Evolutionary artificial neural network for temperature control in a batch polymerization reactor

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Francisco Javier Sánchez-Ruiz https://orcid.org/0000-0001-6896-5798
Elizabeth Argüelles Hernandez https://orcid.org/0009-0006-7185-5518
José Terrones-Salgado https://orcid.org/0000-0003-0393-811X
Luz Judith Fernández Quiroz https://orcid.org/0009-0002-6435-6551


The integration of artificial intelligence techniques introduces fresh perspectives in the implementation of these methods. This paper presents the combination of neural networks and evolutionary strategies to create what is known as evolutionary artificial neural networks (EANNs). In the process, the excitation function of neurons was modified to allow asexual reproduction. As a result, neurons evolved and developed significantly. The technique of a batch polymerization reactor temperature controller to produce polymethylmethacrylate (PMMA) by free radicals was compared with two different controls, such as PID and GMC, demonstrating that artificial intelligence-based controllers can be applied. These controllers provide better results than conventional controllers without creating transfer functions to the control process represented.