Modeling and Control of Pressure in a Chloride Gas Plant by Neural Networks

Abstract This paper is concerned with an application of the neuro-PID controller architecture with a pressure control problem of a chemical plant which produces soda and chloride by electrolysis of salt water. In order to reduce the electrical cost, this plant adopts a switching method of electric current twice a day, which causes fluctuations of pressure inside electrolytic tanks. To stabilize the pressure in the tank, the convenient PID controllers have been devised. In this plant, we discuss the development of a backpropagation(BP) neural network(NN) control scheme to find PID gains. The simulation results indicate that neuro-control schemes are superior and more easily implemented.