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Kiani S, Hadavimoghaddam F, Atashrouz S, Nedeljkovic D, Hemmati-Sarapardeh A, Mohaddespour A. Modeling of ionic liquids viscosity via advanced white-box machine learning. Sci Rep 2024; 14:8666. [PMID: 38622138 PMCID: PMC11018629 DOI: 10.1038/s41598-024-55147-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2023] [Accepted: 02/20/2024] [Indexed: 04/17/2024] Open
Abstract
Ionic liquids (ILs) are more widely used within the industry than ever before, and accurate models of their physicochemical characteristics are becoming increasingly important during the process optimization. It is especially challenging to simulate the viscosity of ILs since there is no widely agreed explanation of how viscosity is determined in liquids. In this research, genetic programming (GP) and group method of data handling (GMDH) models were used as white-box machine learning approaches to predict the viscosity of pure ILs. These methods were developed based on a large open literature database of 2813 experimental viscosity values from 45 various ILs at different pressures (0.06-298.9 MPa) and temperatures (253.15-573 K). The models were developed based on five, six, and seven inputs, and it was found that all the models with seven inputs provided more accurate results, while the models with five and six inputs had acceptable accuracy and simpler formulas. Based on GMDH and GP proposed approaches, the suggested GMDH model with seven inputs gave the most exact results with an average absolute relative deviation (AARD) of 8.14% and a coefficient of determination (R2) of 0.98. The proposed techniques were compared with theoretical and empirical models available in the literature, and it was displayed that the GMDH model with seven inputs strongly outperforms the existing approaches. The leverage statistical analysis revealed that most of the experimental data were located within the applicability domains of both GMDH and GP models and were of high quality. Trend analysis also illustrated that the GMDH and GP models could follow the expected trends of viscosity with variations in pressure and temperature. In addition, the relevancy factor portrayed that the temperature had the greatest impact on the ILs viscosity. The findings of this study illustrated that the proposed models represented strong alternatives to time-consuming and costly experimental methods of ILs viscosity measurement.
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Affiliation(s)
- Sajad Kiani
- Faculty of Science and Engineering, Swansea University, Swansea, SA1 8EN, UK
| | - Fahimeh Hadavimoghaddam
- Key Laboratory of Continental Shale Hydrocarbon Accumulation and Efficient Development (Northeast Petroleum University), Ministry of Education, Northeast Petroleum University, Daqing, 163318, Heilongjiang, China
- Institute of Unconventional Oil & Gas, Northeast Petroleum University, Daqing, 163318, China
| | - Saeid Atashrouz
- Department of Chemical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.
| | - Dragutin Nedeljkovic
- College of Engineering and Technology, American University of the Middle East, Egaila, 54200, Kuwait
| | - Abdolhossein Hemmati-Sarapardeh
- Department of Petroleum Engineering, Shahid Bahonar University of Kerman, Kerman, Iran.
- College of Construction Engineering, Jilin University, Changchun, China.
| | - Ahmad Mohaddespour
- Department of Chemical Engineering, McGill University, Montreal, QC, H3A 0C5, Canada.
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2
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Li H, Baghban A. Insights into the prediction of the liquid density of refrigerant systems by artificial intelligent approaches. Sci Rep 2024; 14:2343. [PMID: 38282108 PMCID: PMC10822862 DOI: 10.1038/s41598-024-53007-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Accepted: 01/25/2024] [Indexed: 01/30/2024] Open
Abstract
This study presents a novel model for accurately estimating the densities of 48 refrigerant systems, categorized into five groups: Hydrofluoroethers (HFEs), Hydrochlorofluorocarbons (HCFCs), Perfluoroalkylalkanes (PFAAs), Hydrofluorocarbons (HFCs), and Perfluoroalkanes (PFAs). Input variables, including pressure, temperature, molecular weight, and structural groups, were systematically considered. The study explores the efficacy of both the multilayer perceptron artificial neural network (MLP-ANN) and adaptive neuro-fuzzy inference system (ANFIS) methodologies in constructing a precise model. Utilizing a comprehensive dataset of 3825 liquid density measurements and outlier analysis, the models achieved R2 and MSE values of 0.975 & 0.5575 and 0.967 & 0.7337 for MLP-ANN and ANFIS, respectively, highlighting their remarkable predictive performance. In conclusion, the ANFIS model is proposed as an effective tool for estimating refrigerant system densities, particularly advantageous in scenarios where experimental measurements are resource-intensive or sophisticated analysis is required.
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Affiliation(s)
- Huaguang Li
- Intelligent Manufacturing College, Qingdao Huanghai University, Qingdao, 266427, Shandong, China.
| | - Alireza Baghban
- Process Engineering Department, National Iranian South Oilfields Company (NISOC), Ahvaz, Iran.
