Detail publikace

Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

Amirkhani, Farid Dashti, Amir Jokar, Mojtaba Mohammadi, Amir H. Chofreh, Abdoulmohammad Gholamzadeh Varbanov, Petar Sabev Zhou, John L.

Originální název

Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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 neurofuzzy 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 (GARBF). 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.

Klíčová slova

Absorption; Blended amines; CO2 capture; Data-driven model; Soft computing approach

Autoři

Amirkhani, Farid; Dashti, Amir; Jokar, Mojtaba; Mohammadi, Amir H.; Chofreh, Abdoulmohammad Gholamzadeh; Varbanov, Petar Sabev; Zhou, John L.

Vydáno

10. 12. 2023

Nakladatel

ELSEVIER SCI LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND

Místo

ELSEVIER SCI LTDTHE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND

ISSN

0959-6526

Periodikum

Journal of Cleaner Production

Číslo

430

Stát

Spojené království Velké Británie a Severního Irska

Strany počet

10

URL

https://www.sciencedirect.com/science/article/pii/S095965262303593X?via%3Dihub

BibTex

@article{BUT187682,
  author="Amirkhani, Farid and Dashti, Amir and Jokar, Mojtaba and Mohammadi, Amir H. and Chofreh, Abdoulmohammad Gholamzadeh and Varbanov, Petar Sabev and Zhou, John L.",
  title="Estimation of CO2 solubility in aqueous solutions of commonly used blended amines: Application to optimised greenhouse gas capture",
  journal="Journal of Cleaner Production",
  year="2023",
  number="430",
  pages="10",
  doi="10.1016/j.jclepro.2023.139435",
  issn="0959-6526",
  url="https://www.sciencedirect.com/science/article/pii/S095965262303593X?via%3Dihub"
}