Automated determination of optimal component design for a binary solvent for absorption-based acid gas removal

Automated determination of optimal component design for a binary solvent for absorption-based acid gas removal

Yogesh Patil, Yash Shankar Chikorde, Madiah Binti Omar, Mohd Hakimi Bin Rosli, ROSDIAZLI Bin Ibrahim

download PDF

Abstract. Natural gases containing impurities, namely carbon dioxide (CO2), heavy hydrocarbons, hydrogen sulfide (H2S), and water vapour, need treatment for removing acidic gases (CO2 and H2S) to reduce corrosion and enhance the heat capacity of the gas. This gas is commercially known as “sour”, and typically, sour gas is any gas that contains significant levels of hydrogen sulfide. The presence of carbon dioxide can affect natural gas quality, which can also lead to CO2 freezing issues; hence reliable techniques for reducing CO2 and H2S from natural gases is necessary. New blends of amines show CO2 and H2S uptake capacity comparable to traditional MEA benchmark solutions. This work aimed to create different regression models using open-source software and estimate the best fit model for a given amine solvent. For this purpose, data were obtained from simulation using Aspen HYSYS V12.1 for MDEA (40-45 wt.%), MDEA +PZ (42-50wt.% + 0-2.5wt.%), DEA (21-26wt.%). Regression models for different amine solvent blends were developed and validated. The study showed that the XGB Regression model was best suited for the MDEA solution, while MDEA + PZ and DEA were best suited for multiple linear regression. The data is generated using simulation from ASPEN HYSYS and models were created in python correlating the simulation-generated values with the model results. These models showed low MSE, RMSE and high R2 values for the tried solvents.

Natural gas, Acid gas, Machine learning, Regression

Published online 5/20/2023, 8 pages
Copyright © 2023 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: Yogesh Patil, Yash Shankar Chikorde, Madiah Binti Omar, Mohd Hakimi Bin Rosli, ROSDIAZLI Bin Ibrahim, Automated determination of optimal component design for a binary solvent for absorption-based acid gas removal, Materials Research Proceedings, Vol. 29, pp 281-288, 2023


The article was published as article 31 of the book Sustainable Processes and Clean Energy Transition

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

[1] T. Chakravarty, U.K. Phukan, R.H. Weilund, Reaction of acid gases with mixtures of amines, in: 1985.
[2] S.-Y. Oh, M. Binns, H. Cho, J.-K. Kim, Energy minimization of MEA-based CO2 capture process, Appl. Energy. 169 (2016) 353–362.
[3] M.R.M. Abu-Zahra, L.H.J. Schneiders, J.P.M. Niederer, P.H.M. Feron, G.F. Versteeg, CO2 capture from power plants: Part I. A parametric study of the technical performance based on monoethanolamine, Int. J. Greenh. Gas Control. 1 (2007) 37–46.
[4] D.D.D. Pinto, H. Knuutila, G. Fytianos, G. Haugen, T. Mejdell, H.F. Svendsen, CO2 post combustion capture with a phase change solvent. Pilot plant campaign, Int. J. Greenh. Gas Control. 31 (2014) 153–164.
[5] F. Bashipour, S.N. Khorasani, A. Rahimi, H2S Reactive Absorption from Off-Gas in a Spray Column: Insights from Experiments and Modeling, Chem. Eng. Technol. 38 (2015) 2137–2145.
[6] J. Ye, C. Jiang, H. Chen, Y. Shen, S. Zhang, L. Wang, J. Chen, Novel Biphasic Solvent with Tunable Phase Separation for CO2 Capture: Role of Water Content in Mechanism, Kinetics, and Energy Penalty, Environ. Sci. Technol. 53 (2019) 4470–4479.
[7] J. Zhan, B. Wang, L. Zhang, B.-C. Sun, J. Fu, G. Chu, H. Zou, Simultaneous Absorption of H2S and CO2 into the MDEA + PZ Aqueous Solution in a Rotating Packed Bed, Ind. Eng. Chem. Res. 59 (2020) 8295–8303.
[8] G. Guido, M. Compagnoni, L. Pellegrini, I. Rossetti, Mature versus emerging technologies for CO2 capture in power plants: Key open issues in post-combustion amine scrubbing and in chemical looping combustion, Front. Chem. Sci. Eng. 12 (2018).
[9] M. Koolivand Salooki, R. Abedini, H. Adib, H. Koolivand, Design of neural network for manipulating gas refinery sweetening regenerator column outputs, Sep. Purif. Technol. 82 (2011) 1–9.
[10] P. Nimmanterdwong, R. Changpun, P. Janthboon, S. Nakrak, H. Gao, Z. Liang, P. Tontiwachwuthikul, T. Sema, Applied Artificial Neural Network for Hydrogen Sulfide Solubility in Natural Gas Purification, ACS Omega. 6 (2021) 31321–31329.
[11] N.F. Salehuddin, M.B. Omar, R. Ibrahim, K. Bingi, A Neural Network-Based Model for Predicting Saybolt Color of Petroleum Products, Sensors. 22 (2022).
[12] M.H.M Tarik, M.B. Omar, M.F. Abdullah, R. Ibrahim, Optimization of neural network hyperparameters for gas turbine modelling using Bayesian optimization, IET, 2018.
[13] Tarik, M. H. M., Omar, M., Abdullah, M. F., & Ibrahim, R. (2017, November). Modelling of dry low emission gas turbine using black-box approach. In TENCON 2017-2017 IEEE Region 10 Conference (pp. 1902-1906). IEEE.
[14] Omar, M. B., Ibrahim, R., Abdullah, M. F., & Tarik, M. H. M. (2020). Modelling of Dry-Low Emission Gas Turbine Fuel System using First Principle Data-Driven Method. Journal of Power Technologies, 100(1).
[15] Omar, M. B., Ibrahim, R., Mantri, R., Chaudhary, J., Ram Selvaraj, K., & Bingi, K. (2022). Smart Grid Stability Prediction Model Using Neural Networks to Handle Missing Inputs. Sensors, 22(12), 4342.
[16] Hakimi, M., Omar, M. B., & Ibrahim, R. (2023). Application of Neural Network in Predicting H2S from an Acid Gas Removal Unit (AGRU) with Different Compositions of Solvents. Sensors, 23(2), 1020.
[17] Rosli, N. S., Ibrahim, R., Ismail, I., & Omar, M. (2022). Modeling of high voltage induction motor cooling system using linear regression mathematical models. Plos one, 17(11), e0276142.
[18] Mahesh, A., Aadhavan, B. A., Meenaa, V. V., Omar, M. B., Ibrahim, R. B., Salehuddin, N. F., & Sujatha, R. (2022, August). Employment of ANN for Predictive Motor Maintenance and Bearing Fault Detection Using Park’s Vector Analysis. In 2022 IEEE 5th International Symposium in Robotics and Manufacturing Automation (ROMA) (pp. 1-6). IEEE.