Design of Artificial Neural Network for Prediction of Hydrogen Sulfide and Carbon Dioxide Concentrations in a Natural Gas Sweetening Plant

• Autorzy: Alardhi Saja Mohsen , Al-Jadir Thaer , Hasan Ahmed Mudheher , Jaber Alaa Abdulhady , Al Saedi Laith Majeed

Identyfikatory

DOI

10.12912/27197050/157092

• Języki publikacji: EN

Abstrakty

EN

Gas sweetening is a fundamental step in gas treatment processes for environmental and safety concerns. One of the most extensively used and largely recognized solvents for gas sweetening is methyl diethanolamine (MDEA). One of the most crucial metrics for measuring the effectiveness of gas treatment units is the amount of acid gas that has been treated with MDEA solution. As a result, it should be regularly monitored to avoid operational issues in downstream processes and excessive energy consumption. In this study, the artificial neural network (ANN) approach was followed to predict the H₂S and CO₂ sour gases concentrations of sweetening process. The model was built using dataset gathered from a real operation plant in Iraq, collected from February 2019 to February 2020, and used as input to the neural network. The data include H₂S and CO₂ concentrations of the feed gas, temperature, pressure, and flow rate of the unit. The designed ANN model showed good accuracy in modeling the process under investigation, even for a wide range of parameter variability. The testing outcomes demonstrated a high coefficient of determination (R²) of greater than 0.99, while the overall training performance showed a low mean squared error (MSE) of less than 0.0003.

Słowa kluczowe

EN

natural gas; dataset; ANN modeling; sour gas; absorption; sweetening process; MDEA; methyldiethanolamine

• Wydawca: Lubelski Oddział Polskiego Towarzystwo Inżynierii Ekologicznej ; Lublin University of Technology ; WNGB Wydawnictwo Naukowe
• Czasopismo: Ecological Engineering & Environmental Technology
• Rocznik: 2023
• Tom: Vol. 24, iss. 2
• Strony: 55--66
• Opis fizyczny: Bibliogr. 24 poz., rys., tab.

Twórcy

autor

Alardhi Saja Mohsen

• Nanotechnology and Advanced Materials Research Center, University of Technology – Iraq, Baghdad, Iraq

• https://orcid.org/0000-0003-1828-9140

autor

Al-Jadir Thaer

• Environment Research Center, University of Technology – Iraq, Baghdad, Iraq

• https://orcid.org/0000-0002-3634-0264

autor

Hasan Ahmed Mudheher

• Control and Systems Engineering Department, University of Technology – Iraq, Baghdad, Iraq

• https://orcid.org/0000-0002-5056-7465

autor

Jaber Alaa Abdulhady

• Mechanical Engineering Department, University of Technology – Iraq, Baghdad, Iraq

