Title: Use of a neural-network-based approach for a reliable modelling of a distillation column

Authors: Yahya Chetouani

Addresses: Departement Genie Chimique, Universite de Rouen, Rue Lavoisier, 76821 Mont Saint Aignan Cedex, France

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.

Keywords: reliability; safety; nonlinear modelling; model reduction; neural networks; distillation column; product quality; process modelling.

DOI: 10.1504/IJMIC.2010.035281

International Journal of Modelling, Identification and Control, 2010 Vol.11 No.1/2, pp.71 - 78

Published online: 20 Sep 2010 *

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