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Dynamic Modeling of a Fermentation Process with Ex-Situ Butanol Recovery (ESBR) for Continuous Biobutanol Production Moon-Ho Eom, Boeun Kim, Hong Jang, Sang-Hyun Lee, Woohyun Kim, Yong-An Shin, and Jay H. Lee Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.5b01031 • Publication Date (Web): 19 Oct 2015 Downloaded from http://pubs.acs.org on October 25, 2015

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Dynamic Modeling of a Fermentation Process with ExSitu Butanol Recovery (ESBR) for Continuous Biobutanol Production Moon-Ho Eom†1,2, Boeun Kim†1, Hong Jang1, Sang-Hyun Lee2, Woohyun Kim††1, Yong-An Shin2, Jay H. Lee1* 1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology (KAIST), 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea 2. R&D Center, GS Caltex Corporation, 359 Expo-ro, Yuseong-gu, Daejeon 305-380, Republic of Korea.

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ABSTRACT

A dynamic model for a fermentation process equipped with an ex-situ butanol recovery (termed ‘ESBR’ hereafter) system is proposed for continuous production of biobutanol. Since the proposed ESBR system integrates a fermenter with a stirred-tank-type adsorption column, the dynamic model includes kinetic models for both the fermentation (the Monod/Luedeking-Piret model) and the adsorption (the extended Langmuir model). Parameters in the kinetic models are initially determined using data from batch and fed-batch fermentation experiments with in-situ butanol recovery (ISBR). The initially developed model is then used to find a feasible operating condition for an experimental ESBR system, and its parameter values are further tuned using experimental data from the proposed ESBR system for accurate predictions in the butanol and glucose concentration range seen in the ESBR operation. The approach to improving the model accuracy consists of two steps: 1) identifying the critical parameters by performing a sensitivity analysis and, 2) re-estimating the selected parameters using data obtained during cyclic operation of the proposed ESBR system. Accordingly, the developed model based on the kinetics for both fermentation and adsorption can describe and predict the behavior of the proposed ESBR system. Thus, the proposed systematic approach provides a reliable platform for the optimal scale-up design and control studies of the ESBR system.

KEYWORDS Biobutanol; Ex-Situ Butanol Recovery; Acetone-Butanol-Ethanol (ABE) fermentation; Adsorption; Cyclic Steady State; Dynamic Modeling


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1. INTRODUCTION The Acetone-Butanol-Ethanol (ABE) fermentation is a well-known process for the production of butanol from glucose and other carbohydrates.1 However, the inhibition of cell growth by butanol results in low fermentation performance (e.g., low productivity, low butanol concentration), which is a major obstacle for commercial viability of the ABE fermentation. To overcome this problem, a new fermentation process integrated with simultaneous product recovery has recently been suggested; the idea is to maintain the butanol concentration below the threshold of toxicity by removing it from the fermentation broth during the fermentation. Numerous recovery technologies, including liquid-liquid extraction, pervaporation, gas stripping and adsorption, have been considered as candidates for integration with the fermentation process.2, 3 Among them, adsorption technology has received the most attention, owing to its high energy efficiency and simplicity.4,

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Various adsorbents have been

investigated for this purpose.6, 7 For integrating adsorption with fermentation, two major design options are available: in-situ butanol recovery (ISBR) and ex-situ butanol recovery (ESBR) (see Figure 1). In the former option, adsorbents highly selective for butanol are put inside the fermenter, where both fermentation and adsorption occur simultaneously.8 Thus, in the case of ISBR, the fermentation can only be done in batch mode since it must be stopped once the capacity of the adsorbents is reached. This can limit the time of each run significantly. The ESBR option involves a system composed of two physically separated parts: a fermenter and a separation process consisting of adsorption column. In this system, the fermentation broth is circulated through the adsorption column to maintain the butanol concentration below the threshold level. The feed medium is continuously fed into the fermenter to keep the glucose concentration within a proper range. In contrast with the ISBR system, the proposed ESBR system can operate as a continuous process by manipulating multiple columns: while one fresh adsorption column is in operation, the other used columns can be regenerated by removing the adsorbed product. Such continuous processing can significantly enhance the volumetric productivity.


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Despite the apparent advantage of the ESBR system, there are several challenges in designing and operating such a process on a commercial scale, given the complex dynamic nature of the operation. According to a recent review9, 10, even though the commercialization of ESBR fermentation processes requires deeper understanding and more developments from the viewpoint of systems engineering, only a few model based simulation studies on fermentation processes integrated with pervaporation11, liquid extraction12, and vacuum flash13 have been undertaken thus far. Furthermore, no study on dynamic modeling and optimization of ESBR by adsorption has appeared in the literature. The main objective of this work is to develop a mathematical model for the purpose of guiding optimal process design and determining operating procedures for ESBR by adsorption to be used for continuous production of biobutanol. In our previous study14, we developed a kinetic model for the adsorption system, which is a major part of the proposed ESBR system. To develop a complete dynamic model of the proposed ESBR system, proper forms of kinetic models for the ABE fermentation should be chosen, and its parameters have to be estimated using experimental data obtained under conditions similar to those of the real operation. However, from the beginning, it is hard to identify a proper condition suitable for maintaining the proposed ESBR system in the desirable operation range, due to the complex nature of operation. Therefore, we initially constructed fermentation kinetic models using batch and fed-batch fermentation experiments with ISBR. Since the models developed using the experimental data of the ISBR system may not suitably explain the dynamic behavior of the proposed ESBR system as the operating ranges covered by the two systems differ substantially, they are improved using the data collected from an actual ESBR system by re-estimation of significant parameters. The developed systematic approach for dynamic modeling of the complex cyclic operation of the reactor/separator can also be applied practically for other types of recombinant microbe, substrate, and recovery process in pilot-scale as well as laboratory-scale.

