analytical model of corn cob pyroprobe-ftir data

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doi:10.1016/j.bi

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Biomass and Bioenergy 30 (2006) 486–492

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Analytical model of corn cob Pyroprobe-FTIR data

Jie Feng1, Qin YuHonga, Alex E.S. Greenb,�

aKey Laboratory of Coal Science and Technology, Taiyuan University of Technology, Taiyuan, Shanxi 030024, PR ChinabClean Combustion Technology Laboratory, University of Florida, Gainesville, FL 32611-6550, USA

Received 21 March 2005; received in revised form 4 August 2005; accepted 15 September 2005

Available online 3 February 2006

Abstract

Pyrolysis of various forms of biomass could convert this primary energy source into valuable liquid or gaseous fuels or chemicals. In

this study a CDS 2000 Pyroprobe, with a Bio-Rad FTS165 FTIR detector are used to measure yields of 3 products and 7 families of

products from corn cobs pyrolysis at temperatures up to 900 �C using a wide range of heating rates. An analytical semi-empirical model is

then used to approximately represent these results using a relatively small number of parameters. The compact representation can be

used in applications to conveniently extrapolate and interpolate these results to other temperatures and heating rates.

r 2006 Elsevier Ltd. All rights reserved.

Keywords: Biomass; Pyrolysis; Analytical model

1. Introduction

Pyrolysis is the first and most basic thermo-chemical stepin the combustion of biomass and coal and also in theirconversion into liquid or gaseous fuels or chemicals. Whilethere have been many attempts at modeling coal pyrolysis[1–8] and biomass pyrolysis [9–17], no easily applicablemodel of coal or biomass pyrolysis is currently accepted asa universal predictive model. Undoubtedly this is related tothe complexity of pyrolysis that usually involves manyparallel reactions each of which have different reactantfeatures.

For applications of pyrolysis to solid fuel conversion it isreasonable to assume that nature responds smoothly tochanges of the controlling variables. The scientific methodsuggests that we should first conduct experiments for awide range of controllable variables and then attempt toorganize these results with a phenomenological model. Ifthe model reasonably represents the experimental results itshould be useful in applications and should also be usefulin the development of a model based upon fundamentals.The classic example is Tycho Brahe’s measurements of the

e front matter r 2006 Elsevier Ltd. All rights reserved.

ombioe.2005.09.004

ing author. Tel.: +1352 392 2002; fax: +1 352 392 2001.

ess: [email protected] (A.E.S. Green).

olar from Key Laboratory of Coal Science and Technol-

niversity of Technology, PR China.

positions of the planets, Kepler’s accurate representation ofthese positions by his phenomenological laws and New-ton’s later development of the law of Gravity and theCalculus that elegantly explained Kepler’s laws. This workis, in effect, a pedestrian analogue of the first two steps.First a large body of corn cob pyrolysis data is assembled.Then a phenomenological formula that has evolved inorganizing other pyrolysis measurements [18–28] is used toorganize this data.

2. Experimental corn cob results with a Pyroprobe

Pyroprobe is a type of mini pyrolysis reactor that can beused with a wide range of heating rates with small samples[2]. Weight loss is not measured, since to satisfy therequirement of negligible heat and mass transfer a specialsample holder must be used. A CDS 2000 Pyroprobe isused and the heating rate is varied from 6–20 000 �C=s, arange that covers most of pyrolysis processes considered instudying kinetic processes.The cobs of Zea may L, in particular the ‘‘Jin-Dan 42

corn’’ sample used was grown in the XiaoMa village nearTaiyuan in 2004 and collected directly from field. Afterdust washing, corn leaf and corn seed removal the samplewas ground to suitable particle size [2]. The experimentswere carried out at the Taiyuan University of Technology.

