abdullah m. alsahly*, elamin m. elkanzi and s. m. zakir

Journal of Scientific Research and Studies Vol. 5(6), pp. 142-152, July, 2018 ISSN 2375-8791 Copyright © 2018 Author(s) retain the copyright of this article http://www.modernrespub.org/jsrs/index.htm

Full Length Research Paper

Lipase-catalyzed production of biodiesel: Process simulation and economic analysis

Abdullah M. Alsahly*, Elamin M. Elkanzi and S. M. Zakir Hossain

Department of Chemical Engineering, University of Bahrain, Isa Town 33349, Kingdom of Bahrain.

*Corresponding author. E-mail: [email protected], Tel: (+966) 541335858

Accepted 22 July, 2018

A technical and economic comparison between the alkaline and enzymatic biodiesel production processes was evaluated based on a rate of production of ~11200 tons/year biodiesel, using Aspen Plus and HYSYS simulation platforms. The simulations were based on the kinetics of the two processes. For the enzymatic process, the kinetic rate was based on Ping-Pong-Bi-Bi mechanism and incorporating an expression for the effect of enzyme loading on the rate of reaction. Two simulation options were done for the enzymatic process: a once-through process where the glycerol is separated at the exit of the continuous stirred tank reactors (CSTRs) and the other with an inter-reactor separation of glycerol. The results of kinetic reactor simulation and reactor staging design may be more realistic than those based on assumed reactor conversion in one or two stages reported by previous investigators. The economic evaluation revealed that the enzymatic process with inter-reactor separation of glycerol may be more economically attractive than the other two alternatives. Key words: Biodiesel, economic evaluation, enzyme loading, kinetic reactor simulation.

INTRODUCTION The limitations of “first generation” biofuel production technology, relating to direct food versus fuel conflict, cost competitiveness, and GHG emissions, led to a rapid and extensive research in industries and academia (Brennan and Owende, 2010; Zakir Hossain et al., 2016). The traditional homogeneous alkaline and acid catalyzed processes are still the favored route for the industrial production of biodiesel. However, due to the drawbacks, such as the high production cost, non-recovery of catalyst, expensive corrosion resistant equipment, and the expensive wastewater treatment related to these processes, attention is now focused on biodiesel production catalyzed by enzymes. However, to date, only a few plants have employed the enzymatic process for the industrial production of biodiesel (Du et al., 2008). This may be due to the relatively high manufacturing cost in which the cost of enzyme is the most effective.

Several reviews have addressed the recent progress in improving the enzymatic production of biodiesel as regards to reducing the running costs via process optimization, bioreactor design, process simulation and

techno-economic evaluation (Go et al., 2016; Marx, 2016; Poppe et al., 2015; Teixeira et al., 2014; Tran et al., 2017).

Although there are a significant number of simulation studies of alkaline and enzymatic processes of biodiesel production, the kinetic reactor simulation is less considered in the literature. Simulation of the kinetic reactor is necessary, because besides it obtains general mass and energy balances, it also serves as a tool in the reactor design and optimization of the reactor operating conditions. Since the transesterification reactions are reversible, the simulations of the alkaline process that considered conversion reactor used excess methanol to drive the reaction to the products, thus increasing conversion and yield (Lee et al., 2011). However, this technique may not be applicable for the enzymatic process because excess methanol could inhibit the reaction and reduce the activity of the enzyme (Harding et al., 2008). On the other hand, there are few studies that considered simulating the enzymatic reactor as a kinetic reactor (Chesterfield et al., 2006). Although there

MMMMRRRRPPPP

143 J. Sci. Res. Stud.

Figure 1. Liquid-liquid equilibrium data for the ternary system of methanol, glycerol and biodiesel (a) predicted by the simulator with Dortmund-Modified UNIFAC thermodynamic model at 35°C and atmospheric pressure (b) experimental data reported by Doungsri and Sookkumnerd (2011).

are various kinetic studies on enzyme-catalyzed transesterification reactions that incorporated the effect of enzyme loading on the rate of reaction (Cheirsilp et al., 2008; Pawar and Yadav, 2015), to our knowledge, none of the process simulations considered the effect of enzyme loading on the rate of reaction nor the inter-reactor glycerol separation technique.

