Optimisation of a continuous flash fermentation for butanol production using the response surface methodology

chemical engineering research and design 8 8 ( 2 0 1 0 ) 562–571

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Optimisation of a continuous flash fermentation for butanol production using the response surface methodology Adriano Pinto Mariano a,∗ , Caliane Bastos Borba Costa a , Dejanira de Franceschi de Angelis c, Francisco Maugeri Filho b, Daniel Ibraim Pires Atala b, Maria Regina Wolf Maciel a , Rubens Maciel Filho a a

Laboratory of Optimization, Design and Advanced Control, LOPCA School of Chemical Engineering - University of Campinas, UNICAMP, P.O. Box 6066, 13083-970 Campinas, SP, Brazil b Laboratory of Bioprocess Engineering, School of Food Engineering, University of Campinas, UNICAMP, Brazil c Department of Biochemistry and Microbiology, Institute of Biosciences, São Paulo State University, UNESP, P.O. Box 199, 13506-900 Rio Claro, SP, Brazil

a b s t r a c t Factorial design and response surface techniques were used in combination with mathematical modelling and computational simulation to optimise an innovative industrial bioprocess, the production of biobutanol employing the flash fermentation technology. A parametric analysis performed by means of a full factorial design at two levels determined the influence of operating variables on butanol yield and productivity. A second set of simulations were carried out based on the central composite rotatable design. This procedure generated simplified statistical models that describe butanol yield and productivity as functions of the significant operating variables. From these models, response surfaces were obtained and used to optimise the process. For a range of substrate concentration from 130 to 180 g/l, the optimum operating ranges ensure butanol productivity between 7.0 and 8.0 g/l h, butanol yield between 19 and 22%, substrate conversion above 90% and final butanol concentration around 25 g/l. © 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. Keywords: Flash fermentation; Biobutanol; Mathematical modelling; Optimisation; Response surfaces

1.

Introduction

Optimisation through factorial design and response surface analysis is a common practice in biotechnology. Usually this technique is applied for the optimisation of culture conditions and for the determination of optimal values for processing parameters, such as pH, temperature, aeration and feeding rate, among others. Nowadays the use of this technique to establish optimal process designs for industrial-scale fermentations, as well as for real time process integration purposes, is increasing and demonstrating to be efficient, especially when accompanied by the use of mathematical modelling and computational simulation (Silva et al., 1999; Kalil et al., 2000; Costa et al., 2001). For this reason, this approach is employed in this work for the optimisation of an industrial-scale fermentation for butanol production.

∗

Carrying out the butanol fermentation under optimised operating conditions is essential to run a biobutanol industry that can compete effectively with the current butanol derived from the petrochemical route, since the ABE fermentation, as normally the fermentation to produce butanol is called, is characterised by its low productivity. In this fermentation, acetone, butanol and ethanol (ABE) are produced in the ratio 3:6:1, with butanol being the major product. Product toxicity results in low butanol concentration in the reactor. In addition, the use of dilute sugar solution results in large process volumes. Mainly because of these problems and due to high costs related to the distillation of dilute product streams, the production of biobutanol on a commercial scale has been considered to be uneconomical (Ishizaki et al., 1999; Ezeji et al., 2007).

Corresponding author. Tel.: +5519 3521 3909. E-mail address: [email protected] (A.P. Mariano). Received 1 December 2008; Received in revised form 15 October 2009; Accepted 2 November 2009 0263-8762/$ – see front matter © 2009 The Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved. doi:10.1016/j.cherd.2009.11.002

chemical engineering research and design 8 8 ( 2 0 1 0 ) 562–571

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Nomenclature F0 F Fc Fp Fpu Fr Fre Fv Ki Pflash Psat i P0 Pi Pr Pv rx rs rPi S0 S Sr Sv Tferm Tflash V xi X0 X Xc Xp Xv yi i

