Russell NTBakker HHChaplin RI2000-012000CONTROL ENGINEERING PRACTICE, 2000, 8 (1), pp. 49 - 590967-0661https://hdl.handle.net/10179/14812This paper presents the development of a modular neural network model of a three-effect, falling-film evaporator. The model comprises a number of sub-networks each modelling a specific element of the overall system. The modular structure was employed in order to provide benefits in terms of improved model training and performance. The performance of the modular neural model is demonstrated for long-range prediction by comparing it with process data, an analytical simulation and a linear ARX model. The results show that the modular neural model can satisfactorily predict over a horizon of arbitrary length and is suited for implementation within a predictive control scheme. Benefits in terms of model flexibility and interpretability are also discussed. (C) 2000 Elsevier Science Ltd. All rights reserved.49 - 59neural networkssimulationpredictionmodular modellingevaporatorsmodel-based predictive controlJournal article10.1016/S0967-0661(99)00123-94201Massey_Dark0102 Applied Mathematics0906 Electrical and Electronic Engineering0913 Mechanical Engineering