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Nomenclature

ASEM analytical semi-empirical modelCCTL clean combustion technology laboratoryPFTIR pyrolysis FTIRDASNF dry ash sulfur and nitrogen freeLðTÞ logistic or learning functionY yield of pyrolysis compounds

W the asymptotic amount of product weightD parameter fixing slope of learning curver heating ratep generalizing learning curve parameterT0 medium point of learning curveT1=2 median point of p ¼ 2 learning curveTa;Tb parameters fixing r dependence of T0

W a;W b parameters fixing r dependence of W

J. Feng et al. / Biomass and Bioenergy 30 (2006) 486–492 487

The ultimate and proximate analyses of the samples aregiven in Table 1. Sample preparation and start up and runprocedure have been described previously [2]. We herereport results for corn cob samples with heating rates 1000,100, 10 �C=s, 100, and 10 �C=min for kinetics scans up to900 �C. Bio-Rad FTS165 FTIR is used as the detector tomonitor the whole process. The setting resolution is 4 cm�1

and the scan ranges from 700 to 4000 cm�1 wavenumber.The panoramic FTIR results are expressed in 3 dimensions;X-axis shows the wave number ðcm�1Þ, Y shows theintensity of absorption (%), Z shows the time (s). Variousindividual products and functional groups are identified attheir standard absorption wavenumbers with the adsorp-tion of 3590 cm�1 taken as water (hydroxyl), 2950 cm�1

paraffin, 2358 cm�1 CO2, 2320 cm�1 CO, 2177 cm�1 olefin,

1798 cm�1, carbonyl, 1745 cm�1 aldehydes, 1380 cm�1

alcohol, 1275 cm�1 phenol and 1175 cm�1 ether. Due tothe adsorption derivation caused by heating processing,5 cm�1 are regarded as tolerance. The dY=dt vs. time datawas extracted from the panoramic diagram and afterintegration and changing time to temperature according toheating rate, we get individual yields, ðY iÞ vs. temperatureðTÞ for the 3 products and the 7 functional groups namedabove. Fig. 1 shows the FTIR panoramic diagrams forthree of the heating rates used.

2.1. Analytical semi-empirical model

In a number of attempts to find an underlying order ofpyrolysis yields for all products and all feedstock lyingalong nature’s coalification path the Clean CombustionTechnology Laboratory (CCTL) of the University Floridahas developed an analytical semi-empirical model (ASEM)of pyrolysis yields. The thrust of the initial systematicstudies [23–25] was to determine how the model yieldparameters depended upon [O], the oxygen weight percen-tage, and [H], the hydrogen wt% of dry ash, sulfur and

Table 1

Ultimate and proximate analysis of sample (* by difference)

Ultimate analysis (wt% daf) Proximate analysis (wt% ad)

C H N S O* Moisture Ash Volatile Fix-carbon

47.63 4.91 0.84 0.14 46.48 3.64 1.53 77.67 17.16

nitrogen free (DASNF) feedstock. In addressing thedependencies of the parameters upon the characteristicsof the products, apart from H2O, CO, CO2, and H2 theproducts were divided into family groups, e.g. paraffins,olefins, alcohols, aromatics, etc. and formulas wereproposed for the dependence of the yield parameters uponthe carbon number within each group [25–28]. Forapplication to the present corn cob PFTIR data we onlyneed the component of the ASEM methodology in whichyield ðY Þ vs. temperature ðTÞ of particular products orfunctional groups are represented by

Y ðTÞ ¼W ½LðTÞ�p, (1)

where

LðTÞ ¼ 1=½1þ expððT0 � TÞ=DÞ�. (2)

In effect each product is assigned 4 parametersðW ;T0;D0; pÞ to represent its yield vs. temperature. LðTÞ,the well-known logistic function, is often called the‘‘learning curve’’. When raised to a power, (i.e., Lp) itmight be called a ‘‘generalized learning curve’’ that canserve as a convenient and robust mathematical spline. Withcorn cob data preliminary searches indicated that withoutsignificant sacrifice of fit the special case p ¼ 2 can be usedto represent the rise and leveling off of all yield curves withtemperature. Thus to represent the 10 sets of yield data wewill use here

Y ¼W=½1þ expððT0 � TÞ=DÞ�2. (3)

