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All Journal Bulletin of Chemical Reaction Engineering & Catalysis
Reza Seif Mohaddecy
Catalytic Reaction Engineering Department, Catalysis and Nanotechnology Division, Research Institute of Petroleum Industry (RIPI), West Blvd., Azadi Sports Complex, P.O. Box 14665-137, Tehran
Chemical Engineering, Chemistry & Bioengineering Chemistry

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Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique Sepehr Sadighi; Reza Seif Mohaddecy; Ali Norouzian
Bulletin of Chemical Reaction Engineering & Catalysis 2015: BCREC Volume 10 Issue 2 Year 2015 (August 2015)
Publisher : Masyarakat Katalis Indonesia - Indonesian Catalyst Society (MKICS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.9767/bcrec.10.2.7171.210-220

Abstract

In this paper, a hybrid model for estimating the activity of a commercial Pt-Re/Al2O3 catalyst in an industrial scale heavy naphtha catalytic-reforming unit (CRU) is presented. This model is also capable of predicting research octane number (RON) and yield of gasoline. In the proposed model, called DANN, the decay function of heterogeneous catalysts is combined with a recurrent-layer artificial neural network. During a life cycle (919 days), fifty-eight points are selected for building and training the DANN (60%), nineteen data points for testing (20%), and the remained ones for validating steps. Results show that DANN can acceptably estimate the activity of catalyst during its life in consideration of all process variables. Moreover, it is confirmed that the proposed model is capable of predicting RON and yield of gasoline for unseen (validating) data with AAD% (average absolute deviation) of 0.272% and 0.755%, respectively. After validating the model, the octane barrel level (OCB) of the plant is maximized by manipulating the inlet temperature of reactors, and hydrogen to hydrocarbon molar ratio whilst all process limitations are taken into account. During a complete life cycle results show that the decision variables, generated by the optimization program, can increase the RON, process yield and OCB of CRU to about 1.15%, 3.21%, and 4.56%, respectively. © 2015 by Authors, Published by BCREC Group. This is an open access article under the CC BY-SA License (https://creativecommons.org/licenses/by-sa/4.0)
Co-Authors Ali Norouzian Ali Norouzian Sepehr Sadighi