Applying modern optimization techniques for prediction reaction kinetics of Iraqi heavy naphtha hydrodesulferization

Abstract EN

In this study, a powerful modern optimization techniques such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Artificial neural network (ANN) were applied to estimate the optimal reaction kinetic parameters for Heavy naphtha Hydrodesulferization (HDS), the hydrodesulferization unit located in AL-Daura refinery-Baghdad/Iraq.

The reactions was carried out in a fixed-bed reactor packed with Co-Mo/γ-Al2O3 catalyst and the operating was 315-400 °C temperature 35 bar Pressure and 0.5-2.1 hr-1 liquid hourly space velocity.

The result showed that hydrodesulferization of heavy naphtha follows the pseudo-first order reaction kinetics.

This study signifies that the reaction kinetic parameters calculated by Genetic Algorithm was found to be more accurate and gives the highest correlation coefficient (R2= 0.9507) than the other two methods.

ANN technology by using the topology of (3-3-1-1) provides an effective tool to simulate and understand the non-linear behavior of the process.

The model result showed very good agreement with the experimental data with less than 5%.

mean absolute error.