plant Analysis Current techno-economic analyses typically take one of two forms. The first takes a “bottom-up” approach by confining the scope of the study to a single power plant. In such a study, the performance of all equipment and systems are considered, and the associated costs and resource requirements of each are used in a discounting cash flow analysis to determine a total plant capital cost (TPC) in $/kW and cost of electricity (COE) generation at that plant in $/MWh. Several plant configurations are considered for comparison in each study, with a typical study examining the COE and TPC for a reference plant and the same plant with carbon capture and sequestration systems installed. This then allows determination of a cost of CO2 capture reported in dollars per metric tonne of CO2 ($/tCO2), calculated as below.

CCS2

2 CCS

$/kWhCost of CO CapturedtCO /kWh

=

If the analysis assumes that additional power capacity must be built to make up for the energy requirement of the CO2 capture system, this cost of capture may be presented instead as a CO2 avoidance cost, calculated using the equation below.

CCS Ref2

2 Ref 2 CCS

$/kWh $/kWhCost of CO Avoided tCO /kWh tCO /kWh

−=

−

The performance of the plant under the specified configurations also allows determination of the plant’s CO2 emissions.

The level of detail involved in a single plant analysis is an advantage over more general analysis methods, and this detail allows a clear assessment of how various plant performance or economic parameters may affect the overall plant economics and its CO2 emissions. This level of detail offers a useful platform to examine the sensitivity of plant economics to a given performance specification or economic assumption. A good example of this technique is contained in Rao 2002, which demonstrates results from the Integrated Environmental Control Module (IECM) software developed at Carnegie Mellon University’s Department of Engineering and Public Policy (Rao & Rubin, 2002).

Top-Down Approach – Macro-Scale Energy Analysis Another method of techno-economic analysis of CCS systems considers energy use on a nationwide or even worldwide scale to understand the effects of various input parameters on energy use and the future impact of CCS in a global energy system. Such an analysis makes assumptions about national energy market trends such as population growth, consumer energy use behavior, and economic production structure (Katzer et al, 2007). From there, various input parameters can be specified. These may include the costs and performance of electricity production for various generation options, fuel prices, policy limitations, and a given CO2 market value specified over a period of time. Models then calculate long term projections of total energy consumption, contribution to energy consumption of the available generation options, and total CO2 emissions in the region of interest. When such an analysis incorporates plants with CCS, it will typically calculate the market penetration of CCS within the fossil fuel generation sector.

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Because this type of analysis typically involves long term projections of energy use behavior, uncertainty about how parameters change over long time periods may make quantitative conclusions difficult, but macro-scale energy analyses can be used to investigate various qualitative cause-and-effect relationships. Input performance and economic parameters or environmental constraints (typically CO2 emissions) can in this manner show macroeconomic tradeoffs between competing performance, economic, and environmental concerns. A valuable feature of this methodology is that its system level perspective allows it to consider interactions between energy generation sources as well as regional variations in energy use, which cannot be captured by considering a single plant alone.

A good example of a macro-scale energy analysis can be found in MIT’s “The Future of Coal” report (Katzer et al, 2007).

Limitations of Typical Techno-Economic Analyses While both single plant and macro-scale energy analyses are valuable technology assessment tools for the reasons described above, they possess limitations in scope that may reduce the accuracy of calculated economic indicators. While a macro-scale analysis does consider an entire power generation system, it calculates based on annually varying trends rather than any short term dynamic behavior. Thus, it ignores the variation in electricity supply and demand and its implications for plant dispatch and electricity cost. A macro-scale analysis often assumes average performance and economic parameters to apply to a fleet of a particular generation source, and this limited detail of individual plant performance may not provide an adequate representation of the power generation system it seeks to describe.

A major limitation of most single plant analyses is that economic indicators are based on static or average values of plant performance parameters. Specifications such as CO2 removal efficiency and total plant capacity are assumed constant, whereas in reality these values may vary significantly throughout the day, year, or life of the plant depending on how it is operated. Electricity demand variation over a period of hours and days can have a large effect on plant operation and the resulting plant economics, and these effects are ignored by the single plant analysis approach. In contrast to a macro-scale analysis, a single plant study is limited in scope to specific siting and technology, so it may be difficult to extend the case study plant performance in an integrated power grid.