2. THEORY 2.1 Description of ESBR system and modeling approach ACS Paragon Plus Environment

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The proposed ESBR system is composed of a feed tank, a main fermenter, and two adsorption columns. The operation steps are illustrated in Figure 2: First, as a start-up phase, the fermentation is conducted in a batch manner without feeding and circulation until the cell and product concentrations reach preset control levels, and then the continuous, cyclic operation is started. In the continuous operation mode, feed media is fed into the main fermenter to keep the substrate concentration in the fermenter in an optimal range for microbial growth. In addition, the fermentation broth is continuously circulated between the fermenter and the adsorption column, which allows the butanol concentration in the fermenter to be maintained below the control level by adsorption. When the capacity of the adsorbents is exceeded and the butanol concentration reaches a specific value, the recirculation flow is routed to the other adsorption column so that fermentation and adsorption may continue. In the meantime, the used (or saturated) adsorption column, which removes the product from the column, is regenerated. Due to the cyclic nature of the operation, a steady-state based design cannot be used; instead the design and optimization methods should take the dynamic characteristics of the periodic operation into account. In consideration of the complex dynamic nature of the proposed ESBR system, the development of the dynamic model follows the following procedure: 1. Development of an unstructured model for describing the fermentation kinetics relating cell growth, glucose consumption and product formation, and estimation of model parameters using data obtained from an experimental ISBR system. 2. Establishment of a dynamic model for ESBR by adsorption including the mass balance equations of cell, products, and glucose by integrating the fermentation and adsorption kinetic models. 3. Identification of the critical parameters by sensitivity analysis and updating of the model by tuning the critical parameters with additional experimental data obtained from the experimental ESBR system.

2.2 Kinetic model for the fermentation: Cell growth, Product formation, and Glucose uptake ACS Paragon Plus Environment

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To develop a dynamic model for the proposed process, kinetic models describing cell growth, product formation, and glucose uptake are needed. In this study, we establish the kinetic model based on unstructured mathematical models which have generally been used to describe and predict the state of the ABE fermentation.15-18 In the ABE fermentation, the cell growth rate can be inhibited for three reasons: i) high substrate concentration, ii) high butanol concentration and, iii) high cell concentration. Thus, the specific growth rate of cell is described as follows by modifying the Monod equation to reflect the inhibition factors.19-21

iB

iX

 CB   X  µg = × 1−  ×1−  Ks + S + S 2 / KI  PB   PX 

µmS

(1)

Next, the kinetic models for the butanol and ethanol production rates are developed using the Luedeking-Piret model that consists of a growth-associated part and a non-growth-associated part.22 Among the products, only butanol and ethanol are considered as valuable products in this study, because the amounts of acetone, acetic acid, and butyric acid produced are negligible. The kinetic models for the two are expressed as below:

rB =

1 dC B = αBµg + βB X dt

(2)

rE =

1 dC E = α Eµg + βE X dt

(3)

Finally, the rate of glucose consumption can be given in a stoichiometric equation form related to the amount of assimilation for cell growth, product formation, and maintenance energy, as shown below:


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 1 dC B dS 1 1 dC E  =− + µg X −   − ms X dt Yx / s YE / s dt   YB / s dt

(4)

Eq 4 can be combined with the production rate model to yield,

 1  β  dS α α  β = − + B + E  × µ g X −  B + E + ms  × X dt  Yx / s YB / s YE / s   YB / s YE / s 

(5)

and eq 5 can be simplified to eq 6 since the terms in the parentheses become constant values:

−

dS = (α G lu µ g + β G lu ) X dt

(6)

This equation depends on only the cell growth rate and cell concentration. In addition, the butanol inhibition effect on glucose uptake23 is introduced in the equation as,

iB'

 C  1 dS rs = − × = αGlu × µ g + βGlu × 1 − B'  X dt  PB 

(7)

2.3 Model for ISBR system Figure 3(A) shows the schematic diagram with design and operation variables for the modeling of ISBR. In the ISBR system, feed media is continuously fed into the fermenter to prevent glucose shortage, such that the system operates in fed-batch mode. Mass balances for cell, glucose and products in the fermenter are represented by the following equations:


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dX = ( µ net − D ) × X dt

(8)

µnet = µg − kd

(9)

dS = (S f − S ) × D − rs × X dt

(10)

where D is dilution rate, defined as D=F/V, F is the feed flow rate and V is liquid volume.

dCB dA = rB × X + B dt dt

(11)

dCE dA = rE × X + E dt dt

(12)

where dAi/dt is the concentration change of component i by adsorption. This was derived in our previous study14 as follows:

dAi m dqi m =− = − × ki ( qi ,eq − qi ) dt Vr dt Vr qi , eq =

qi , m Bi C r ,i

(13)

(14)

n

1 + ∑ Bi C r ,i i =1

2.4 Model for the ESBR system For the model of the proposed ESBR system, the schematic diagram with design and operation variables of the system is shown in Figure 3(B). The mass balance equations of cell, glucose, butanol and ethanol for the fermenter and the adsorption column are developed separately, and then the mass balance equations for the two parts are connected through the circulation flow. The overall models can


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be represented by eqs 15 –26. Subscripts r and ad in the equations indicate the property or concentration of components in the fermenter and the adsorption column, respectively. For the fermenter, the mass balance equations of all the components are defined as follows:

dX r ( Fc ,ad × X ad − Fc,r × X r ) = + (µnet ,r − Dr ) × X r dt Vr

(15)

dSr ( F × S f + Fc ,ad × Sad − Fc , r × S r ) = + rr , s × X r − S r × Dr dt Vr

(16)

dCr , B dt dCr , E dt

=

=

( Fc,ad × Cad ,B − Fc,r × Cr , B ) Vr

+ rr ,B × X r − Cr , B × Dr

( Fc ,ad × Cad , E − Fc ,r × Cr , E ) Vr

+ rr , E × X r − Cr , E × Dr

(17)

(18)

where Dr is given by Dr = (F- Fc,r + Fc,ad)/Vr, F is the feed flow rate, and Fc,r and Fc,ad are the circulation flow rates from the fermenter to the adsorption column and vice versa. During the circulation step, when Fc,ad is equal to Fc,r, then Dr becomes D=F/Vr, as defined in the model of ISBR system. Thus, eqs 15 – 18 can be simplified to the following:

,

F dX r = ( µnet ,r − D) × X r + ( X ad − X r ) × c ,r dt Vr

(19)

F dSr = (S f − Sr ) × D + (Sad − Sr ) × c,r − rr ,s × X r dt Vr

(20)

dCr , B dt dCr , E dt

= (Cad , B − Cr , B ) ×

= (Cad , E − Cr , E ) ×

Fc,r Vr Fc,r Vr

+ rr , B × X r − Cr , B × D

(21)

+ rr , E × X r − Cr , E × D

(22)


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Unlike the fermenter, in the adsorption column, there is no feeding during the continuous operation mode so the concentrations of the components are changed by the adsorption as well as the circulation. Therefore, the dilution rate terms are excluded from the mass balance equations, and the adsorption kinetics are introduced as shown below:

F dX ad = µnet ,ad × X ad + ( X r − X ad ) × c ,r dt Vad

(23)

F dSad = (Sr − Sad ) × c,r − rad ,s × X ad dt Vad

(24)

dCad , B dt dCad , E dt

= (Cr , B − Cad , B ) ×

= (Cr , E − Cad , E ) ×

Fc ,r Vad

Fc,r Vad

+ rad , B × X ad +

+ rad , E × X ad +

dAB dt

dAE dt

(25)

(26)

The concentration changes of butanol and ethanol by adsorption, dAB/dt and dAE/dt, are calculated from the adsorption isotherm model, eqs 13 and 14.

