Optimization of the Culture Medium for the Bioethanol Production Using Genetic Algorithm
Systematic methods and tools for managing the complexity
Process Simulation and Optimization (T4-9P)
Keywords: bioethanol, fermentation, Saccharomyces cerevisiae, optimization, genetic algorithm
Fuel ethanol, which has a higher octane rating than gasoline, accounts for approximately two-thirds of the world’s total annual ethanol production of more than 31 billion liter. The primary reasons for considering the expanded use of biofuel ethanol concern sustainable resource supply, enhanced security and the realization of macroeconomic benefits for rural communities and the economy at large. In the light of the aspects relating to oil exhaustion, a possible interruption in oil supply caused by political meddling, a superior net performance of biofuel ethanol in comparison with gasoline, and a significant reduction in pollution levels when fossil fuel is replaced by bioethanol as a sustainable energy source for fuel transportation, it can be expected that the demand for cheap, renewable substrates and cost-effective ethanol production processes will become ever more urgent. In this regard, plant biomass is the only foreseeable sustainable source of fuel bioethanol because of its relatively low cost and plentiful supply. Bioethanol is generally produced from the fermentation of sugar or materials that can be converted into sugar such as starch or cellulose. Commonly the process involves the conversion of biomass or starch crops into sugars, fermentation of the sugars into ethanol with the ethanol extracted in its final form by distillation.
One of the basic problems in the bioprocesses development is the optimization of initial experimental conditions. Considering that, there is a great deal of parameters that can influence the outcome of bioprocess, it is of general concern to find a good optimization method. Genetic algorithm (GA) is a stochastic optimization method based on the principals of evolution. It is quite commonly used for experimental optimization, but is also used for parameter estimation of the nonlinear systems. Using genetic algorithm, it has been experimentally verified, that process improvements can be achieved for both microbial and enzymatic conversions and for cell cultures, despite the large number of medium components under simultaneous consideration.
Experimental optimization using GA was applied to investigate influence of the composition of synthetic culture medium on fermentation of sugars in the bioethanol production process. Anaerobic fermentation of glucose by Saccharomyces cerevisiae was carried out in shake flasks. Effect of initial concentrations of medium composition on final ethanol concentration and ethanol productivity was analyzed.
Presented Wednesday 19, 13:30 to 15:00, in session Process Simulation and Optimization (T4-9P).
