Modeling and optimization of enzymatic synthesis of caffeic acid phenethyl ester using artificial neural network and genetic algorithm

Khanzadi, Kobra (2014) Modeling and optimization of enzymatic synthesis of caffeic acid phenethyl ester using artificial neural network and genetic algorithm. Masters thesis, University of Zabol.

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Abstract

In this study, the reaction of caffeic acid and 2-phenyl ethanol in the presence of immobilized lipase from Candida Antarctica ( Novozym 435) was modeled and optimized in isooctane system using artificial neural network and genetic algorithm methods in order to obtain caffeic acid phenethyl ester. For this purpose, a 5- level -4 variable central composite rotatable design was used for modeling the enzymatic reaction using artificial neural network. Independent variables were temperature, time, molar ratio of substrates and enzyme amount; while the molar conversion of caffeic acid to ester was considered as a dependent variable. The Levenberg-Marquardt algorithm was used as learning algorithm of artificial neural network. Therefore, first, the modeling was carried out by artificial neural network and using Levenberg-Marquardt algorithm. The best model includes a network of four inputs, 10 neurons in hidden layer and one output (4-10-1). After modeling by artificial neural network, genetic algorithm was used for optimization of the model. The optimized conditions were: time: 60 hrs, temperature: 62˚C, molar ratio of substrates: 0321 (2-phenyl ethanol: caffeic acid) and enzyme amount: 322 PLU. Under these conditions, the actual and predicted of molar conversion of caffeic acid to ester were 21012 and 100054, respectively.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Modeling; optimization; enzymatic synthesis; caffeic acid phenethyl ester; artificial neural network; genetic algorithm
Subjects: Q Science > QD Chemistry
Depositing User: admin admin1 admin2
Date Deposited: 24 Oct 2016 08:52
Last Modified: 24 Oct 2016 08:52
URI: http://eprints.uoz.ac.ir/id/eprint/884

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