Abstract
•Hydrotreating process is applied on the whole crude oil and not on its heavy products.•Correlations for sulfur, vanadium, nitrogen and nickel removal, are developed.•Influence of T, H2 PP, and LHSV, and their interactions on the hydrotreating process is studied.•An NLP algorithm is developed to find optimum operating conditions for hydrotreating processes.
In recent years, research has been directed towards upgrading of heavy crude oil as unconventional oil recovery rises. Catalytic hydrotreating of crude oil is an important upgrading option that is rarely discussed in literature. The main aim of crude oil hydrotreating is to reduce adverse environmental effects caused by the concentration of contaminants, increase productivity and improve the quality of middle distillate cuts. In this work, Response surface methodology (RSM) has been adopted to study the influence of various process parameters, such as hydrogen partial pressure, temperature and liquid hourly space velocity on the hydrotreating performance. The significance of these parameters is identified by using the analysis of variance (ANOVA) method. The resulting correlations are capable of predicting sulfur, vanadium, nitrogen and nickel conversions that are in excellent agreement with experimental data. The operating parameters are optimized with LINGO optimization software to achieve maximum conversions of contaminants during hydrotreating processes.