Simulation and Multi-Objective Optimization of a Trickle-Bed Reactor for Diesel Hydrotreating by a Heterogeneous Model Using Non-Dominated Sorting Genetic Algorithm II

Abstract

Hydrotreating processes for diesel fuels are becoming increasingly important, owing to environmental regulations limiting sulfur and aromatic compounds, which are becoming more strict. Among investigations into hydrotreating modeling, most of the studies use a homogeneous model to simulate the reactor. However, for a real industrial reactor, that is not always an accurate assumption. Furthermore, reports of efforts to find the best operating conditions are very scarce. In this study, a heterogeneous model of three main reactionshydro-desulfurization, hydro-denitrogenation, and hydro-dearomatizationwas applied to simulate the hydrotreating reactor. The model results show a good agreement with literature experimental data. The effects of important operational parameters such as temperature, pressure, liquid hourly space velocity, and H 2 /oil ratio on various hydrotreating reactions are also evaluated. Finally, a multi-objective optimization based on non-dominated sorting genetic algorithm II is carried out to find the best operating conditions for a diesel fuel hydrotreating reactor.

A Dynamic Kinetic Model for Methanol to Light Olefins Reactions over a Nanohierarchical SAPO‐34 Catalyst: Catalyst Synthesis, Model Presentation, and Validation at the Bench Scale

“…) using the fourth‐order Runge–Kutta method and estimating rate equations. Also the optimization objective function was applied to minimize the fitness function .…”

“…Then, the natural logarithm of the mean rate constants at different space velocities was plotted against 1/T and the preexponential factor and activation energy for each reaction were calculated using the Arrhenius plot. A population size of 200 was chosen with mutation probability of 0.1 and crossover probability of 0.7 (by the trial and error method to obtain the best performance and minimum error) [2,[34][35][36][37]. Gaussian and arithmetic methods were selected as mutation and crossover rules [2,[34][35][36][37].…”

“…A population size of 200 was chosen with mutation probability of 0.1 and crossover probability of 0.7 (by the trial and error method to obtain the best performance and minimum error) [2,[34][35][36][37]. Gaussian and arithmetic methods were selected as mutation and crossover rules [2,[34][35][36][37]. The fitness function was equal to the amount of difference between mass fraction at an experimental and computational mode that was obtained by solving the reactor model (Eq.…”

“…(5)) using the fourth-order Runge-Kutta method and estimating rate equations. Also the optimization objective function was applied to minimize the fitness function [34][35][36][37]. Figure 4 shows Arrhenius plots of the reactions rate constants.…”

Simulating and Optimizing Hydrogen Production by Low-pressure Autothermal Reforming of Natural Gas using Non-dominated Sorting Genetic Algorithm-II

“…The Evolutionary Strategy (ES) has been used for the selection and optimization of heterogeneous catalytic materials [26][27][28] . Genetic Programming has been employed very rarely.…”