OPTIMIZATION OF MICROALGAE PRODUCTION CONDITIONS USING GENETIC ALGORITHM AND RESPONSE SURFACE METHODOLOGY

Abstract

Renewable biofuels are required as a substitute to fossil-derived transport fuels, which are of limitedavailability and contribute to global warming. Microalgae are a potential source of renewable energy, andthey can be converted into energy such as biodiesel. The aim of this study was optimization of microalgaecultivation conditions in a Flat Plate Photobioreactor (FPPBR) for biodiesel production at pilot scale usingGenetic Algorithm (GA) and Response Surface Methodology (RSM). The simulations were done at photonflux density of 70, 95 and 120 μmolphotonsm -2s -1 growth rates of 0.1, 0.2 and 0.3 h-1 and biomassconcentration 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 growingmicroalgae 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 GAmethod can also be obtained at 70 μmolphotonsm -2s -1, growth rate of 0.3 h-1 and biomass concentration of3 gL-1. These results show that GA and RSM can be effectively used to optimize the cultivation conditionsof microalgae in photobioreactors. Validation of the model should be done by physical experiments andfurther research should be done to improve the model and simultaneously study more regressor variables.