Modeling of extractive fermentation processes for fuel ethanol production plays a crucial role when different process alternatives are being analyzed in the framework of conceptual process design, especially when process synthe­sis procedures are applied. Considering that technologies for ethanol production from lignocellulosic biomass are not currently mature, the analysis of different options intended to reduce lignocellulosic ethanol production costs is a task of great significance. In a previous work (Sanchez et al., 2006), the co-fermentation process integrated by means of reaction-separation approach was assessed. The objective of that work was to model the extractive fermentation process for fuel ethanol production from lignocellulosic biomass analyzing cultivation kinetics coupled with liquid extraction.

To describe the continuous process of extractive fermentation for fuel ethanol production, и-dodecanol was selected as an extracting agent (solvent). A feed aque­ous stream containing sugars and nutritive components is added to a CSTR where a solvent stream is continuously fed as well. Fed sugars are generated during the pretreatment of lignocellulosic biomass in which major polysaccharides are broken down into elementary sugars like hexoses (glucose) and pentoses (mainly xylose). Formed sugars are converted into ethanol in the reactor. Ethanol is distributed between aqueous and organic (solvent) phases, diminishing its concentration in the culture aqueous broth and allowing reduction of the product inhibition effect on the microorganisms. The ethanol-enriched solvent phase is continuously removed from the reactor through a decanting unit. This stream is sent to a flash unit in order to recover the obtained ethanol and to regenerate the solvent, which can be recycled to the CSTR.

With the aim of developing a rigorous model that describes both the fermen­tation and liquid-liquid extraction processes, the kinetics cultivation is coupled with an extraction model. The liquid-liquid equilibrium was described through an algorithm based on the mass balance equations developed for the isothermal flash in the case of two liquid immiscible phases. Activities of components in each phase were calculated by means of the UNIFAC model, since this model has demonstrated to be the most appropriate for description of equilibrium when two or more liquid phases are present for this case. This algorithm was integrated into the ModELL-R software, which was especially designed by the research group to which the authors of this book belong. The software couples two convergence algorithms (Newton-Raphson and False Position Method) in order to calculate the liquid fraction of each phase. ModELL-R was developed in Delphi package v7.0 (Borland Software Corp., Austin, TX, USA).

The kinetic model of alcoholic fermentation was taken from Leksawasdi et al. (2001; see Chapter 7, Case Study 7.1). This model describes the simultane­ous consumption by a recombinant strain of Z. mobilis of two main substrates contained in the lignocellulosic hydrolyzates: glucose and xylose. The following assumptions were considered for the development of the overall model of extractive fermentation:

• The substrate uptake, biomass formation, and product biosynthesis are car­ried out only in the aqueous phase; hence, no reactions occur in the organic (solvent) phase.

• Ethanol is the main component migrating to the solvent phase; small amounts of water can migrate to the organic phase depending on the solvent.

• No migration of substrates and biomass to the solvent phase takes place.

• Solvent is biocompatible with the microorganisms and does not have effect on the fermentation process.

• Stirring of bioreactor ensures total mixing between liquid phases and does not produce damage to the growing cells.

The configuration corresponding to continuous extractive fermentation involves the continuous feeding of culture medium and solvent to the reactor and the continuous removal of the liquid aqueous phase and solvent phase from the reactor in a separate way with the help of a decanter (see Figure 9.14). In this case, the flowrate (in L/h) of influent aqueous stream (FA) is greater than the flowrate of effluent aqueous stream (QA) because of the migration of ethanol to the sol­vent phase. Similarly, the flowrate of influent solvent stream (FE) is less than the flowrate of effluent solvent stream (QE). Mass balance equations representing this process are as follows:

where rX is the cell growth rate (in g/(L x h)), rS1 and rS2 are the glucose and xylose consumption rates, respectively (in g/(L x h)), and rP is the ethanol formation rate (in g/(L x h)); X, S1, S2, and P are the concentrations of cell biomass, glucose, xylose, and ethanol in the aqueous effluent from the bioreactor (in g/L), and X0, S10, S20, and P0 are the corresponding concentrations in the aqueous feed stream (in g/L); P* and P0* are the ethanol concentrations in the solvent effluent and in the solvent feed streams, respectively (in g/L). The balance can be applied for the case when the solvent contains small amounts of ethanol as a result of noncomplete regeneration of the extracting agent. This system of equations is nonlinear due to the equations describing the process kinetics and is solved through multivariate Newton-Raphson algorithm. For this, a constant solvent volume/aqueous volume ratio is assumed. The relationship between both effluent flow rates is fixed. The ethanol concentration in the solvent phase (P*) that is in equilibrium with ethanol concentration in the aqueous phase is determined using the distribution coefficient &EtOH as shown by equation (9.18), which is calculated by the algorithm for liquid — liquid equilibrium. The determination of all variables involved in the model is per­formed using the algorithm shown in Figure 9.15 incorporated into the software ModELL-R. For specified inlet aqueous dilution rate (DAi = FA/VA) and solvent feed flow rate/aqueous feed flow rate ratio (R = FE/FA), the program requires concentra­tions of cell biomass, substrates, and ethanol in feed streams.

The simulation of alcoholic extractive fermentation from biomass was performed with a glucose concentration of 100 g/L and an xylose concentration of 50 g/L in the feed aqueous stream. These concentrations correspond to those of lignocel — lulosic hydrolyzates. The behavior of this process for R = 2 in dependence of inlet dilution rate (DAi) is shown in Figure 9.16. The cell washout occurs at dilution rates near 0.33 h-1. These results were obtained for a solvent volume/aqueous volume ratio (VE/VA) of 2. Higher ethanol productivities are found in the range 0.25 to 0.30 h-1. In order to elucidate the best operating value of DAi, GAMS software (General Algebraic Modeling System, GAMS Development Corp., Washington, DC, USA) was used for maximizing total ethanol productivity. For this, a simple linear rela­tionship for Equation (9.18) was considered. The optimal DAi was 0.265 h-1.

The coupled algorithm was used for process simulation varying the solvent feed flow rate/aqueous feed flow rate ratio (R) for an inlet dilution rate of 0.265 h-1. Best results were obtained for values greater than 4 that correspond to an increased

amount of consumed substrates. Both total productivity and productivity for etha­nol recovered from solvent phase are approaching constant values. For R higher than 8, the model predicts the formation of homogeneous mixture without extrac­tion. The simulation of this process modifying both R and DAi shows that the zone of manipulating variables with higher productivities (inlet concentrations of glu­cose and xylose in the aqueous stream of 100 g/L and 50 g/L, respectively) corre­sponded to dilution rates near washout conditions, and to higher values of R within the range 1.29 to 7.9. If the concentration of both substrates in the feed stream is varied, the problem becomes more complex. Physically, the increase in substrate can be achieved as a result of evaporation of initial hydrolyzate obtained from bio­mass pretreatment. For this reason, the proportion of glucose and xylose in the inlet aqueous stream should be constant and equal to 2:1. Best values of total productiv­ity and ethanol productivity recovered from solvent phase correspond to an inlet concentration of total sugars of about 600 g/L. The simulation was carried out until the concentration of sugars was less than or equal to 600 g/L, which corresponds to maximum solubility of these sugars in water.

In this way, a model describing extractive co-fermentation was developed. This model allows for doing the analysis of this reaction-separation integration process in order to consider it in subsequent process synthesis methodologies. This is espe­cially valuable for such process synthesis approaches as the hierarchical decompo­sition (see Chapter 2) that requires the development of proper models for simulation of alternative configurations during each hierarchical level of analysis.

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(b)

FIGURE 9.16 Continuous extractive co-fermentation using я-dodecanol. Effect of inlet aqueous dilution rate (DAi) on (a) effluent concentrations of glucose (51), xylose (S2), etha­nol in aqueous phase (P), and ethanol in solvent phase (P*); (b) total ethanol produc­tivity (PrT), productivity for ethanol recovered from aqueous phase (PrA), productivity for ethanol recovered from solvent phase (PrE), and effluent concentration of cells (X). Concentration of sugars in feed aqueous stream: glucose, 100 g/L; xylose, 50 g/L.