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Abdullah M, Chellappan Lethesh K, Baloch AA, Bamgbopa MO. Comparison of molecular and structural features towards prediction of ionic liquid ionic conductivity for electrochemical applications. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
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Duong DV, Tran HV, Pathirannahalage SK, Brown SJ, Hassett M, Yalcin D, Meftahi N, Christofferson AJ, Greaves TL, Le TC. Machine learning investigation of viscosity and ionic conductivity of protic ionic liquids in water mixtures. J Chem Phys 2022; 156:154503. [PMID: 35459305 DOI: 10.1063/5.0085592] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Ionic liquids (ILs) are well classified as designer solvents based on the ease of tailoring their properties through modifying the chemical structure of the cation and anion. However, while many structure-property relationships have been developed, these generally only identify the most dominant trends. Here, we have used machine learning on existing experimental data to construct robust models to produce meaningful predictions across a broad range of cation and anion chemical structures. Specifically, we used previously collated experimental data for the viscosity and conductivity of protic ILs [T. L. Greaves and C. J. Drummond, Chem. Rev. 115, 11379-11448 (2015)] as the inputs for multiple linear regression and neural network models. These were then used to predict the properties of all 1827 possible cation-anion combinations (excluding the input combinations). These models included the effect of water content of up to 5 wt. %. A selection of ten new protic ILs was then prepared, which validated the usefulness of the models. Overall, this work shows that relatively sparse data can be used productively to predict physicochemical properties of vast arrays of ILs.
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Affiliation(s)
- Dung Viet Duong
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Hung-Vu Tran
- Department of Chemistry, University of Houston, 4800 Calhoun Road, Houston, Texas 77204-5003, USA
| | | | - Stuart J Brown
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Michael Hassett
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Dilek Yalcin
- CSIRO Manufacturing, Clayton, VIC 3168, Australia
| | - Nastaran Meftahi
- ARC Centre of Excellence in Exciton Science, School of Science, RMIT University, Melbourne, VIC 3001, Australia
| | - Andrew J Christofferson
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Tamar L Greaves
- School of Science, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
| | - Tu C Le
- School of Engineering, STEM College, RMIT University, GPO Box 2476, Melbourne, VIC 3001, Australia
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Ahmadianfar I, Shirvani-Hosseini S, He J, Samadi-Koucheksaraee A, Yaseen ZM. An improved adaptive neuro fuzzy inference system model using conjoined metaheuristic algorithms for electrical conductivity prediction. Sci Rep 2022; 12:4934. [PMID: 35322087 PMCID: PMC8943002 DOI: 10.1038/s41598-022-08875-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 03/14/2022] [Indexed: 11/09/2022] Open
Abstract
Precise prediction of water quality parameters plays a significant role in making an early alert of water pollution and making better decisions for the management of water resources. As one of the influential indicative parameters, electrical conductivity (EC) has a crucial role in calculating the proportion of mineralization. In this study, the integration of an adaptive hybrid of differential evolution and particle swarm optimization (A-DEPSO) with adaptive neuro fuzzy inference system (ANFIS) model is adopted for EC prediction. The A-DEPSO method uses unique mutation and crossover processes to correspondingly boost global and local search mechanisms. It also uses a refreshing operator to prevent the solution from being caught inside the local optimal solutions. This study uses A-DEPSO optimizer for ANFIS training phase to eliminate defects and predict accurately the EC water quality parameter every month at the Maroon River in the southwest of Iran. Accordingly, the recorded dataset originated from the Tange-Takab station from 1980 to 2016 was operated to develop the ANFIS-A-DEPSO model. Besides, the wavelet analysis was jointed to the proposed algorithm in which the original time series of EC was disintegrated into the sub-time series through two mother wavelets to boost the prediction certainty. In the following, the comparison between statistical metrics of the standalone ANFIS, least-square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), generalized regression neural network (GRNN), wavelet-LSSVM (WLSSVM), wavelet-MARS (W-MARS), wavelet-ANFIS (W-ANFIS) and wavelet-GRNN (W-GRNN) models was implemented. As a result, it was apparent that not only was the W-ANFIS-A-DEPSO model able to rise remarkably the EC prediction certainty, but W-ANFIS-A-DEPSO (R = 0.988, RMSE = 53.841, and PI = 0.485) also had the edge over other models with Dmey mother in terms of EC prediction. Moreover, the W-ANFIS-A-DEPSO can improve the RMSE compared to the standalone ANFIS-DEPSO model, accounting for 80%. Hence, this model can create a closer approximation of EC value through W-ANFIS-A-DEPSO model, which is likely to act as a promising procedure to simulate the prediction of EC data.
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Affiliation(s)
- Iman Ahmadianfar
- Department of Civil Engineering, Behbahan Khatam Alanbia University of Technology, Behbahan, Iran.
| | | | - Jianxun He
- Department of Civil Engineering, University of Calgary, Calgary, AB, Canada
| | | | - Zaher Mundher Yaseen
- Adjunct Research Fellow, USQ's Advanced Data Analytics Research Group, School of Mathematics Physics and Computing, University of Southern Queensland, QLD 4350, Toowoomba, Australia.,New Era and Development in Civil Engineering Research Group, Scientific Research Center, Al-Ayen University, Thi-Qar, 64001, Iraq
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Sahandi PJ, Salimi M, Iranshahi D. Insights on the speed of sound in ionic liquid binary mixtures: Investigation of influential parameters and construction of predictive models. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2020.115067] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
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Mousavi SP, Atashrouz S, Rezaei F, Peyvastegan ME, Hemmati-Sarapardeh A, Mohaddespour A. Modeling thermal conductivity of ionic liquids: A comparison between chemical structure and thermodynamic properties-based models. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2020.114911] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Taghvaei H, Amooie MA, Hemmati-Sarapardeh A, Taghvaei H. A comprehensive study of phase equilibria in binary mixtures of carbon dioxide + alcohols: Application of a hybrid intelligent model (CSA-LSSVM). J Mol Liq 2016. [DOI: 10.1016/j.molliq.2016.09.119] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Atashrouz S, Hemmati-Sarapardeh A, Mirshekar H, Nasernejad B, Keshavarz Moraveji M. On the evaluation of thermal conductivity of ionic liquids: Modeling and data assessment. J Mol Liq 2016. [DOI: 10.1016/j.molliq.2016.09.106] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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