feed broth flow rate (m3 /h) fermentor outflow rate (m3 /h) inlet flow rate of the flash tank (m3 /h) permeate flow rate (m3 /h) fermentor purge flow rate (m3 /h) liquid outlet flow rate of the flash tank (m3 /h) return stream flow rate (m3 /h) condensate vapour outlet flow rate of the flash tank (m3 /h) equilibrium constant flash tank pressure (kPa) vapour pressure of component i (kPa) inlet product concentration (g/l) fermentor product concentration (g/l) product concentration in the liquid outlet flow of the flash tank (g/l) product concentration in the vapour outlet flow of the flash tank (g/l) rate of cell growth (g/l h) rate of substrate utilisation (g/l h) rate of products production (g/l h) inlet substrate concentration (g/l) fermentor substrate concentration (g/l) substrate concentration in the liquid outlet flow of the flash tank (g/l) substrate concentration in the vapour outlet flow of the flash tank (g/l) fermentor temperature (◦ C) flash tank temperature (◦ C) total volume of the system (m3 ) liquid molar fraction of component (i) inlet biomass concentration (g/l) fermentor biomass concentration (g/l) biomass concentration in the inlet flow of the flash tank (g/l) biomass concentration in the permeate (g/l) biomass concentration in the vapour outlet flow of the flash tank (g/l) vapour molar fraction of component i activity coefficient

During the past two decades a significant amount of research has been performed on the development of alternative technologies designed to remove the butanol continuously from the fermentation broth (e.g. adsorption, gas stripping, ionic liquids, liquid–liquid extraction, pervapouration, aqueous two-phase separation, supercritical extraction, perstraction, etc.) (Ezeji et al., 2007). These recovery techniques reduce the effect of product inhibition allowing an increase in the substrate concentration, which results in a reduction in the process streams, higher productivity and lower distillation costs (Groot et al., 1992). In the process presented in this work, the continuous recovery of the butanol is carried out by the flash fermentation technology (Roffler et al., 1984; Silva et al., 1999; Costa et al., 2000, 2001; Costa and Maciel Filho, 2004; Atala, 2004; Mariano et al., 2008), in which the fermentor remains at atmospheric pressure and the broth is circulated to a vacuum chamber where butanol is continuously boiled off. A statistical methodology (factorial design) was applied to this process in order to

Fig. 1 – General scheme of the continuous flash fermentation process.

determine the most important operating variables for the optimisation of the process and the technique of surface response was used to find the best ranges of operating conditions that maximise butanol yield and productivity.

2. Process description and mathematical modelling Fig. 1 depicts the flash fermentation process, which is a continuous fermentor connected to a cell retention system (filter) and an in-line product recovery equipment (flash tank). The broth is continuously circulated through the cell retention system in order to increase the biomass concentration in the fermentor. Product toxicity is reduced by partially recovering the solvents in the flash tank, which is placed in the recirculation line between the cell filter and the fermentor. Cell bleeding is carried out in the purge stream in order to avoid excessive cell growth. Thus in the flash fermentation process there are three interconnected units, as follows: fermentor, cell retention system (tangential microfiltration) and vacuum flash vessel. The process starts as a conventional continuous fermentation until steady state is reached. Then, the flash tank separation system is turned on (i.e. vacuum is applied and pressure in the flash tank is reduced to 6.50 kPa), where a partial separation of the solvents and water mixture occurs. The liquid fraction (Fr) returns to the fermentor and the vapour fraction (Fv) after being condensed is combined with the purge (Fpu) and permeate (Fp) streams. These three streams (Fv, Fp, and Fpu) compose the final stream that is sent to distillation. The Fre stream (return) can be activated to regulate the inlet flow rate of the flash tank (Fc). The cell retention system allows the fermentor to be operated at high dilution rates without cell washout. The cells remain suspended in the liquid medium and a membrane is used as a means of preventing the cells from being removed with the out flow. For the mass balance, it is assumed that all cells are retained in the filter and solubilised compounds (substrate and products) freely pass through the membrane. Thus in the permeate, the cell concentration is equal to zero (Xp = 0) and the concentrations of solubilised compounds (S and P) are the same as in the fermentor.

