DOI: https://doi.org/10.9767/bcrec.10.2.7171.210-220

Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique

Sepehr Sadighi  -  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, Iran, Islamic Republic of
*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, Iran, Islamic Republic of
Ali Norouzian  -  Chemical Engineering Faculty, Islamic Azad University of Mahshahr, P.O. Box 63519, Khouzestan,, Iran, Islamic Republic of

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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 BCREC UNDIP. All rights reserved.

Received: 27th July 2014; Revised: 31st May 2015; Accepted: 31th May 2015

How to Cite: Sadighi, S., Mohaddecy, R.S., Norouzian, A. (2015). Optimizing an Industrial Scale Naphtha Catalytic Reforming Plant Using a Hybrid Artificial Neural Network and Genetic Algorithm Technique. Bulletin of Chemical Reaction Engineering & Catalysis, 10(2): 210-220. (doi:10.9767/bcrec.10.2.7171.210-220)

Permalink/DOI: http://dx.doi.org/10.9767/bcrec.10.2.7171.210-220

 

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Keywords: Reforming; Heavy naphtha; Artificial neural network; Deactivation; Life model

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Section: Original Research Articles
Language : EN
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