ARTIFICIAL NEURAL NETWORKS IN PROCESS ESTIMATION AND CONTROL

In this contribution, the suitability of the artificial neural network methodology for solving some process engineering problems is discussed. First the concepts involved in the formulation of artificial neural networks are presented. Next the suitability of the technique to provide estimates of difficult to measure quality variables is demonstrated by application to industrial data. Measurements from established instruments are used as secondary variables for estimation of the "primary" quality variables. The advantage of using these estimates for feedback control is then demonstrated. The possibility of using neural network models directly within a model-based predictive control strategy is also considered, making use of an on-line optimization routine to determine the future inputs that will minimize the deviations between the desired and predicted outputs. Control is implemented in a receding horizon fashion. Application of the predictive controller to a nonlinear distillation system is used to indicate the potential of the neural network based control philosophy.