Application of Artificial Neural Network in Chemical Separation Process

Abstract

The chemical separation process is a necessary unit operation to obtain qualified purity products. The design and operation control of the separation device are very important. Due to the numerous parameters that affect the separation efficiency and product purity, it is very difficult to optimize by traditional methods. Artificial neural network (ANN) has strong fault tolerance because of its self-learning, self-organization, self-adaptive and strong nonlinear function approximation ability. ANN can be used to map the complex nonlinear relationship between dependent variables and independent variables, and can be used for the design and control of chemical separation devices. This paper summarizes the characteristics of artificial neural network and several important artificial neural network models, and discusses the application of artificial neural network in different chemical separation processes.