Identification of a chemical process reactor using soft computing techniques

Abstract

This paper discusses the application of artificial neural networks (ANNs) in the area of identification and control of nonlinear dynamical systems. Since chemical processes are getting more complex and complicated, the need of schemes that can improve process operations is highly demanded. ANNs are capable of learning from examples, perform non-linear mappings, and have a special capacity to approximate the dynamics of nonlinear systems in many applications. This paper describe the application of neural network for modeling reactor level, reactor pressure, reactor cooling water temperature, and reactor temperature problems in the Tennessee Eastman (TE) chemical process reactor. The potential of neural network technology in the process industries is great. Its ability to model process dynamics makes it powerful tool for modeling and control processes. A comparison between the applications of ANNs to model the TE plant is compared with other soft computing techniques like fuzzy logic (FL) and adaptive neuro-fuzzy inference systems (ANFIS).