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Originally this process was developed for the ethanol fermentation in laboratory scale at the Laboratory of Bioprocess Engineering (FEA/UNICAMP). This laboratory has experience in the study of ethanol fermentation processes. Among them, the process developed by Andrietta and Maugeri (1994) using mathematical modelling is distinguished and was implemented in several Brazilian distilleries (Guarani, Costa Pinto and others). The efficiency of the flash fermentation process was experimentally validated for the ethanol fermentation (Atala, 2004) and these experimental results are in agreement with previous works based on mathematical modelling and computer simulation, which demonstrated the technical feasibility of the continuous flash fermentation for the ethanol fermentation (Silva et al., 1999; Costa et al., 2000, 2001; Costa and Maciel Filho, 2004). Here the use of the flash fermentation technology is proposed for the ABE fermentation. The study is carried out through computer simulation of a mathematical model based on experimental kinetic parameters (Mulchandani and Volesky, 1986), thus ensuring the physical meaning of the results. Assuming constant volume of broth in the system, the mass balance equations for the continuous flash fermentation process are given by Eqs. (1)–(4), where the kinetic parameters were determined experimentally by Mulchandani and Volesky (1986), whose model was developed on the basis of the following assumptions: (1) (2) (3) (4) (5) (6) (7) (8)

Carbon substrate (glucose) limitation only. No nitrogen and nutrient limitation. Product inhibition. Acetic acid and butyric acid are intermediate metabolites and are reduced to acetone and butanol, respectively. Acetone and butanol are also synthesised directly from carbon substrate. Ethanol is synthesised from carbon substrate only. Fermentation is performed at (a) optimal temperature of 37 ◦ C; (b) optimal pH of 4.5; (c) under anaerobic conditions. All the cells (Clostridium acetobutylicum) are metabolically active and viable.

Fpu dX X = rx − V dt

(1)

dS Fp F0 Fpu = rs + S0 − S− S dt V V V

(2)

Fpu Fp Fv dPi = rPi − P − P − P dt V i V i V vi

(3)

Fig. 2 – Simulation results of glucose and products (butanol, acetone and ethanol). The vertical dashed line indicates the time when the flash tank separation system is turned on. and a multicomponent system (water, butanol, acetone, ethanol, acetic acid and butyric acid) was considered. The vapour–liquid equilibrium of the mixture was calculated by was calculated by Antoine’s equation Eq. (6); the value of Psat i and the value of the activity coefficient ( i ) by the UNIQUAC model. The results generated by the model of the flash tank were validated by the HYSYS® simulator. sat

Ki =

P yi = i i xi P

Eqs. (1)–(6) were solved using a Fortran program with integration with an algorithm based on the fourth order Runge–Kutta method. Figs. 2 and 3 show the dynamic behaviour of the flash continuous fermentation process. When the flash system is switched on, significant changes in the process parameters are observed. The concentration of butanol in the fermentor lowers, which represents a significant reduction in the inhibitory effect, and, as a consequence, biomass concentration increases, resulting in a higher conversion of substrate. At this point, it is worthwhile stressing the benefits of the proposed process for ABE products with emphasis on the butanol: due to the continuous removal of the fermentor products, butanol concentration lowers and the cell activity increases.

where i stands for butanol, acetone, ethanol, butyric acid and acetic acid. Fp = F0 − Fpu − Fv

(4)

The dynamics of the flash tank are much faster than that of the fermentation process, so a ‘pseudo’-steady state was assumed for the flash tank. The mass balance over the flash tank is given by Eq. (5). Fc = Fv + Fr

(5)

The modelling of the flash tank was based on the isothermal and isobaric evaporation model (Sandler, 1999)

(6)

Fig. 3 – Simulation results of biomass and the intermediates (acetic acid and butyric acid). The vertical dashed line indicates the time when the flash tank separation system is turned on.

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Table 1 – Operating conditions and steady-state concentration values of the continuous flash fermentation process. Parameter V F0 S0 Tferm Fpu F S Pbutanol Pacetone Pethanol Pacetic acid Pbutyric acid X Fp Fre Fc Xc Fv Pvbutanol Pvacetone Pvethanol Pvacetic acid Pvbutyric acid Fr Sr Xr Prbutanol Pracetone Prethanol Pracetic acid Prbutyric acid Tflash Pflash

Value

Unit m3 m3 /h g/l ◦ C m3 /h m3 /h g/l g/l g/l g/l g/l g/l g/l m3 /h m3 /h m3 /h g/l m3 /h g/l g/l g/l g/l g/l m3 /h g/l g/l g/l g/l g/l g/l g/l ◦ C kPa

400 100 145 37 25 443.3 8.0 8.1 4.3 0.69 3.69 4.29 29.6 43.3 0 400 32.8 31.7 75.8 47.3 4.61 5.94 4.14 368.3 8.7 35.7 2.27 0.56 0.36 3.50 4.30 37 6.50

The operating conditions considered for the ABE fermentation as well as steady-state concentration values are listed in Table 1. An industrial-scale process was taken into account for the design.

