- Email: [email protected]
Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: Genetic algorithm
Accepted Manuscript Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: genetic algorithm Mehri Esfahanian, Ali Shokuhi Rad, Saeed Khoshhal, Ghasem Najafpour, Behnam Asghari PII: DOI: Reference:
S0960-8524(16)30509-0 http://dx.doi.org/10.1016/j.biortech.2016.04.022 BITE 16381
To appear in:
Bioresource Technology
Received Date: Revised Date: Accepted Date:
26 February 2016 4 April 2016 5 April 2016
Please cite this article as: Esfahanian, M., Rad, A.S., Khoshhal, S., Najafpour, G., Asghari, B., Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: genetic algorithm, Bioresource Technology (2016), doi: http://dx.doi.org/10.1016/j.biortech.2016.04.022
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: genetic algorithm Mehri Esfahanian1*, Ali Shokuhi Rad1, Saeed Khoshhal2, Ghasem Najafpour2, Behnam Asghari2 1
Department of Chemical Engineering, Islamic Azad University, Qaemshahr Branch,
Qaemshahr, Iran 2
Department of Chemical Engineering, Babol Noushirvani University of Technology,
Babol, Iran
∗
Corresponding author Email address: [email protected] , [email protected] Tell/Fax number: +98-11-42294270 Postal address: Azad University of Qaemshahr, Nezami Road, Qaemshahr, Iran
1
Abstract In this paper, genetic algorithm was used to investigate mathematical modeling of ethanol fermentation in a continuous conventional bioreactor (CCBR) and a continuous membrane bioreactor (CMBR) by ethanol permselective polydimethylsiloxane (PDMS) membrane. A lab scale CMBR with medium glucose concentration of 100 g L-1 and Saccharomyces cerevisiae microorganism was designed and fabricated. At dilution rate of 0.14 h-1, maximum specific cell growth rate and productivity of 0.27 h-1 and 6.49 g L-1 h-1were respectively found in CMBR. However, at very high dilution rate, the performance of CMBR was quite similar to conventional fermentation on account of insufficient incubation time. In both systems, genetic algorithm modeling of cell growth, ethanol production and glucose concentration were conducted based on Monod and Moser kinetic models during each retention time at unsteady condition. The results showed that Moser kinetic model was more satisfactory and desirable than Monod model. Keywords: mathematical modeling; genetic algorithm; ethanol fermentation; CMBR; kinetic models
2
1 Introduction Nowadays, climate changes caused by greenhouse gas emission and environmental contamination, continuous consumption of fossil fuels, increasing global energy demand, evacuation of conventional fuel resources and uncertainties related to oil price have led to an increasing interest in the production of renewable energy sources such as biofuels (Balat and Balat, 2009; Kheyrandish et al., 2015; Limayem and Ricke, 2012). Bioethanol is vastly considered as one of the most promising, renewable and realistic substitute to fossil fuels such as petroleum and its production has been developed by many countries (Fan et al., 2014c; Fu et al., 2016; Shinozaki and Kitamoto, 2011). As in case of China, it was predicted that 10 million tons of ethanol would be utilized in 2020 as fuel (Fang et al., 2010). By modified spark-ignition engines, ethanol could be combined with petrol or burned in its pure form (Nigam and Singh, 2011). Furthermore, it is extensively used in food, plastic and chemical industries (Qureshi and Blaschek, 2001). However, obtaining an economically practicable industrial process for ethanol production from different natural sources requires that the yeast to be capable of fermenting most of the sugars present in broth with high ethanol yield and productivity (Sanda et al., 2011). Because of the accumulation of ethanol as product in conventional ethanol fermentation process, the cell growth inhibition and viability of the ethanol-producing microorganism occur and these lead to low ethanol productivity and high wastewater discharge (Fan et al., 2014c; Stanley et al., 2010). Some separation technologies such as gas/steam stripping, extraction, adsorption, distillation and pervaporation are used for product separation (Vane, 2008). Most of them require further concentration and purification steps, greater equipment and higher labor costs, unable to reuse microbes, lower ethanol productivity and higher energy consumption for ethanol recovery from the broth (Nguyen et al., 2011; Stanley et al., 2010). 3
Membrane technology is one of the most effective and energy saving processes for ethanol production. Integration of membrane separation with biological process in only one unit is a very attractive configuration for the reactions where the continuous metabolites elimination is necessary to maintain high productivity (Moueddeb et al., 1996). Pervaporation, a membrane process, is an interesting technique for ethanol separation among various types of in situ product separation methods, (Fan et al., 2014b; Fan et al., 2014c; Samanta and Ray, 2015). Pervaporation is a very simple and useful technique for product separation from the broth during fermentation, as it reduces the ethanol inhibition. It does not require extra chemicals (Ding et al., 2011). During the integration, ethanol is produced via fermentation and recovered by pervaporation simultaneously; leading to low ethanol concentration in the broth. Therefore, the overall fermentation performances such as carbon source utilization rate and productivity would enhance due to the detoxification of ethanol, which is the end-product (Fu et al., 2016). Polydimethylsiloxane (PDMS), with high selectivity for separation of organic chemical compounds from water, is the most important and effective polymeric membrane which has a good chemical stability and biocompatibility (Ding et al., 2011; Huang et al., 2009; Li et al., 2004). Moreover, researches showed that it is an appropriate polymer for pervaporation separation of organic solvents from water such as ethanol/water and butanol/water mixtures (Li et al., 2013). Furthermore, it was evaluated to show an excellent performance in ethanol, butanol, and furfural separation (Qin et al., 2014). Thus, it is suitable for pervaporation membrane bioreactor fabrication (MBR) in which bioconversion and biological product separation occur(Huang et al., 2009). Kinetic study and modeling may be effective for ethanol fermentation process development, since they result in product quality increase, high process control and process cost reduction (Fan et al., 2014a). Owing to simultaneous ethanol elimination 4
by the membrane pervaporation, ethanol inhibition can be decreased in MBR. Therefore, in long fermentation period, high ethanol productivity could be accomplished. However, during long-term operation of continuous membrane bioreactor (CMBR) in each dilution rate, fermentation with membrane pervaporation process was unsteady and its time profiles showed zigzag shapes (Fan et al., 2014a). Therefore, it was difficult to develop the mathematical kinetic models for CMBR process. In the present study, the bioethanol production enhancement along with CMBR by pervaporation using an ethanol permselective PDMS membrane was investigated. The CMBR performance compared with continuous conventional bioreactor (CCBR) at different dilution rates. Based on the experimental results, it should be feasible to mathematically characterization of the CMBR and CCBR process. For both systems mathematical modeling using kinetic models was implemented with regard to the pervaporation separation and ethanol fermentation at unsteady state condition. In each dilution rate, Monod and Moser kinetic parameters were specified by genetic algorithm. The aim of present work is mathematical models development and determination of kinetic parameters to describe the broth and permeate ethanol production, cell growth and glucose consumption in the CMBR and CCBR processes at unsteady state condition. 2 Material and methods 2.1 Microorganism and medium Ethanol fermentation was performed by the pure stock culture of Saccharomyces cerevisiae, originated from Persian Type Culture Collection (PTCC 24860) and supplied by Iranian Research Organization of Science and Technology (IROST). According to the existing literatures for ethanol fermentation in CMBR with high glucose concentration (Fan et al., 2014c; Nomura et al., 2002) and based on some 5
preliminary experiments conducted at different concentrations of glucose and nitrogen sources, the feed media composition included in 100 g L-1 of glucose, 3.0 g L-1 of yeast extracts and 5 g L-1 of NH4Cl. In addition, potassium hydrogen phthalate 0.1 M and sodium hydroxide 0.1 M were used as buffer solutions to maintain the media optimum pH value of 5.2 (Esfahanian et al., 2013). The details of medium preparation were mentioned at previous studies (Esfahanian et al., 2012; Najafpour, 2015). 2.2 Membrane A membrane was used for produced ethanol concentration by pervaporation process. An asymmetric PDMS, a dense hydrophobic/organophilic and ethanol-permselective flat sheet membrane with an effective thickness of 3-5 µm PDMS as top layer supplied by Pervatech Company (Netherland) was used. 2.3 CMBR and CCBR processes The schematic of a laboratory-scale MBR is illustrated in Fig. 1a. It could be readily assembled and disassembled between experiments for cleaning and sterilization. A glass column with 30 cm height, 10 cm internal diameter (I.D) and 11 cm output diameter (O.D) was used as framework. The membrane was fixed at the bottom of the cell (8 cm I.D). Pervaporation process along with MBR was used for continuous fermentation. The fermentation chamber contained 1260 mL fermentation broth on the top and a pervaporation cell of 50.24 cm2 effective areas at the bottom. Fig. 1b shows a threedimensional image of the continuous fermentation-pervaporation system. A thermal jacket was used in MBR to keep the fermentation broth at optimum temperature of 32 o
C (Esfahanian et al., 2013). The pressures of atmospheric and 3 mmHg were applied on
the feed and permeate-side, respectively. A cold trap containing liquid nitrogen at -196 o
C was used for condensation and collection of permeated vapor. Under similar physicochemical and geometrical condition, the performance of
CMBR was investigated compared to conventional fermentation. When both systems 6
have achieved to steady state after 12 hours of batch fermentation, the fresh media containing 100 g L-1 of glucose was fed to the bioreactor of CCBR and CMBR with flow rate of 50.4-302.4 mL h-1. Pervaporation process was continuously performed before steady state condition in CMBR. Steady state condition was confirmed when the cell, glucose and ethanol concentrations remained constant. At the end of a stationary phase, the flow rate of fresh feed was changed. Based on the total working volume of the bioreactor, the range of dilution rate varied from 0.04 to 0.24 h-1. 2.4 Analytical methods By using of a spectrophotometer (Unico, USA) and based on the developed calibration curve, the cell concentration was evaluated via optical density measurement at 620 nm. Glucose concentration was determined by color-metric method using DNS reagent (Thomas and Chamberlin, 1974), after cell removing from 2 mL samples by centrifuge at 7000 g for 7 min by a micro centrifuge (Hermle, model: Z 233 M-2 (Germany)). Additionally, ethanol concentration was measured by a gas chromatograph (Agilent, 7890A) equipped with a flame ionization detector (FID) and the stainless steel packed column of 1.83 m length and 2.1 mm I.D., 80/100 mesh Porapak Q (Supelco, USA). The oven temperature was 120 oC fixed for 1 minute. Then, it was changed to 185 oC with a rate of 40 ℃ and remained at this temperature for 8.5 minutes as the set temperature. In addition, nitrogen with flow rate of 30 mL min-1 was used as sweep gas and the detector temperature was fixed at 225 oC. Besides, 50 µL of 2propanol or propionic acid (Merck, Germany) with concentration of 10% (v/v) as internal standard was added into the 0.5 mL of each sample by a micropipette (Labnet, Germany). Fig. 1 2.5 Kinetic models and mathematical modeling
7
During the continuous ethanol fermentation in CMBR by pervaporation, ethanol removal from fermentation broth could decrease the ethanol inhibition (Qureshi and Blaschek, 2001; Vane, 2005). In order to expand a simple valid model for the cell growth in the CMBR, the following assumptions are considered: - Homogeneous broth in the reactor due to the perfect mixing - No limitation in mass and heat transfer during fermentation - The same glucose, cell and ethanol composition along with the culture - Models application for any feed flow rate in CMBR - Equal concentration of glucose, cell and ethanol in the both to those in overflow stream at steady state condition Basically, expressions based on differential equation terms can be employed to describe the models of microbial kinetics for growth and fermentation processes. The change of fermented product rate, substrate consumption and biomass were related to ethanol (P), glucose (S) and biomass (X) concentrations in using the suitable functional forms given by some kinetic growth rate models (Birol et al., 1998). Due to the concentration of preventive substrate, the growth rate of the microorganism can be described by Monod equation as follows: =
.
