In this paper, we have attempted to make a comparative assessment of the three techniques for extraction of lipids from microalgal biomass, viz. Soxhlet extraction, the Bligh and Dyer method, and sonication. The approach is mechanistic in the sense that we have tried to determine the physical mechanism of extraction of lipids (cell disruption or diffusion across a cell wall) from microalgae using microscopic analysis of extracted biomass. We have also assessed the relative influence of the solvent (or extractant) selectivity and the intensity of convection in the medium on the overall lipid yield. None of the techniques used produced complete disruption of the cells, not even sonication. Thus, the prominent mechanism of lipid extraction was diffusion across a cell wall. Moreover, the selectivity of the solvent was found to be the most dominating factor in overall lipid extraction by diffusion than the intensity of bulk convection in the medium.
SUMMARYAmong several liquid alternative fuels, biobutanol has shown great promise because of its very similar properties to gasoline. This review provides an overview of research activities in acetone-butanol-ethanol (ABE) fermentation over the past two and a half decades. We have addressed seven important facets of ABE fermentation, viz. biochemistry, microbial cultures, alternative substrates, solvent recovery, fermentation mode and reactor designs, mathematical modeling, and economics. Development of mutant strains having higher yield, selectivity and tolerance to inhibition, and search for cheap alternative substrates for fermentation are most important thrust areas in biobutanol production. New and efficient processes have been developed for in situ removal and recovery of the ABE solvents. Several rigorous kinetic and physiological models for fermentation have been formulated, which form useful tool for optimization of the process. These research activities have been reviewed in this paper. Finally, we have summarized studies on the economic viability of large-scale ABE fermentation processes employing various process designs, substrates, and microbial cultures. With the use of new strains, inexpensive substrates, and superior reactor designs, economic potential of ABE fermentation has been found to be highly attractive. Research efforts in science, engineering, and economics of ABE fermentation have brought biobutanol close to commercialization as liquid alternate fuel.
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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