Proposal of Grid Level Analysis of CCS Systems In order to conduct a techno-economic analysis that satisfies some of the limitations of single plant and macro-scale studies, I propose an intermediate approach that takes place at the level of the electric grid. Such a study considers a full power generation system made up of several types of electricity generation source but retains plant level detail of key performance and economic parameters. A primary input will be the time-varying electricity demand and supply, which will interact with performance and economics of all power generation technologies to determine economical plant dispatch. Thus, the resulting economic indicators including COE and the cost of CO2 capture or avoidance are determined by the dynamic electricity market rather than the economics of a single plant or a globally averaged electricity system. Plant performance parameters such as CO2 removal efficiency and plant output can now be considered as variable over a time scale of interest rather than static as in a single plant analysis. The detail of such an

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analysis allows CO2 emissions and other environmental measures to be determined both at the plant and grid level.

This analysis method will be used to study the feasibility and effects of varying CO2 capture efficiency or turning CO2 capture systems on and off in response to grid supply and demand. It is possible that such an operational strategy could reduce the overall costs of implementing CCS for two reasons. First, lost generation capacity from the energy requirement of CO2 capture could be recovered during peak load conditions, eliminating the requirement to invest capital expenditures for additional makeup capacity. Second, peak load conditions are typically associated with higher energy costs, so turning CO2 capture systems off when electricity is most expensive can improve revenue from the base case of running expensive capture systems all the time. Such an operational strategy will result in greater CO2 emissions than when capture systems always operate at full capacity, but electricity supply and demand scenarios can be used to find operational practices that optimize system performance and economics within a CO2 emissions constraint.

Post-combustion (PC) capture using chemical absorption is uniquely suited for analysis of varied or on/off operation because unlike other CO2 capture technologies, it offers the ability for this type of control. For example, an IGCC unit with CCS cannot incorporate controlled operation of the CO2 capture system through such a wide range of operating points because the capture system is integrated into the gasification unit, and changing the gasification process would result in a mixture of fuel gases that the unit’s gas turbine is not designed for. PC chemical absorption offers several options to reduce CO2 capture efficiency or effectively turn capture systems on and off, and these control strategies will be investigated within a separate body of work. In addition, PC capture offers the possibility of retrofitting currently installed plants, so an analysis that carefully considers site specific retrofit requirements may include the possibility of retrofitting an existing plant.

Illustration of Grid Dynamics and Implications of On/Off or Varied CO2 Capture Operation There are several possible effects that varied CO2 capture operation may have on electric grid operations based on the time scale or dynamic variable of interest. Figure 1 below displays the total electricity load in the Electricity Reliability Council of Texas (ERCOT) grid region on August 23, 2005, the day of 2005 when peak load was the greatest. This figure demonstrates the current use of coal and nuclear capacity solely for baseload generation, and natural gas both for the remaining baseload and for all variation in load throughout the day. Other sources such as wind, hydroelectric, and DC connections to other grids constitute substantially smaller portions of generation.

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Figure 1: Hourly ERCOT load on summer day in 2005 (Jones, 2006) Figures 2 and 3 demonstrate that ERCOT uses a similar approach to electricity dispatch throughout the year.

Figure 2: Hourly ERCOT load on spring day in 2005 (Jones, 2006)

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Figure 3: Hourly ERCOT load on winter day in 2005 (Jones, 2006) To visualize the potential effect of turning CO2 capture systems on and off in response to electricity demand, one can imagine a scenario where every coal-fired power plant has a CO2 capture unit installed that reduces plant capacity by 30%, a value typical of single plant analyses considering currently available MEA absorption technology (Davidson, 2007). Thus, Figure 4 shows that for August 23, 2005, the installed coal capacity of 14,640MW (DOE, 2007) is reduced to 10,248MW from 1:00am until approximately 1:30pm, with the lost coal capacity being made up for by currently installed natural gas (NG) units. However, when peak demand dictates that full coal capacity is needed, CO2 capture units are switched off, after which they ramp up to full capacity with a system response time arbitrarily shown in the figure as one hour. When this peak load capacity is no longer needed, CO2 capture can be switched back on. This example is extreme, and limitations to retrofit availability, system response time, and ability to regain full coal plant capacity in a CCS-reduced or “off” state will likely prevent this exact scenario from existing. However, this example demonstrates the potential for varied or on/off operation of CO2 capture to provide a method of meeting demand variation in addition to the activation of NG spinning reserve. If all or most of the installed coal capacity can be regained by turning CO2 capture off, little to no additional generation capacity must be added to the grid, saving a significant amount of capital cost to a system-wide implementation of CCS.