2.5 Parameter estimation for fermentation models The developed fermentation kinetic models include 16 parameters that need to be estimated: parameters for the Monod equation (µm, KS), coefficients for the inhibitory effects of substrate (KI), butanol concentration (PB, iB, P’B and i’B) and cell concentration (PX and iX), the cell death rate constant (kd), substrate consumption parameters (αGlu and βGlu), and yield coefficients and other parameters related to product formation (αB, αB, βB and βE). The kinetic models established using batch experiment data may have difficulties in predicting phenomena in fed-batch or continuous processes, due to the dilution effect on the cell growth.24 In addition, ordinary fed-batch operations are not appropriate for the ABE fermentation since cell growth stops quickly due to the butanol inhibition effect. Therefore, the ACS Paragon Plus Environment

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fed-batch fermentation experiments with the ISBR system are just used for developing an initial mathematical model for the proposed ESBR system. The model is then validated with a new set of fedbatch data under different conditions. The unknown parameters are estimated using the conventional least-squares estimation (LSE) method, for the dependent variables such as the concentrations of cell, glucose and products. To solve the optimization problem, a feasible parameter range for each parameter is established (see Table 1), and fmincon in MATLAB R2014b Optimization Toolbox is employed for solving the constrained nonlinear optimization problem based on the interior point algorithm.

2.6 Model improvement for the ESBR system To improve the fidelity of the model, the parameters need to be refined using additional data collected from an actual ESBR system. Given a large number of parameters and relatively small amounts of data available, adjusting all parameters can be ineffective. Therefore, we identify the critical parameters through a sensitivity analysis, and perform a re-estimation of those parameters.

2.6.1 Sensitivity analysis of model parameters In the proposed ESBR system, the adsorption column is switched in a periodic manner, which results in a cyclic dynamic behavior in the concentrations. After a sufficiently large number of cycles, the system approaches the Cyclic Steady State (CSS), in which the initial state of a cycle is identical to the end state of the cycle. The CSS condition is determined through the successive substitution method, where the simulation is executed until the difference between the states of the two successive cycles is smaller than a relative tolerance (< 10-3).25 Accordingly, in order to identify the significant parameters in this study, sensitivities of the model parameters around the reached CSS (the stationary region) are calculated.


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After the system reaches the CSS, the sensitivity analysis is conducted with ±10% variations in each of 16 kinetic parameters presented in Section 2.4. The sensitivity can be measured by comparing the end states simulated over one cycle time of the CSS with perturbed and unperturbed parameters. Endpoint deviations (ED) of the three main states (cell, glucose, and butanol) as a result of each parameter perturbation are assessed. Note that ethanol is excluded from the sensitivity analysis since its concentration is relatively small and constant compared to the others. ED is calculated as below:

ED ip ( % ) =

C r ,i ( p + ∆ p , t f ) − C rref,i ( p , t f ) C rref,i ( p , t f )

× 100,

i = X , S , and B

(27)

where Cr,i ( p,t f ) and Cr ,i ( p + ∆p, t f ) represent the simulated concentration of ith component in the fermenter at time t f (corresponding to the terminal time of a cycle) associated with unperturbed ref parameter p and perturbed parameter p+Δp, respectively. Cr ,i ( p, t f ) is the terminal concentration of

the ith component in the reference cycle (i.e., the cyclic steady state).

2.6.2 Index of model accuracy The critical parameters identified by the sensitivity analysis are re-estimated using data from the actual ESBR system to improve the accuracy of the model. For evaluating the model accuracy quantitatively, the sum of squared relative error (SSRE) between experimental data and predicted values is defined as follow:

2

 Ci exp (t ) − Ci pred (t )  SSRE = ∑  , Ci exp (t ) t =0   tf

exp

where Ci (t) and Ci

pred

∀t = [0, t f ], i = X , S ,and B

(28)

(t ) are ith component concentration in the fermenter at time t from the

experiments and prediction, respectively. ACS Paragon Plus Environment

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3. MATERIAL and METHODS 3.1 Bacteria strains and culture The bacterial strain used for this study was recombinant Clostridium acetobutylicum, obtained by deletion of butyrate and acetate formation genes and overexpression of the adhE1-ctfAB operon (C. acetobutylicum ∆pta, ∆buk).

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For the fermentation test, the strain was first cultured in an anaerobic

chamber (Coy Laboratory products, Glass Lake, MI) containing 90% (v/v) N2, 5% (v/v) H2 and 5% (v/v) CO2 at 37°C. 2×YTG medium (16 g/L Bactotryptone, 10 g/L yeast extract, 4 g/L NaCl, 5 g/L glucose and 1.5% (w/v) agar) was used for plate culture. Clostridial growth medium (CGM, pH 5.8) was used as a basal culture medium for all strains. The CGM medium contained 0.75 g/L K2HPO4, 0.75 g/L KH2PO4, 0.7 g/L MgSO4•7H2O, 0.017 g/L MnSO4•5H2O, 0.01 g/L FeSO4•7H2O, 2 g/L (NH4)2SO4, 1 g/L NaCl, 2 g/L asparagine, 0.004 g/L p-aminobenzoic acid, 5 g/L yeast extract, and 4.08 g/L CH3COONa∙3H2O. For recombinant strains, erythromycin was added to the media at the final concentration of 40 µg/mL.

