INTELLIGENT MODELING IN THE CHEMICAL PROCESS INDUSTRY WITH NEURAL NETWORKS - A CASE-STUDY

Nowadays the increasing complexity of most processes increases the dem and for performant models. Most of these processes are highly non-line ar and dynamic ones, which require complex modelling techniques. Neura l networks are eligible modelling candidates for such processes, since they have the ability to map a variety of input-output patterns quite easily. Moreover certain types of networks (the so-called spatio-temp oral networks) can not only model spatial but also temporal patterns. Nevertheless a continuous search for improvement is mandatory. Therefo re in this paper combinations of spatio-temporal neural network types with other modelling techniques are discussed whilst applied to a comp lex problem from the chemical process industry, i.e. a polymerisation reactor. (C) 1998 Elsevier Science Lid. All rights reserved.