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Figure 4: Possible use of On/Off CO2 Capture to meet peak summer load Figure 5 below displays the load variation throughout the day in the ERCOT region on the maximum peak and minimum base load days of 2006. The maximum peak load of 63.1GW was achieved on August 17, and the minimum base load of 21.5GW occurred on January 2. The general trend of the load throughout the day is the same for both days, but there is a dramatic difference between overall electricity needs of these days. If the operational strategy shown in Figure 4 was used during these two extreme days to regain the lost 30% of coal capacity during the period of time when the load is above the difference between the daily peak and 30% coal capacity, it can be seen that the amount of time when capture systems are “off” can vary significantly depending on the load profile of a given day. For days when system load is well below total capacity, as on January 2, on/off operation of CO2 capture is not used to prevent a requirement for building additional system capacity. It is simply being used as a strategy to meet peak demand that could complement the use of NG spinning reserve.

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Figure 5: Load profiles on the days of minimum base and maximum peak load in 2006 shown with possible use of On/Off CO2 Capture to meet peak daily demand (ERCOT,

2006)

Below is a graph of ERCOT balancing energy (BE) price on the same two days as in the previous figure. Balancing energy price is the spot market electricity price for power used to achieve an immediate balance between generation and load. In the ERCOT region, BE price accounts for less than of 5% of total generation, but BE price is considered to be representative of the actual electricity price contained in confidential contracts between private companies (2005 State of the Market Report, 2006; PUCT Report to the 80th Texas Legislature, 2007). The volatility in this spot market energy price is evident, and this is because BE prices are greatly influenced by short term factors such weather, congestion, and outages, which can be unpredictable. However, the general trend in price follows closely with the trend in demand, with peak hourly demand and peak price aligning very closely if large outliers are ignored. The arbitrarily placed line at $100/MWh indicates that one could define a threshold energy price, above which CO2 capture systems would be turned off. It is expected that the generation cost to the plant operator will be much lower with capture systems off, so the electricity could then be sold during this high price period to increase revenue above a scenario when capture systems are always on at full capture capacity. This additional revenue may offset some of the costs of CO2 capture, which is projected to increase generation costs significantly while in operation.

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Figure 6: Energy prices on the days of minimum base and maximum peak load in 2006 with a possible threshold price when CO2 Capture is turned off (ERCOT, 2006)

Figure 7 below was produced by a General Electric study done for ERCOT to analyze grid operations under several scenarios of large scale wind power installation in the ERCOT region. The particular graph shown depicts the total load on April 23, 2006, the calculated wind generation if 15,000MW of wind capacity were installed, and the difference between the two, shown as “load minus wind.” As of 11/16/07, the ERCOT region had 4,112MW of wind power installed, 1,478MW under construction, and another 8,012MW planned, so this scenario is far from unrealistic (PUCT Summary of Changes, 2007). The figure shows that wind power in Texas, which is primarily located in the northwest region of the state, generates the most power during nighttime hours when electricity demand is lower. However, if it is again assumed that 30% of installed coal capacity can be recovered simply by turning CO2 capture systems off, a possible operational strategy may be to turn off CO2 capture when wind generation is low in order to recover capacity lost to poorer wind conditions. In this respect, on/off operation of CO2 capture is being used to balance variation in energy supply rather than demand.

If solar power were ever to approach a significant percentage of total generation capacity, a similar argument could be made by considering its natural variation, which is best during midday but begins to decrease in the evening hours when power demand may still be quite high. CO2 capture systems could be turned off in this situation to make up for declining solar power supply.

