Abstract
Renewable biofuels are required as a substitute to fossil-derived transport fuels, which are of limited availability and contribute to global warming. Microalgae are a potential source of renewable energy, and they can be converted into energy such as biodiesel. The aim of this study was optimization of microalgae cultivation conditions in a Flat Plate Photobioreactor (FPPBR) for biodiesel production at pilot scale using Genetic Algorithm (GA) and Response Surface Methodology (RSM). The simulations were done at photon flux density of 70, 95 and 120 µmolphotonsm-2s-1 growth rates of 0.1, 0.2 and 0.3 h-1 and biomass concentration of 2, 2.5 and 3 gL-1. Design expert software was used to generate the experimental design, statistical analysis, and regression models. The results showed that the optimum point for growing microalgae in a FPPBR with RSM method can be achieved at photon flux density of 70 µmolphotonsm-2s-1, growth rate of 0.3 h-1 and biomass concentration of 3 gL-1. On the other hand, optimum point with GA method can also be obtained at 70 µmolphotonsm-2s-1, growth rate of 0.3 h-1 and biomass concentration of 3 gL-1. These results show that GA and RSM can be effectively used to optimize the cultivation conditions of microalgae in photobioreactors. Validation of the model should be done by physical experiments and further research should be done to improve the model and simultaneously study more regressor variables.
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