Use of neural networks in the mathematical modelling of the enzymic synthesis of amoxicillin catalysed by penicillin G acylase immobilized in chitosan

This study focuses in the mathematical modelling of the enzymic synthesis of amoxicillin by the reaction of p-hydroxyphenylglycine methyl ester and 6-aminopenicillanic acid (6APA), catalyzed by penicillin G acylase (PGA) immobilized on glutaraldehyde-chitosan, at 25°C and pH 6.5. Previous work on the kinetics and mechanism of reaction showed that the use of neural networks seems to be an interesting alternative to simulate experimental data of antibiotic production. Therefore, two feedforward neural networks, with one hidden layer, were trained and used to forecast the rates of amoxicillin and p-hydroxyphenylglycine (POHPG) net production. First of all, some parameters that affect the network performed were investigated, such as the number of nodes between the input and hidden layers and the number of interactions during the learning phase. Afterwards, hybrid models that coupled artificial neural networks to mass-balance equations were used to reproduce the performance of batch reactors for the production of amoxicillin. This approach provided accurate results, within the range of substrate concentration studied.