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Figure 7: Load and expected wind generation on April 23, 2006 with 15,000MW of wind capacity in the ERCOT grid, shown with use of On/Off CO2 Capture to offset wind

generation lost during the day (GE, 2007) The idea of using on/off operation to eliminate the need for makeup generation capacity can also be viewed in terms of demand variation over the course of the year, where CO2 capture is only turned off during the days when the grid demand is sufficiently likely to exceed a certain value. Again, if all of ERCOT’s coal generation now had CO2 capture systems using 30% of coal capacity, Figure 8 below demonstrates that the above operational strategy would result in capture systems being turned off between approximately June 12 and September 1.

If this operational methodology were applied, the scheduled downtime of capture systems might be akin to scheduled plant downtime, where scheduled maintenance activities would take place. In this scheme, system response time is less of an issue because of the longer time scales. However, keeping systems off for such a long period of time would obviously have negative implications for overall CO2 emissions and would extend the time required to recover investment costs of the capture system. A more logical approach to on/off or varied operation to reduce the need for makeup capacity may combine strategies for hourly and seasonal variation in power demand to turn capture systems off only during the peak demand hours of the specific days throughout the year when it is absolutely necessary to maintain enough generation and reserve margin capacity. This strategy would also result in lower CO2 emissions than the approaches described earlier.

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Figure 8: Daily peak load in the ERCOT grid throughout 2006, shown with the time period when CO2 Capture may be turned off (ERCOT, 2006)

Figure 9 below displays the ERCOT balancing energy price along with Houston Ship Channel NG prices throughout 2006. The Houston Ship Channel is one of the larger NG shipping hubs in Texas. An average balancing energy price was determined for each day, then these daily averages were averaged with the two days prior to and after the day of interest to create a 5-day moving average BE price. This is done to eliminate some of the volatility in BE price data in order to make the price trends more apparent. Because ERCOT currently uses NG exclusively for any variation in electricity demand, energy price is very closely tied to its variation. While the exact implications are unknown at this time, there is a possibility that because varied or on/off CO2 capture operation offers another option of meeting demand and supply variation, it may help break NG’s current grip on Texas energy prices.

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Figure 9: Five day moving average of ERCOT Balancing Energy price and the Houston Ship Channel Natural Gas price over the year 2006 (ERCOT, 2006; ICE, 2006)

Another important consideration for on/off operation is the regional variation in electric grids. In ERCOT, 72.1% of 2006 generation capacity comes from natural gas, and another 20.4% is from coal, while grid operations dictated that NG and coal provided 46.6% and 37.4% of energy production, respectively (Jones, 2006). Other electric grids may have a very different capacity mix, which could significantly alter operational strategies relating to on/off or varied CO2 capture.

There is also regional variation in electricity demand, which Figure 10 demonstrates below by plotting the peak loads experienced in each month of 2006 for ERCOT and the Reliability First Corporation (RFC), which operates the electric grid in several Midwest and central east coast states. Monthly peak loads shown are normalized by the 2006 annual peak load in the given region because of the large difference in absolute load between the regions. While Texas has a large demand peak in the hot summer months for energy use related to cooling, the more northern states in the RFC region have a smaller seasonal peak in the winter months for heating energy.

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Figure 10: Normalized monthly peak loads for the ERCOT and RFC grids in 2006 (ERCOT, 2006; RFC, 2006)

Electric grid dynamics can take several forms, and the variation of supply and demand involves a great deal of interacting parameters that result in the data shown above. Several of the above graphs and descriptions of hypothetical situations describe scenarios where varied or on/off operation of CO2 capture may be used to improve load handling and decrease the implementation costs of CCS. A fully integrated electric grid analysis would have to consider all these operational strategy choices together to determine the optimal way to operate individual plants and the electric grid.