3.

Results and discussion

The evaluation of the flash fermentation process performance with respect to butanol yield and productivity and sugar conversion is very important. This evaluation was obtained by a set of simulations, in which changes in the inlet substrate concentration were performed (Fig. 4). As the most attractive characteristic of the technologies for continuous butanol recovery from the fermentation broth is the increase in productivity by processing a concentrated feed stream, the range of the substrate concentration chosen (100–300 g/l) was considerably higher than the typical maximum concentration found in batch processes (60 g/l) (Ezeji et al., 2007). Fig. 4 shows that with a variation in the substrate concentration from 100 to 150 g/l, the butanol yield achieves its maximum value of 20.5% and the butanol productivity

Fig. 4 – Effect of sugar concentration on butanol yield and productivity and on sugar conversion for F0 = 100 m3 /h, tr = 4.0 h, Fpu = 25 m3 /h, Fc = 400 m3 /h, and Pflash = 6.50 kPa. increases from 4.51 to 7.70 g/l h (for the operating conditions considered in Table 1). However, for the same substrate variation, the sugar conversion decreases from 98.5 to 92.9%. For concentrations above 150 g/l, yield and conversion decrease continuously and productivity is practically constant. Therefore, to obtain high productivity without significant losses in yield and conversion, the optimisation of the operating parameters must be carried out as described below. The flash fermentation process was optimised using the response surface methodology, which is a procedure that does not require model simplifications and the explicit formulation of an objective function. Initially, the parametric analysis was performed by means of a full factorial design at two levels (Cox et al., 1978) using five selected input variables: inlet substrate concentration (S0 ), residence time (tr), purge flow (Fpu), feed flow of the flash tank (Fc) and the flash tank pressure (Pflash ). The parameters or responses studied were butanol yield and productivity. Table 2 shows the coded factor levels and real values for the five variables. According to the factorial design at two levels, the total simulated experiments were 25 , i.e., 32 experiments plus a central point (Table 3). Only one central point is used since there are no experimental errors involved. The software Statistica (Statsoft, v. 7.0) was used to analyse the results. In the simulations the flow rate of the fresh medium (F0 ) was considered constant (100 m3 /h), so that variations in residence time led to variations in the system volume. Yield and productivity were defined as follows: Butanol yield =

Fpu.Pbut + Fp.Pbut + Fv.Pvbut S0

Butanol productivity =

(7)

Fpu.Pbut + Fp.Pbut + Fv.Pvbut V

(8)

Figs. 5 and 6 depict the Pareto charts for the effects calculated from the yield and productivity data (Table 3) considering a significance level of 95% (represented by the dotted line). A negative effect means that there is a decrease in the response

Table 2 – Coded factor levels and real values for full factorial design 25 . S0 (g/l) Level +1 Central point (0) Level −1

140 120 100

tr (h) 5.0 4.0 3.0

Fpu (m3 /h) 30 25 20

Fc (m3 /h) 200 150 100

Pflash (kPa) 6.60 6.49 6.38

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Table 3 – Full factorial design for the simulated experiments with the results for butanol yield and productivity. Trial 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

S0

tr

+1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 0

+1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 0

Fpu +1 +1 +1 +1 −1 −1 −1 −1 +1 +1 +1 +1 −1 −1 −1 −1 +1 +1 +1 +1 −1 −1 −1 −1 +1 +1 +1 +1 −1 −1 −1 −1 0

Fc +1 +1 +1 +1 +1 +1 +1 +1 −1 −1 −1 −1 −1 −1 −1 −1 +1 +1 +1 +1 +1 +1 +1 +1 −1 −1 −1 −1 −1 −1 −1 −1 0

