Solving process engineering problems using artificial neural networks

Abstract

Artificial neural networks are made up of highly inter-connected layers of simple 'neuron' like nodes. The neurons act as nonlinear processing elements within the network. An attractive property of artificial neural networks is that given the appropriate network topology, they are capable of characterising nonlinear functional relationships. Furthermore, the structure of the resulting neural network based process model may be considered generic, in the sense that little prior process knowledge is required in its determination. The methodology therefore provides a cost efficient and reliable process modelling technique.