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. 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.
Highlights
The need for transportation fuels, especially gasoline, will show a steady growth in the future, contributing to demand petroleum processes
The parity plots for the research octane number (RON) and gasoline flow rate simulated by the artificial neural network (ANN) models are presented in Figures 3 and 4
A recurrent layer neural network model was developed for the simulation of an industrial fixed-bed catalytic naphtha reformer
Summary
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. 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. 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
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