From this article’s brief and concise review of the literature, process simulation for both alkaline and enzymatic processes is to be investigated focusing on process simulation and optimization, enzyme loading and economic evaluation. PROCESS SIMULATION The alkaline process was simulated using Aspen Plus (V9.0) while the enzymatic process was simulated using Aspen HYSYS (V9.0) with the imported database of Aspen Properties. The use of Aspen Plus for the alkaline process was due to the three-step reaction in which the oil content was considered as a mixture of triglycerides, diglycerides, and monoglycerides (concentration of 0.948, 0.052, and 0.000 g/g, respectively) where they are fully defined. In contrast to the enzymatic process, as most studies consider one-step reaction, the oil content was simulated as pure triglycerides. The components: triglycerides, diglycerides, monoglycerides, and methyl ester (biodiesel) were modelled as triolein, diolein, monoolein, and methyl oleate, respectively. Both simulations were validated with respect to the quality of properties prediction and the liquid-liquid equilibrium was

modelled by the Dortmund-Modified UNIFAC group contribution. A good fit between the predicted liquid-liquid equilibrium for fatty acid methyl ester, methanol and glycerol (Figure 1a) with the experimental data (Figure 1b) reported by Doungsri and Sookkumnerd (2011) was obtained. The simulations were based on a rate of production of biodiesel of ~1300 kg/h (~11200 tons/year), an isothermal CSTR reactor operated at a temperature of 50°C for the enzymatic process and 60°C for alkaline, and with n-hexane as a solvent in the enzymatic process. ALKALINE CATALYZED BIODIESEL PROCESS Biodiesel can be produced by the transesterification reaction of a tri-di-monoglycerides with a primary alcohol (e.g. methanol) in the presence of an alkaline chemical catalyst, such as sodium hydroxide or sodium methoxide. The chemical reactions in Equations 1, 2, and 3 represent the transesterification reactions of tri, di and monoglycerides with methanol.

(1)

(2)

(3) where TG, DG, MG, GL, FAME, and MeOH are triglycerides, diglycerides, monoglycerides, glycerol, fatty acid methyl ester, and methanol, respectively. Based on the reaction mechanism, most of the published kinetic

TG + MeOH ⇄ DG + FAME#

DG + MeOH ⇄ MG + FAME#

MG + MeOH ⇄ GL + FAME

Alsahly et al. 144

Figure 2. Methanol to oil feed molar ratio effect on the oil equilibrium conversion.

Figure 3. Alkaline catalyzed biodiesel process flow diagram.

models consider a second order reaction rates in molar concentrations with the assumption of neglecting the partial solubility of methanol and oil (Tuluc et al., 2015). The kinetic parameters of the reactions with sodium hydroxide as catalyst in the study published by Bambase et al. (2007) were considered to simulate the kinetic reactor.

The alkaline catalyst transesterification reactions in Equations 1-3 are dominated by chemical equilibrium. The reactions are relatively fast and can approach equilibrium. Moreover, the equilibrium conversion of oil is favored by reasonably low temperatures and high methanol to oil molar ratios (Tuluc et al., 2015). Figure 2 is a sensitivity analysis performed for an isothermal reaction in a CSTR operated at 60°C and shows the effect of excess methanol feed on the oil equilibrium conversion. The region confined between 4 and 6

mol/mol of feed molar ratio in Figure 2 is the equilibrium limitation associated with the alkaline catalyzed biodiesel process.

A methanol to oil feed molar ratio of 8 mol/mol is chosen in this study which corresponds to an oil equilibrium conversion of ~94%. By using the second order rate laws of the three-step reactions, the Levenspiel’s plot reveals an optimum combination of two CSTRs connected in series with a total volume of 1.5 m

3

with a volume of 0.58 m3

of the first CSTR, and single-pass oil conversion of 74.89%, and a volume of 0.92 m

3

for the second CSTR and single-pass oil conversion of ~93%. The simulation results are as shown in Figure 3 and Table 1.

The separation system associated with the alkaline process is shown in Figure 3, to accomplish product specifications, composed of methanol recovery column


Table 1. Flow summary table for the alkaline catalyzed biodiesel process.