3.2 Adsorbent Dowex optipore L-493, supplied from Dow Korea, was used for ISBR and ESBR fermentation experiments. This is a poly-(styrene-co-divinylbenzene) polymer resin, and is an effective adsorbent for the recovery of 1-butanol from the ABE fermentation broth due to its high capacity and selectivity for 1butanol.14, 27

3.3 Analytical Method


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Concentrations of 1-butanol and ethanol in the aqueous solution were measured by gas chromatography (GC) using Agilent 6890N Series/5873 Network (Agilent Technologies, Palo Alto, CA, USA). The GC was equipped with a flame ionization detector (FID) and a 300 mm x 7.8 m glass 80/120 Carbopack BAW packed column (Supelco Inc., Bellefonte, PA). Glucose concentration was analyzed by high performance chromatography system (Agilent 1200 series, Wilmington, DE) equipped with a reflective index detector and an Aminex 87H column (Bio-Rad, Hercules, CA). 0.01 M H2SO4 was used as the mobile phase with a flow rate of 0.6 mL/min and the oven temperature is adjusted to 80°C for optimum column performance. Cell concentration was monitored by measuring absorbance at 600 nm with a DR5000 spectrophotometer (HACH, Loveland, CO).

3.4 Experiments Three kinds of experiments were performed for the development of a dynamic model for the proposed ESBR system. First, several batch fermentation tests were performed using the medium supplemented with varying concentrations of glucose for estimation of the kinetic parameters of the Monod model. Second, two fed-batch fermentation experiments with ISBR were conducted for the estimation and validation of fermentation kinetic parameters. Finally, a continuous fermentation experiment with the proposed ESBR system was carried out for the refinement and validation of the model.

3.4.1 Batch fermentation Batch fermentation was conducted in a 7 L Bioflo 310 fermenter (New Brunswick Scientific Co., Enfield, CT) with a 2 L working volume. A single colony of the Clostridium strain was inoculated into a Falcon tube containing 40 mL CGM and cultured anaerobically up to absorbance of 1.0 at 600 nm. This seed culture was transferred to 360 mL CGM. When the cell density in the flask reached an absorbance of 1.0 at 600 nm, the flask culture was inoculated into the fermenter. The fermentation was maintained ACS Paragon Plus Environment

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at pH 5.0 by adding ammonia solution (28%, w/v) at 37°C. The culture was agitated at 200 rpm and flushed with oxygen-free nitrogen at a flow rate of 50 mL min-1 to maintain anaerobic condition. To decide the kinetic parameters of eq 1 (µm and the parameters related to the substrate, KS and KI), several batch fermentation tests were conducted using the medium supplemented with glucose of varying concentration (0, 1.0, 2.0, 2.6, 3.2, 6.0, 11.3, 22.4, 41.4, and 86.5 g/L). The parameters were determined from the data collected during the early exponential growth phase when no product inhibition occurred.

3.4.2 Fed-batch fermentation with the ISBR system For fed-batch fermentation experiments with the ISBR system, the same strain, equipment and media were used, and 200 g of the adsorbent was added to the fermenter for the removal of butanol from the fermentation broth. The fermentation initially started with a volume of 2 L and a glucose concentration of 77 g/L. Feed media was continuously fed into the fermenter with a peristaltic pump to prevent the shortage of glucose, so glucose concentration was controlled above 10 g/L. The feed media was purged with N2 gas for 1 hour prior to use and then a Tedlar bag (SKC, PA) containing oxygen-free N2 gas was connected for maintaining the anaerobic condition during the fermentation. To obtain experimental data for the estimation and validation of the remaining 13 model parameters, two experiments were conducted under different conditions: a low glucose concentration of 200 g/L and a high feeding rate of 0.099 L/h (1st fed-batch experiment), and a high glucose concentration of 700 g/L and a low feeding rate of 0.017 L/h (2nd fed-batch experiment). Broth samples were taken from the fermenter at a 1.5 h intervals and the concentrations of cell, glucose, butanol, and ethanol were analyzed. The amounts of adsorbed butanol and ethanol were calculated using the extended Langmuir model (eq 14). As a result, the actual concentrations of the butanol and ethanol produced at a sampling point were calculated by the sum of components in both the liquid and the adsorbent divided by the liquid volume.


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3.4.3 Continuous fermentation with the ESBR system The continuous fermentation experiment was carried out in the pilot-scale ESBR system. This system was composed of a 300 L fermenter and two 50 L adsorption columns (Fermentec, Cheongju, Korea) filled with 10 kg of the adsorbent as described in Section 2.1. At the early stage of fermentation, the fermenter was operated in a batch manner with an initial volume of 200 L, and then the operation was changed to a continuous mode when the butanol concentration reached a target level, about 7 g/L. During the continuous mode, the fermentation broth was circulated at 250 L/h by a sanitary rotary pump (JEC, Hwaseong-si, Korea), and the feed media containing 196 g/L of glucose was fed to maintain the glucose concentration (0 - 10 g/L) in the main fermenter. The adsorption columns were periodically switched at 1.5 - 3 h intervals. This way, the butanol concentration was maintained within the control range of 6 - 8 g/L. Liquid samples were taken from both the fermenter and the adsorption columns at every column switching time, and the timevarying concentrations of cell, glucose, butanol, and ethanol were measured. For the complete butanol recovery and adsorbent regeneration, the use of 20 kg of steam was found to be sufficient. The effluent from the adsorption column was condensed in a condenser to recover the butanol.

4. RESULTS AND DISCUSSION 4.1 Development of fermentation kinetic models from the ISBR system Under substrate-limiting conditions, eq 2 can be modified to,

µg =

µm S

(29)