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3
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Song Z, Chen J, Cheng J, Chen G, Qi Z. Computer-Aided Molecular Design of Ionic Liquids as Advanced Process Media: A Review from Fundamentals to Applications. Chem Rev 2024; 124:248-317. [PMID: 38108629 DOI: 10.1021/acs.chemrev.3c00223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
The unique physicochemical properties, flexible structural tunability, and giant chemical space of ionic liquids (ILs) provide them a great opportunity to match different target properties to work as advanced process media. The crux of the matter is how to efficiently and reliably tailor suitable ILs toward a specific application. In this regard, the computer-aided molecular design (CAMD) approach has been widely adapted to cover this family of high-profile chemicals, that is, to perform computer-aided IL design (CAILD). This review discusses the past developments that have contributed to the state-of-the-art of CAILD and provides a perspective about how future works could pursue the acceleration of the practical application of ILs. In a broad context of CAILD, key aspects related to the forward structure-property modeling and reverse molecular design of ILs are overviewed. For the former forward task, diverse IL molecular representations, modeling algorithms, as well as representative models on physical properties, thermodynamic properties, among others of ILs are introduced. For the latter reverse task, representative works formulating different molecular design scenarios are summarized. Beyond the substantial progress made, some future perspectives to move CAILD a step forward are finally provided.
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Affiliation(s)
- Zhen Song
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jiahui Chen
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Jie Cheng
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Guzhong Chen
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
| | - Zhiwen Qi
- State Key laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, 130 Meilong Road, Shanghai 200237, China
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4
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Gao N, Yang Y, Wang Z, Guo X, Jiang S, Li J, Hu Y, Liu Z, Xu C. Viscosity of Ionic Liquids: Theories and Models. Chem Rev 2024; 124:27-123. [PMID: 38156796 DOI: 10.1021/acs.chemrev.3c00339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2024]
Abstract
Ionic liquids (ILs) offer a wide range of promising applications due to their unique and designable properties compared to conventional solvents. Further development and application of ILs require correlating/predicting their pressure-viscosity-temperature behavior. In this review, we firstly introduce methods for calculation of thermodynamic inputs of viscosity models. Next, we introduce theories, theoretical and semi-empirical models coupling various theories with EoSs or activity coefficient models, and empirical and phenomenological models for viscosity of pure ILs and IL-related mixtures. Our modelling description is followed immediately by model application and performance. Then, we propose simple predictive equations for viscosity of IL-related mixtures and systematically compare performances of the above-mentioned theories and models. In concluding remarks, we recommend robust predictive models for viscosity at atmospheric pressure as well as proper and consistent theories and models for P-η-T behavior. The work that still remains to be done to obtain the desired theories and models for viscosity of ILs and IL-related mixtures is also presented. The present review is structured from pure ILs to IL-related mixtures and aims to summarize and quantitatively discuss the recent advances in theoretical and empirical modelling of viscosity of ILs and IL-related mixtures.
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Affiliation(s)
- Na Gao
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Ye Yang
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Zhiyuan Wang
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Xin Guo
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Siqi Jiang
- Sinopec Engineering Incorporation, Beijing 100195, P. R. China
| | - Jisheng Li
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Yufeng Hu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
- State Key Laboratory of Heavy Oil Processing, China University of Petroleum Beijing at Karamay, Karamay 834000, China
| | - Zhichang Liu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
| | - Chunming Xu
- State Key Laboratory of Heavy Oil Processing and High Pressure Fluid Phase Behavior & Property Research Laboratory, China University of Petroleum, Beijing 102249, P. R. China
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5
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Schieppati D, Mohan M, Blais B, Fattahi K, Patience GS, Simmons BA, Singh S, Boffito DC. Characterization of the acoustic cavitation in ionic liquids in a horn-type ultrasound reactor. ULTRASONICS SONOCHEMISTRY 2024; 102:106721. [PMID: 38103370 PMCID: PMC10765111 DOI: 10.1016/j.ultsonch.2023.106721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 12/19/2023]
Abstract
Most ultrasound-based processes root in empirical approaches. Because nearly all advances have been conducted in aqueous systems, there exists a paucity of information on sonoprocessing in other solvents, particularly ionic liquids (ILs). In this work, we modelled an ultrasonic horn-type sonoreactor and investigated the effects of ultrasound power, sonotrode immersion depth, and solvent's thermodynamic properties on acoustic cavitation in nine imidazolium-based and three pyrrolidinium-based ILs. The model accounts for bubbles, acoustic impedance mismatch at interfaces, and treats the ILs as incompressible, Newtonian, and saturated with argon. Following a statistical analysis of the simulation results, we determined that viscosity and ultrasound input power are the most significant variables affecting the intensity of the acoustic pressure field (P), the volume of cavitation zones (V), and the magnitude of the maximum acoustic streaming surface velocity (u). V and u increase with the increase of ultrasound input power and the decrease in viscosity, whereas the magnitude of negative P decreases as ultrasound power and viscosity increase. Probe immersion depth positively correlates with V, but its impact on P and u is insignificant. 1-alkyl-3-methylimidazolium-based ILs yielded the largest V and the fastest acoustic jets - 0.77 cm3 and 24.4 m s-1 for 1-ethyl-3-methylimidazolium chloride at 60 W. 1-methyl-3-(3-sulfopropyl)-imidazolium-based ILs generated the smallest V and lowest u - 0.17 cm3 and 1.7 m s-1 for 1-methyl-3-(3-sulfopropyl)-imidazolium p-toluene sulfonate at 20 W. