Mohammad-Reza Mohammadi , Fahimeh Hadavimoghaddam , Saeid Atashrouz , Ali Abedi , Abdolhossein Hemmati-Sarapardeh , Ahmad Mohaddespour
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Moreover, Valderrama-Patel-Teja (VPT), Zudkevitch-Joffe (ZJ), Peng-Robinson (PR), Redlich-Kwong (RK), and Soave-Redlich-Kwong (SRK) equations of state (EOSs) were applied for the solubility predictions in the SO<sub>2</sub> + ILs systems.</p></div><div><h3>Significant findings</h3><p>The results illustrated that DBN model is the most reliable predictive tool for the SO<sub>2</sub> solubility in ILs by having an average absolute percent relative error (AAPRE) of 3.56%. Furthermore, the proposed simple to use GMDH mathematical correlation also provides good estimations with an AAPRE of 8.05%. Despite the weaker performance of the EOSs than the intelligent models, the PR EOS presented better estimations among other EOSs for the SO<sub>2</sub> solubility in ILs.</p></div>","PeriodicalId":381,"journal":{"name":"Journal of the Taiwan Institute of Chemical Engineers","volume":"133 ","pages":"Article 104220"},"PeriodicalIF":5.5000,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Toward predicting SO2 solubility in ionic liquids utilizing soft computing approaches and equations of state\",\"authors\":\"Mohammad-Reza Mohammadi , Fahimeh Hadavimoghaddam , Saeid Atashrouz , Ali Abedi , Abdolhossein Hemmati-Sarapardeh , Ahmad Mohaddespour\",\"doi\":\"10.1016/j.jtice.2022.104220\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><p>The use of novel and green solvents like ionic liquids (ILs) for the capture of air pollutant gases has gained extensive attention in recent years. However, getting reliable and fast predictions of gases solubility in ILs is complex.</p></div><div><h3>Methods</h3><p><span>Four soft computing methods including deep belief network (DBN), group method of data handling (GMDH), genetic programming (GP), and K-nearest neighbor (KNN) were utilized for estimating the solubility of sulfur dioxide (SO</span><sub>2</sub>) in ILs. A total of 374 experimental data points of SO<sub>2</sub> solubility in 15 types of ILs were collected and used for model development. Moreover, Valderrama-Patel-Teja (VPT), Zudkevitch-Joffe (ZJ), Peng-Robinson (PR), Redlich-Kwong (RK), and Soave-Redlich-Kwong (SRK) equations of state (EOSs) were applied for the solubility predictions in the SO<sub>2</sub> + ILs systems.</p></div><div><h3>Significant findings</h3><p>The results illustrated that DBN model is the most reliable predictive tool for the SO<sub>2</sub> solubility in ILs by having an average absolute percent relative error (AAPRE) of 3.56%. Furthermore, the proposed simple to use GMDH mathematical correlation also provides good estimations with an AAPRE of 8.05%. 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Toward predicting SO2 solubility in ionic liquids utilizing soft computing approaches and equations of state
Background
The use of novel and green solvents like ionic liquids (ILs) for the capture of air pollutant gases has gained extensive attention in recent years. However, getting reliable and fast predictions of gases solubility in ILs is complex.
Methods
Four soft computing methods including deep belief network (DBN), group method of data handling (GMDH), genetic programming (GP), and K-nearest neighbor (KNN) were utilized for estimating the solubility of sulfur dioxide (SO2) in ILs. A total of 374 experimental data points of SO2 solubility in 15 types of ILs were collected and used for model development. Moreover, Valderrama-Patel-Teja (VPT), Zudkevitch-Joffe (ZJ), Peng-Robinson (PR), Redlich-Kwong (RK), and Soave-Redlich-Kwong (SRK) equations of state (EOSs) were applied for the solubility predictions in the SO2 + ILs systems.
Significant findings
The results illustrated that DBN model is the most reliable predictive tool for the SO2 solubility in ILs by having an average absolute percent relative error (AAPRE) of 3.56%. Furthermore, the proposed simple to use GMDH mathematical correlation also provides good estimations with an AAPRE of 8.05%. Despite the weaker performance of the EOSs than the intelligent models, the PR EOS presented better estimations among other EOSs for the SO2 solubility in ILs.
期刊介绍：
Journal of the Taiwan Institute of Chemical Engineers (formerly known as Journal of the Chinese Institute of Chemical Engineers) publishes original works, from fundamental principles to practical applications, in the broad field of chemical engineering with special focus on three aspects: Chemical and Biomolecular Science and Technology, Energy and Environmental Science and Technology, and Materials Science and Technology. Authors should choose for their manuscript an appropriate aspect section and a few related classifications when submitting to the journal online.
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