Ks + S + S 2 / KI


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where the inhibition terms for the product and cell concentrations are excluded from eq 2. As the first step, the parameters, µm, KS, and KI, in eq 29 are estimated by fitting the batch fermentation data obtained with various initial concentrations of glucose through nonlinear regression (Figure 4). As a result, the estimated values of µm, KS, and KI for the growth of the recombinant Clostridium acetobutylicum under substrate-limiting conditions are 0.238 h-1, 0.357 g/L, and 272.3 g/L, respectively. Next, the remaining unknown parameters in the fermentation model equations (eqs 1 – 7) are estimated by fitting the model to the profiles of cell growth, glucose consumption, butanol production and ethanol production from the 1st fed-batch experiment (Figure 5). The estimation results are summarized in Table 1. The continuous operation of ESBR by adsorption is set to proceed in the solventogenic phase where the butanol concentration is maintained at a level above 5 g/L. The data of a batch experiment in Figure S1 shows that the fermentation process comes into the solventogenic phase from the acidogenic phase after 6 h. Therefore, experimental data obtained past the 6 h mark after the start-up are used for the parameter estimation. The cell concentration increases steadily as the fermentation proceeds (Figure 5(A)), but the cell growth becomes totally inhibited when the butanol concentration in the broth reaches values around 12.57 g/L (PB). Beyond this upper limit of the butanol concentration, the cell concentration decreases. Figure 5(B) shows the accumulated glucose consumption versus time. The growth associated parameter for glucose consumption, αGlu, is estimated as 2.322, and the non-growth associated parameter, βGlu, is estimated as 1.844 h-1. In Figures 5(C) and (D), the production profiles of butanol and ethanol show similar patterns during the exponential cell growth phase. However, in the stationary phase, the rate of butanol production slows down as the cell growth rate decreases and eventually stops, while the ethanol concentration still increases, even when the cell growth is ceased. The fitted curves from the parameter estimation also show the same trend with experimental results. The growth associated parameter of the butanol production, αB, is estimated as 1.771, while the non-growth associated parameter, βB, is estimated to be 0. On the contrary, the estimated value of the growth associated parameter for ethanol ACS Paragon Plus Environment

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formation, αE, equals 0, and the non-growth associated parameter βE is 0.0176 h-1. These results indicate that butanol production follows a growth associated pattern, whereas ethanol production follows a nongrowth associated pattern. To validate the kinetic models and the estimated parameters, the simulated results are compared with data from a 2nd fed-batch experiment carried out under a different condition. Although the two experiments are conducted under extreme conditions as reported in Section 3.4.2, the fermentation kinetic model predicts the dynamics of the ABE fermentation without any modification, especially during the exponential growth phase (Figure 6).

4.2 Model improvement for the ESBR system From the operation setting in section 4.3, the concentration profiles of cell, glucose, butanol and ethanol in the proposed ESBR system are obtained, as shown in Figure 7. The circulation between the fermenter and the adsorption column starts after 12 h of batch operation, when the butanol concentration reaches about 7 g/L. The feed medium containing 196 g/L of glucose is fed into the fermenter from the 15 h mark. The adsorption columns are switched at intervals of 2 – 3 h during the first four cycles and then, after 19 h, the switching time is shortened to 1.5 – 2.0 h due to the increase in the butanol production rate along with the rise in cell concentration. The butanol concentration is maintained between 5.0 – 8.0 g/L by the continuous butanol removal but ethanol accumulates in the broth due to its lower affinity to the adsorbent. In addition, the recombinant microbe used in this study shows a remarkably low level of acetone production compared to the traditional ABE fermentation (see Figure 7), so acetone is excluded from the dynamic modeling of the proposed ESBR system. After approximately 35 h, the concentrations of cell and ethanol are maintained at levels of 8.5 g/L and 5 g/L, respectively. Thus, the system reaches a stable cyclic operation condition. In this condition, fermentation broth samples are collected from the fermenter at 10 min intervals over three cycles (Cycle


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#1 – #3). Time profiles of the cell, glucose, and butanol concentrations in Cycle #1 are analyzed and used for validating the model initially developed from the batch and fed-batch experiments. As shown in Figure 8(A), there are significant deviations in the predictions of the cell, butanol and glucose concentrations (Figure 8(B) and (C)). The model tends to underestimate the cell and butanol concentration and overestimate the glucose concentration. Therefore, the model is further refined next by: i) identification of the critical parameters and ii) re-estimating the critical parameters using the data from Cycle #1.

4.2.1 Sensitivity analysis: identification of critical parameters In the sensitivity analysis of the model parameters, the reference trajectory is the concentration profile at the CSS resulting from successive simulation under given design and operation variables. The design variables are fixed as described in Section 4.3, and the operation variables are decided based on simulation of the initially constructed model, ensuring that the system reaches the CSS: the feed concentration is 150 g/L, the feeding rate is 4.7 L/h, and the circulation rate is 250 L/h. Compared with the reference cycle, the endpoint deviation from the reference trajectory for each change in the model parameters was calculated by using eq 27. Figure 9 shows the results of the sensitivity analysis for the concentrations of cell, butanol, and glucose. In Figure 9(A), µm, kd, PX, and iX have substantial effects on the cell concentration. Among the four parameters, the maximum growth rate, µm, is an intrinsic parameter and thus left unchanged; so kd, PX, and iX are selected as the significant parameters to be re-estimated. Figures 9(B) and (C) show that PX, and iX also can affect the butanol concentration as well as the glucose concentration considerably. In addition, αB is a significant parameter for the variation of butanol concentration, due to its direct relevance to the butanol production in eq 2 (Figure 9(B)). In the sensitivity analysis for the glucose concentration (Figure 9(C)), P’B, i’B, αGlu and βGlu are influential compared to other parameters. However, since the butanol inhibition term for glucose uptake and βGlu are multiplied in the second term


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in right hand side of eq 7, they have the same effect on the glucose concentration. For simple and efficient parameter re-estimation, αGlu and βGlu are chosen as significant parameters for the glucose concentration. In conclusion, six parameters, namely kd, PX, iX, αB, αGlu, and βGlu, are identified as the critical parameters in the proposed ESBR system.

4.2.2 Parameter re-estimation To improve the model accuracy in the proposed ESBR system, we carry out parameter reestimation for the 6 most sensitive parameters using Cycle #1 data. Then, the re-estimated parameters are evaluated with the dataset from Cycles #2 and #3. The re-estimated parameters are reported in Table 1. As a result, the SSRE of Cycle #1 decreases from 8.0272 to 0.3726, and the problems of overestimation of the glucose concentration and underestimation of the cell and butanol concentration are mitigated by the increase in αB and αGlu. However, the lowered glucose concentration and raised butanol concentration lead to a reduction in the specific growth rate and cell concentration accordingly. To compensate for this and reflect the stronger inhibition effect of cell mass concentration during cyclic operation, the values of PX and iX are reduced by 3 % and 54 % compared with the initially chosen values, respectively. The model, after being updated by the re-estimation of the selected parameters, is used for simulating Cycles #1, #2, and #3, and its predictions are compared to the experimental data, as well as predictions from the initially constructed model (Figure 10). As a result, the simulated results with the updated model show remarkable improvements in predicting the dynamics seen during the cyclic operation. Specifically, the SSREs of Cycle #2 and Cycle #3 are reduced from 18.1344 and 6.0179 to 1.0288 and 0.6864, respectively, a reduction by about 93 %. Through the parameter re-estimation of six critical parameters identified by the sensitivity analysis, a model has been obtained that is more consistent with the dynamic behaviors of the proposed ESBR system.


