Artificial neural networks in process engineering

Abstract

Artificial neural networks are made up of highly interconnected 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. The concepts involved in the formulation of artificial neural networks are introduced. Their suitability for solving some process engineering problems is discussed and illustrated using results obtained from both simulation studies and recent applications to industrial process data. In the latter, neural network models were used to provide estimates of biomass concentration in industrial fermentation systems and of top product composition of an industrial distillation tower. Measurements from established instruments such as off-gas carbon dioxide in the fermenter and overheads temperature in the distillation column were used as the secondary variables for the respective processes. The advantage of using these estimates for feedback control is demonstrated. The possibility of using neural network models directly in model based control strategies is also considered. The range of applications is an indication of the utility of artificial neural network methodologies within a process engineering environment.