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Revista de Chimie (Rev. Chim.), Year 2007, Volume 58, Issue 11,





Neural Network Based Modelling of Ethyl-2-Chloro-Phenoxy
Acetate Chlorosulphonation Process

Abstract:

The most difficult and costly step in the industrial manufacturing of the Romener® drug is the chlorosulphonation of ethyl 2-chloro-phenoxyacetate, which is influenced by three main parameters: molar ratio between the reagents, temperature and time. In order to establish the optimum reaction conditions, a planned factorial experiment of first-degree order was accomplished, in which the real values of the parameters and their limits of variation were chosen arbitrarily. Feedforward neural networks with a single hidden layer were used in direct and inverse modeling of the process, to predict the yield of the reaction for different reaction conditions and the reaction conditions for a pre-established yield. The neural modeling of the cholorosulphonation process enabled to settle the optimum values of the parameters for a maximum yield in ethyl 2-chloro-4-chlorosulphonyl-phenoxyacetate. A real and complete characterization of the chlorosulphonation process was accomplished by neural network based modeling. Keywords: chlorosulphonation, ethyl 2-chloro-4-chlorosulphonyl-phenoxyacetate, Romener®, neural networks, mathematical modeling


Issue: 2007, Volume 58, Issue 11
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