ARTIFICIAL NEURAL NETWORK ANALYSIS OF IMMOBILIZED LIPASE CATALYZED SYNTHESIS OF BIODIESEL FROM RAPESEED SOAPSTOCK
Refined vegetable oils are the predominant feedstocks for the production of biodiesel. However, their relatively high costs render the resulting fuels unable to compete with petroleum-derived fuel. Artificial neural network (ANN) analysis of immobilized Candida rugosa lipase (CRL) on chitosan catalyzed preparation of biodiesel from rapeseed soapstock with methanol was carried out. Methanol substrate molar ratio, enzyme amount, water content and reaction temperature were four important parameters employed. Back-Propagation algorithm with momentous factor was adopted to train the neural network. The momentous factor and learning rate were selected as 0.95 and 0.8. ANN analysis showed good correspondence between experimental and predicted values. The coefficient of determination (R2) between experimental and predicted values was 99.20%. Biodiesel conversion of 75.4% was obtained when optimum conditions of immobilized lipase catalysed for biodiesel production were methanol substrate molar ratio of 4.4:1, enzyme amount of 11.6%, water content of 4% and reaction temperature of 45°. Methyl ester content was above 95% after short path distillation process. Biodiesel conversion was increased markedly by neural network analysis.
KeywordsArtificial Neural Network Fatty Acid Methyl Ester Molecular Distillation Candida Rugosa Lipase Momentous Factor
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