Modular modelling of an evaporator for long-range prediction

This paper presents the development of a neural network model of a pilot-scale, three-effect falling-film evaporator for use in a model predictive control system. The data used in its development are from a simulation model of the evaporator. The approach taken in the neural network modelling is to divide the full model into a group of sub-networks, each modelling a specific element of the overall system. The model decomposition is determined through prior knowledge of the system. Localised computation is also used within the sub-networks to simplify the model further. The modular nature of the model gives it the capability of representing theoretical information through its topology as well as empirical information through the weights of the sub-networks. The performance of the modular evaporator neural network model is demonstrated for n-step-ahead prediction by comparing it with the analytical model of the evaporator. The results show that the model can perform satisfactory long-range predictions and hence is well suited for implementation within a model predictive control scheme. (C) 1997 Elsevier Science Limited.