Pflash +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 +1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 −1 0

Butanol yield (%) 16.6 19.3 13.8 17.5 18.7 20.0 16.0 19.1 12.7 17.3 10.9 14.9 13.9 19.0 12.4 16.8 16.9 19.3 14.7 18.4 18.3 19.6 16.5 19.2 12.6 17.3 11.2 15.4 13.5 18.6 12.3 17.0 17.4

Butanol prod. (g/l h) 4.65 3.87 6.44 5.85 5.23 3.99 7.48 6.35 3.56 3.45 5.11 4.97 3.90 3.79 5.77 5.6 4.72 3.87 6.86 6.13 5.12 3.92 7.70 6.41 3.53 3.47 5.25 5.13 3.78 3.73 5.76 5.66 5.21

parameter (yield or productivity) for every increase in the variable and vice-versa. Based on the estimated effects, the relevant variables in the process are S0 , tr, Fpu and Fc. The pressure in the flash tank (Pflash ) did not influence yield and productivity, thus this variable was rejected in the next step in the optimisation study. The insignificance of the flash tank pressure was also verified by Silva et al. (1999) when optimising the production of ethanol by the continuous flash fermentation. Although Pflash is not an important variable for the optimisation of the process, variations in its value may result in

Fig. 6 – Pareto chart—effects of the operating variables on the butanol productivity.

Fig. 5 – Pareto chart—effects of the operating variables on the butanol yield.

total or null vapourisation in the flash tank. For the operating conditions considered in this work, the pressure of the bubble and dew points are 6.60 and 6.35 kPa, respectively (this range is a function of the concentrations of the products and temperature). These values may also vary because of the presence of biomass, glucose and other substances that were not considered in the flash calculation. Atala (2004) worked with a pilot plant of ethanol fermentation with the process here considered and observed that, when the plant was running with water–ethanol solution, the pressure necessary for vapourisation was around 13.3 kPa. However, when sugar-cane molasses were present into the equipment, the pressure was of 26.7 kPa.

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Regardless the variations, it is very important to observe that to increase productivity and yield (Figs. 5 and 6), the feed flow of the flash tank (Fc) must be greater than the feed broth flow (F0 ). For this reason, the pressure must be set taking into account the vapour flow in the flash tank, which could be greater than the system feed flow (F0 ). In this way, to prevent the fermentor from emptying, in the next step of the optimisation, the pressure was set to 6.50 kPa. With this value, the vapour flow is lower than F0 (100 m3 /h), ranging between 7 and 11% of Fc. Analysing the effect of the purge flow (Fpu), lower values were responsible for an increase in the biomass concentration, which varied between 14 and 27 g/l. Thus, as expected, higher biomass concentrations resulted in gains of productivity and yield. The maximum biomass concentration in a conventional batch or continuous process is usually only a few grams per litre of broth (<4 g/l) (Mulchandani and Volesky, 1986; Ezeji et al., 2007). Since the rate of bacterial growth and product formation is proportional to the number of cells present, high cell densities resulted in increased volumetric productivity of the fermentor. Furthermore, Tashiro et al. (2005) demonstrated that it is possible to operate a continuous ABE fermentation with cell recycling (membrane filtration) without culture degeneration and with stability of ABE production. They also recommend operating the fermentor at a maximum cell concentration of approximately 30 g/l in order to avoid bubbling and broth outflow. The variables S0 and tr had opposite effects on yield and productivity, which means that the values that increase yield decrease productivity and vice-versa. In the case of S0 , for example, with an increase from the −1 level to the +1 level, i.e. from 100 to 140 g/l (Table 3, trials 1 and 2, respectively), yield decreased from 19.3 to 16.6% and productivity varied from 3.87 to 4.65 g/l h (gain of approximately 20%). Thus, to find the best combinations of variables that leads to high levels of productivity without significant losses of yield, a second

Table 4 – Coded factor levels and real values for the central composite rotatable design (CCRD) − 24 + star configuration. S0 (g/l) Level +2 Level +1 Central point (0) Level −1 Level −2