Stream FAME GLYCEROL WATER MEOH OIL S8 S18 W-WATER2 W-WATER NAOH

Temperature (°C) 148.3 264.8 25.0 25.0 25.0 28.6 25.0 70.9 148.3 25.0

Pressure (kPa) 10 50 110 100 100 20 20 40 10 100

Mole flows (mmol/s) 1225.556 434.722 1040.833 1308.056 417.222 2333.333 30.556 2675.278 67.500 926.111

Mass flows (kg/h) 1303.70 152.19 67.50 150.89 1300.79 266.68 58.01 180.90 4.96 67.47

Mass Fractions g/g:

Methanol 0.0000 0.0000 0.0000 1.0000 0.0000 0.9881 0.0000 0.0567 0.0192 0.0000

Triolein 0.0000 0.0000 0.0000 0.0000 0.9480 0.0000 0.3922 0.0000 0.0000 0.0000

Diolein 0.0000 0.0000 0.0000 0.0000 0.0520 0.0000 0.2272 0.0000 0.0000 0.0000

Monoolein 0.0027 0.0000 0.0000 0.0000 0.0000 0.0000 0.3333 0.0000 0.0000 0.0000

Methyl oleate 0.9970 0.0000 0.0000 0.0000 0.0000 0.0000 0.0473 0.0000 0.1180 0.0000

Glycerol 0.0000 0.8788 0.0000 0.0000 0.0000 0.0000 0.0000 0.0200 0.0000 0.0000

Sodium hydroxide 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.2000

Water 0.0003 0.0000 1.0000 0.0000 0.0000 0.0119 0.0000 0.9233 0.8628 0.8000

Phosphoric acid 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Trisodium phosphate 0.0000 0.1211 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

(T-101) for the recycle of excess methanol, water-washing column (T-102) to purify the light non-polar phase, biodiesel distillation column (T-103) where the biodiesel meets the required specifications and the unreacted oil is recycled, catalyst neutralization reactor (R-103), and glycerol purification column (T-104). ENZYMATIC CATALYZED BIODIESEL PROCESS Unlike the alkaline-catalyzed process, the enzymatic-catalyzed process has much slower kinetics resulting in a very slow rate of reaction relative to the alkaline process. The reported experimental reaction time of the alkaline process ranges between several minutes and two hours for more than 95% single-pass oil conversion. However, for the enzymatic process, the reported

experimental reaction time ranges between several hours and up to 3 days for a single-pass oil conversion that ranges between 70 and 95%. Hence, the enzymatic-catalyzed reaction is kinetically limited (Luo et al., 2006; Ying and Chen, 2007; Zhao et al., 2007). Kinetic reactor simulation Three reaction mechanism pathways were proposed in the literature (Amini et al., 2017), direct transesterification of glycerides into fatty acid methyl ester, two consecutive steps consisting of hydrolysis followed by esterification and simultaneous reactions of both transesterification and hydrolysis followed by esterification. Cheirsilp et al. (2008) found that the rate constants for transesterification reaction are much higher than those for the hydrolysis-

esterification reaction and hence, transesterification reactions are dominant. The overall enzymatic catalyzed transesterification reaction (Equation 4) for biodiesel production is presented as follows:

(4) The Ping-Pong-Bi-Bi mechanism is widely accepted for the methanolysis of triglycerides into glycerol and fatty acid methyl ester. The resulting rate expression known as Ping-Pong kinetic rate law model (Zarejousheghani et al., 2016) can be represented as follows:

� =��.��.��

��.��

.��.�� (5)

Where � is the reaction rate, V�� is the

TG + 3MeOH ⇄ GL + 3FAME#

Alsahly et al. 146

Figure 4. (a) Levenspiel's plot for different enzyme loading including the base-case loading of 83.69 kg (b) Economic potential 3 as a function of the conversion for all enzyme loading alternatives (including the base-case).

maximum reaction rate, C#$ and C% are the molar concentrations of triglycerides and methanol,

respectively. K�'( and K�)

are the apparent Michaels

constants for triglycerides and methanol, respectively. The inhibition by methanol was neglected in this work as most authors agreed that the presence of an organic solvent (e.g. n-hexane) even with a feed molar ratio of methanol to oil exceeding the stoichiometric ratio will make the inhibition by methanol negligible (Chen et al., 2011; Du et al., 2007; Talukder et al., 2009; Zarejousheghani et al., 2016). However, in this work, the reactants are dissolved in n-hexane in their stoichiometric feed ratio.

It is to be noted that the term V�� , in Equation 5 is dependent on the temperature and the total concentration

of the enzyme and bears a linear relationship with the total enzyme concentration and can be represented as follows (Fogler, 2006):

*+,- = .. /01 (6)

where k is a constant dependent on the system

temperature and C23 is the total enzyme concentration in

the system. Kraai et al. (2008) experimentally observed a linear relationship between the initial reaction rate and the enzyme concentration for the esterification of oleic acid with 1-butanol in a biphasic system consisting of water and n-heptane that agrees with Equation 6. It is necessary for scaling up of the process to industrial one,

V�� term is to be replaced using Equation 6 with the


Table 2. Reactor staging results of the enzymatic catalyzed process with 83.69 kg of enzyme load.

Reactor number Rn

Single-pass conversion up to reactor Rn (%)

Reactor volume (m3) Total volume (m

3)

1 10.01 9.66 9.66

2 20.00 11.80 21.50

3 30.00 14.30 35.80

4 35.00 7.91 43.70

5 40.00 8.74 52.40

6 45.00 9.70 62.10

7 50.00 10.80 73.00

8 55.00 12.20 85.20

9 60.00 13.80 99.00

10 65.00 15.90 115.00

11 69.00 14.40 129.00

Table 3. Reactor staging design and fixed cost for 100 kg of enzyme load with a fixed cost of $100,000.

Reactor number Rn

Single pass conversion up to Rn (%)

Reactor volume (m

3)

Total volume (m

3)

Reactor cumulative FC

a (x 10

6$)

1 9.92 8.00 8.00 0.22

2 20.03 10.00 18.00 0.46

3 30.03 12.00 30.00 0.73

4 40.01 14.60 44.60 1.02

5 45.05 8.20 52.80 1.24

6 50.01 9.00 61.80 1.47

7 55.01 10.20 72.00 1.71

8 60.02 11.60 83.60 1.98

9 65.00 13.30 96.90 2.26

10 70.00 15.60 112.50 2.57

11 72.50 8.55 121.00 2.79

12 75.00 9.37 130.40 3.03

13 77.50 10.45 140.90 3.28

14 80.00 11.68 152.50 3.54

15 82.50 13.37 165.90 3.82

16 85.00 15.56 181.50 4.13 a Fixed cost calculated by CAPCOST spreadsheet with a CEPCI2016 of 556.8.

appropriate units associated with the kinetic model to account for enzyme loading.

The reaction parameters V�� , K�'(, and K�)

with the

pre-exponential factor values of 4.62 x 1023

, 7.51 x 104,

and 7.39 x 1026

with activation energies of (E�) of 147.42, 15.88, and 135.42 J/mmol, respectively reported by Zarejousheghani et al. (2016) were considered in this

study. Adjusting the term V�� to accommodate the large-scale production for an oil feed of 1750 kg/h (both processes have the same production capacity but they differ in the feed rate due to the different single-pass oil conversion associated with each process) with an enzyme mass concentration of 0.5 g/g (5 mass% based

on oil mass), the resulted total enzyme amount is 83.69

kg and hence, the V�� value is calculated to be 2.05 x

1024

mmol.mL-1

.s-1

. The temperature-dependent constant

k in Equation 6 can be calculated from the experimental conditions reported by Zarejousheghani et al. (2016) in which the reactor temperature was kept constant at an optimum value of 50°C, since the enzyme starts to deactivate at temperatures greater than 60°C (Al-Zuhair et al., 2007; Zarejousheghani et al., 2016). Using the rate law of Equation 5 with stoichiometric feed molar ratio for an oil feed of 1750 kg/h, the Levenspiel's plot in Figure 4a (the base-case curve with the enzyme load of 83.68 kg) was used in the design of the reactor section. A series of

Alsahly et al. 148

Figure 5. Process flow diagram for the (a) Enzymatic catalyzed biodiesel with once-through separation of glycerol (b) Enzymatic catalyzed biodiesel with inter-reactor separation of glycerol.

CSTRs that are necessary to give the desired conversion were chosen from the plot. CSTR was considered as intensive mixing is required for this system to minimize the mass transfer limitation and creates an even distribution of the enzyme catalyst in the reactor. Table 2 illustrates the results of the reactor staging of the enzymatic process. These reactors volume results (or single-pass conversion) are consistent with those reaction times reported for the enzymatic catalyzed process (Luo et al., 2006; Ying and Chen, 2007; Zhao et al., 2007).

Sotoft et al. (2010) in their simulation of the enzymatic biodiesel production plant in a single CSTR assumed single-pass oil conversion of 95%. As evident from Figure 4a, this conversion would require a reactor volume of 2000 m

3.

The effect of enzyme loading and process optimization A sensitivity analysis was carried out to study the effect of the enzyme loading on the conversion or the total reactor volume. Values of the enzyme loadings of 50, 75, 100, and 120 kg were used to generate the Levenspiel's plot shown in Figure 4a. This figure reveals that as the amount of the enzyme increases, lower volumes are

required to achieve the same desired conversion. To find the optimum loading, an objective function was chosen such that the extreme maximum (or minimum) would be the desired condition. For the case of varying the amount of enzyme loading, the costs that will be affected are the installed cost associated with each reactor volume, the fixed cost of the enzyme and the variable cost of enzyme losses due to enzyme life time deactivation. To account for both fixed costs and operating costs, an objective function that considers the time value of money has been considered. Before defining the objective function, the economic potential (

), defined in Equation 7 (Douglas, 1999), was calculated for each loading with the prices of methanol, oil, biodiesel, glycerol, n-hexane and enzyme reported as 0.67, 0.70, 1.40, 0.98, 0.41, and 1000 ($/kg) respectively (ICIS, 2006; Sotoft et al., 2010).

(7)

where EP6 is an economic screening tool to differentiate between process alternatives. The objective function was then defined as follows:

(8)

781 = :;�;<=; − :?@ A?B;CD?E FGHB

783 = 781 − 0.187L:;?FBGC M/ + 7<NOA; M/P#


Table 4. Flow summary table for the enzymatic catalyzed biodiesel process with once-through separation of glycerol.

Stream 1 5 10 57 51 45 53 48

Temperature (°C) 25.0 25.0 25.0 25.0 25.0 148.3 262.2 338.1

Pressure (kPa) 101.3 101.3 101.3 120.0 120.0 10.0 50.0 10.0

Molar flow (mmole/s) 1237.500 18.333 420.833 4550.833 162.778 1259.444 411.944 9.444

Mass flow (kg/h) 142.76 5.71 1341.19 1334.67 469.30 1327.07 136.57 27.30

Mass composition g/g:

Methanol 1.0000 0.0000 0.0000 0.0379 0.0000 0.0000 0.0000 0.0000

Triolein 0.0000 0.0000 1.0000 0.0000 0.9466 0.0000 0.0000 0.9466

Methyl oleate 0.0000 0.0000 0.0000 0.0086 0.0534 0.9947 0.0001 0.0534

Glycerol 0.0000 0.0000 0.0000 0.0008 0.0000 0.0002 0.9999 0.0000

Water 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

n-Hexane 0.0000 1.0000 0.0000 0.9528 0.0000 0.0051 0.0000 0.0000

Table 5. Flow summary table for the enzymatic catalyzed biodiesel process with inter-reactor separation of glycerol.

Stream 1 5 10 75 68 79 66 72

Temperature (°C) 25.0 25.0 25.0 262.2 338.1 25.0 148.3 25.0

Pressure (kPa) 101.3 101.3 101.3 50.0 10.0 120.0 10.0 120.0

Molar flow (mmole/s) 1268.889 19.722 420.000 421.111 7.500 5502.222 1285.000 137.778

Mass flow (kg/h) 146.37 6.12 1338.53 139.67 21.63 1402.14 1355.37 397.47

Mass composition g/g:

Methanol 1.0000 0.0000 0.0000 0.0000 0.0000 0.1395 0.0000 0.0000

Triolein 0.0000 0.0000 1.0000 0.0000 0.9466 0.0000 0.0000 0.9466

Methyl oleate 0.0000 0.0000 0.0000 0.0003 0.0534 0.0256 0.9952 0.0534

Glycerol 0.0000 0.0000 0.0000 0.9997 0.0000 0.0001 0.0000 0.0000

Water 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

n-Hexane 0.0000 1.0000 0.0000 0.0000 0.0000 0.8348 0.0047 0.0000

Table 6. Reactor volume, cost, manufacturing cost, total capital investment, conversion and NPV for each process.

Process type No. of stages

Reactor volume

(m3)

Reactor costa

(x 103$)

Conversion

(%)

COMb

($/y)

Capital investment (x 10

6$)

NPV

(x 106$)

Alkaline 2 1.50 126 93 14.53 x106

10.17 3.94

Enzymatic (OT) 12 130.42 3030 75 14.46 x106 10.37 3.95

Enzymatic (IR) 8 83.60 1980 75 14.47 x106 9.15 5.17

a This cost calculated by CAPCOST spreadsheet with a CEPCI2016 of 556.8; b Cost of manufacturing

EPQ is the economic potential 3 such that EP6 was modified to consider the parameters of Reactor FC and Enzyme FC, which are the fixed costs for the reactor and the amount of enzyme loaded initially, respectively. The parameter 0.187 in Equation 8 is the result of converting the fixed costs into annualized costs, assuming 10 years of the plant life and an interest rate of 13.5% (Douglas, 1999). All five alternatives of enzyme loadings were sized, and the terms in Equation 8 were calculated. CAPCOST was used to estimate the fixed installed costs of the reactor section. Table 3 shows a sample result for the case of 100 kg load of the enzyme.

Figure 4b shows the results of all alternatives for EPQ as a function of conversion. This figure shows that the

maximum EPQ can be achieved with enzyme loading of 100 kg and an optimum oil single-pass conversion of 75%. Once-through and inter-reactor glycerol separation The enzymatic catalyzed biodiesel once-through process with 100 kg of enzyme load was simulated using Aspen HYSYS and shown in Figure 5a with the main streams flow summary in Table 4. Another option was simulated to account for inter-reactor separation of glycerol. A fresh side stream of methanol is fed at each stage such that the molar ratio of methanol to oil is kept constant throughout the reactor stages as shown in Figure 5b with the main streams flow summary in Table 5. Here a decrease in the total volume from 130.4 to 83.6 m

3 is

achieved for the same oil conversion of 75%. Table 6 contains a summary of the main results of the

kinetic reactor design section of the three processes alkaline, enzymatic with once-through separation (OT) and enzymatic with inter-reactor separation (IR) of glycerol.

The separation system associated with the enzymatic process is simple compared with the alkaline process. In the absence of chemical catalysts, a single decanter (for OT process) is enough to separate the two phases and no water-washing column is needed. The biodiesel purification column (T-100) and glycerol purification column (T-101) are the main separation equipment to recycle the unreacted raw materials and meet the required products specification.

ECONOMIC EVALUATION AND COMPARISON The three biodiesel processes alkaline, enzymatic with once-through separation, and enzymatic with inter-reactor separation of glycerol were economically evaluated and compared. Aspen Process Economic Analyzer (APEA, included in both Aspen Plus and Aspen HYSYS) was used to estimate the total capital cost, equipment fixed installed costs, the utility cost, waste

Alsahly et al. 150 treatment cost, and the operating labor cost of each of the three plants. The manufacturing cost could be estimated as follows (Turton et al., 2003):

(9)

where COM represents the cost of manufacturing, FCI represents the equipment fixed installed costs, CST

represents the cost of the operating labor. CU#, CV#, and

CW% represent the utility cost, waste treatment cost, and the cost of raw materials, respectively. For the alkaline process, the raw materials cost were calculated with the prices of sodium hydroxide, phosphoric acid, and glycerol reported as 0.40, 0.45, and 0.89 ($/kg) respectively (ICIS, 2006; Sotoft et al., 2010). Figure 6 shows the distribution of the equipment fixed installed costs of the three processes taken from APEA.

Finally, Table 6 contains the end results for the total capital investment taken from APEA and the cost of manufacturing calculated from Equation 9. The capital investment and the cost of manufacturing for the three processes are, in general, close to each other as seen in Table 6. The profitability of the three processes was analyzed using the NPV for assumed plant life of 10 years and interest rate of 13.5%. For the cash flow (Equation 10), the revenue was calculated using prices of $1.4/ kg biodiesel, $0.98/ kg glycerol produced from the enzymatic process and $0.89/ kg of glycerol produced from the alkaline process. The cash flow is defined as the profit before tax:

(10)

The NPV for each of the three processes are $3.947 ×10\, $3.957 × 10\ and $5.172 × 10\ respectively. This shows that the enzymatic process with inter-reactor separation may be more economically attractive than the other two processes. Conclusions The alkaline and two enzyme-catalyzed processes were simulated and evaluated economically for a plant capacity of ~11200 tons/year biodiesel. The simulations used the kinetics of the chemical and enzymatic transesterification reactions. The conversions and reactor sizes results were found more appropriate than those based on assumed conversions reported previously. In the enzymatic process, the increase in enzyme loading reduced the reactor volume and hence industrial production of biodiesel by enzymes may be attractive. The economic comparison between the three processes was carried out using the NPV criterion and revealed that the enzymatic process with inter-reactor separation is more economically attractive.

/_` = 0.180M/a + 2.73/_b + 1.23L/cd + /ed + /:` P#

8CGfDB g;fGC; B?h = :;�;<=; − /_`#


Figure 6. Distribution of equipment fixed installed costs for the process (a) Alkaline (b) Enzymatic with once-through separation of glycerol (c) Enzymatic with inter-reactor separation of glycerol.

Recommendations Future work may consider optimizing the process with the objective function being maximization of profit with enzyme loading, temperature, and rate of production as constraints. Acknowledgement This work is an unfunded student projects' research. The

authors would like to acknowledge the College of Engineering, University of Bahrain for providing the computer and software facilities. REFERENCES

Al-Zuhair S, Ling FW, Jun LS (2007). Proposed kinetic mechanism of the production of biodiesel from palm oil using lipase. Process Biochem. 42(6):951-960.

Amini Z, Ilham Z, Ong HC, Mazaheri H, Chen WH (2017). State of the art and prospective of lipase-catalyzed transesterification reaction for biodiesel production. Energy Convers. Manag. 141:339-353.

(a)

(b)

(c)

Reactors22%

Towers (including

utilities and horizental drumps)

57%

Water-washing

tower11%

Pumps3%

Heat exchangers

7%

Total FCI ($) = 3.37x106

Reactors60%

Towers (including


23%

Pumps5%

Heat exchangers

9%Decanters

3%

Total FCI ($) = 3.18x106

Reactors37%

Towers (including


25%

Pumps4%

Heat exchangers

10%

Decanters 24%

Total FCI ($) = 3.39x106

Bambase ME, Nakamura N, Tanaka J, Matsumura M (2007). Kinetics of

hydroxide-catalyzed methanolysis of crude sunflower oil for the production of fuel-grade methyl esters. J. Chem. Technol. Biotechnol. 82(3):273-280.

Brennan L, Owende P (2010). Biofuels from microalgae: A review of technologies for production, processing, and extractions of biofuels and co-products. Renew. Sustain. Energy Rev. 14(2):557-577.

Cheirsilp B, H-Kittikun A, Limkatanyu S (2008). Impact of transesterification mechanisms on the kinetic modeling of biodiesel production by immobilized lipase. Biochem. Eng. J. 42(3):261-269.

Chen HC, Ju HY, Wu TT, Liu YC, Lee CC, Chang C, Chung YL, Shieh CJ (2011). Continuous production of lipase-catalyzed biodiesel in a packed-bed reactor: Optimization and enzyme reuse study. J. Biomed. Biotechnol. 2011: Article ID 950725.

Chesterfield D, Rogers PL, Al-Zaini E, Adesina AA (2006). Steady-state simulation of a novel extractive reactor for enzymatic biodiesel production. Fuel Process. Technol. 108:101-111.

Douglas JM (1999). Conceptual design of chemical processes. McGra-Hill (Clark BJ and Bradley JW Eds.), New York, p. 55.

Doungsri S, Sookkumnerd T (2011,November). Solubility curves of FAME + Methanol + Glycerol. Paper presented at the TICHE International Conference. Retrieved from http://chemserver.eng.psu.ac.th/tiche2011/TCHE/.

Du W, Li W, Sun T, Chen X, Liu D (2008). Perspectives for biotechnological production of biodiesel and impacts. Appl. Microbiol. Biotechnol. 79(3):331-337.

Du W, Liu D, Li L, Dai L (2007). Mechanism exploration during lipase-mediated methanolysis of renewable oils for biodiesel production in a tert-butanol system. Biotechnol. Prog. 23(5):1087-1090.

Fogler HS (2006). Elements of Reaction Engineering, 4th ed. Prentice Hall (Amundson NR Ed.), Boston, p. 400.

Go AW, Sutanto S, Ong LK, Tran-Nguyen PL, Ismadji S, Ju YH (2016). Developments in in-situ (trans) esterification for biodiesel production: A critical review. Renew. Sustain. Energy Rev. 60:284-305.

Harding KG, Dennis JS, von Blottnitz H, Harrison STL (2008). A life-cycle comparison between inorganic and biological catalysis for the production of biodiesel. J. Clean. Prod. 16(13):1368-1378.

ICIS (2006). Indicative chemical prices A-Z. Retrieved on May 2, 2017. Available online: https://www.icis.com/chemicals/channel-info-chemicals-a-z/.

Kraai GN, Winkelman JGM, de Vries JG, Heeres HJ (2008). Kinetic studies on the Rhizomucor miehei lipase catalyzed esterification reaction of oleic acid with 1-butanol in a biphasic system. Biochem. Eng. J. 41(1):87-94.

Lee S, Posarac D, Ellis N (2011). Process simulation and economic analysis of biodiesel production processes using fresh and waste vegetable oil and supercritical methanol. Chem. Eng. Res. Des. 89(12):2626-2642.

Alsahly et al. 152 Luo Y, Zheng Y, Jiang Z, Ma Y, Wei D (2006). A novel psychrophilic

lipase from Pseudomonas fluorescens with unique property in chiral resolution and biodiesel production via transesterification. Appl. Microbiol. Biotechnol. 73(2):349-355.

Marx S (2016). Glycerol-free biodiesel production through transesterification: A review. Fuel Process. Technol. 151:139-147.

Pawar SV, Yadav GD (2015). Kinetics and mechanism of regioselective monoacetylation of 3-aryloxy-1,2-propandiols using immobilized Candida antarctica lipase. J. Ind. Eng. Chem. 31:335-342.

Poppe JK, Fernandez-Lafuente R, Rodrigues RC, Ayub MAZ (2015). Enzymatic reactors for biodiesel synthesis: Present status and future prospects. Biotechnol. Adv. 33(5):511-525.

Sotoft LF, Rong BG, Christensen KV, Norddahl B (2010). Process simulation and economical evaluation of enzymatic biodiesel production plant. Bioresour. Technol. 101(14):5266-5274.

Talukder MMR, Wu JC, Van Nguyen TB, Fen NM, Melissa YLS (2009). Novozym 435 for production of biodiesel from unrefined palm oil: Comparison of methanolysis methods. J. Mol. Catal. B Enzym. 60(3-4):106-112.

Teixeira CB, Madeira Junior JV, Macedo GA (2014). Biocatalysis combined with physical technologies for development of a green biodiesel process. Renew. Sustain. Energy Rev. 33:333-343.

Tran DT, Chang JS, Lee DJ (2017). Recent insights into continuous-flow biodiesel production via catalytic and non-catalytic transesterification processes. Appl. Energy 185:376-409.

Tuluc A, Iancu P, Plesu V, Bonet-Ruiz J, Bozga G, Bumbac G (2015). Biodiesel plant optimisation study by using aspen-HYSYS® process simulator. Rev. Chim. 66(4):565-569.

Turton R, Bailie RC, Whiting WB, Shaeiwitz JA, Bhattacharyya D (2003). Analysis, synthesis, and design of chemical processes, 2nd Ed. Prentice Hall (Goodwin B Ed.), New Jersey, p. 207.

Ying M, Chen G (2007). Study on the production of biodiesel by magnetic cell biocatalyst based on lipase-producing Bacillus subtilis. Appl. Biochem. Biotechnol. 137-140(1-2):793-803.

Zakir Hossain SM, Sultana N, Irfan MF, Elkanzi EMA, Al-Aali YAM, Taha A, Manirul Haque S (2016). Optimization of biodiesel production from spent palm cooking oil using fractional factorial design combined with the response surface methodology. Am. J. Appl. Sci. 13(11):1255-1263.

Zarejousheghani F, Kariminia HR, Khorasheh F (2016). Kinetic modelling of enzymatic biodiesel production from castor oil: Temperature dependence of the Ping Pong parameters. Can. J. Chem. Eng. 94(3):512-517.

Zhao X, El-Zahab B, Brosnahan R, Perry J, Wang P (2007). An organic soluble lipase for water-free synthesis of biodiesel. Appl. Biochem. Biotechnol. 143(3):236-243.

abdullah m. alsahly*, elamin m. elkanzi and s. m. zakir

Documents