Toward predicting SO2 solubility in ionic liquids utilizing soft computing approaches and equations of state

IF 5.5 3区工程技术 Q1 ENGINEERING, CHEMICAL Journal of the Taiwan Institute of Chemical Engineers Pub Date : 2022-04-01 DOI:10.1016/j.jtice.2022.104220

Mohammad-Reza Mohammadi , Fahimeh Hadavimoghaddam , Saeid Atashrouz , Ali Abedi , Abdolhossein Hemmati-Sarapardeh , Ahmad Mohaddespour

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引用次数: 11

Abstract

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.

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利用软计算方法和状态方程预测离子液体中二氧化硫的溶解度

近年来，利用离子液体等新型绿色溶剂捕集大气污染气体得到了广泛的关注。然而，获得可靠和快速的气体溶解度预测是复杂的。方法采用深度信念网络(DBN)、群体数据处理方法(GMDH)、遗传规划(GP)和k近邻(KNN) 4种软计算方法估算二氧化硫(SO2)在土壤中的溶解度。共收集了15种il中SO2溶解度的374个实验数据点，并用于模型开发。此外，应用valderrma - patel - teja (VPT)、zudkevich - joffe (ZJ)、Peng-Robinson (PR)、Redlich-Kwong (RK)和Soave-Redlich-Kwong (SRK)状态方程(eos)预测SO2 + ILs体系的溶解度。结果表明，DBN模型是最可靠的SO2溶解度预测工具，平均绝对相对误差(AAPRE)为3.56%。此外，本文提出的GMDH数学相关也提供了良好的估计，AAPRE为8.05%。尽管EOS的性能不如智能模型，但PR EOS在其他EOS中对SO2在ILs中的溶解度的估计较好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of the Taiwan Institute of Chemical Engineers 工程技术-工程：化工

CiteScore

9.10

自引率

14.00%

发文量

362

审稿时长

35 days

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.