Using ‘‘eyeball’’ parameter adjustments followed by non-linear least square (NLLS) searches we have to determinethe three parameters W , T0 and D for the 3 individualproducts and 7 functional groups at five heating rates ðrÞ.We found that for each group the D parameters could beheld constant with respect to heat rate ðrÞ again withoutsignificant degradation of fit. Table 2 gives the parametersobtained that give fits to data within experimentalaccuracy. These excellent fits raise the question as towhether the W and T0 parameters vary in a simple mannerwith r. After some experimentation we found that for all 10products yields can approximately be represented by

T0 ¼ Ta þ Tb ln r, (4)

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1000 °C/sec

05

1015

20

3000 2000 1000Wavenumber (cm-1)

10 °C/sec

3000 2000 1000

Wavenumber (cm-1)

10 °C/min

3000 2000 1000

Wavenumber (cm-1)

Abs

orba

nce

Abs

orba

nce

Time (sec)

Time (

sec)

020

406080100

05

1015

20

Time (min)

0.06

0.04

0.02

0

Abs

orba

nce 0.06

0.08

0.04

0.02

0

0.04

0.02

0

Fig. 1. PFTIR results of different heating rate.

J. Feng et al. / Biomass and Bioenergy 30 (2006) 486–492488

and

W ¼W a þW b ln r, (5)

where Ta, Tb, W a, and W b are adjusted parameters. Theleft set of Fig. 2 shows the smoothed Y ðTÞ patterns foreach functional group as represented by Eqs. (3)–(5)with the parameters given in Table 3. The sixth columnof Table 3 gives the temperature at which the yield for eachproduct reaches W=2, i.e. its median value. By algebra itfollows that

T ð1=2Þ ¼ T0 �D lnð21=2 � 1Þ ¼ T0 þ 0:88D. (6)

The successive rows in Table 3 have been ordered by thismagnitude. The last column gives the number of oxygenatoms in the products.

2.2. Time dependence

Using the relationship T ¼ rtþ Ti the analytical repre-sentation Y ðTÞ can be translated into an analytical Y ðtÞ

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Table 2

Detailed fitting parameters for yield vs. temperature ðp ¼ 2Þ

d Water CO2 CO Paraffin Olefin Carbonyl Aldehydes Alcohol Phenol Ether

135 155 160 115 115 125 165 165 135 145

1000K s�1 W 0.54 1.99 2.05 0.85 0.74 1.44 2.00 0.77 1.20 0.96

T0 640 575 630 645 827 630 663 600 630 675

100K s�1 W 0.665 2.856 2.830 1.021 0.375 1.497 2.325 0.953 1.435 1.342

T0 589 588 619 622 721 561 613 592 622 580

10K s�1 W 0.75 3.43 3.00 2.57 0.41 1.69 2.68 1.14 1.72 1.86

T0 548 549 563 737 585 523 576 580 600 547

1:67K s�1 W 2.48 7.99 7.84 2.56 2.61 3.50 4.32 4.12 4.12 4.25

T0 686 514 568 669 618 443 435 530 530 528

0:167K s�1 W 2.86 8.65 8.40 2.95 3.31 4.65 5.99 4.61 5.71 4.96

T0 495 446 467 507 511 393 411 414 479 433

water

10C/Sec100C/Sec

100C/min

1000C/Sec

10C/min

water

CO2 CO2

CO CO

0.001

0.01

0.1

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10

200 400 600 800Temperature



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ld (

%)

0.1 1 10 100 1000 10000Time

0.1 1 10 100 1000 10000Time (sec)

0.1 1 10 100 1000 10000Time (sec)

Fig. 2. The comparison between the experiment results with ASEM model (temperature and time).