Summary of Research Goals My work will analyze the effects of varied and on/off CO2 capture operation under several dynamic electricity supply and demand scenarios such as those described above. I hope to quantify the performance, economic, and environmental tradeoffs of this mode of operation, with the goal of determining optimal operation of CO2 capture systems for a given grid scenario. This optimization would include sensitivity studies to determine which parameters have the most effect on proposed operational strategies, and a statistical approach may also be incorporated to determine the realistic limits of a given level of parameter variation. If successful, a long term deliverable of the project would be a tool that could help guide industry in the implementation and use of CO2 capture systems for when they are incorporated into an electric grid.

This analysis will focus on integration of CCS at the system level of the electric grid, and in addition to analyzing system-wide effects on performance, economics, and emissions, another major research goal is to address the technical feasibility of varied or on/off control. Analyzing dynamic operation of the capture process itself is outside the scope of this work, but technical constraints on the electric transmission system, other generation sources, and a CO2 transport/storage network are possible system level technical issues that may be addressed.

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While cost improvements can be expected over time, CO2 capture and sequestration is likely to remain a relatively expensive process that adds cost to the production of electricity. However, dynamic electricity supply and demand offers an opportunity to use varied or on/off operation of CO2 capture to reduce the costs associated with implementing CCS.

References 2005 State of the Market Report for the ERCOT Wholesale Electricity Markets. 2006, Potomac

Economics, Ltd. Aaron, D. and C. Tsouris, Separation of CO2 from Flue Gas: A Review. Separation Science and

Technology, 2005. 40(1): p. 321 - 348. Davidson, R.M., Post-combustion carbon capture from coal fired plants – solvent scrubbing.

2007, IEA Clean Coal Centre. DOE, NETL's 2007 Coal Power Plant DataBase, N.C.-. Public.xls, Editor. 2007. ERCOT, 2006 ERCOT Hourly Load Data, E.H.L. Data.xls, Editor. 2006. ERCOT, Balancing Energy Services Market Clearing Prices for Energy Annual Report,

MCPER_MCPEL_2006.xls, Editor. 2006. GE, Phase 1 Review: Variability and Predictability Characterization, in ERCOT Wind Impact /

Integration Analysis 2007. ICE, ICE Day Ahead Natural Gas Price Report, NG_Price_2006_HousShipChan.xlsx, Editor.

2006. Jones, S., Electric Reliability and Resource Adequacy Update. 2006, ERCOT: Dallas. Katzer, J., et. al., The Future of Coal: Options for a Carbon Constrained World. 2007,

Massachusetts Institute of Technology: Cambridge. PUCT, Scope of Competition in Electric Markets in Texas, in Report to the 80th Texas

Legislature. 2007, Public Utility Commission of Texas. PUCT, Summary of Changes to Generation Capacity (MW) in Texas By Status and Resource

Type (updated 11/16/07), P.g.t.o.E.J. 2008.xls, Editor. 2007. Rao, A.B. and E.S. Rubin, A Technical, Economic, and Environmental Assessment of Amine-

Based CO2 Capture Technology for Power Plant Greenhouse Gas Control. Environ. Sci. Technol., 2002. 36(20): p. 4467-4475.

RFC, Historical and Projected Peak Demand and Energy - Monthly, R. 06-EIA-411.xls, Editor. 2006.

Appendix – Annotated Bibliography The references below represent journal articles, presentations, and reports that have been read and are considered relevant to a techno-economic study of CCS systems at the electric grid level. The descriptions below summarize the main point of each document and why it may be useful to system analysis of CCS. (2006) This third party report to ERCOT on its electricity markets is very useful in understanding the terminology and market forces that determine the price of energy in Texas. It has a good description of balancing energy markets as well as ERCOT’s electricity demands and its current

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strategy and outlook for meeting them. Coupled with the PUCT report described below, this document is extremely helpful in understanding the economics of electricity in Texas.

2007 This is a presentation done by GE about its analysis for ERCOT to study practical implementation and operation of the ERCOT grid under scenarios of large new wind capacity installation ranging up to 15,000MW total. It is useful to understand the potential dynamic nature of electricity supply in TX from renewable sources that are not as controlled as fossil fuels. The results would be useful inputs in investigation of using on/off or varied CO2 capture to satisfy supply variation of wind generation in the ERCOT region.

