(617b) Aveva Process Simulation-Oriented Thermodynamic Property Calculation for a Python Hydrodesulfurization Model

The global shift in crude oil quality, driven by an increase in heavy oil production, has required refineries to process denser feedstocks. These new feedstocks, characterized by high viscosities, significant impurities, and low hydrogen-to-carbon ratios, result in diminished distillate production, thus complicating the refining process [1-4]. Moreover, with the rising concerns about climate change, the industrial sector is expanding its production portfolio. The aim in such expansion is to enhance product quality and optimize operational efficiency taking into account the mitigation of pollutant emissions, such as sulfur [5]. In this context, the quest for solutions that reduce environmental impact and boost fossil energy source efficiency is gaining momentum. Consequently, the adoption of cleaner technologies and innovative, well-established processes like catalytic hydrotreating (HDT) is becoming crucial. These advancements aim to achieve more profitable, sustainable, and socially responsible practices [3,6-8].In light of these developments and challenges in the refining industry, our objective in this work is to leverage the integration between the Python programming environment and the Aveva Process Simulation (APS) software to develop a customized computer model for simulating the HDT process, with a focus on HDS.

Catalytic hydrotreating plays a vital role in enhancing the quality of various oil fractions, ranging from naphtha to vacuum residues. It serves as both a preliminary stage for conversion processes and the final stage in producing fuels that comply with stringent environmental standards, such as gasoline and diesel [3]. This process involves subjecting oil fractions to hydrogenation reactions in the presence of a heterogeneous catalyst, typically composed of metals like molybdenum and nickel. Among these reactions, hydrodesulfurization (HDS) is notable. In HDS, sulfur from organosulfur molecules is captured by the catalyst and reacts with hydrogen gas to form hydrogen sulfide (H2S) [6]. Effective operation of the HDT process requires adjusting and optimizing operating conditions based on feed stream properties and desired product characteristics, ensuring efficiency, safety, and economic viability [3,9,10]. However, the process's complexity often necessitates study and sizing using process simulators, which may not always meet industry demands, leading to gaps in development and optimization [3]. Despite available HDT models, their inflexibility to adapt to specific conditions, such as feedstock variations, compromises simulation accuracy and effectiveness.

In light of this, our work aims to leverage the integration between the Python programming environment and the Aveva Process Simulation (APS) software for simulating the HDT process at a pseudo-steady state, with a focus on HDS, as proposed in the literature [7, 3]. This approach enables the development of a customized and adaptable computer model, where Python is used to apply the mathematical model of a trickle-bed reactor and APS is employed to calculate the necessary thermodynamic properties and physicochemical parameters. With this, the result is an optimized HDS model capable of representing the specific conditions of the process, providing greater control over variables and parameters and enabling a more accurate representation of the involved reaction rates, mass transfers, and operating conditions.

Moreover, the use of APS provides the essential level of thermodynamic precision for simulation, enhancing the robustness of the model developed in Python. Consequently, the integration of the Python programming environment with APS software proves highly beneficial for modeling and simulating complex processes, such as HDS. Emphasizing the ease of implementing this methodology, the rapid integration between the platforms resulted in an effective and practical solution for detailed process analysis.

References

[1] Bai, Y.; Lian, Y.; Zhao, J.; Cao, Z.; Sun, J.; Zhang, H. Thermal-Insulation and Temperature-Resistant Foamed Gel for Thermal Management of Heavy Oil Steam Flooding. J Mol Liq 2022, 359, 119304, doi:10.1016/j.molliq.2022.119304.

[2] Guan, D.; Chen, Z.; Chen, X.; Zhang, Y.; Qi, Q.; Shi, Q.; Zhao, S.; Xu, C.; Zhang, L. Molecular-Level Heavy Petroleum Hydrotreating Modeling and Comparison with High-Resolution Mass Spectrometry. Fuel 2021, 297, 120792, doi:10.1016/j.fuel.2021.120792.

[3] Pereira Neto, A.T.; Fernandes, T.C.R.L.; da Silva Junior, H.B.; de Araújo, A.C.B.; Alves, J.J.N. Three-Phase Trickle-Bed Reactor Model for Industrial Hydrotreating Processes: CFD and Experimental Verification. Fuel Processing Technology 2020, 208, 106496, doi:10.1016/j.fuproc.2020.106496.

[4] Saleh, T.A. Characterization, Determination and Elimination Technologies for Sulfur from Petroleum: Toward Cleaner Fuel and a Safe Environment. Trends in Environmental Analytical Chemistry 2020, 25, e00080, doi:10.1016/j.teac.2020.e00080.

[5] Chehadeh, D.; Ma, X.; Al Bazzaz, H. Recent Progress in Hydrotreating Kinetics and Modeling of Heavy Oil and Residue: A Review. Fuel 2023, 334, 126404, doi:10.1016/j.fuel.2022.126404.

[6] Ancheyta, J. Modeling and Simulation of Catalytic Reactors for Petroleum Refining; John Wiley & Sons, Inc.: Hoboken, New Jersey, 2011;

[7] Korsten, H.; Hoffmann, U. Three‐phase Reactor Model for Hydrotreating in Pilot Trickle‐bed Reactors. AIChE Journal 1996, 42, 1350–1360, doi:10.1002/aic.690420515.

[8] Leal, E.; Morales-Leal, F.; Ancheyta, J.; Alonso, F.; Torres-Mancera, P. Maximizing Data Analysis from Batch Reactor Experiments: Application to Catalytic Hydrotreating of Petroleum Distillates. Ind Eng Chem Res 2023, 62, 17631–17645, doi:10.1021/acs.iecr.3c02667.

[9] Ancheyta, J.; Alvarez‐Majmutov, A.; Leyva, C. Hydrotreating of Oil Fractions. In Multiphase Catalytic Reactors; Wiley, 2016; pp. 295–329.

[10] Chowdhury, R.; Pedernera, E.; Reimert, R. Trickle‐bed Reactor Model for Desulfurization and Dearomatization of Diesel. AIChE Journal 2002, 48, 126–135, doi:10.1002/aic.690480113.