Artificial Neural Network Control of Chemical Processes

Abstract

This paper presents an artificial neural network based control scheme for studying the control of continuous stirred tank reactor, distillation column and neutralization process and this method is compared with conventional proportional- /integral-derivative controller. A multi-layer back-propagation neural network is employed to model the nonlinear relationships between the inputs variables and controlled variables of processes in order to regulate the manipulating variables to a variety of operating conditions and acquire a more flexible learning ability. The robustness of this control structure is studied in the case of setpoint changes and load disturbances. The experimental results suggest that such neural controllers can provide excellent setpoint-tracking and disturbance rejection. The neural network based control has higher speed of response and the offset has a smaller average value than that of the conventional controller. The control action based on the neural network controller shows less oscillation and an improvement in the controlled variables stabilization time with respect to the conventional controller and gives a better control performance.

Keywords

• artificial neural network controller
• reverse model controller
• continuous stirred reactor
• neutralization process
• conventional controller