Aaron and Tsouris, 2005 This study reviews several CO2 capture technologies at various stages of development and determines that membrane diffusion is the most promising method due to low energy requirements, but costs are unknown and new materials need to be developed. Chemical absorption is seen as the most immediately promising technology, especially if energy requirements decrease with technological development. This report is a good overview of CO2 capture technologies that highlight some advantages and disadvantages of various options. It is not, however, able to compare all technologies with consistent metrics.

Alie, 2005 A linear algebra model has been designed to minimize electricity costs on an electric grid subject to several realistic dispatch constraints in order to determine the effects of CO2 limits on the electric grid. The model uses an objective function with constraints related to electricity supply/demand balance, plant output, transmission capacity, and total grid CO2 emissions. This model appears to be directed towards a much more realistic determination of costs and performance of an electric system with CCS installed. This body of work is likely to be a valuable resource for any grid level model created to investigate on/off or varied CO2 capture operation.

Alie, 2006 An IEEE grid reliability model is used to determine CO2 mitigation costs and electricity costs under a varying CO2 emissions constraint for scenarios where one plant in the system has CO2 capture installed. For the particular electricity system case study, the model finds that a 20% CO2 reduction can occur without increasing COE, but it is not clear how costs are incorporated, and the model does not appear to consider hourly demand variation. A plant with flexible CO2 capture is investigated and determined to be a better option than a static capture plant, but the level of flexibility and its effect on system performance and economics is not reported.

Bergerson and Lave, 2007 This paper finds that CO2 needs to be $20/tonne to justify building IGCC with CCS and $46/tonne to justify pulverized coal with CCS, but sensitivity studies found that the high uncertainty in CO2 price makes decisions difficult between IGCC and PC with and without CCS. It states that in order to properly invest in CCS, industry needs to know dates that CO2 regulations will be applied and will still require government subsidies to close the gap between a

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typical 10% private discount rate and 3% societal discount rate. Uncertainly also makes preinvestment in “capture ready” plants unreasonable. I think that the qualitative results and general trends in this report are useful, but the specific cost values may not be representative of what would occur in an integrated electric system.

Davidson, 2007 This paper reviews chemical absorption techniques for CO2 capture, focusing on key concerns with the process (solvent degradation, solvent regeneration, cost, etc.) and development of different solvent options. It also describes current world activities in CO2 capture using chemical absorption. The study acknowledges amine absorption as the current leading technology and promotes retrofitting plants that have high enough thermal efficiency such that the energy penalty of CO2 capture would not be economically disastrous. This study provides a good overview of chemical absorption for CO2 capture, but because it is essentially an annotated bibliography, it does not represent new research findings.

Johnson and Keith, 2001 This study considers CCS within a dynamic energy system model in order to determine a cost of capture based on grid dispatch rather than commonly reported values from single plant analyses. Results show that CCS becomes economical at a lower CO2 value than typical single plant analyses, but it also predicts large scale fuel switching to natural gas before installation of CCS. However, the study acknowledges the importance of natural gas prices to the economics of dispatch, and natural gas prices today may be high enough to significantly change these results. The model describes coal with CCS as a critical technology for emissions reductions because despite the high capital cost, such a plant would be insensitive to NG prices and the value of CO2.

Jones, 2006 The generation reserve margin in the ERCOT region is projected to fall increasingly below the standard 12.5% of peak load starting in 2008, indicating that TX is approaching a capacity crisis without significant new installation. New proposed or planned generation to 2011 is primarily coal and wind power at about 15GW and 14GW respectively, but about 6GW of new NG is also planned. Coal and nuclear power is used only for baseload with NG being used for any load variation, resulting in NG accounting for the largest percentage of energy production at 46.6% with coal second at 37.4%. It is unlikely that ERCOT’s projections investigate the effects of CO2 regulations on the potential energy supply crisis.