200 180 160 140 120

tr (h) 5.0 4.5 4.0 3.5 3.0

Fpu (m3 /h) 33 31 29 27 25

Fc (m3 /h) 500 425 350 275 200

Pflash = 6.50 kPa.

set of simulated experiments were carried out based on the central composite rotatable design (CCRD). To accomplish this goal, the optimisation of the process was performed by response surface analysis following the factorial design of 24 plus star configuration, with the most relevant variables (S0 , tr, Fpu and Fc) determined by the parametric analysis. Table 4 shows the coded factor levels and real values for the variables in this second study and Table 5 shows the whole set of simulated experiments with the results for butanol yield and productivity. Based on the parametric analysis, the feed flow of the flash tank (Fc) varied up to 500 m3 /h, since an increase in this variable has positive effects on butanol yield and productivity. Quadratic models (Eqs. (9) and (10)) were obtained from the second factorial design (Table 5). These equations represent, in a predictive way, the process performance in terms of butanol yield and productivity as functions of the most significant variables. Table 6 shows the analyses of variance (ANOVA) for the quadratic models. Both responses present high correlation coefficients (higher than 0.950). Furthermore, the F-test confirms the statistical significance of the models with 95% of confidence, since the calculated F values were more than 12 times greater than the listed ones. As a rule of thumb, a

Table 5 – Central composite rotatable design (CCRD) for the simulated experiments with the results for butanol yield and productivity. Trial 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

S0

tr

+1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 +1 −1 2 −2 0 0 0 0 0 0 0

+1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 +1 +1 −1 −1 0 0 2 −2 0 0 0 0 0

Fpu +1 +1 +1 +1 −1 −1 −1 −1 +1 +1 +1 +1 −1 −1 −1 −1 0 0 0 0 2 −2 0 0 0

Fc +1 +1 +1 +1 +1 +1 +1 +1 −1 −1 −1 −1 −1 −1 −1 −1 0 0 0 0 0 0 2 −2 0

Butanol yield (%) 18.8 19.8 16.8 19.1 19.9 20.1 17.9 19.6 15.2 18.9 13.8 17.3 16.0 19.7 14.6 18.1 15.3 19.1 20.1 16.5 17.6 19.6 20.2 14.4 18.5

Butanol prod. (g/l h) 7.53 6.17 8.65 7.65 7.97 6.24 9.21 7.83 6.10 5.87 7.11 6.90 6.41 6.13 7.51 7.26 7.63 5.73 6.44 8.78 7.04 7.84 8.10 5.76 7.42

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Table 6 – Analyses of variance of the models. Source of variation

Regression Residual Total Correlation coefficient (R) F listed value

Sum of squares

Mean square

Yield

Prod.

Yield

Prod.

95.97 1.934 97.90 0.953

23.00 0.134 23.14 0.986

6.85 0.193

1.64 0.013

Degrees of freedom

14 10 24

F-value Yield

Prod.

35.5

126.15

F14;10 = 2.86 (95%)

Table 7 – Optimum operating ranges (Fc and tr) as function of S0 (Fpu = 25 m3 /h; Pflash = 6.50 kPa). Yield, productivity and conversion were obtained by simulating the deterministic model. S0 (g/l)

tr = 3.5 h, Fc = 400 m3 /h Butanol yield (%) Butanol prod. (g/l h) Conversion (%)

110

120

130

140

18.4 5.78 97.9

18.9 6.47 97.3

19.4 7.21 96.2

19.8 7.93 94.3

S0 (g/l) 150

160

170

20.6 6.88 96.3

21.2 7.53 94.2

21.3 8.06 90.4

3

tr = 4.5 h, Fc = 450 m /h Butanol yield (%) Butanol prod. (g/l h) Conversion (%)

S0 (g/l) 180

190

200

22.0 7.93 92.4

22.0 8.35 87.3

21.5 8.58 80.2

3

tr = 5.0 h, Fc = 500 m /h Butanol yield (%) Butanol prod. (g/l h) Conversion (%)