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Paraffin Paraffin

olefin olefin

carbonyl carbonyl

200 400 600 800Temperature (C)



0.001

0.01

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10

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ld

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%)

0.1 1 10 100 1000 10000Time

0.1 1 10 100 1000 10000Time

0.1 1 10 100 1000 10000Time (sec)

Fig. 2. (Continued)


given by

Y ¼W=½1þ expððt0 � tÞ=dÞ�2, (7)

where, by algebra, t0 ¼ ðT0 � TiÞ=r and d ¼ D=r. The rightset of Fig. 2 shows the experimental vs. smooth Y ðtÞ for thevarious products. As one can see, there are somedepartures of the smoothed Y ðTÞ and Y ðtÞ representationsfrom the data particularly at low yields. However, overallthe smoothed analytic representations provide a reasonablebasis for interpolation or extrapolation of the experimentalresults. If the parameters in Table 2 were used to derive theparameters in Eq. (7) all curves would pass through thedata points.

3. Discussion

One could also fit the direct PFTIR rate data with thisASEM methodology by using RðtÞ ¼ dY ðtÞ=dt with thederivative of the Y ðtÞ (Eq. (7)). This approach has advantagesif one is interested in the finer details of the experimental data.However, for most applications the averaging approachedused here gives a useful overview of expected yields. ThisASEM approach to the organization of experimentalpyrolysis data appears simpler to implement that organiza-tion with various types of kinetic models [1–3,9–14,17] whileavoiding complications with the Boltzmann integral [13].It might also be noted that the absence of weight loss

data in the analysis of Pyroprobe pyrolysis products

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aldehydes

0.001

0.01

0.1

1

10

200 400 600 800Temperature (C)

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ld (

%)

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aldehydes

0.1 1 10 100 1000 10000

0.1 1 10 100 1000 10000

0.1 1 10 100 1000 10000

0.1 1 10 100 1000 10000

Time (sec)

alcohol alcohol


Temperature

Temperature

Time

phenol

200 400 600 800

phenol

Time

ether

200 400 600 800

ether

Time

Fig. 2. (Continued)


substantially simplifies the ASEM analysis as compared,for example, with possible applications to TG-FTIR data[29,30] or TG-MS data [17].

4. Conclusions

The analytic semi-empirical model appears to provide auseful way of distilling the essence of the thermo-chemicalresponse of corn cob to temperature and heating rate in aPFTIR experimental system. Table 2, in effect, accurately

compresses the 750 data points used from the Pyroprobeoutputs into 111 adjusted constants. Table 3 gives anoverview of the 750 data points in terms of 51 adjustedparameters. The fact that the parameters, particularly Tb

and W b vary over small ranges suggests that some averagesmight be used leading to a further reduction in the numberof parameters needed to represent overall corn cob yieldbehavior. Perhaps the variation of Ta or T1=2 or W a canbe rationalized in terms of the molecular properties ofthe molecules or functional groups. Thus this specific

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Table 3

Ta, Tb, W a, W b and D, T1=2, O

Ta Tb W a W b D T1=2 Oxygen

Carbonyl 429.82 31.12 3.05 �0.26 115 539.9 1

Aldehydes 445.39 36.20 4.16 �0.33 125 566.7 1

Ether 480.92 27.81 3.28 �0.33 135 606.5 1

Alcohol 483.11 23.07 2.73 �0.29 165 633.2 1

Phenol 520.92 19.95 3.42 �0.33 165 669.7 1

Olefin 553.24 38.19 1.54 �0.16 145 691.4 0

Paraffin 601.85 12.14 2.43 �0.22 115 704.4 0

CO2 490.97 16.87 6.09 �0.58 155 630.2 2

CO 519.24 19.43 5.82 �0.55 160 663.5 1

Water 561.38 10.83 1.73 �0.18 135 681.4 1


analytical model of pyrolysis yields can at least serve as achallenge to find better models using fewer parameters thatare better based in fundamental chemistry and physics.

Acknowledgments

This work was supported in part by funds from the MickA. Naulin Foundation, NASA’s University of FloridaEnvironmental Systems Commercial Space TechnologyCenter program and Taiyuan University of Technology,China (NNSF No. 20476068).

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analytical model of corn cob pyroprobe-ftir data

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