Among different liquid biofuels that have emerged in the recent past, biobutanol produced via fermentation processes is of special interest due to very similar properties to that of gasoline. For an effective design, scale-up, and optimization of the acetone-butanol-ethanol (ABE) fermentation process, it is necessary to have insight into the micro- and macro-mechanisms of the process. The mathematical models for ABE fermentation are efficient tools for this purpose, which have evolved from simple stoichiometric fermentation equations in the 1980s to the recent sophisticated and elaborate kinetic models based on metabolic pathways. In this article, we have reviewed the literature published in the area of mathematical modeling of the ABE fermentation. We have tried to present an analysis of these models in terms of their potency in describing the overall physiology of the process, design features, mode of operation along with comparison and validation with experimental results. In addition, we have also highlighted important facets of these models such as metabolic pathways, basic kinetics of different metabolites, biomass growth, inhibition modeling and other additional features such as cell retention and immobilized cultures. Our review also covers the mathematical modeling of the downstream processing of ABE fermentation, i.e. recovery and purification of solvents through flash distillation, liquid-liquid extraction, and pervaporation. We believe that this review will be a useful source of information and analysis on mathematical models for ABE fermentation for both the appropriate scientific and engineering communities.

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Rice straw is one of the potential economic feedstock for biobutanol production through ABE fermentation. However, the rice straw hydrolysate-based fermentation medium needs to be supported with nutritional elements. In this study, an attempt is made to optimize the rice straw hydrolysate-based fermentation medium employing Clostridium acetobutylicum MTCC 481 using Taguchi design of experiments (DOE) statistical model. Initially, a set of 12 nutrient components viz. MgNO3·6H2O, FeNO3, NH4NO3, yeast extract, PABA, biotin, PABA + biotin mixture, CaCl2, KCl, NaCl, MgSO4 and CH3COONa were screened through classical (one-variable-at-a-time) method. Based on the results, four components (PABA, yeast extract, MgSO4 and CH3COONa) were found to have significant impact, and were further subjected to statistical optimization through Taguchi DOE method. These experiments revealed that RSH supported with 3 g L−1 of yeast extract and 4 mg L−1 PABA to RSH was the most optimum fermentation medium. Experiments using 2 L bioreactor with this optimum fermentation medium showed nearly complete utilization of soluble sugars with the production of 8.7 g L−1 of total solvents and 6 g L−1 of butanol. The experimental data were fitted to kinetic models reported in the literature to determine the kinetic parameters of the fermentation process. An interesting result was revealed from this analysis that the under optimized fermentation medium, the kinetic parameters for both shake flask and bioreactor level were similar. This essentially means that effect of scale of operation is rendered insignificant when fermentation medium is under optimum conditions.Electronic supplementary materialThe online version of this article (doi:10.1007/s13205-013-0120-x) contains supplementary material, which is available to authorized users.

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