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 Ibrahimdownload 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.
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