Using Hybrid Neural Networks in Scaling up an FCC Model from a Pilot Plant to an Industrial Unit
Chemical Engineering and Processing 42 (2003) 697Á/713 www.elsevier.com/locate/cep Using hybrid neural networks in scaling up an FCC model from a pilot plant to an industrial unit G.M. Bollas b, S. Papadokonstadakis a, J. Michalopoulos a, G. Arampatzis a, A.A. Lappas b, I.A. Vasalos b, A. Lygeros a,* a School of Chemical Engineering, National Technical University of Athens, Zografou Campus, Heroon Polytechniou 9, Athens, GR-157 80, Greece b Chemical Process Engineering Research Institute (CPERI), Centre for Research and Technology Hellas (CERTH), P.O. Box 361, 57001 Thermi-Thessaloniki, Greece Received 13 May 2002; received in revised form 30 September 2002; accepted 30 September 2002 Abstract The scaling up of a pilot plant fluid catalytic cracking (FCC) model to an industrial unit with use of artificial neural networks is presented in this paper. FCC is one of the most important oil refinery processes. Due to its complexity the modeling of the FCC poses great challenge. The pilot plant model is capable of predicting the weight percent of conversion and coke yield of an FCC unit. This work is focused in determining the optimum hybrid approach, in order to improve the accuracy of the pilot plant model. Industrial data from a Greek petroleum refinery were used to develop and validate the models. The hybrid models developed are compared with the pilot plant model and a pure neural network model. The results show that the hybrid approach is able to increase the accuracy of prediction especially with data that is out of the model range.
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