PNNARMA model: an alternative to phenomenological models in chemical reactors |
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| Affiliation: | 1. Department of Computer Science, Carlos III University of Madrid, Avda. de la Universidad, 30, 28911 Leganés, Madrid, Spain;2. European Commission, Joint Research Center, Institute for Systems, Informatics and Safety, TP 250, 21020 Ispra (VA), Italy;1. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD;2. Division of General Medicine, Department of Medicine, Columbia University, New York, NY;3. Division of Nephrology, Department of Medicine, Johns Hopkins University, Baltimore, MD;1. State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, 310027, PR China;2. State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, 310027, PR China;1. School of Chemical and Biological Engineering, Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, South Korea;2. Condensation Polymerization PJT, Corporate R&D, LG Chem Research Park, 188 Munji-ro, Yuseong-gu, Daejeon 34122, South Korea |
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| Abstract: | This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose. However, in this work it is shown that since the parameters of parallel identification models are estimated using multilayer feed-forward networks, the approximation of dynamic systems could be not suitable. The solution proposed in this work consists of building up parallel models using a particular recurrent neural network. This network allows to identify the parameter sets of the parallel model in order to generate process simulators. Hence, it is possible to guarantee better dynamic predictions. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The results suggest that parallel models based on the recurrent neural network proposed in this work can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits. |
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