Use of neural networks for process control. Experimental applications

In this paper the problem of design and elaboration of artificial neural networks as direct process controllers is developed. The neural controller is a feedforward multi-layer network, and the controller design methodology is based on the modelling of the process inverse dynamics. The advantage of this method is that it is not necessary to perform initial closed-loop experiments with a classical controller to generate the learning data base. By this way, multivariable controllers can be easily developed, taking into account the dynamics and the interactions of the different control loops. The efficiency of such a control methodology is exemplified through its application to different chemical processes a semi-batch pilot plant chemical reactor a liquid-liquid extraction column a low pressure chemical vapour deposition reactor.