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Summarizing the results and putting them in perspective, the main contributions of this paper can be stated as below: 1. The ABE fermentation process coupled with an adsorption system has been investigated experimentally5,

8, 28

, but this is a first study that addresses the dynamic modeling targeted for the

continuous cyclic operation in a systematic manner. 2. The proposed ABE fermentation kinetic model is the first attempt to consider the inhibitory effects by biomass as well as butanol.29 From the results, it is verified that both inhibition effects are significant enough to justify their inclusion in the kinetic model for the ABE fermentation. 3. The proposed two-step procedure for the parameter estimation of the model yields a more accurate and consistent model for the cyclic operation of the proposed ESBR system. 4. In the proposed ESBR system, the continuous biobutanol production is achieved through the periodic switching of adsorption column. This study provides a continuous operation protocol for the industrial biofuel production based on the concept of in-situ product recovery by energy-efficient adsorption.

6. CONCLUSIONS A dynamic model of the proposed ESBR system is successfully developed by integrating the kinetic models of fermentation and adsorption in the mass balance equations. Due to the difficulty in obtaining data from an experimental ESBR system right from the beginning, the model parameters are first estimated using data from batch and fed-batch systems with ISBR, which can be performed more easily. However, the developed model is not readily applicable to the targeted ESBR system as different operating ranges are exposed during the cyclic operation of ESBR by adsorption, resulting in inaccuracies in the model prediction of butanol and glucose concentrations under the cyclic operation. Thus, the major parameters, identified by the sensitivity analysis at the cyclic steady state, are reestimated using cyclic operation data, which are obtained from a continuous fermentation experiment


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using an actual ESBR system designed based on the initial model. Through this, the accuracy of the model in terms of predicting the cyclic behaviors of the cell, butanol and glucose is greatly improved. The developed model serves as a basis for future design and operation and control studies of the ESBR system.

7. AUTHOR INFORMATION * Corresponding authors Prof. Jay H. Lee: E-mail address: [email protected] ; Tel.: 82-42-350-3926; Fax: 82-42-350-3910 † Co-first authors that equally contributed to this work †† His current address: Hydrogen Laboratory, New & Renewable Energy Research Division, Korea Institute of Energy Research (KIER), 152 Gajeong-ro, Yuseong-gu, Daejeon 305-343, South Korea

8. ACKNONOWLEDGEMENTS This work was supported by the R&D Center for Organic Wastes to Energy Business (under the Wastes to Energy Technology Development Program) funded by the Ministry of Environment, Republic of Korea (Project No.: 2013001580001), and the Advanced Biomass R&D Center (ABC) of Global Frontier Project funded by the Ministry of Science, ICT and Future Planning (ABC-2011-0031354).

SUPPORTING INFORMATION Supporting figure of experimental data from the batch fermentation. This information is available free of charge via the Internet at http://pubs.acs.org/.


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NOMENCLATURE Ai: concentration of component i by adsorption [g/L] Bi: adsorption-equilibrium constant for component i [L/g] Ci: concentration of component i [g/L] Ci,eq: concentration of component i at equilibrium [g/L] Cad,i: concentration of component i in adsorption column [g/L] Cr,i: concentration of component i in fermenter [g/L]

Crref,i : terminal concentration of component i of the reference cycle [g/L] Dr: dilution rate [h-1] F: flow rate of feed media [L/h] Fc,ad: flow rate of circulation from adsorption column to fermenter [L/h] Fc,r: flow rate of circulation from fermenter to adsorption column [L/h] iB: product inhibition constant to cell growth i’B: product inhibition constant to glucose consumption iX: cell concentration inhibition constant to cell growth kd: specific death rate of cell [h-1] ki: adsorption kinetic parameter for component i [/min] KI: substrate inhibition constant [g/L] KS: substrate saturation constant [g/L] m: mass of adsorbent [kg] ACS Paragon Plus Environment

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ms: specific maintenance coefficient [h-1] p: parameter

PB: butanol concentration at which cell growth stops [g/L] P’B: butanol concentration at which glucose consumption stops [g/L] PX: cell concentration at which cell growth stops [g/L] qi: amount of adsorbed component i per unit mass of adsorbent [g/kgadsorbent] qi,eq: amount of adsorbed component i per unit mass of adsorbent at equilibrium [g/kgadsorbent] qi,m: maximum adsorption capacity for component i per unit mass of adsorbent [g/kgadsorbent] rB: specific rate of butanol formation [h-1] rE: specific rate of ethanol formation [h-1] rS: specific rate of glucose consumption [h-1] S: concentration of substrate [g/L] Sr: concentration of substrate in fermenter [g/L] Sad: concentration of substrate in adsorption column[g/L] Sf: concentration of substrate in feed media [g/L] Sr: concentration of substrate in fermenter [g/L] Sad: concentration of substrate in adsorption column [g/L] tf : final time of one reference cycle for sensitivity analysis [h]

YB/s : stoichiometric yield coefficient of butanol [g/g] YE/s : stoichiometric yield coefficient of ethanol [g/g] ACS Paragon Plus Environment

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Yx / s : stoichiometric yield coefficient of cell [g/g] V: volume [L] Vad: volume of adsorption column [L] Vr: volume of fermenter [L] X: concentration of cell mass [g/L] Xr: concentration of cell mass in fermenter [g/L] Xad: concentration of cell mass in adsorption column [g/L]

Greek letter αi: yield coefficient for the production of component i βi: non-growth related effect of the cell mass on the production of component i [h-1] µg: specific growth rate of cell [h-1] µg,r: specific growth rate of cell in fermenter [h-1] µg,ad: specific growth rate of cell in adsorption column [h-1] µm: maximum growth rate of cell [h-1] µnet: net growth rate of cell [h-1] µnet,r: net growth rate of cell in fermenter [h-1] µnet,ad: net growth rate of cell in adsorption column [h-1]


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References 1.

