Determining the Optimum Temperature Path Versus the Catalyst Working Time in a Trickle Bed Diesel Hydrodesulfurization Reactor

Abstract

This study consists of four main parts. First, a heterogeneous reactor model was developed to simulate a diesel hydrodesulfurization (HDS) reactor with catalyst deactivation. Second, operating conditions were investigated. Third, the simulation results from the first part were modeled using the response surface method and artificial neural networks (ANNs) to shorten the temperature path optimization time. Among the different modeling methods, the feed-forward ANN method employing the Bayesian Regularization (BR) training method with 10 neurons in the hidden layer demonstrated the highest accuracy. Finally, the temperature path of the trickle bed reactor was optimized. A three-dimensional curve depicting sulfur output content versus temperature and catalyst operation time was plotted using the most effective ANN approach as a fitness function. When the sulfur content met the Euro-6 requirement, the temperature path versus catalyst working period was optimized.