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. Ninety-seven 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.
Key words: Modeling, simulation, artificial neural network, catalytic reforming, naphtha cycle life.
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