(1)
where, [g L-1] is the substrate inhibition constant and [h-1] is defined as maximum specific cell growth rate. Correspondingly, Moser kinetic model represents inhibition of free substrate concentration: =
.
(2)
where, n is the degree of substrate utilization. 2.5.1 Cell growth
8
For any dilution rate or retention time, biomass mass balance in CMBR could be illustrated as follows: . − . − . + . =
. !
(3)
"
where, Fm, F0 and F were permeate, input and output (overflow) volumetric flow rates [L h-1], respectively. Also, Xm, X0 and X were cell concentrations at permeate, input and output streams [g L-1], respectively. Furthermore; rx, V and t were the cell production rate [g L-1 h-1], bioreactor working volume [L] and fermentation time [h] during each dilution rate. Similarly, it was found that biomass did not permeate through the membrane and there
was not any biomass in feed flow stream. Therefore, Eq. 3 could be defined as follows: . − . =
. !
(4)
"
By substituting Malthus law ( = μ. ) (Bailey and Ollis 1976) and considering the constant working volume, the Eq. 4 could be changed to:
. . − . = . "
(5)
Since the input and permeate flow rate difference was equal to output flow rate, Eq. 5 could be converted to: "
= . +
$
%$.
(6)
2.5.2 Glucose consumption In every dilution rate, substrate (glucose) mass balance utilization rate could be expressed as follows: . & − . & − . & − . =
. !
(7)
"
where, Sm, S0 and S were substrate concentrations at permeate side, input and output streams [g L-1]. Also, rs was the substrate consumption rate [g L-1 h-1]. The substrate did not permeate through the membrane, Sm=0. By using the yield of biomass concentration based on substrate utilization (Y(⁄) ), the Eq. 7 could be 9
illustrated as follows: F . S + F- − F !S +
../ 01⁄2
45
V = V.
(8)
46
So: "
=
$% % !$ .
.
+7
(9)
8⁄
2.5.3 Ethanol production For any dilution rate, product (ethanol) mass balance formation in CMBR could be given by following equation: . 9 − . 9 − . 9 + : . =
:. !
(10)
"
where Pm, P0 and P were ethanol concentrations at permeate, input and output streams of bioreactor [g L-1]. Also, rp was the ethanol production rate [g L-1 h-1]. Due to the lack of ethanol in input feed flow, P0 =0, Eq. 10 could be reduced as follows: −. 9 − . 9 + : . = .
:
(11)
"
By using the yield of product formation based on biomass concentration (;<⁄ ), Eq. 11 could be changed as follows: : "
=
$ :: !$%.:
+ . . ;<⁄
(12)
2.5.4 The obtained mathematical models for CMBR and CCBR In any retention time of continuous fermentation, a set of differential equations consisting of Eqs. 6, 9 and 12 obtained in terms of specific growth rate which were replaced by Monod and Moser growth rate (Eqs. 1 and 2). The substrate inhibition constant ( ) are defined as , and < for cell growth, glucose consumption and ethanol production, respectively. By substituting Fm =0, the set of CMBR equations could be reduced to CCBR. The obtained mathematical models for both bioreactors are summarized in Table 1. 10
Table 1 2.5.5 Determination of kinetic model parameters by genetic algorithm The analysis and understanding of a physical system is often abstracted into a mathematical form (Katare et al., 2004). Both derived sets of equations, indicative of CMBR and CCBR bioethanol production performance, were composed of complex differential equations which could not be integrated separately to obtain the cell, glucose and ethanol concentrations. Therefore, special mathematical approaches are required to solve the problem. Many engineering and chemical engineering problems are solved by using genetic algorithm (Shopova and Vaklieva-Bancheva, 2006). In this study, an optimization procedure based on genetic algorithm approach was employed. Besides being able to solve a wide variety of problems such as curve fitting by nonlinear regression analysis, optimization techniques are getting special attentions as an efficient way to deal with complex modeling issue. System identification method based on genetic algorithm is one of these ways to perform this duty. Genetic algorithm, a popular optimization technique to alleviate the problems, may involve in consequence complexity models. By stepwise initial guess increase, it searches for the final optimum parameters to minimize the roots mean square error (RMSE) between the predicted parameters and corresponding experimental data. Based on the physical model, the experimental data were estimated by model parameters and hence the resulting functions would have minimum deviation compared to experimental data. In this work, calculations were started by using an initial guess in the genetic algorithm. In order to describe the behavior of the system, the initial guess was inserted in the differential equations. The initial guess parameters were increased step by step to reduce the deviation between experimental data and model results. The system of differential equations solved by fourth degree Range–Kutta method and its results were
11
compared with experimental data. For this purpose, a program code was developed by the Matlab software package (Version 7.14). 3 Results and Discussion 3.1 Cell, ethanol and glucose concentrations Continuous ethanol fermentation was carried out in both conventional and membrane bioreactors. The cell, ethanol and glucose concentrations during the fermentation at different retention times are illustrated in Fig. 2. As it can be seen, at first dilution rate of 0.04 h-1, the cell and broth ethanol concentration of about 31.78 and 40.10 g L-1 in the CMBR and 25.50 and 40.54 g L-1 in the CCBR were respectively observed (Fig 2.a) for long term operation of CMBR (123 h) and CCBR (140 h). Totally, the cell concentration had increased over 25% in CMBR compared to CCBR. Immediately after of steady state or
decline stage observation, the other run could be started. Fig. 2 shows that longer time was needed to achieve steady state in the conventional system compared to CMBR. Because of low retention time and no sufficient time for cell growth and ethanol
production, the cell and broth ethanol concentration would drastically decreased by the feed flow rate increment, as the cell and ethanol concentration of about 0.9 and 3.9 g L-1
for CMBR and 0.7 and 4.1 g L-1 for CCBR were respectively observed at high dilution rate of o.24 h-1 or low retention time of 7 h. At high dilution rate, the performance of both reactors was almost similar to each other. Besides low retention time, it may be attributed to the microorganisms fouling on membrane surface during 300 h of CMBR fermentation. Furthermore, glucose concentration was decreased at first dilution rate as shown in Fig. 2b. It was due to the longer incubation time of microorganisms, higher ethanol production and more glucose consumption. The glucose concentration in CMBR and CCBR were 2.5 g L-1 and 10.95 g L-1, respectively. The feed flow rate increase resulted in cell and ethanol concentration decline as well as low glucose
12
consumption, where the glucose concentration of about 80.10 and 83 g L-1 were respectively observed for CMBR and CCBR at feed flow rate of 302.4 mL h-1. Fig. 2c shows the permeate ethanol concentration of CMBR. On account of the cumulative ethanol concentration in the broth, the ethanol concentration increased to 215.61 g L-1 at permeate side, at first dilution rate of 0.04 h-1. Moreover, ethanol concentration reduction in the permeate side by feed flow rate enhancement or incubation time decrease can be ascribed to the lower ethanol concentration in the broth or membrane fouling during 300 h continuous fermentation; where at the high dilution rate of 0.24 h-1, the ethanol concentration significantly dropped to 27.17 g L-1 for the time period from 270 h to 277 h.
Fig. 2 3.2 Comparison of CMBR and CCBR performance As presented in Fig. 3, the performance of CMBR and CCBR at various dilution rates was investigated. Fig. 3a, represents much higher cell concentration and glucose consumption in CMBR compared to conventional fermentation for each fermentation period, owing to simultaneous ethanol reduction by membrane and lower substrate inhibition cause to more cell growth. Moreover, due to the cell washout and insufficient time for microorganisms’ growth, the cell production rate decreased after dilution rate of 0.14 h-1. On the other hand, the obtained data showed that ethanol concentration in CMBR broth was lower than that of the conventional system, attributable to the continuous ethanol extraction by pervaporation and ethanol inhibition reduction as illustrated in Fig. 3b. Furthermore, during each dilution rate, in pervaporation process, the obtained results presented that the ethanol concentration of the permeate vapor was 6 to 7 times higher than in the broth. Fig. 3
13
Table 2 summarizes the results of the cell, glucose and the ethanol concentration in the broth and permeate side at different dilution rates in both continuous fermentation systems. The broth ethanol concentrations in CCBR and CMBR were in the range of 4.10-40.54 g L-1and 3.90-40.10 g L-1, respectively. Furthermore, the ethanol concentration in permeate side of CMBR was in the range of 27.71-215.61 g L-1. As it was expected, with increasing in dilution rate, the cell concentration and ethanol production were decreased because of low cell retention time and further glucose concentration, leading to higher substrate inhibition. Table 2 Ethanol productivity is one of the most important factors for CMBR performance assessment compared to CCBR. The productivity is related to the ethanol concentration and the dilution rate and is also defined as ethanol concentration divided by retention time. On the other hand, ethanol concentration could be expressed as the broth ethanol concentration in CCBR and permeate ethanol concentration of CMBR. The ethanol productivity in CMBR and CCBR were in the range of 1.07 – 6.49 g L-1 h-1 and 0.311.09 g L-1 h-1, respectively as presented in Fig. 4. Up to the dilution rate of 0.14 h-1, the ethanol productivity increased and then declined because of cell washout. Surprisingly, maximum productivity did not occur at the maximum ethanol concentration or at the maximum conversion of substrate. Therefore, optimization could be based on the permeate ethanol and substrate concentration, productivity and other factors. Consequently, optimum dilution rate of 0.14 h-1 was selected, based on the maximum ethanol productivity. The maximum ethanol productivity of the CMBR was 6.49 g L-1 h1
, which was much higher than the maximum productivity of 1.13 g L-1 h-1 in CCBR. Fig. 4
3.3 Kinetics and simulation by genetic algorithm
14
The mathematical model in this work was described to investigate variation of cell, substrate and product formation during continuous ethanol fermentation in CMBR by pervaporation and conventional system. The proposed mathematical model was solved by using Monod and Moser kinetic models. The genetic algorithm determined empirical constants that may vary with fermentation condition at different feed flow rates. Then, the simulation of continuous ethanol fermentation at various retention times was performed. The equation sets are presented in Table 1. At different retention times for both systems, the obtained data of continuous fermentation experiments were used to simulate biomass and ethanol production as well as substrate consumption by S. cerevisiae microorganism. Experimental data curve fitting was tested for both kinetic models. The criterion of models validity is normally reflected in logical constant parameters. The second one is the good fitting of experimental data at various region of cell growth, formation of product, consumption of substrate at different retention times, separately. The third one is the approximation of standard deviation of the residues between the calculated and experimental data or RMSE (Birol et al., 1998). The genetic algorithm can be optimized the constant values using of minimum error procedure. As the wash out phenomenon was occurred at dilution rate of 0.25 h-1 in the CCBR -1
and 0.27 h in the CMBR, the maximum specific growth rate for both fermentation system were the same values (Bailey and Ollis, 1976). The constant values at different dilution rates for both kinetic models are given in Tables 3 and 4, separately. Also, Figs. 5 and 6 compare the experimental data with Monod and Moser models in continuous ethanol fermentation with and without pervaporation for growth kinetics, product formation and substrate consumption, respectively. Table 3 Table 4 Fig. 5 15
Fig. 6 The kinetics of bioethanol fermentation in the CMBR by both models is shown in Figs. 5a and 6a. They represent the relationship between the accumulative ethanol formation and fermentation time, including the plotted curves obtained by Monod and Moser models and experimental data. In addition, broth ethanol concentration in CMBR was less than CCBR owing to elimination of produced ethanol through the membrane, reducing the ethanol inhibition on the cell growth, ethanol production improvement and low glucose concentration as illustrated in Figs. 5b and 6b. Therefore, they result in more cell growth as shown in Figs. 5c and 6c. It can be observed that the ethanol concentration in each time period followed a reducing trend, due to the gradual deterioration of the culture environment, which could lead to the dropping of ethanol productivity. Furthermore, ethanol production drastically decreased with increasing of dilution rate on account of inadequate time for cell growth. Therefore, the ethanol concentration in permeate side of CMBR reduced with dilution rate increment as showed in Figs. 5d and 6d. Generally, the proposed cell growth, substrate consumption and ethanol formation models confirmed these procedures as presented in Figs. 5 and 6. Based on the Le Chatelier’s principle, the substrate consumption would gradually increase with reducing of product concentration in CMBR. Consequently, it was expected that substrate inhibition on the cell growth and ethanol formation in the CMBR to be lower than CCBR. Based on the genetic algorithm the results of both models represented approximately lower Ksx, Ksp and Kss in CMBR compared to CCBR, as mentioned in Tables 2 and 3. At low dilution rate of 0.04 h-1 in CMBR, the proposed mathematical model by Monod calculated Ksx, Ksp and Kss of about 38.850 , 25.981 and 44.156 g L-1, while the values of 71.580, 26.669 and 37.160 g L-1 were gain in CCBR, respectively. As illustrated in Table 2, at dilution rate of 0.04, higher Kss of 0.14 and 0.19 h-1 were calculated in 16
CMBR compared to CCBR. This was possibly due to the formation of some byproducts in the CCBR. Although genetic algorithm calculated the parameters based on the minimum RMSE, it was possible to calculate other coefficients with larger errors. In case of larger errors, the model could calculate lower Kss in the CMBR compared to CCBR. Here, all calculations were performed based on the minimum error by genetic algorithm. Additionally, at dilution rate of 0.24 h-1, lower Ksp of 35 g L-1 obtained in CCBR than Ksp of 59.992 g L-1 in CMBR. Besides based on the minimum error calculation, it was probably due to the insufficient data at low retention time, or inaccuracy of the model. Furthermore, at high dilution rate, the behavior of the CMBR shifted to the CCBR as a result of low incubation time and maybe membrane fouling during 300 h long term fermentation. On the other hand, based on the set of equations (see Table 1), the RMSE and correlation coefficient of non-linear regression were in the range of 0.693-3.811 and 0.988-0.999 in CMBR, whereas the values of about 1.0774.324 and 0.991-0.995 were gained in CCBR, respectively. The presented mathematical model based on Moser kinetic resulted in lower substrate inhibition coefficients on cell growth, ethanol production and substrate consumption in CMBR compared to CCBR. Only at dilution rate of 0.09 h-1, Ksx of 18.05 g L-1 obtained in CMBR, whereas this was of about 7.890 g L-1 in CCBR; this may contributed to the calculations based on the minimum RMSE. As mentioned previously, the lower Ksx of 99 g L-1 attained in the conventional system can be attributed to the low accuracy and data limitation at high dilution rate of 0.24 h-1. Despite the fact that the RMSE and correlation coefficient were in the range of 0.7282.753 and 0.995-0.999 in CMBR, they were in the range of 0.911-2.950 and 0.9960.999 in CCBR, respectively.
17
Regardless of the satisfactory results of both Monod and Moser kinetic models simulation, it seems that Moser kinetic model showed lower RSME and higher correlation coefficient than Monod one. 4 Conclusions The genetic algorithm modeling of CMBR and CCBR performance by Monod and Moser kinetic models was proposed to describe the cell growth, substrate concentration and ethanol production during ethanol fermentation at different retention times and in unsteady state condition. The results were compared with the experimental data. The models' simulating kinetic parameters of both fermentation systems were comprehensively analyzed and compared to each other and hence some features of kinetic behavior of substrate consumption, ethanol production and cell growth in continuous conventional and pervaporation processes could be brought to light. Acknowledgments The authors wish to appreciate Mr. K. Alinezhad from I.O.O.C Company for his operational and technical assistance. We also thank Afagh and KGW Isotherm companies for their close collaboration and technical supply of equipment and spare parts, and thank I.F.C.O Company for the financial support. References 1.Bailey, J. E., Ollis D. F., 1994. Biochemical engineering fundamentals, second ed. McGraw-Hill Book Company, Singapore 2.Balat, M., Balat, H., 2009. Recent trends in global production and utilization of bioethanol fuel. Appl. Energ. 86, 2273-2282. 3.Birol, G., Doruker, P., Kirdar, B., Önsan, Z. ĺ., Ülgen, K., 1998. Mathematical description of ethanol fermentation by immobilised Saccharomyces cerevisiae. Process Biochem. 33, 763-771
18
4.Ding, W. W., Wu, Y. T., Tang, X. Y., Yuan, L., Xiao, Z. Y., 2011. Continuous ethanol fermentation in a closed-circulating system using an immobilized cell coupled with PDMS membrane pervaporation. J. Chem. Technol. Biot. 86, 82-87. 5.Esfahanian, M., Ghorbanfarahi, A., Ghoreyshi, A., Najafpour, G., Younesi, H., Ahmad, A. L., 2012. Enhanced bioethanol production in batch fermentation by pervaporation using a PDMS membrane bioreactor. Int. J. Eng. 25, 249-258. 6.Esfahanian, M., Nikzad, M., Najafpour, G., Ghoreyshi, A. A., 2013. Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network. Chem. Ind. Chem. Eng. Q. 19, 241-252. 7.Fan, S., Chen, S., Tang, X., Xiao, Z., Deng, Q., Yao, P., Sun, Z., Zhang, Y., Chen, C., 2014a. Kinetic model of continuous ethanol fermentation in closed-circulating process with pervaporation membrane bioreactor by Saccharomyces cerevisiae. Bioresour. Technol. 177, 169-175. 8.Fan, S., Xiao, Z., Tang, X., Chen, C., Zhang, Y., Deng, Q., Yao, P., Li, W., 2014b. Inhibition effect of secondary metabolites accumulated in a pervaporation membrane bioreactor on ethanol fermentation of Saccharomyces cerevisiae. Bioresour. Technol. 162, 8-13. 9.Fan, S., Xiao, Z., Zhang, Y., Tang, X., Chen, C., Li, W., Deng, Q., Yao, P., 2014c. Enhanced ethanol fermentation in a pervaporation membrane bioreactor with the convenient permeate vapor recovery. Bioresour. Technol. 155, 229-234. 10.Fang, X., Shen, Y., Zhao, J., Bao, X., Qu, Y., 2010. Status and prospect of lignocellulosic bioethanol production in China. Bioresour. Technol. 101, 4814-4819. 11.Fu, C., Cai, D., Hu, S., Miao, Q., Wang, Y., Qin, P., Wang, Z., Tan, T., 2016. Ethanol fermentation integrated with PDMS composite membrane: An effective process. Bioresour. Technol. 200, 648-657.
19
12.Huang ,Y., Fu, J., Pan, Y., Huang, X., Tang, X., 2009. Pervaporation of ethanol aqueous solution by polyphosphazene membranes: Effect of pendant groups. Sep. Purif. Technol. 66, 504-509. 13.Katare, S., Bhan, A., Caruthers, J. M., Delgass, W. N., Venkatasubramanian, V., 2004. A hybrid genetic algorithm for efficient parameter estimation of large kinetic models. Comput. Chem. Eng. 28, 2569-2581. 14.Kheyrandish, M., Asadollahi, M. A., Jeihanipour, A., Doostmohammadi, M., Rismani-Yazdi, H., Karimi, K., 2015. Direct production of acetone-butanol-ethanol from waste starch by free and immobilized Clostridium acetobutylicum. Fuel. 142, 129133. 15.Li, L., Xiao, Z., Tan, S., Pu, L., Zhang, Z., 2004. Composite PDMS membrane with high flux for the separation of organics from water by pervaporation. J. Membr. Sci. 243, 177-187. 16.Li, S., Qin, F., Qin, P., Karim, M. N., Tan, T., 2013. Preparation of PDMS membrane using water as solvent for pervaporation separation of butanol/water mixture. Green Chem. 15, 2180-2190. 17.Limayem, A., Ricke, S. C., 2012. Lignocellulosic biomass for bioethanol production: current perspectives, potential issues and future prospects. Prog. Energy Combust. Sci. 38, 449-467. 18.Moueddeb, H., Sanchez, J., Bardot, C., Fick, M., 1996. Membrane bioreactor for lactic acid production. J. Membr. Sci. 114, 59-71. 19.Najafpour, G., 2015. Biochemical engineering and biotechnology, second ed. Elsevier, Amsterdam. 20.Nguyen, V. D., Auresenia, J., Kosuge, H., Tan, R. R., Brondial, Y., 2011. Vacuum fermentation integrated with separation process for ethanol production. Biochem. Eng. J. 55, 208-214. 20
21.Nigam, P. S., Singh, A., 2011. Production of liquid biofuels from renewable resources. Prog. Energy Combust. Sci. 37, 52-68. 22.Nomura, M., Bin, T., Nakao, S.-i., 2002. Selective ethanol extraction from fermentation broth using a silicalite membrane. Sep. Purif. Technol. 27, 59-66. 23.Qin, F., Li, S., Qin, P., Karim, M. N., Tan, T., 2014. A PDMS membrane with high pervaporation performance for the separation of furfural and its potential in industrial application. Green Chem. 16, 1262-1273. 24.Qureshi, N., Blaschek, H., 2001. ABE production from corn: a recent economic evaluation. J. Ind. Microbiol. Biotechnol. 27, 292-297. 25.Samanta, H. S., Ray, S. K., 2015. Separation of ethanol from water by pervaporation using mixed matrix copolymer membranes. Sep. Purif. Technol. 146, 176-186. 26.Sanda, T., Hasunuma, T., Matsuda, F., Kondo, A., 2011. Repeated-batch fermentation of lignocellulosic hydrolysate to ethanol using a hybrid Saccharomyces cerevisiae strain metabolically engineered for tolerance to acetic and formic acids. Bioresour. Technol. 102, 7917-7924. 27.Shinozaki, Y., Kitamoto, H. K., 2011. Ethanol production from ensiled rice straw and whole-crop silage by the simultaneous enzymatic saccharification and fermentation process. J. Biosci. Bioeng. 111, 320-325. 28.Shopova, E. G., Vaklieva-Bancheva, N. G., 2006. Basic-A genetic algorithm for engineering problems solution. Comput. Chem. Eng. 30, 1293-1309. 29.Stanley, D., Bandara, A., Fraser, S., Chambers, P., Stanley, G. A., 2010. The ethanol stress response and ethanol tolerance of Saccharomyces cerevisiae. J. Appl. Microbiol. 109, 13-24. 30.Thomas, L. C., Chamberlin, G. J., 1974. Colorimetric chemical analytical methods, Tintometer, England.
21
31.Vane, L. M., 2005. A review of pervaporation for product recovery from biomass fermentation processes. J. Chem. Technol. Biot. 80, 603-629. 32.Vane, L. M., 2008. Separation technologies for the recovery and dehydration of alcohols from fermentation broths. Biofuels, Bioprod. Biorefin. 2, 553-588.
22
Figure legends Fig. 1 a) Design of MBR b) 3D image of continuous pervaporation system Fig. 2 CMBR and CCBR profiles during dilution rates of 0.04-0.24 h-1, continuously (a) Ethanol and cell concentration (b) glucose concentration (c) ethanol concentration in permeate side of MBR Fig. 3 CMBR and CCBR profiles at dilution rates of 0.04-0.24 h-1 (a) Cell and glucose concentration and cell production rate (b) Broth and permeate ethanol concentration Fig. 4 Ethanol productivity as function of dilution rate in continuous fermentation experiments Fig. 5 Comparison of predicted Monod model and experimental data in CMBR and CCBR for (a) ethanol formation in the broth (b) glucose consumption (c) cell growth (d) ethanol in permeate side Fig. 6 Comparison of predicted Moser model and experimental data in CMBR and CCBR for (a) ethanol formation in the broth (b) glucose consumption (c) cell growth (d) ethanol in permeate side
23
Table legends Table 1 Mathematical models for CMBR and CCBR Table 2 Details of cell, ethanol and glucose concentration at dilution rates of 0.04-0.24 h-1 Table 3 Kinetic Monod model parameters for CMBR and CCBR at various dilution rates Table 4 Kinetic Moser model parameters for CMBR and CCBR at various dilution rates
24
(a)
(b)
Fig. 1 25
Ethanol and cell concentration (g L-1 )
Broth Ethanol Conc. (CMBR) Cell Conc. (CMBR) 45
Broth Ethanol Conc. (CCBR) Cell Conc. (CCBR)
40 35 30 25 20 15 10 5 0 0
50
100
150
200
250
300
350
250
300
350
250
300
350
Time (h)
(a)
Glucose concentration (g L-1 )
CMBR
CCBR
90 80 70 60 50 40 30 20 10 0 0
50
100
150
200
Time (h)
(b) 240
Ethanol concentration (g L-1)
200 160 120 80 40 0 0
50
100
150 200 Time (h)
(c) Fig. 2 26
Cell Conc. (CMBR)
Cell Conc. (CCBR)
Glucose Conc. (CCBR)
Cell Prod. Rate. (CMBR)
Cell Prod. Rate (CCBR)
90 80 70 60 50 40 30 20 10 0
1.2 1 0.8 0.6 0.4 0.2
Cell production rate (g L-1 h-1)
Cell and glucose concentration (g L-1 )
Glucose Conc. (CMBR)
0 0
0.04 0.08 0.12 0.16 0.2 0.24 0.28 Dilution rate (h-1)
in broth (CMBR) In broth (CCBR) In permeate (CMBR)
50 45 40 35 30 25 20 15 10 5 0
225 200 175 150 125 100 75 50 25 0
0
0.04 0.08 0.12 0.16 0.2 0.24 0.28 Dilution rate (h-1)
(b) Fig. 3
27
Permeate ethanol concentration (g L-1)
Broth ethanol concentration (g L-1)
(a)
CMBR
CCBR
7
Productivity (g L-1 h-1)
6 5 4 3 2 1 0 0
0.04 0.08 0.12 0.16 Dilution rate (h-1)
Fig. 4
28
0.2
0.24 0.28
(a)
(b)
(c)
(d)
Fig. 5
29
(a)
(b)
(c)
(d) Fig. 6
30
(d)
Table 1 Bioreactors
CMBR
CCBR
Monod
Moser
= . & − = . + . => + &
= . & @ − = . + . => + & @
=& & − &! + . & . & = + . => + & ;⁄
=& & − &! + . & . & @ = + . => + & @ ;⁄
=9 9 − 9 ! − . 9 . & = + . . ;:? => < + &
=9 9 − 9 ! − . 9 . & @ = + . . ;:? => < + & @
= . & . = . − => + &
= . & @ . = . − => + & @
=& & − &! . & = + . => + & ;⁄
=& & − &! . & @ = + . => + & @ ;⁄
=9 . & . 9 = . . ;:? − => < + &
=9 . & @ . 9 = . . ;:? − => < + & @
31
Table 2 Broth concentration (g L-1)
-1
Permeate ethanol
Dilution rate (h ) concentration Ethanol
Glucose
Cell
CMBR
40.10
2.50
31.78
CCBR
40.54
10.95
25.50
CMBR
37.78
4.03
31.20
CCBR
38.51
15.47
24.03
CMBR
29.52
16.31
26.19
CCBR
30.53
26.12
20.60
CMBR
17.42
38.34
16.23
CCBR
18.13
45.87
13.10
CMBR
3.90
80.10
0.90
CCBR
4.10
83.00
0.70
0.04
(g L-1) 215.61
0.09
204.83
0.14
168.78
0.19
108.51
0.24
27.17
32
Table 3 Parameters
CMBR
CCBR
D (h-1)
Ksx(gL-1)
Ksp (gL-1)
0.04
38.850
25.981
0.09
8.680
0.14
Kss(gL-1)
YX/S(gg-1)
YP/X(gg-1)
R2
RMSE
44.156
0.277
1.158
0.994
3.301
8.800
0.898
0.820
1.370
0.999
0.693
14.540
16.051
27.510
0.208
1.400
0.996
2.381
0.19
22.289
25.702
37.697
0.224
1.269
0.988
1.726
0.24
80
59.992
80
1
0.259
0.994
3.811
0.04
71.580
26.669
37.160
0.453
0.730
0.995
3.039
0.09
28.230
20.770
17.360
0.378
1.336
0.991
1.196
0.14
23.230
16.240
13.145
0.350
1.310
0.991
1.077
0.19
24.860
35.451
25
0.261
1.519
0.991
1.095
0.24
120
35
90
2
1
0.993
4.324
33
Table 4 Parameters
CMBR
CCBR
D (h-1)
n
Ksx(gL-1)
Ksp(gL-1)
Kss(gL-1)
YX/S(gg-1)
YP/X(gg-1)
R2
RMSE
0.04
0.570
16.390
14.100
25.685
0.113
1.547
0.995
2.057
0.09
1.850
18.500
16.100
3.850
0.680
1.160
0.999
0.728
0.14
1.200
24.690
18.335
1.149
0.268
1.917
0.996
2.753
0.19
1.120
33.380
31.880
33.830
0.281
1.158
0.998
1.876
0.24
4.6E-13
122
13.570
12.370
0.690
1.930
0.998
2.734
0.04
0.780
41.550
25.420
27.590
0.380
1.060
0.996
2.314
0.09
0.513
7.890
26.370
10.354
0.232
1.183
0.999
0.911
0.14
1.255
51.830
26.910
29.060
0.348
1.171
0.999
1.296
0.19
1.135
40.836
41.084
35.660
0.282
1.311
0.999
1.086
0.24
0.200
99
50
70
1
1
0.998
2.950
34
Highlights •
Genetic algorithm was used for modeling of CMBR and CCBR performances
•
Mathematical modeling was based on Monod and Moser kinetic models
•
Modeling was performed at the unsteady state condition at different retention times
•
The model described the cell growth, substrate concentration and ethanol production
•
The Moser kinetic model results showed good agreement with experimental data
35
S0960-8524(16)30509-0 http://dx.doi.org/10.1016/j.biortech.2016.04.022 BITE 16381
To appear in:
Bioresource Technology
Received Date: Revised Date: Accepted Date:
26 February 2016 4 April 2016 5 April 2016
Please cite this article as: Esfahanian, M., Rad, A.S., Khoshhal, S., Najafpour, G., Asghari, B., Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: genetic algorithm, Bioresource Technology (2016), doi: http://dx.doi.org/10.1016/j.biortech.2016.04.022
This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Mathematical modeling of continuous ethanol fermentation in a membrane bioreactor by pervaporation compared to conventional system: genetic algorithm Mehri Esfahanian1*, Ali Shokuhi Rad1, Saeed Khoshhal2, Ghasem Najafpour2, Behnam Asghari2 1
Department of Chemical Engineering, Islamic Azad University, Qaemshahr Branch,
Qaemshahr, Iran 2
Department of Chemical Engineering, Babol Noushirvani University of Technology,
Babol, Iran
∗
Corresponding author Email address: [email protected] , [email protected] Tell/Fax number: +98-11-42294270 Postal address: Azad University of Qaemshahr, Nezami Road, Qaemshahr, Iran
1
Abstract In this paper, genetic algorithm was used to investigate mathematical modeling of ethanol fermentation in a continuous conventional bioreactor (CCBR) and a continuous membrane bioreactor (CMBR) by ethanol permselective polydimethylsiloxane (PDMS) membrane. A lab scale CMBR with medium glucose concentration of 100 g L-1 and Saccharomyces cerevisiae microorganism was designed and fabricated. At dilution rate of 0.14 h-1, maximum specific cell growth rate and productivity of 0.27 h-1 and 6.49 g L-1 h-1were respectively found in CMBR. However, at very high dilution rate, the performance of CMBR was quite similar to conventional fermentation on account of insufficient incubation time. In both systems, genetic algorithm modeling of cell growth, ethanol production and glucose concentration were conducted based on Monod and Moser kinetic models during each retention time at unsteady condition. The results showed that Moser kinetic model was more satisfactory and desirable than Monod model. Keywords: mathematical modeling; genetic algorithm; ethanol fermentation; CMBR; kinetic models
2
1 Introduction Nowadays, climate changes caused by greenhouse gas emission and environmental contamination, continuous consumption of fossil fuels, increasing global energy demand, evacuation of conventional fuel resources and uncertainties related to oil price have led to an increasing interest in the production of renewable energy sources such as biofuels (Balat and Balat, 2009; Kheyrandish et al., 2015; Limayem and Ricke, 2012). Bioethanol is vastly considered as one of the most promising, renewable and realistic substitute to fossil fuels such as petroleum and its production has been developed by many countries (Fan et al., 2014c; Fu et al., 2016; Shinozaki and Kitamoto, 2011). As in case of China, it was predicted that 10 million tons of ethanol would be utilized in 2020 as fuel (Fang et al., 2010). By modified spark-ignition engines, ethanol could be combined with petrol or burned in its pure form (Nigam and Singh, 2011). Furthermore, it is extensively used in food, plastic and chemical industries (Qureshi and Blaschek, 2001). However, obtaining an economically practicable industrial process for ethanol production from different natural sources requires that the yeast to be capable of fermenting most of the sugars present in broth with high ethanol yield and productivity (Sanda et al., 2011). Because of the accumulation of ethanol as product in conventional ethanol fermentation process, the cell growth inhibition and viability of the ethanol-producing microorganism occur and these lead to low ethanol productivity and high wastewater discharge (Fan et al., 2014c; Stanley et al., 2010). Some separation technologies such as gas/steam stripping, extraction, adsorption, distillation and pervaporation are used for product separation (Vane, 2008). Most of them require further concentration and purification steps, greater equipment and higher labor costs, unable to reuse microbes, lower ethanol productivity and higher energy consumption for ethanol recovery from the broth (Nguyen et al., 2011; Stanley et al., 2010). 3
Membrane technology is one of the most effective and energy saving processes for ethanol production. Integration of membrane separation with biological process in only one unit is a very attractive configuration for the reactions where the continuous metabolites elimination is necessary to maintain high productivity (Moueddeb et al., 1996). Pervaporation, a membrane process, is an interesting technique for ethanol separation among various types of in situ product separation methods, (Fan et al., 2014b; Fan et al., 2014c; Samanta and Ray, 2015). Pervaporation is a very simple and useful technique for product separation from the broth during fermentation, as it reduces the ethanol inhibition. It does not require extra chemicals (Ding et al., 2011). During the integration, ethanol is produced via fermentation and recovered by pervaporation simultaneously; leading to low ethanol concentration in the broth. Therefore, the overall fermentation performances such as carbon source utilization rate and productivity would enhance due to the detoxification of ethanol, which is the end-product (Fu et al., 2016). Polydimethylsiloxane (PDMS), with high selectivity for separation of organic chemical compounds from water, is the most important and effective polymeric membrane which has a good chemical stability and biocompatibility (Ding et al., 2011; Huang et al., 2009; Li et al., 2004). Moreover, researches showed that it is an appropriate polymer for pervaporation separation of organic solvents from water such as ethanol/water and butanol/water mixtures (Li et al., 2013). Furthermore, it was evaluated to show an excellent performance in ethanol, butanol, and furfural separation (Qin et al., 2014). Thus, it is suitable for pervaporation membrane bioreactor fabrication (MBR) in which bioconversion and biological product separation occur(Huang et al., 2009). Kinetic study and modeling may be effective for ethanol fermentation process development, since they result in product quality increase, high process control and process cost reduction (Fan et al., 2014a). Owing to simultaneous ethanol elimination 4
by the membrane pervaporation, ethanol inhibition can be decreased in MBR. Therefore, in long fermentation period, high ethanol productivity could be accomplished. However, during long-term operation of continuous membrane bioreactor (CMBR) in each dilution rate, fermentation with membrane pervaporation process was unsteady and its time profiles showed zigzag shapes (Fan et al., 2014a). Therefore, it was difficult to develop the mathematical kinetic models for CMBR process. In the present study, the bioethanol production enhancement along with CMBR by pervaporation using an ethanol permselective PDMS membrane was investigated. The CMBR performance compared with continuous conventional bioreactor (CCBR) at different dilution rates. Based on the experimental results, it should be feasible to mathematically characterization of the CMBR and CCBR process. For both systems mathematical modeling using kinetic models was implemented with regard to the pervaporation separation and ethanol fermentation at unsteady state condition. In each dilution rate, Monod and Moser kinetic parameters were specified by genetic algorithm. The aim of present work is mathematical models development and determination of kinetic parameters to describe the broth and permeate ethanol production, cell growth and glucose consumption in the CMBR and CCBR processes at unsteady state condition. 2 Material and methods 2.1 Microorganism and medium Ethanol fermentation was performed by the pure stock culture of Saccharomyces cerevisiae, originated from Persian Type Culture Collection (PTCC 24860) and supplied by Iranian Research Organization of Science and Technology (IROST). According to the existing literatures for ethanol fermentation in CMBR with high glucose concentration (Fan et al., 2014c; Nomura et al., 2002) and based on some 5
preliminary experiments conducted at different concentrations of glucose and nitrogen sources, the feed media composition included in 100 g L-1 of glucose, 3.0 g L-1 of yeast extracts and 5 g L-1 of NH4Cl. In addition, potassium hydrogen phthalate 0.1 M and sodium hydroxide 0.1 M were used as buffer solutions to maintain the media optimum pH value of 5.2 (Esfahanian et al., 2013). The details of medium preparation were mentioned at previous studies (Esfahanian et al., 2012; Najafpour, 2015). 2.2 Membrane A membrane was used for produced ethanol concentration by pervaporation process. An asymmetric PDMS, a dense hydrophobic/organophilic and ethanol-permselective flat sheet membrane with an effective thickness of 3-5 µm PDMS as top layer supplied by Pervatech Company (Netherland) was used. 2.3 CMBR and CCBR processes The schematic of a laboratory-scale MBR is illustrated in Fig. 1a. It could be readily assembled and disassembled between experiments for cleaning and sterilization. A glass column with 30 cm height, 10 cm internal diameter (I.D) and 11 cm output diameter (O.D) was used as framework. The membrane was fixed at the bottom of the cell (8 cm I.D). Pervaporation process along with MBR was used for continuous fermentation. The fermentation chamber contained 1260 mL fermentation broth on the top and a pervaporation cell of 50.24 cm2 effective areas at the bottom. Fig. 1b shows a threedimensional image of the continuous fermentation-pervaporation system. A thermal jacket was used in MBR to keep the fermentation broth at optimum temperature of 32 o
C (Esfahanian et al., 2013). The pressures of atmospheric and 3 mmHg were applied on
the feed and permeate-side, respectively. A cold trap containing liquid nitrogen at -196 o
C was used for condensation and collection of permeated vapor. Under similar physicochemical and geometrical condition, the performance of
CMBR was investigated compared to conventional fermentation. When both systems 6
have achieved to steady state after 12 hours of batch fermentation, the fresh media containing 100 g L-1 of glucose was fed to the bioreactor of CCBR and CMBR with flow rate of 50.4-302.4 mL h-1. Pervaporation process was continuously performed before steady state condition in CMBR. Steady state condition was confirmed when the cell, glucose and ethanol concentrations remained constant. At the end of a stationary phase, the flow rate of fresh feed was changed. Based on the total working volume of the bioreactor, the range of dilution rate varied from 0.04 to 0.24 h-1. 2.4 Analytical methods By using of a spectrophotometer (Unico, USA) and based on the developed calibration curve, the cell concentration was evaluated via optical density measurement at 620 nm. Glucose concentration was determined by color-metric method using DNS reagent (Thomas and Chamberlin, 1974), after cell removing from 2 mL samples by centrifuge at 7000 g for 7 min by a micro centrifuge (Hermle, model: Z 233 M-2 (Germany)). Additionally, ethanol concentration was measured by a gas chromatograph (Agilent, 7890A) equipped with a flame ionization detector (FID) and the stainless steel packed column of 1.83 m length and 2.1 mm I.D., 80/100 mesh Porapak Q (Supelco, USA). The oven temperature was 120 oC fixed for 1 minute. Then, it was changed to 185 oC with a rate of 40 ℃ and remained at this temperature for 8.5 minutes as the set temperature. In addition, nitrogen with flow rate of 30 mL min-1 was used as sweep gas and the detector temperature was fixed at 225 oC. Besides, 50 µL of 2propanol or propionic acid (Merck, Germany) with concentration of 10% (v/v) as internal standard was added into the 0.5 mL of each sample by a micropipette (Labnet, Germany). Fig. 1 2.5 Kinetic models and mathematical modeling
7
During the continuous ethanol fermentation in CMBR by pervaporation, ethanol removal from fermentation broth could decrease the ethanol inhibition (Qureshi and Blaschek, 2001; Vane, 2005). In order to expand a simple valid model for the cell growth in the CMBR, the following assumptions are considered: - Homogeneous broth in the reactor due to the perfect mixing - No limitation in mass and heat transfer during fermentation - The same glucose, cell and ethanol composition along with the culture - Models application for any feed flow rate in CMBR - Equal concentration of glucose, cell and ethanol in the both to those in overflow stream at steady state condition Basically, expressions based on differential equation terms can be employed to describe the models of microbial kinetics for growth and fermentation processes. The change of fermented product rate, substrate consumption and biomass were related to ethanol (P), glucose (S) and biomass (X) concentrations in using the suitable functional forms given by some kinetic growth rate models (Birol et al., 1998). Due to the concentration of preventive substrate, the growth rate of the microorganism can be described by Monod equation as follows: =
.
(1)
where, [g L-1] is the substrate inhibition constant and [h-1] is defined as maximum specific cell growth rate. Correspondingly, Moser kinetic model represents inhibition of free substrate concentration: =
.
(2)
where, n is the degree of substrate utilization. 2.5.1 Cell growth
8
For any dilution rate or retention time, biomass mass balance in CMBR could be illustrated as follows: . − . − . + . =
. !
(3)
"
where, Fm, F0 and F were permeate, input and output (overflow) volumetric flow rates [L h-1], respectively. Also, Xm, X0 and X were cell concentrations at permeate, input and output streams [g L-1], respectively. Furthermore; rx, V and t were the cell production rate [g L-1 h-1], bioreactor working volume [L] and fermentation time [h] during each dilution rate. Similarly, it was found that biomass did not permeate through the membrane and there
was not any biomass in feed flow stream. Therefore, Eq. 3 could be defined as follows: . − . =
. !
(4)
"
By substituting Malthus law ( = μ. ) (Bailey and Ollis 1976) and considering the constant working volume, the Eq. 4 could be changed to:
. . − . = . "
(5)
Since the input and permeate flow rate difference was equal to output flow rate, Eq. 5 could be converted to: "
= . +
$
%$.
(6)
2.5.2 Glucose consumption In every dilution rate, substrate (glucose) mass balance utilization rate could be expressed as follows: . & − . & − . & − . =
. !
(7)
"
where, Sm, S0 and S were substrate concentrations at permeate side, input and output streams [g L-1]. Also, rs was the substrate consumption rate [g L-1 h-1]. The substrate did not permeate through the membrane, Sm=0. By using the yield of biomass concentration based on substrate utilization (Y(⁄) ), the Eq. 7 could be 9
illustrated as follows: F . S + F- − F !S +
../ 01⁄2
45
V = V.
(8)
46
So: "
=
$% % !$ .
.
+7
(9)
8⁄
2.5.3 Ethanol production For any dilution rate, product (ethanol) mass balance formation in CMBR could be given by following equation: . 9 − . 9 − . 9 + : . =
:. !
(10)
"
where Pm, P0 and P were ethanol concentrations at permeate, input and output streams of bioreactor [g L-1]. Also, rp was the ethanol production rate [g L-1 h-1]. Due to the lack of ethanol in input feed flow, P0 =0, Eq. 10 could be reduced as follows: −. 9 − . 9 + : . = .
:
(11)
"
By using the yield of product formation based on biomass concentration (;<⁄ ), Eq. 11 could be changed as follows: : "
=
$ :: !$%.:
+ . . ;<⁄
(12)
2.5.4 The obtained mathematical models for CMBR and CCBR In any retention time of continuous fermentation, a set of differential equations consisting of Eqs. 6, 9 and 12 obtained in terms of specific growth rate which were replaced by Monod and Moser growth rate (Eqs. 1 and 2). The substrate inhibition constant ( ) are defined as , and < for cell growth, glucose consumption and ethanol production, respectively. By substituting Fm =0, the set of CMBR equations could be reduced to CCBR. The obtained mathematical models for both bioreactors are summarized in Table 1. 10
Table 1 2.5.5 Determination of kinetic model parameters by genetic algorithm The analysis and understanding of a physical system is often abstracted into a mathematical form (Katare et al., 2004). Both derived sets of equations, indicative of CMBR and CCBR bioethanol production performance, were composed of complex differential equations which could not be integrated separately to obtain the cell, glucose and ethanol concentrations. Therefore, special mathematical approaches are required to solve the problem. Many engineering and chemical engineering problems are solved by using genetic algorithm (Shopova and Vaklieva-Bancheva, 2006). In this study, an optimization procedure based on genetic algorithm approach was employed. Besides being able to solve a wide variety of problems such as curve fitting by nonlinear regression analysis, optimization techniques are getting special attentions as an efficient way to deal with complex modeling issue. System identification method based on genetic algorithm is one of these ways to perform this duty. Genetic algorithm, a popular optimization technique to alleviate the problems, may involve in consequence complexity models. By stepwise initial guess increase, it searches for the final optimum parameters to minimize the roots mean square error (RMSE) between the predicted parameters and corresponding experimental data. Based on the physical model, the experimental data were estimated by model parameters and hence the resulting functions would have minimum deviation compared to experimental data. In this work, calculations were started by using an initial guess in the genetic algorithm. In order to describe the behavior of the system, the initial guess was inserted in the differential equations. The initial guess parameters were increased step by step to reduce the deviation between experimental data and model results. The system of differential equations solved by fourth degree Range–Kutta method and its results were
11
compared with experimental data. For this purpose, a program code was developed by the Matlab software package (Version 7.14). 3 Results and Discussion 3.1 Cell, ethanol and glucose concentrations Continuous ethanol fermentation was carried out in both conventional and membrane bioreactors. The cell, ethanol and glucose concentrations during the fermentation at different retention times are illustrated in Fig. 2. As it can be seen, at first dilution rate of 0.04 h-1, the cell and broth ethanol concentration of about 31.78 and 40.10 g L-1 in the CMBR and 25.50 and 40.54 g L-1 in the CCBR were respectively observed (Fig 2.a) for long term operation of CMBR (123 h) and CCBR (140 h). Totally, the cell concentration had increased over 25% in CMBR compared to CCBR. Immediately after of steady state or
decline stage observation, the other run could be started. Fig. 2 shows that longer time was needed to achieve steady state in the conventional system compared to CMBR. Because of low retention time and no sufficient time for cell growth and ethanol
production, the cell and broth ethanol concentration would drastically decreased by the feed flow rate increment, as the cell and ethanol concentration of about 0.9 and 3.9 g L-1
for CMBR and 0.7 and 4.1 g L-1 for CCBR were respectively observed at high dilution rate of o.24 h-1 or low retention time of 7 h. At high dilution rate, the performance of both reactors was almost similar to each other. Besides low retention time, it may be attributed to the microorganisms fouling on membrane surface during 300 h of CMBR fermentation. Furthermore, glucose concentration was decreased at first dilution rate as shown in Fig. 2b. It was due to the longer incubation time of microorganisms, higher ethanol production and more glucose consumption. The glucose concentration in CMBR and CCBR were 2.5 g L-1 and 10.95 g L-1, respectively. The feed flow rate increase resulted in cell and ethanol concentration decline as well as low glucose
12
consumption, where the glucose concentration of about 80.10 and 83 g L-1 were respectively observed for CMBR and CCBR at feed flow rate of 302.4 mL h-1. Fig. 2c shows the permeate ethanol concentration of CMBR. On account of the cumulative ethanol concentration in the broth, the ethanol concentration increased to 215.61 g L-1 at permeate side, at first dilution rate of 0.04 h-1. Moreover, ethanol concentration reduction in the permeate side by feed flow rate enhancement or incubation time decrease can be ascribed to the lower ethanol concentration in the broth or membrane fouling during 300 h continuous fermentation; where at the high dilution rate of 0.24 h-1, the ethanol concentration significantly dropped to 27.17 g L-1 for the time period from 270 h to 277 h.
Fig. 2 3.2 Comparison of CMBR and CCBR performance As presented in Fig. 3, the performance of CMBR and CCBR at various dilution rates was investigated. Fig. 3a, represents much higher cell concentration and glucose consumption in CMBR compared to conventional fermentation for each fermentation period, owing to simultaneous ethanol reduction by membrane and lower substrate inhibition cause to more cell growth. Moreover, due to the cell washout and insufficient time for microorganisms’ growth, the cell production rate decreased after dilution rate of 0.14 h-1. On the other hand, the obtained data showed that ethanol concentration in CMBR broth was lower than that of the conventional system, attributable to the continuous ethanol extraction by pervaporation and ethanol inhibition reduction as illustrated in Fig. 3b. Furthermore, during each dilution rate, in pervaporation process, the obtained results presented that the ethanol concentration of the permeate vapor was 6 to 7 times higher than in the broth. Fig. 3
13
Table 2 summarizes the results of the cell, glucose and the ethanol concentration in the broth and permeate side at different dilution rates in both continuous fermentation systems. The broth ethanol concentrations in CCBR and CMBR were in the range of 4.10-40.54 g L-1and 3.90-40.10 g L-1, respectively. Furthermore, the ethanol concentration in permeate side of CMBR was in the range of 27.71-215.61 g L-1. As it was expected, with increasing in dilution rate, the cell concentration and ethanol production were decreased because of low cell retention time and further glucose concentration, leading to higher substrate inhibition. Table 2 Ethanol productivity is one of the most important factors for CMBR performance assessment compared to CCBR. The productivity is related to the ethanol concentration and the dilution rate and is also defined as ethanol concentration divided by retention time. On the other hand, ethanol concentration could be expressed as the broth ethanol concentration in CCBR and permeate ethanol concentration of CMBR. The ethanol productivity in CMBR and CCBR were in the range of 1.07 – 6.49 g L-1 h-1 and 0.311.09 g L-1 h-1, respectively as presented in Fig. 4. Up to the dilution rate of 0.14 h-1, the ethanol productivity increased and then declined because of cell washout. Surprisingly, maximum productivity did not occur at the maximum ethanol concentration or at the maximum conversion of substrate. Therefore, optimization could be based on the permeate ethanol and substrate concentration, productivity and other factors. Consequently, optimum dilution rate of 0.14 h-1 was selected, based on the maximum ethanol productivity. The maximum ethanol productivity of the CMBR was 6.49 g L-1 h1
, which was much higher than the maximum productivity of 1.13 g L-1 h-1 in CCBR. Fig. 4
3.3 Kinetics and simulation by genetic algorithm
14
The mathematical model in this work was described to investigate variation of cell, substrate and product formation during continuous ethanol fermentation in CMBR by pervaporation and conventional system. The proposed mathematical model was solved by using Monod and Moser kinetic models. The genetic algorithm determined empirical constants that may vary with fermentation condition at different feed flow rates. Then, the simulation of continuous ethanol fermentation at various retention times was performed. The equation sets are presented in Table 1. At different retention times for both systems, the obtained data of continuous fermentation experiments were used to simulate biomass and ethanol production as well as substrate consumption by S. cerevisiae microorganism. Experimental data curve fitting was tested for both kinetic models. The criterion of models validity is normally reflected in logical constant parameters. The second one is the good fitting of experimental data at various region of cell growth, formation of product, consumption of substrate at different retention times, separately. The third one is the approximation of standard deviation of the residues between the calculated and experimental data or RMSE (Birol et al., 1998). The genetic algorithm can be optimized the constant values using of minimum error procedure. As the wash out phenomenon was occurred at dilution rate of 0.25 h-1 in the CCBR -1
and 0.27 h in the CMBR, the maximum specific growth rate for both fermentation system were the same values (Bailey and Ollis, 1976). The constant values at different dilution rates for both kinetic models are given in Tables 3 and 4, separately. Also, Figs. 5 and 6 compare the experimental data with Monod and Moser models in continuous ethanol fermentation with and without pervaporation for growth kinetics, product formation and substrate consumption, respectively. Table 3 Table 4 Fig. 5 15
Fig. 6 The kinetics of bioethanol fermentation in the CMBR by both models is shown in Figs. 5a and 6a. They represent the relationship between the accumulative ethanol formation and fermentation time, including the plotted curves obtained by Monod and Moser models and experimental data. In addition, broth ethanol concentration in CMBR was less than CCBR owing to elimination of produced ethanol through the membrane, reducing the ethanol inhibition on the cell growth, ethanol production improvement and low glucose concentration as illustrated in Figs. 5b and 6b. Therefore, they result in more cell growth as shown in Figs. 5c and 6c. It can be observed that the ethanol concentration in each time period followed a reducing trend, due to the gradual deterioration of the culture environment, which could lead to the dropping of ethanol productivity. Furthermore, ethanol production drastically decreased with increasing of dilution rate on account of inadequate time for cell growth. Therefore, the ethanol concentration in permeate side of CMBR reduced with dilution rate increment as showed in Figs. 5d and 6d. Generally, the proposed cell growth, substrate consumption and ethanol formation models confirmed these procedures as presented in Figs. 5 and 6. Based on the Le Chatelier’s principle, the substrate consumption would gradually increase with reducing of product concentration in CMBR. Consequently, it was expected that substrate inhibition on the cell growth and ethanol formation in the CMBR to be lower than CCBR. Based on the genetic algorithm the results of both models represented approximately lower Ksx, Ksp and Kss in CMBR compared to CCBR, as mentioned in Tables 2 and 3. At low dilution rate of 0.04 h-1 in CMBR, the proposed mathematical model by Monod calculated Ksx, Ksp and Kss of about 38.850 , 25.981 and 44.156 g L-1, while the values of 71.580, 26.669 and 37.160 g L-1 were gain in CCBR, respectively. As illustrated in Table 2, at dilution rate of 0.04, higher Kss of 0.14 and 0.19 h-1 were calculated in 16
CMBR compared to CCBR. This was possibly due to the formation of some byproducts in the CCBR. Although genetic algorithm calculated the parameters based on the minimum RMSE, it was possible to calculate other coefficients with larger errors. In case of larger errors, the model could calculate lower Kss in the CMBR compared to CCBR. Here, all calculations were performed based on the minimum error by genetic algorithm. Additionally, at dilution rate of 0.24 h-1, lower Ksp of 35 g L-1 obtained in CCBR than Ksp of 59.992 g L-1 in CMBR. Besides based on the minimum error calculation, it was probably due to the insufficient data at low retention time, or inaccuracy of the model. Furthermore, at high dilution rate, the behavior of the CMBR shifted to the CCBR as a result of low incubation time and maybe membrane fouling during 300 h long term fermentation. On the other hand, based on the set of equations (see Table 1), the RMSE and correlation coefficient of non-linear regression were in the range of 0.693-3.811 and 0.988-0.999 in CMBR, whereas the values of about 1.0774.324 and 0.991-0.995 were gained in CCBR, respectively. The presented mathematical model based on Moser kinetic resulted in lower substrate inhibition coefficients on cell growth, ethanol production and substrate consumption in CMBR compared to CCBR. Only at dilution rate of 0.09 h-1, Ksx of 18.05 g L-1 obtained in CMBR, whereas this was of about 7.890 g L-1 in CCBR; this may contributed to the calculations based on the minimum RMSE. As mentioned previously, the lower Ksx of 99 g L-1 attained in the conventional system can be attributed to the low accuracy and data limitation at high dilution rate of 0.24 h-1. Despite the fact that the RMSE and correlation coefficient were in the range of 0.7282.753 and 0.995-0.999 in CMBR, they were in the range of 0.911-2.950 and 0.9960.999 in CCBR, respectively.
17
Regardless of the satisfactory results of both Monod and Moser kinetic models simulation, it seems that Moser kinetic model showed lower RSME and higher correlation coefficient than Monod one. 4 Conclusions The genetic algorithm modeling of CMBR and CCBR performance by Monod and Moser kinetic models was proposed to describe the cell growth, substrate concentration and ethanol production during ethanol fermentation at different retention times and in unsteady state condition. The results were compared with the experimental data. The models' simulating kinetic parameters of both fermentation systems were comprehensively analyzed and compared to each other and hence some features of kinetic behavior of substrate consumption, ethanol production and cell growth in continuous conventional and pervaporation processes could be brought to light. Acknowledgments The authors wish to appreciate Mr. K. Alinezhad from I.O.O.C Company for his operational and technical assistance. We also thank Afagh and KGW Isotherm companies for their close collaboration and technical supply of equipment and spare parts, and thank I.F.C.O Company for the financial support. References 1.Bailey, J. E., Ollis D. F., 1994. Biochemical engineering fundamentals, second ed. McGraw-Hill Book Company, Singapore 2.Balat, M., Balat, H., 2009. Recent trends in global production and utilization of bioethanol fuel. Appl. Energ. 86, 2273-2282. 3.Birol, G., Doruker, P., Kirdar, B., Önsan, Z. ĺ., Ülgen, K., 1998. Mathematical description of ethanol fermentation by immobilised Saccharomyces cerevisiae. Process Biochem. 33, 763-771
18
4.Ding, W. W., Wu, Y. T., Tang, X. Y., Yuan, L., Xiao, Z. Y., 2011. Continuous ethanol fermentation in a closed-circulating system using an immobilized cell coupled with PDMS membrane pervaporation. J. Chem. Technol. Biot. 86, 82-87. 5.Esfahanian, M., Ghorbanfarahi, A., Ghoreyshi, A., Najafpour, G., Younesi, H., Ahmad, A. L., 2012. Enhanced bioethanol production in batch fermentation by pervaporation using a PDMS membrane bioreactor. Int. J. Eng. 25, 249-258. 6.Esfahanian, M., Nikzad, M., Najafpour, G., Ghoreyshi, A. A., 2013. Modeling and optimization of ethanol fermentation using Saccharomyces cerevisiae: Response surface methodology and artificial neural network. Chem. Ind. Chem. Eng. Q. 19, 241-252. 7.Fan, S., Chen, S., Tang, X., Xiao, Z., Deng, Q., Yao, P., Sun, Z., Zhang, Y., Chen, C., 2014a. Kinetic model of continuous ethanol fermentation in closed-circulating process with pervaporation membrane bioreactor by Saccharomyces cerevisiae. Bioresour. Technol. 177, 169-175. 8.Fan, S., Xiao, Z., Tang, X., Chen, C., Zhang, Y., Deng, Q., Yao, P., Li, W., 2014b. Inhibition effect of secondary metabolites accumulated in a pervaporation membrane bioreactor on ethanol fermentation of Saccharomyces cerevisiae. Bioresour. Technol. 162, 8-13. 9.Fan, S., Xiao, Z., Zhang, Y., Tang, X., Chen, C., Li, W., Deng, Q., Yao, P., 2014c. Enhanced ethanol fermentation in a pervaporation membrane bioreactor with the convenient permeate vapor recovery. Bioresour. Technol. 155, 229-234. 10.Fang, X., Shen, Y., Zhao, J., Bao, X., Qu, Y., 2010. Status and prospect of lignocellulosic bioethanol production in China. Bioresour. Technol. 101, 4814-4819. 11.Fu, C., Cai, D., Hu, S., Miao, Q., Wang, Y., Qin, P., Wang, Z., Tan, T., 2016. Ethanol fermentation integrated with PDMS composite membrane: An effective process. Bioresour. Technol. 200, 648-657.
