Synopsis
In this chapter, 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 for the modelling of dynamic (time dependent) systems 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 control strategy is also considered, making use of an on-line optimisation routine to determine the ‘optimal’ settings for standard industrial controllers. Application of the control algorithm to a nonlinear distillation system is used to indicate the potential of the neural network based control philosophy.
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Willis, M.J., Montague, G.A., Peel, C. (1995). On the Application of Artificial Neural Networks to Process Control. In: Murray, A.F. (eds) Applications of Neural Networks. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-2379-3_8
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DOI: https://doi.org/10.1007/978-1-4757-2379-3_8
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