Fig. 7 – Comparison between yield and productivity obtained by the statistical model and the deterministic model. model has statistical significance if the calculated F value is at least 3–5 times greater than the listed value (Kalil et al., 2000; Barros Neto et al., 2001). The very good prediction accuracy of the statistical models can be visualised with the comparison between yield and productivity predicted by the statistical model and that calculated by the deterministic model (Fig. 7). It is important to explain that the models represented by Eq. (9) and (10) are in coded form for the factors, i.e., when yield or productivity are to be calculated, coded values of S0 , tr, Fpu and Fc (values inside the range of −2.0 to 2.0) must be used, and not the real (decoded) values of these variables. Butanol yield = 18.50 − 1.133.S0 −0.325.S20 +0.767.tr−0.425.Fpu + 1.250.Fc − 0.300.Fc2 + 0.575.S0 .Fc

(9)

Butanol productivity = 7.420 + 0.427.S0 − 0.186.S20 − 0.599.tr − 0.174.Fpu + 0.527.Fc − 0.123.Fc2 + 0.281.S0 .Fc − 0.072.tr.Fc

(10)

Nowadays there is a strong interest in these kind of simplified models since they turn possible to make optimisation calculations (using optimisation algorithms like the Sequential Quadratic Programming, Genetic Algorithms, etc.) in a computational time suitable for real time process integration applications, while the time required for the optimisation problem solution, when the detailed deterministic model is used, hampers its utilisation in such kind of applications.

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Fig. 8 – Response surfaces for butanol yield (surface area) and butanol productivity (lines) in function of S0 (g/l), Fc (102 m3 /h) and tr (h): (a) tr = 3.5 h, (b) tr = 4.0 h, and (c) tr = 4.5 h. For all cases, Fpu = 25 m3 /h and Pflash = 6.50 kPa.

In this work, the quadratic models (Eqs. (9) and (10)) were used to generate response surfaces that were used to map the optimal region of the process (Fig. 8). The variations in butanol yield (surface area) and butanol productivity (lines) are shown as function of S0 , Fc and tr. The variable Fpu was set to its lower level (25 m3 /h) because this value ensures (for the operating conditions listed in Table 4) that the system is running with an optimised biomass concentration around 30 g/l (biomass

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concentration varied between 20 and 30 g/l for the simulations listed in Table 5). The response surfaces constitute a useful tool in the search of operating conditions that conciliate high substrate concentration (S0 ) with high levels of productivity and yield. In this sense, the variables tr and mainly Fc are the ones to be manipulated. According to the surfaces, values of Fc between 400 and 500 m3 /h are needed for the process to achieve maximum butanol yield (∼20%) with significant butanol productivity. It is important to stress that values of Fc higher than 500 m3 /h decrease yield and productivity and, besides, it can cause the fermentor to empty, since, mainly for these operating conditions, the vapour flow can be greater than the system feed flow (F0 = 100 m3 /h). Based on the response surfaces, optimum operating ranges (Fc and tr) that are dependent on the feed substrate concentration (S0 ), were determined (Table 7). For each condition, values of butanol yield, productivity and substrate conversion are shown. As it can be seen, as S0 increases, it is necessary to increase the residence time and the flash tank feed flow. For values of S0 above 180 g/l there is a significant decrease in substrate conversion (<90%) and, for this reason, there might be a considerable loss of raw material if the plant is operated with a sugar concentration above this value. Thus, when the plant is operated inside the optimum ranges (130 g/l < S0 < 180 g/l; 3.5 h < tr < 5.0 h; 400 m3 /h < Fc < 500 m3 /h; Fpu = 25 m3 /h; Pflash = 6.50 kPa), it is possible to obtain butanol productivity between 7.0 and 8.0 g/l h, butanol yield between 19.0 and 22.0% and substrate conversion above 90%. The decision on which operating conditions the process is more profitable must be based on an economic analysis covering the raw material cost and the selling price of the solvents. A simulation experiment using the deterministic model (Eqs. (1)–(6)) considering values for F0 , S0 , tr, Fc, Fpu and Pflash of 100 m3 /h, 140 g/l, 3.5 h, 450 m3 /h, 25 m3 /h and 6.50 kPa, respectively, resulted in butanol yield and productivity and substrate conversion of 19.7%, 7.87 g/l h and 95.8%, respectively. Moreover, the final concentration of butanol, acetone and ethanol were, respectively, 27.5, 16.0 and 1.9 g/l (total solvents concentration = 45.4 g/l), which represents 32.5% of yield of solvents and productivity of 13.0 g/l h. The solvents yield is inside the typical range of 29–33% for industrial-scale processes found throughout the world in the last century (Jones and Woods, 1986; Volesky and Votruba, 1992; Gapes, 2000). Productivity is one of the aspects that stand out on the process proposed in this work. For the sake of comparison, in the review available in Ezeji et al. (2007), the following values of productivity can be found: in batch reactors productivity is limited to less than 0.50 g/l h while in continuous reactors with cell recycle using a filter this value is up to 6.5 g/l h. Those authors also list the productivity obtained with other recovery technologies (gas stripping and pervapouration) operating in different modes. For these cases, the maximum value of productivity is 1.16 g/l h. Gains in productivity with the continuous flash fermentation process, as the ones here observed for the ABE fermentation, were also verified in previous works of ethanol fermentation (Silva et al., 1999; Costa et al., 2000, 2001; Costa and Maciel Filho, 2004). The results of these studies based on mathematical modelling and computer simulation were later verified in a laboratory-scale plant by Atala (2004). Other aspect to be emphasised about the flash technology is the final butanol concentration achieved (27.5 g/l for the given example). Phillips and Humphrey (1983) presented curves correlating the final butanol concentration with

