Predictive modeling for an industrial naphtha reforming plant using a recurrent-layer artificial neural network

CATEGORY: NAPHTHA
Journal of Engineering and Technology Research, Vol. 5(6), pp. 200-206, July 2013, DOI: 10.5897/JETR2013-0310
Predictive modeling for an industrial naphtha reforming plant using a recurrent-layer artificial neural network
Sepehr Sadighi and S. Reza Seif Mohaddecy*
Catalysis and Nanotechnology Division, Catalytic Reaction Engineering Department, Research Institute of Petroleum Industry (RIPI), Iran.
Abstract
In this research, a layered-recurrent artificial neural network (ANN) using back-propagation method was developed for simulation of a fixed-bed industrial catalytic-reforming unit, called Platformer. Ninetyseven data points were gathered from the industrial catalytic naphtha reforming plant during the complete life cycle of the catalyst (about 919 days). A total of 80% of data were selected as past horizontal data sets, and the others were selected as future horizontal ones. After training, testing and validating the model using past horizontal data, the developed network was applied to predict the volume flow rate and research octane number (RON) of the future horizontal data versus days on stream. Results show that the developed ANN was capable of predicting the volume flow rate and RON of the gasoline for the future horizontal data with the AAD% of 0.238 and 0.813%, respectively. Moreover, the AAD% of the predicted octane barrel against the actual values was 1.447%, confirming the excellent capability of the model to simulate the behavior of the under study catalytic reforming plant.
The present study was aimed at investigating the predictability of artificial neural network (ANN) models for an industrial naphtha reforming unit, called Platformer. This investigation discusses the use of mathematical models to find the behavior of the Platformer that is, yield and research octane number (RON) of the product from the existing data. This work can be significant because of considering the life of the catalyst or days on stream to predict the significant output variables.
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