Katzer, 2007 This comprehensive report assesses current and future use of coal in the U.S. and the world to determine its implications for climate change. It contains results from macro-scale energy analyses done under several scenarios that consider varying CO2 regulations, natural gas prices, growth in nuclear power, and national responses to the call for emissions reductions. It looks at all aspects of the CCS system, then goes on to focus on coal use in China and India, current analysis and R&D, and public perception before presenting recommended actions to make coal a viable fuel in a carbon constrained world. Some of the details of the analyses seem suspect and some data outdated, but the overall emphasis of its findings is the glaring need for significant work in CCS and cleaner coal technology, especially in the form of multiple large scale

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demonstration projects. This study is a useful resource and collection of information relating to coal technology and CCS research, but its results may not be directly useful for input into a grid analysis.

Metz, 2005 The IPCC SRCCS is an extremely comprehensive and valuable resource that explores the technical, economic, and environmental issues of every aspect of CO2 capture and sequestration. It is useful for determining an international perspective of the current status of CCS in order to identify and address knowledge gaps. Its references to techno-economic studies, however, seem to focus on plant level and macro-scale analyses, so there appears to be a significant void in the realm of system level CCS analysis that incorporates electric grid demands.

Nelson, 2007 This report, done by a consortium of industry representatives to present an industry perspective of CCS, makes several conclusions and recommendations about how to decrease coal emissions and make cleaner coal technology (including CCS) more viable. It recommends several large scale demo projects of CCS and emphasizes the need for much more research to be directed towards CCS and cleaner coal technology. It also outlines several administrative recommendations to streamline the advancement of CCS technologies. The industry point of view given by this report is very valuable because ultimately these companies will bear a large burden for implementing CCS.

PUCT, 2007 This is a report to the TX legislature to describe the current status of electricity markets in TX, including the ERCOT region. It focuses on the effect of natural gas prices on electricity prices, lingering effects of Hurricane Katrina, TX legislative activities, and other resource adequacy and market concerns. It is informative regarding the terminology involved in TX energy markets and for understanding the market forces involved in pricing TX electricity.

Rao and Rubin, 2002 This article details how CO2 capture with MEA absorption was integrated into Carnegie Mellon’s Integrated Environmental Control Model (IECM), which is a public software package that performs single plant performance and economic analysis. A case study comparing a reference coal plant with a new CCS coal plant and a plant retrofitted with CCS demonstrated that estimates of CO2 avoidance cost depends strongly on assumptions about the reference plant. Retrofits are found to result in larger energy penalties than new CCS plant due to lack of optimal heat integration. The IECM also allows statistical analysis to be done based on parameter uncertainty. Overall, this is a good example of a typical single plant analysis methodology, but it is more rigorous than most. However, it is limited to knowledge of MEA absorption at the time of software development, so discrepancies may arise between the IECM calculated performance and that which would exist in the newest technology.

Rao and Rubin, 2006 The IECM model is used to find optimal levels of CO2 capture efficiency in a coal plant with MEA absorption while also investigating practical concerns such how a system would be designed for a specified capture efficiency. It finds an optimal performance at about 86%

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capture efficiency, but cost plots show interesting behavior due to practical issues of the number and size of capture trains associated with a given capture efficiency. Capital costs increased monotonically with capture efficiency, and CO2 avoidance costs tended to decrease with capture efficiency with a given capture train and number. Bypassing the capture unit is found to be a less expensive method of achieving lower capture efficiency than running an absorption-stripping unit below optimal levels. These are interesting results at the plant level, but all performance values are taken as constant over plant life, so it is difficult to extend the economic results to a dynamic electricity distribution system. Rao, Rubin et al., 2006 An expert elicitation study was performed to determine the perceived status of current CO2 capture technology, what level of performance is expected in the future, and what are the most critical R&D priorities. Results indicate a wide range of opinion regarding future system performance. Regeneration heat is expected on average to decrease by 23%, and many cited new solvents as a driver of improved system performance, though it is acknowledged that future solvents are likely to be more expensive. Highest R&D priorities were placed in decreasing regeneration energy, cheaper solvent storage/disposal, improved heat integration, and more efficient base plant designs. These results allow interesting insight into the thoughts of CO2 capture experts, but the small sample size (12) and possible biases when divulging information may be reason to refrain from placing too much value in the quantitative results.

