Application of neural networks to chemical process control
An artificial neural network is used to model and control the pH of the erythromycin acetate salt. Experiments were mainly conducted to determine the time delay of chemical reactions at the Abbott Chemical Plant located in Barceloneta, Puerto Rico. The suggested methodology includes three main steps: (1) the cross-correlation function is used to detect time delay, (2) a feedforward neural network is used to model the input and output variables of a nonlinear dynamic process, and (3) an optimization technique is used to solve the control equation and implement the corrective action. The selected neural network algorithm works as an adaptive procedure. The implemented algorithm reads the last 60 observations from four variables to generate a recommendation for controlling the pH of the erythromycin acetate salt. (C) 1999 Elsevier Science Ltd. All rights reserved.
neural network chemical process adaptive control dynamic system hooke and jeeves (hj) ph control