Modeling the Tennessee Eastman chemical process reactor using bio-inspired feedforward neural network (BI-FF-NN)

This study explores the application of bio-inspired algorithms (BIAs) in training artificial neural networks (ANNs) in the area of the nonlinear chemical process modeling. Motivated by the increasing complexity and operational efficiency of chemical processes, the need for schemes that can improve model identification of highly nonlinear systems is demanded. As a case study, the Tennessee Eastman (TE) chemical reactor problem is considered. The key issue is to find a particular architecture for an ANN that best model the TE chemical process. We propose the use of BIAs of bat algorithm (BA), firefly algorithm (FA), and artificial bee colony (ABC) algorithm as mechanisms to automatically update the synaptic weights of ANN. These algorithms were conducted to increase the ability of ANN to adapt the dynamic aspect of the TE process and prevent trapping into a local optimum. The proposed modeling framework was devised with extensive experiments and statistical analysis to illustrate the usability and suitability of the entire modeling and identification procedures. The results were explored with a discussion using the mean square error (MSE) and the variance account for (VAF) criteria to assess the degree of identification performance of the TE case study model. The reliability of the presented models was compared with previously developed models for the same subsystems of the TE reactor using competitive, intelligent approaches. The comparative results show that the proposed approach provides superior modeling performance and outperforms its competitors.