Abstract
Artificial neural networks (ANN) are currently being explored in various engineering fields as valuable tools for automatic model-building and knowledge acquisition. This technique was applied to model hydrodesulfurization of atmospheric gas oil in a mini-pilot trickle-bed reactor. Sulfur removal was measured as a function of temperature, pressure and liquid hourly space velocity (LHSV) for three sulfur feed concentrations. The potential of a two-stage process was also tested. A set of experimental data was used to teach a three-layer neural network. The capability of the artificial neural network to predict the performance was tested with a different set of data. The agreement between predicted and experimental values was good. Temperature, LHSV and staging of the process were determined to be important parameters, while pressure had a little effect over the range tested in this study.
Original language | English |
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Pages (from-to) | 907-911 |
Number of pages | 5 |
Journal | Fuel |
Volume | 75 |
Issue number | 7 |
DOIs | |
State | Published - 1 Jan 1996 |
Keywords
- Artificial neural networks
- Hydrodesulfurization
- Mathematical modelling
ASJC Scopus subject areas
- General Chemical Engineering
- Fuel Technology
- Energy Engineering and Power Technology
- Organic Chemistry