19
12.Huang ,Y., Fu, J., Pan, Y., Huang, X., Tang, X., 2009. Pervaporation of ethanol aqueous solution by polyphosphazene membranes: Effect of pendant groups. Sep. Purif. Technol. 66, 504-509. 13.Katare, S., Bhan, A., Caruthers, J. M., Delgass, W. N., Venkatasubramanian, V., 2004. A hybrid genetic algorithm for efficient parameter estimation of large kinetic models. Comput. Chem. Eng. 28, 2569-2581. 14.Kheyrandish, M., Asadollahi, M. A., Jeihanipour, A., Doostmohammadi, M., Rismani-Yazdi, H., Karimi, K., 2015. Direct production of acetone-butanol-ethanol from waste starch by free and immobilized Clostridium acetobutylicum. Fuel. 142, 129133. 15.Li, L., Xiao, Z., Tan, S., Pu, L., Zhang, Z., 2004. Composite PDMS membrane with high flux for the separation of organics from water by pervaporation. J. Membr. Sci. 243, 177-187. 16.Li, S., Qin, F., Qin, P., Karim, M. N., Tan, T., 2013. Preparation of PDMS membrane using water as solvent for pervaporation separation of butanol/water mixture. Green Chem. 15, 2180-2190. 17.Limayem, A., Ricke, S. C., 2012. Lignocellulosic biomass for bioethanol production: current perspectives, potential issues and future prospects. Prog. Energy Combust. Sci. 38, 449-467. 18.Moueddeb, H., Sanchez, J., Bardot, C., Fick, M., 1996. Membrane bioreactor for lactic acid production. J. Membr. Sci. 114, 59-71. 19.Najafpour, G., 2015. Biochemical engineering and biotechnology, second ed. Elsevier, Amsterdam. 20.Nguyen, V. D., Auresenia, J., Kosuge, H., Tan, R. R., Brondial, Y., 2011. Vacuum fermentation integrated with separation process for ethanol production. Biochem. Eng. J. 55, 208-214. 20
21.Nigam, P. S., Singh, A., 2011. Production of liquid biofuels from renewable resources. Prog. Energy Combust. Sci. 37, 52-68. 22.Nomura, M., Bin, T., Nakao, S.-i., 2002. Selective ethanol extraction from fermentation broth using a silicalite membrane. Sep. Purif. Technol. 27, 59-66. 23.Qin, F., Li, S., Qin, P., Karim, M. N., Tan, T., 2014. A PDMS membrane with high pervaporation performance for the separation of furfural and its potential in industrial application. Green Chem. 16, 1262-1273. 24.Qureshi, N., Blaschek, H., 2001. ABE production from corn: a recent economic evaluation. J. Ind. Microbiol. Biotechnol. 27, 292-297. 25.Samanta, H. S., Ray, S. K., 2015. Separation of ethanol from water by pervaporation using mixed matrix copolymer membranes. Sep. Purif. Technol. 146, 176-186. 26.Sanda, T., Hasunuma, T., Matsuda, F., Kondo, A., 2011. Repeated-batch fermentation of lignocellulosic hydrolysate to ethanol using a hybrid Saccharomyces cerevisiae strain metabolically engineered for tolerance to acetic and formic acids. Bioresour. Technol. 102, 7917-7924. 27.Shinozaki, Y., Kitamoto, H. K., 2011. Ethanol production from ensiled rice straw and whole-crop silage by the simultaneous enzymatic saccharification and fermentation process. J. Biosci. Bioeng. 111, 320-325. 28.Shopova, E. G., Vaklieva-Bancheva, N. G., 2006. Basic-A genetic algorithm for engineering problems solution. Comput. Chem. Eng. 30, 1293-1309. 29.Stanley, D., Bandara, A., Fraser, S., Chambers, P., Stanley, G. A., 2010. The ethanol stress response and ethanol tolerance of Saccharomyces cerevisiae. J. Appl. Microbiol. 109, 13-24. 30.Thomas, L. C., Chamberlin, G. J., 1974. Colorimetric chemical analytical methods, Tintometer, England.
21
31.Vane, L. M., 2005. A review of pervaporation for product recovery from biomass fermentation processes. J. Chem. Technol. Biot. 80, 603-629. 32.Vane, L. M., 2008. Separation technologies for the recovery and dehydration of alcohols from fermentation broths. Biofuels, Bioprod. Biorefin. 2, 553-588.
22
Figure legends Fig. 1 a) Design of MBR b) 3D image of continuous pervaporation system Fig. 2 CMBR and CCBR profiles during dilution rates of 0.04-0.24 h-1, continuously (a) Ethanol and cell concentration (b) glucose concentration (c) ethanol concentration in permeate side of MBR Fig. 3 CMBR and CCBR profiles at dilution rates of 0.04-0.24 h-1 (a) Cell and glucose concentration and cell production rate (b) Broth and permeate ethanol concentration Fig. 4 Ethanol productivity as function of dilution rate in continuous fermentation experiments Fig. 5 Comparison of predicted Monod model and experimental data in CMBR and CCBR for (a) ethanol formation in the broth (b) glucose consumption (c) cell growth (d) ethanol in permeate side Fig. 6 Comparison of predicted Moser model and experimental data in CMBR and CCBR for (a) ethanol formation in the broth (b) glucose consumption (c) cell growth (d) ethanol in permeate side
23
Table legends Table 1 Mathematical models for CMBR and CCBR Table 2 Details of cell, ethanol and glucose concentration at dilution rates of 0.04-0.24 h-1 Table 3 Kinetic Monod model parameters for CMBR and CCBR at various dilution rates Table 4 Kinetic Moser model parameters for CMBR and CCBR at various dilution rates
24
(a)
(b)
Fig. 1 25
Ethanol and cell concentration (g L-1 )
Broth Ethanol Conc. (CMBR) Cell Conc. (CMBR) 45
Broth Ethanol Conc. (CCBR) Cell Conc. (CCBR)
40 35 30 25 20 15 10 5 0 0
50
100
150
200
250
300
350
250
300
350
250
300
350
Time (h)
(a)
Glucose concentration (g L-1 )
CMBR
CCBR
90 80 70 60 50 40 30 20 10 0 0
50
100
150
200
Time (h)
(b) 240
Ethanol concentration (g L-1)
200 160 120 80 40 0 0
50
100
150 200 Time (h)
(c) Fig. 2 26
Cell Conc. (CMBR)
Cell Conc. (CCBR)
Glucose Conc. (CCBR)
Cell Prod. Rate. (CMBR)
Cell Prod. Rate (CCBR)
90 80 70 60 50 40 30 20 10 0
1.2 1 0.8 0.6 0.4 0.2
Cell production rate (g L-1 h-1)
Cell and glucose concentration (g L-1 )
Glucose Conc. (CMBR)
0 0
0.04 0.08 0.12 0.16 0.2 0.24 0.28 Dilution rate (h-1)
in broth (CMBR) In broth (CCBR) In permeate (CMBR)
50 45 40 35 30 25 20 15 10 5 0
225 200 175 150 125 100 75 50 25 0
0
0.04 0.08 0.12 0.16 0.2 0.24 0.28 Dilution rate (h-1)
(b) Fig. 3
27
Permeate ethanol concentration (g L-1)
Broth ethanol concentration (g L-1)
(a)
CMBR
CCBR
7
Productivity (g L-1 h-1)
6 5 4 3 2 1 0 0
0.04 0.08 0.12 0.16 Dilution rate (h-1)
Fig. 4
28
0.2
0.24 0.28
(a)
(b)
(c)
(d)
Fig. 5
29
(a)
(b)
(c)
(d) Fig. 6
30
(d)
Table 1 Bioreactors
CMBR
CCBR
Monod
Moser
= . & − = . + . => + &
= . & @ − = . + . => + & @
=& & − &! + . & . & = + . => + & ;⁄
=& & − &! + . & . & @ = + . => + & @ ;⁄
=9 9 − 9 ! − . 9 . & = + . . ;:? => < + &
=9 9 − 9 ! − . 9 . & @ = + . . ;:? => < + & @
= . & . = . − => + &
= . & @ . = . − => + & @
=& & − &! . & = + . => + & ;⁄
=& & − &! . & @ = + . => + & @ ;⁄
=9 . & . 9 = . . ;:? − => < + &
=9 . & @ . 9 = . . ;:? − => < + & @
31
Table 2 Broth concentration (g L-1)
-1
Permeate ethanol
Dilution rate (h ) concentration Ethanol
Glucose
Cell
CMBR
40.10
2.50
31.78
CCBR
40.54
10.95
25.50
CMBR
37.78
4.03
31.20
CCBR
38.51
15.47
24.03
CMBR
29.52
16.31
26.19
CCBR
30.53
26.12
20.60
CMBR
17.42
38.34
16.23
CCBR
18.13
45.87
13.10
CMBR
3.90
80.10
0.90
CCBR
4.10
83.00
0.70
0.04
(g L-1) 215.61
0.09
204.83
0.14
168.78
0.19
108.51
0.24
27.17
32
Table 3 Parameters
CMBR
CCBR
D (h-1)
Ksx(gL-1)
Ksp (gL-1)
0.04
38.850
25.981
0.09
8.680
0.14
Kss(gL-1)
YX/S(gg-1)
YP/X(gg-1)
R2
RMSE
44.156
0.277
1.158
0.994
3.301
8.800
0.898
0.820
1.370
0.999
0.693
14.540
16.051
27.510
0.208
1.400
0.996
2.381
0.19
22.289
25.702
37.697
0.224
1.269
0.988
1.726
0.24
80
59.992
80
1
0.259
0.994
3.811
0.04
71.580
26.669
37.160
0.453
0.730
0.995
3.039
0.09
28.230
20.770
17.360
0.378
1.336
0.991
1.196
0.14
23.230
16.240
13.145
0.350
1.310
0.991
1.077
0.19
24.860
35.451
25
0.261
1.519
0.991
1.095
0.24
120
35
90
2
1
0.993
4.324
33
Table 4 Parameters
CMBR
CCBR
D (h-1)
n
Ksx(gL-1)
Ksp(gL-1)
Kss(gL-1)
YX/S(gg-1)
YP/X(gg-1)
R2
RMSE
0.04
0.570
16.390
14.100
25.685
0.113
1.547
0.995
2.057
0.09
1.850
18.500
16.100
3.850
0.680
1.160
0.999
0.728
0.14
1.200
24.690
18.335
1.149
0.268
1.917
0.996
2.753
0.19
1.120
33.380
31.880
33.830
0.281
1.158
0.998
1.876
0.24
4.6E-13
122
13.570
12.370
0.690
1.930
0.998
2.734
0.04
0.780
41.550
25.420
27.590
0.380
1.060
0.996
2.314
0.09
0.513
7.890
26.370
10.354
0.232
1.183
0.999
0.911
0.14
1.255
51.830
26.910
29.060
0.348
1.171
0.999
1.296
0.19
1.135
40.836
41.084
35.660
0.282
1.311
0.999
1.086
0.24
0.200
99
50
70
1
1
0.998
2.950
34
Highlights •
Genetic algorithm was used for modeling of CMBR and CCBR performances
•
Mathematical modeling was based on Monod and Moser kinetic models
•
Modeling was performed at the unsteady state condition at different retention times
•
The model described the cell growth, substrate concentration and ethanol production
•
The Moser kinetic model results showed good agreement with experimental data
35