Jones, D. T.; Woods, D. R., Acetone-butanol fermentation revisited. Microbiological Reviews

1986, 50, (4), 484. 2.

Ezeji, T. C.; Li, Y., Advanced product recovery technologies. Biomass to biofuels: strategies for

global industries. Wiley, Chichester 2010, 331-345. 3.

Vane, L. M., Separation technologies for the recovery and dehydration of alcohols from

fermentation broths. Biofuels, Bioproducts and Biorefining 2008, 2, (6), 553-588. 4.

Oudshoorn, A.; van der Wielen, L. A.; Straathof, A. J., Assessment of options for selective 1-

butanol recovery from aqueous solution. Industrial & Engineering Chemistry Research 2009, 48, (15), 7325-7336. 5.

Qureshi, N.; Hughes, S.; Maddox, I. S.; Cotta, M. A., Energy-efficient recovery of butanol from

model solutions and fermentation broth by adsorption. Bioprocess Biosyst Eng 2005, 27, (4), 215-22. 6.

Lin, X.; Wu, J.; Fan, J.; Qian, W.; Zhou, X.; Qian, C.; Jin, X.; Wang, L.; Bai, J.; Ying, H.,

Adsorption of butanol from aqueous solution onto a new type of macroporous adsorption resin: studies of adsorption isotherms and kinetics simulation. Journal of Chemical Technology and Biotechnology 2012, 87, (7), 924-931. 7.

Abdehagh, N.; Tezel, F. H.; Thibault, J., Adsorbent screening for biobutanol separation by

adsorption: kinetics, isotherms and competitive effect of other compounds. Adsorption-Journal of the International Adsorption Society 2013, 19, (6), 1263-1272. 8.

Yang, X.; Tsai, G.-J.; Tsao, G. T., Enhancement of in situ adsorption on the acetone-butanol

fermentation by Clostridium acetobutylicum. Separations Technology 1994, 4, (2), 81-92.


26

Page 27 of 40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

9.

Energy & Fuels

Van Hecke, W.; Kaur, G.; De Wever, H., Advances in in-situ product recovery (ISPR) in whole

cell biotechnology during the last decade. Biotechnol Adv 2014, 32, (7), 1245-55. 10.

Visioli, L. J.; Enzweiler, H.; Kuhn, R. C.; Schwaab, M.; Mazutti, M. A., Recent advances on

biobutanol production. Sustainable Chemical Processes 2014, 2, (1), 15. 11.

Park, C. H.; Geng, C. Q., Mathematical modeling of fed-batch butanol fermentation with

simultaneous pervaporation. Korean Journal of Chemical Engineering 1996, 13, (6), 612-619. 12.

Shukla, R.; Kang, W.; Sirkar, K. K., Acetone–butanol–ethanol (ABE) production in a novel

hollow fiber fermentor–extractor. Biotechnology and Bioengineering 1989, 34, (9), 1158-1166. 13.

Mariano, A. P.; Costa, C. B.; Maciel, M. R.; Maugeri Filho, F.; Atala, D. I.; de Angelis Dde, F.;

Maciel Filho, R., Dynamics and control strategies for a butanol fermentation process. Appl Biochem Biotechnol 2010, 160, (8), 2424-48. 14.

Eom, M. H.; Kim, W.; Lee, J.; Cho, J. H.; Seung, D.; Park, S.; Lee, J. H., Modeling of a

biobutanol adsorption process for designing an extractive fermentor. Industrial & Engineering Chemistry Research 2013, 52, (2), 603-611. 15.

Srivastava, A. K.; Volesky, B., Updated Model of the Batch Acetone-Butanol Fermentation.

Biotechnology Letters 1990, 12, (9), 693-698. 16.

Schoutens, G.; F Kossen, N., A simple model for the continuous production of butanol by

immobilized Clostridia: II: Clostridium species DSM 2152 on glucose and whey permeate. The Chemical Engineering Journal 1986, 32, (3), B51-B56. 17.

Mulchandani, A.; Volesky, B., Modelling of the acetone‐butanol fermentation with cell

retention. The Canadian Journal of Chemical Engineering 1986, 64, (4), 625-631.


27

Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

18.

Page 28 of 40

Park, C.-H.; Geng, C. Q., Mathematical modeling of fed-batch butanol fermentation with

simultaneous pervaporation. Korean Journal of Chemical Engineering 1996, 13, (6), 612-619. 19.

Shuler, M. L.; Kargi, F., Bioprocess engineering. Prentice Hall New York: 2002.

20.

Lee, E. Y., Kinetic analysis of the effect of cell density on hybridoma cell growth in batch

culture. Biotechnology and Bioprocess Engineering 2002, 7, (2), 117-120. 21.

Bai, F. W.; Anderson, W. A.; Moo-Young, M., Ethanol fermentation technologies from sugar

and starch feedstocks. Biotechnol Adv 2008, 26, (1), 89-105. 22.

Luedeking, R.; Piret, E. L., A kinetic study of the lactic acid fermentation. Batch process at

controlled pH. Journal of Biochemical and Microbiological Technology and Engineering 1959, 1, (4), 393-412. 23.

Bowles, L. K.; Ellefson, W. L., Effects of butanol on Clostridium acetobutylicum. Applied and

Environmental Microbiology 1985, 50, (5), 1165-1170. 24.

Wang, F.-S.; Sheu, J.-W., Multiobjective parameter estimation problems of fermentation

processes using a high ethanol tolerance yeast. Chemical Engineering Science 2000, 55, (18), 36853695. 25.

Croft, D. T.; LeVan, M. D., Periodic states of adsorption cycles—I. Direct determination and

stability. Chemical Engineering Science 1994, 49, (11), 1821-1829. 26.

Lee, S. Y.; Jang, Y.-S.; Lee, J. Y.; Jung, K. S.; Kim, J. H., Enhanced ethanol and butanol

producing microorganisms and method for preparing ethanol and butanol using the same. In 2008. 27.

Nielsen, D. R.; Prather, K. J., In situ product recovery of n‐butanol using polymeric resins.

Biotechnology and Bioengineering 2009, 102, (3), 811-821.


28

Page 29 of 40

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28.

Energy & Fuels

Wiehn, M.; Staggs, K.; Wang, Y.; Nielsen, D. R., In situ butanol recovery from Clostridium

acetobutylicum fermentations by expanded bed adsorption. Biotechnology progress 2014, 30, (1), 6878. 29.

