Estimation of CO<inf>2</inf> solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

Amirkhani, F; Dashti, A; Jokar, M; Mohammadi, AH; Gholamzadeh Chofreh, A; Varbanov, PS; Zhou, JL

Estimation of CO<inf>2</inf> solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

Amirkhani, F Dashti, A

Jokar, M Mohammadi, AH Gholamzadeh Chofreh, A Varbanov, PS Zhou, JL

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Journal of Cleaner Production, 2023, 430
Issue Date:: 2023-12-10

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Field	Value	Language
dc.contributor.author	Amirkhani, F
dc.contributor.author	Dashti, A https://orcid.org/0000-0002-3937-7353
dc.contributor.author	Jokar, M
dc.contributor.author	Mohammadi, AH
dc.contributor.author	Gholamzadeh Chofreh, A
dc.contributor.author	Varbanov, PS
dc.contributor.author	Zhou, JL
dc.date.accessioned	2024-04-21T19:46:23Z
dc.date.available	2024-04-21T19:46:23Z
dc.date.issued	2023-12-10
dc.identifier.citation	Journal of Cleaner Production, 2023, 430
dc.identifier.issn	0959-6526
dc.identifier.issn	1879-1786
dc.identifier.uri	http://hdl.handle.net/10453/178140
dc.description.abstract	One of the key concerns in the 21st century, alongside the growing population, is the increase in energy consumption and the resulting global warming. The impact of CO2, a prominent greenhouse gas, has garnered significant attention in the realm of CO2 capture and gas purification. CO2 absorption can be enhanced by introducing some additives into the aqueous solution. In this study, the accuracies of some of the most up-to-date computational approaches are investigated. The employed machine learning methods are hybrid-adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS), least-squares support vector machines (LSSVM) and genetic algorithm-radial basis function (GA-RBF). The developed models were used in estimating the solubility of CO2 in binary and ternary amines aqueous solutions. i.e. blends of monoethanolamine (MEA), triethanolamine (TEA), aminomethyl propanol (AMP), and methyldiethanolamine (MDEA). This modeling study was undertaken over relatively significant ranges of CO2 loading (mole of CO2/mole of solution) as a function of input parameters, which are 0.4–2908 kPa for pressure, 303–393.15 K for temperature, 36.22–68.89 g/mol for apparent molecular weight, and 30–55 wt % for total concentration. In this work, the validity of approaches based on different statistical graphs was investigated, and it was observed that the developed methods, especially the GA-RBF model, are highly accurate in estimating the data of interest. The obtained AARD% values for the developed models are 18.63, 8.25, 12.22, and 7.54 for Hybrid-ANFIS, PSO-ANFIS, LSSVM, and GA-RBF, respectively.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Journal of Cleaner Production
dc.relation.isbasedon	10.1016/j.jclepro.2023.139435
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0907 Environmental Engineering, 0910 Manufacturing Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Environmental Sciences
dc.subject.classification	33 Built environment and design
dc.subject.classification	40 Engineering
dc.title	Estimation of CO<inf>2</inf> solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture
dc.type	Journal Article
utslib.citation.volume	430
utslib.for	0907 Environmental Engineering
utslib.for	0910 Manufacturing Engineering
utslib.for	0915 Interdisciplinary Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/Strength - CTWW - Centre for Technology in Water and Wastewater Treatment
pubs.organisational-group	University of Technology Sydney/Strength - CGT - Centre for Green Technology
utslib.copyright.status	open_access	*
dc.date.updated	2024-04-21T19:46:20Z
pubs.publication-status	Published
pubs.volume	430

Abstract:

One of the key concerns in the 21st century, alongside the growing population, is the increase in energy consumption and the resulting global warming. The impact of CO2, a prominent greenhouse gas, has garnered significant attention in the realm of CO2 capture and gas purification. CO2 absorption can be enhanced by introducing some additives into the aqueous solution. In this study, the accuracies of some of the most up-to-date computational approaches are investigated. The employed machine learning methods are hybrid-adaptive neuro-fuzzy inference system (Hybrid-ANFIS), particle swarm optimization-adaptive neuro-fuzzy inference system (PSO-ANFIS), least-squares support vector machines (LSSVM) and genetic algorithm-radial basis function (GA-RBF). The developed models were used in estimating the solubility of CO2 in binary and ternary amines aqueous solutions. i.e. blends of monoethanolamine (MEA), triethanolamine (TEA), aminomethyl propanol (AMP), and methyldiethanolamine (MDEA). This modeling study was undertaken over relatively significant ranges of CO2 loading (mole of CO2/mole of solution) as a function of input parameters, which are 0.4–2908 kPa for pressure, 303–393.15 K for temperature, 36.22–68.89 g/mol for apparent molecular weight, and 30–55 wt % for total concentration. In this work, the validity of approaches based on different statistical graphs was investigated, and it was observed that the developed methods, especially the GA-RBF model, are highly accurate in estimating the data of interest. The obtained AARD% values for the developed models are 18.63, 8.25, 12.22, and 7.54 for Hybrid-ANFIS, PSO-ANFIS, LSSVM, and GA-RBF, respectively.

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http://hdl.handle.net/10453/178140