Process Optimization in Biofuel Production Using Different Biomass

The huge usage of fossil fuels is responsible for global warming, climate change, and unprecedented weather pattern. The increasing demand for less-polluting energy sources has pointed out that the biomass is a suitable alternate for sustainable energy supply. The burning of biofuels does not contribute to net increase of carbon dioxide concentration in atmosphere because the carbon present in plant source is due to photosynthesis, a process in which plant utilizes atmospheric carbon dioxide to produce food. Biomass (agricultural residues) is mainly used in gasification process for direct heating of process equipment, biodiesel manufacture, bioethanol production, and electricity generation. Biofuels are classified as gaseous fuel, liquid fuel, and solid fuel. The plant-based biofuels are considered as second-generation biofuels but this green—fuels have the potential to compete with fossil fuels in terms of heating value. Since agriculture is practiced as primary occupation to meet the global food requirement, tons of biomass waste is generated. Biomass wastes could be used to produce environmentally friendly biofuels to meet increasing energy demand. The calorific value of biofuels is on par with conventional nonrenewable hydrocarbon fuels, so there is a need for new technologies and processes to enhance biofuel production. Optimization is a mathematical approach to find the maxima or minima of a function subjected to various constraints. In any process, the major objective of optimization is to minimize operating cost and maximize the process efficiency. The optimization principles are applied in transesterification process to maximize biodiesel production from triglycerides. The production of biodiesel is influenced by the process variables, namely, reaction temperature, catalyst weight percent, reaction time, and “alcohol-to-oil” molar ratio. A second-order quadratic model is developed between “percentage yield of biofuel” as dependent variable with “alcohol-to-oil” ratio, catalyst concentration, process time, and reaction temperature as independent variables. The optimum condition for biofuel production is determined through response surface methodology (RSM) using central composite design (CCD) and Box-Behnken design method. The effects of process variables on objective function are analyzed using three-dimensional response surface graphs and two-dimensional contour plots whereas the quality of fitted model is evaluated using analysis of variance (ANOVA), coefficient of determination (R2), and statistical properties of the model. The interactive effects and linear effects of process variables on output response are analyzed through the shape of contour curve and response surface graph.