Abstract
Process development and continuous request for productivity led to an increasing complexity of industrial units. The main aim of this paper is to establish a reliable model of process behaviour both for the steady-state and unsteady-state regimes. Multilayer feedforward neural network in the form of non-linear-auto-regressive with exogenous input (MFNN-NARX) was proposed to predict the product quality of a distillation column. This study shows also another technique for neural model reduction into account the physical knowledge of the process. It shows the choice and the performance of the neural network in the training and test phases. An analysis of the inputs choice, time delay, hidden neurons and their influence on the behaviour of the neural estimator is carried out. Three statistical criteria are used for the validation of the experimental data. Based on these results, the best network MFNN-NARX is composed of one hidden layer of eight nodes and six exogenous inputs. This result shows the good generalisation of the developed reliable model.
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