Mayank, R.; Ranjan, A.; Moholkar, V. S., Mathematical models of ABE fermentation: review

and analysis. Critical Reviews in Biotechnology 2013, 33, (4), 419-447.


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List of Table

Table 1. Values of fermentation kinetic model parameters

List of Figures

Figure 1. (A) In-Situ Butanol Recovery (ISBR) System, (B) Ex-Situ Butanol Recovery (ESBR) System Figure. 2. Operation steps of ESBR by adsorption: (a) batch operation, (b) continuous operation (with the circulation between the fermenter and the adsorption column), (c) column switch and regeneration Figure 3. Schematic diagram with design and operation variables of (A) In-Situ Butanol Recovery (ISBR) System, (B) Ex-Situ Butanol Recovery (ESBR) System Figure 4. Effect of initial glucose concentration on the specific growth rate of C. acetobutylicum Figure 5. Experimental data and model fit of the 1st fed-batch fermentation experiment with ISBR: A) cell concentration, B) accumulated glucose consumption (concentration) C) accumulated butanol production (concentration), and D) accumulated ethanol production (concentration) Figure 6. Experimental data and model predictions of the 2nd fed-batch fermentation experiment with ISBR: A) cell concentration, B) accumulated glucose consumption (concentration) C) accumulated butanol production (concentration), and D) accumulated ethanol production (concentration) Figure 7. Concentration profiles of cell, glucose, butanol, and ethanol of the continuous fermentation experiment with the proposed ESBR system Figure 8. Comparison of experimental data and predictions by the initially developed model at Cycle #1: (A) cell concentration, (B) butanol concentration, and (C) glucose concentration Figure 9. Sensitivity analysis results of (A) cell concentration, (B) butanol concentration, and (C) glucose concentration


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Figure 10. Comparison between predictions of initially developed model and those of updated model at Cycle #1 - Cycle #3: (A) cell concentration, (B) butanol concentration, and (C) glucose concentration

Table 1. Values of fermentation kinetic model parameters Parameters (unit)

Feasible range

Estimated value

Re-estimated value

kd ( h-1)

0 – 0.5

0.0723

0.0902

PB (g/L)

10 – 20

12.57

iB

0 – 10

0.0159

PX (g/L)

5 – 10

9.806

9.499

iX

0 – 10

0.4583

0.2095

P’B (g/L)

10 – 30

20

i’B

0 – 10

6.636

αGlu

0 – 10

2.323

9.103

βGlu ( h-1)

0–5

1.844

0

αB

0–5

1.771

2.59

βB ( h-1)

0–5

0

αE

0–5

0

βE ( h-1)

0–5

0.0176


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M

Feed Media

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M

M

Feed Media

(A)

M

(B)

Figure 1. (A) In-Situ Butanol Recovery (ISBR) System, (B) Ex-Situ Butanol Recovery (ESBR) System


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Figure 2. Operation steps of ESBR by adsorption: (a) batch operation, (b) continuous operation (with the circulation between the fermenter and the adsorption column), (c) column switch and regeneration


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Figure 3. Schematic diagram with design and operation variables of (A) In-Situ Butanol Recovery (ISBR) System, (B) Ex-Situ Butanol Recovery (ESBR) System


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0.25

Specific growth rate (µ), hr-1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.20

0.15

0.10

0.05

0.00 0

20

40

60

80

100

Initial glucose concentration, g/L

Figure 4. Effect of initial glucose concentration on the specific growth rate of C. acetobutylicum


35

16

(A)

14 Cell conc. (Experimental data) Cell conc. (Fit of model) Butanol conc. (Experimental data)

Cell concentration, g/L

8

12 10

6

8 4

6 4

2 2 0

0 0

5

10

15

20

25

30

35

Butanol concentration in broth, g/L

10

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35

(C) 30 Experimental data Fit of model

25 20 15 10 5 0 0

5

10

Time, hr

Experimental data Fit of model

60

40

20

0 0

5

10

15

20

25

30

35

Accumulated ethanol produced (concentration), g/L

(B) 80

15

20

25

30

35

25

30

35

Time, hr

100

Accumulated glucose consumption, g

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Accumulated butanol produced (concentration), g/L

Energy & Fuels

3.0

(D) 2.5 Experimental data Fit of model 2.0

1.5

1.0

0.5

0.0 0

5

Time, hr

10

15

20

Time, hr

Figure 5. Experimental data and model fit of the 1st fed-batch fermentation experiment with ISBR: A) cell concentration, B) accumulated glucose consumption C) accumulated butanol production (concentration), and D) accumulated ethanol production (concentration)


36

9

(A)

Cell concentration, g/L

8

Experimental data Predicted value

7 6 5 4 3 2 1 0

5

10

15

20

25

30

35

35

(C)

30


25 20 15 10 5 0

0

5

10

(B) Experimental data Predicted value

100 80 60 40 20 0 0

5

10

15

20

25

30

35

Accumulated ethanol produced (concentration), g/L

140 120

15

20

25

30

35

25

30

35

Time, hr

Time, hr

Accumulated glucose consumption, g

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Energy & Fuels Accumulated butanol produced (concentration), g/L

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3.0

(D) 2.5


2.0

1.5

1.0

0.5

0.0 0

5

10

15

20

Time, hr

Time, hr

Figure 6. Experimental data and model predictions of the 2nd fed-batch fermentation experiment with ISBR: A) cell concentration, B) accumulated glucose consumption C) accumulated butanol production (concentration), and D) accumulated ethanol production (concentration)


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Energy & Fuels

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Figure 7. Concentration profiles of cell, glucose, butanol, ethanol, and acetone of the continuous fermentation experiment with the proposed ESBR system

Figure 8. Comparison of experimental data and predictions by the initially developed model for the (A) cell concentration, (B) butanol concentration, and (C) glucose concentration during Cycle #1


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Energy & Fuels

Figure 9. Sensitivity analysis results of (A) cell concentration, (B) butanol concentration, and (C) glucose concentration ACS Paragon Plus Environment

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Energy & Fuels

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Figure 10. Comparison between predictions of initially developed model and those of updated model over Cycle #1 to Cycle #3: (A) cell concentration, (B) butanol concentration, and (C) glucose concentration


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Dynamic Modeling of a Fermentation Process with ... - ACS Publications

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