Lipase-catalyzed synthesis of dilauryl azelate ester: process optimization by artificial neural networks and reusability study

Abstract

An application of artificial neural networks (ANNs) to predict the performance of a lipase-catalyzed synthesis for esterification of dilauryl azelate ester was carried out. The central composite rotatable design (CCRD) experimental data were utilized for training and testing of the proposed ANN model. The model was applied to predict various performance parameters of the enzymatic reaction conditions, namely enzyme amount (0.05–0.45 g), reaction time (90–450 min), reaction temperature (40–64 °C) and molar ratio of substrates (AzA : LA, 1 : 3–1 : 9 mol). The incremental back propagation (IBP), batch back propagation (BBP), quick propagation (QP), genetic algorithm (GA), and the Levenberg–Marguardt (LM) algorithms were used in the network. It was found that the optimal algorithm and topology were the incremental back propagation (IBP) and the configuration with 4 inputs, 14 hidden, and 1 output nodes, respectively.