Riahi, Rubin et al., 2004 The cost of CO2 capture is modeled to decrease over time similarly to that experienced by SO2 capture technology, and this “technological learning” curve is implemented in a macro-scale analysis to determine CCS use through 2100 based on a chosen atmospheric CO2 concentration stabilization target. The model predicts fuel switching as the largest contributor to emissions reductions, but CCS also offers a significant contribution, earning large market share in the coal generation sector. Because it takes time for CCS technology to become economical, it is not implemented widely until after several decades. The analysis is typical of a macro-scale analysis in that the trends it reports are interesting, but the limited detail of the plant performance and inability to capture short term grid dynamics can overly simplify the model of the power generation industry.

Tseng, 2007 A model was developed to study short term dynamics in electricity demand and generation, which was validated by retrospective studies of several U.S. generation regions. The model appears to be very detailed and allows simulations based on specified emissions constraints, though nothing in the presentation is mentioned concerning CCS. This model appears to be quite robust, and the work of this author will be a valuable resource in any effort to create another such rigorous model to investigate on/off or varied CCS operation.

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(2006). 2005 State of the Market Report for the ERCOT Wholesale Electricity Markets, Potomac Economics, Ltd.

(2007). Phase 1 Review: Variability and Predictability Characterization. ERCOT Wind Impact/Integration Analysis.

Aaron, D. and C. Tsouris (2005). "Separation of CO2 from Flue Gas: A Review." Separation Science and Technology 40(1): 321 - 348.

Alie, C. (2005). A Generalized Framework for Scheduling the Operation of Power Plants Incorporating CO2 Capture. International Test Network for CO2 Capture: report on 8th Workshop. Austin, TX, IEA Greenhouse Gas R&D Programme. 2005/13: 110-180.

Alie, C., Douglas, P. , and E. Croiset, (2006). A generalized framework for evaluating the performance of CO2 capture processes. GHGT-8. Trondheim, Norway.

Bergerson, J. A. and L. B. Lave (2007). "Baseload Coal Investment Decisions under Uncertain Carbon Legislation." Environ. Sci. Technol. 41(10): 3431-3436.

Davidson, R. M. (2007). Post-combustion carbon capture from coal fired plants – solvent scrubbing, IEA Clean Coal Centre.

Johnson, T. L. and D. W. Keith (2001). "Electricity from Fossil Fuels without CO2 Emissions: Assessing the Costs of Carbon Dioxide Capture and Sequestration in U.S. Electricity Markets." Journal of the Air & Waste Management Association (1995) 51(10): 1452-1459.

Jones, S. (2006). Electric Reliability and Resource Adequacy Update. Dallas, ERCOT. Katzer, J., et. al. (2007). The Future of Coal: Options for a Carbon Constrained World.

Cambridge, Massachusetts Institute of Technology. Metz, B., O. Davidson et.al. (2005). IPCC Special Report on Carbon Dioxide Capture and

Storage. Nelson, G. (2007). Technologies to Reduce or Capture and Store Carbon Dioxide Emissions

(Draft), The National Coal Council. PUCT (2007). Scope of Competition in Electric Markets in Texas. Report to the 80th Texas

Legislature, Public Utility Commission of Texas. Rao, A. B. and E. S. Rubin (2002). "A Technical, Economic, and Environmental Assessment of

Amine-Based CO2 Capture Technology for Power Plant Greenhouse Gas Control." Environ. Sci. Technol. 36(20): 4467-4475.

Rao, A. B. and E. S. Rubin (2006). "Identifying Cost-Effective CO2 Control Levels for Amine-Based CO2 Capture Systems." Ind. Eng. Chem. Res. 45(8): 2421-2429.

Rao, A. B., E. S. Rubin, et al. (2006). "Evaluation of potential cost reductions from improved amine-based CO2 capture systems." Energy Policy 34(18): 3765-3772.

Riahi, K., E. S. Rubin, et al. (2004). "Prospects for carbon capture and sequestration technologies assuming their technological learning." Energy 29(9-10): 1309-1318.

Tseng, P. (2007). Modeling Regional Electricity Generation.

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co capture by aqueous absorption summary of 4th quarterly progress reports...

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