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the energy consumption for distillation. Their calculations demonstrate that when the typical fermentor concentration of 12 g/l increases to 19 g/l, the costs with energy can be halved. It is known that the high costs required to recover the solvents from the broth by distillation are responsible in great part for the economic unfeasibility of the traditional ABE fermentation. Therefore, with the flash technology meaningful economic gains are expected, and moreover, environmental benefits can also be achieved, since lower quantities of wastewater would be generated by the distillation of a more concentrated broth. Finally, it is important to stress that the optimum operating ranges for the flash fermentation were obtained considering the fermentation of glucose by C. acetobutylicum. As soon as kinetic data are obtained considering other strains and substrates, such as lignocellulosic materials, further investigations must focus on determining the effects of these changes on the operation of the flash fermentation.

4.

Conclusions

A mathematical model based on fundamental mass balances and kinetic equations with experimental parameters was employed to simulate an innovative process for the butanol fermentation: the continuous flash fermentation. Using the methods of factorial design and response surface analysis, the influence of operating variables on butanol yield and productivity was investigated. The more relevant process variables were found to be the feed substrate concentration, residence time, purge flow rate and the feed flow of the flash tank. The pressure in the flash tank does not have any effect on yield and productivity. However, this is an important variable since, depending on its value, the vapour flow leaving the flash tank can be greater than the feed flow of the process, and consequently causing the fermentor to empty. The response surfaces analysis was a useful tool in the search of operating conditions that conciliated high substrate concentration with high levels of productivity and yield. The present methodology also makes it possible to generate simplified statistical models that predict both butanol yield and productivity when the system is disturbed in some way. This is useful not only for the additional knowledge of the process, but also for the potentials for process control and real time optimisation. The optimum operating ranges determined were 130 g/l < S0 < 180 g/l; 3.5 h < tr < 5.0 h; 400 m3 /h < Fc < 500 m3 /h; Fpu = 25 m3 /h; Pflash = 6.50 kPa. In this sense, the residence time and mainly the flash tank feed flow are the variables to be manipulated. As S0 increases, it is necessary to increase the residence time and the flash tank feed flow and, since the process has a cell recycling system, the purge flow is decisive to keep the system running with an optimised biomass concentration around 30 g/l. Thus, with the process being operated at optimised conditions, it is possible to obtain butanol productivity between 7.0 and 8.0 g/l h, butanol yield between 19.0 and 22.0% and substrate conversion above 90%. The high level of productivity achieved by the flash fermentation is an important factor that can turn this process into a promising technology for the biobutanol industry. Moreover, with a final butanol concentration greater then 20 g/l, it is expected a meaningful reduction in the distillation costs and environmental benefits due to lower quantities of wastewater generated by the process.

Acknowledgement The authors gratefully acknowledge the Fundac¸ão de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for the financial support (process numbers 2007/00341-1 and 2006/55177-9).

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