Residual Neural Network-Based Observer Design for Continuous Stirred Tank Reactor Systems

Abstract

Continuous stirred-tank reactor (CSTR) is a common reactor in the chemical industry. The accurate monitoring of the concentration of the product and the internal temperature of the reaction vessel is a prerequisite for obtaining the desired product. This paper proposes a novel observer based on the residual neural networks for CSTR systems. Firstly, the mathematical model of the CSTR reaction is given, as well as detailed description of the structure and equations of the residual neural networks and the designed observer. Then the matrix method is used for nonlinear isolation of the residual neural networks and the theory of quadratic constraints for nonlinear activation functions of the neural networks is applied. Thus, the convergence of the proposed observer is analyzed theoretically in detail. Finally, the numerical simulations are implemented to demonstrate that the proposed residual neural network-based observer can quickly and accurately monitor the state changes during the CSTR reaction.