• https://orcid.org/0000-0001-5709-195X

autor

Al Saedi Laith Majeed

• Missan Oil Company, Amarah, Iraq

Bibliografia

• 1. Anagnostis A.P., Elpiniki, B., Dionysis. 2020. Application of Artifi cial Neural Networks for Natural Gas Consumption Forecasting. Sustainability, 12, 6409.
• 2. Al Jarrah A. 2022. Using Jordanian Natural Zeolite for Capturing Hydrogen Sulfide Gas. Ecological Engineering & Environmental Technology, 23(5), 164–168.
• 3. Al Jarrah A.M. 2022. Using Jordanian Natural Zeolite for Capturing Hydrogen Sulfide Gas. Ecological Engineering & Environmental Technology, 23(5), 164–168.
• 4. Alardhi S., Jabbar N., AL-Jadir T., Ibrahim N. K., Dakhil A.M., Al-Saedi N.Dh., Al-Saedi H. Dh., Adnan M. 2022. Artificial neural network model for predicting the desulfurization efficiency of Al-Ahdab crude oil, AIP Conference Proceedings, 2443, 030033.
• 5. Al-Jadir T., Siperstein F.R. 2019. Modeling and simulation of adsorption of methane, ethane, hydrogen sulfide and water from natural gas in (FP)Yeu Metal–Organic Framework. IOP Conference Series: Materials Science and Engineering, 579, 012020.
• 6. Alqaheem Y. 2021. A simulation study for the treatment of Kuwait sour gas by membranes. Heliyon, 7, e05953.
• 7. Burr B., Lyddon L.G. 2008. A Comparison of Physical Solvents for Acid Gas Removal.
• 8. Georgiadis A., Charisiou N. D., Goula M. A. 2020. Removal of Hydrogen Sulfide from Various Industrial Gases: A Review of The Most Promising Adsorbing Materials. Catalysts, 10, 521.
• 9. Kadhim W., Al-Jadir T., Albrazanjy M.G., Al-Rubaiey N.A., Mohammed Dakhil A., Al-Saedi N.D., Rahima M.H.A. 2021. Sulphide pollutants elimination and degradation in petroleum wastewater by ozonation process. IOP Conference Series: Earth and Environmental Science, 779, 012086.
• 10. Koolivand Salooki M., Abedini R., Adib H., Koolivand H. 2011. Design of neural network for manipulating gas refinery sweetening regenerator column outputs’, Separation and Purification Technology, 82, 1–9.
• 11. Liu Y., Liu Z., Morisato A., Bhuwania N., Chinn D., Koros W.J. 2020. Natural gas sweetening using a cellulose triacetate hollow fiber membrane illustrating controlled plasticization benefits. Journal of Membrane Science, 601, 117910.
• 12. Mokhatab S., Poe W.A. 2012. Handbook of Natural Gas Transmission and Processing. Gulf Professional Publishing: Boston.
• 13. Okorie A., Akpabio J.U., Dosunmu A. 2020. Artificial neural network model for predicting the density of oil-based muds in high-temperature, high-pressure wells. Journal of Petroleum Exploration and Production Technology, 10, 1081–1095.
• 14. Pacheco M., Rochelle G.T. 1998. Rate-Based Modeling of Reactive Absorption of CO2 and H2S into Aqueous Methyldiethanolamine. Industrial & Engineering Chemistry Research, 37, 4107–4117.
• 15. Pandey M. 2005. Process Optimization in Gas Sweetening Unit – A Case Study. In International Petroleum Technology Conference.
• 16. Rafati N. 2019. A Novel Low-Cost Process for Sour Gas Sweetening and NGL Recovery. In Abu Dhabi International Petroleum Exhibition & Conference.
• 17. Rahmanpour O., Zargari M.H., Ghayyem M.A. 2015. Application of Artificial Neural Networks (ANNs) to Predict the Rich Amine Concentration in Gas Sweetening Processing Units. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 37, 118–126.
• 18. Ram M., Taklif A., Faridzad A. 2019. Prediction of Natural Gas Prices in European Gas Hubs Using Artificial Neural Network. Petroleum Business Review, 3, 1–14.
• 19. Rene E. Estefanía López M., Hoon Kim J., Suck Park H. 2013. Back Propagation Neural Network Model for Predicting the Performance of Immobilized Cell Biofilters Handling Gas-Phase Hydrogen Sulphide and Ammonia. BioMed Research International, 463401.
• 20. Sanni S., Agboola O., Fagbiele O., Ojima Yusuf E., Eterigho Emetere M. 2020. Optimization of natural gas treatment for the removal of CO2 and H2S in a novel alkaline-DEA hybrid scrubber. Egyptian Journal of Petroleum, 29, 83–94.
• 21. Seqatoleslami N. KoolivandSalooki M. Mohamadi N. 2011. A neural network for the gas sweetening absorption column using genetic algorithm. Pet. Sci. Technol., 29, 1437–1448.
• 22. Stewart M., Arnold K. 2011. Gas Sweetening and Processing Field Manual, Gulf Professional Publishing: Boston.
• 23. Webley P. 2014. Adsorption technology for CO2 separation and capture: a perspective. Adsorption, 20, 225–231.
• 24. Zhu W., Ye H., Zou X., Yang Y., Dong H. 2021. Analysis and optimization for chemical absorption of H2S/CO2 system: Applied in a multiple gas feeds sweetening process’, Separation and Purification Technology, 276, 119301.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).