Artificial neural network (ANN) approach to optimize cultivation conditions of microalga Chlorella vulgaris in view of biodiesel production

Artificial neural networks (ANN) are widely used for the modelling of biological systems due to their ability to interpret non-linear relationships between input and output parameters. Hence, ANN can be used as a tool for optimization of biological systems. In the present study, ANN models were developed to simulate the concentrations of biomass, total lipid, unsaturated lipid and oleic acid of Chlorella vulgaris with cultivation time and pH level as inputs. The predicted values were strongly correlated with experimental values (R-2>0.97 for training data set, R-2>0.92 for validation data set) for all ANN models while indicating superior prediction accuracy over response surface methodology (RSM). The importance of pH and time on different metabolites are diverse as indicated by the Olden's plots, and thus, special attention should be given when optimizing the cultivation of C. vulgaris for biodiesel production. Multi-objective optimization algorithm along with ANN models was used to determine the optimum cultivation time and pH to produce biodiesel. The optimum oleic acid concentration of 745.21 mg/L was observed at pH 7.46 after 16 days of cultivation whereas the concentrations of biomass, total lipid and unsaturated lipid are 2663.34 mg/L, 1266.33 mg/L, and 1072.58 mg/L respectively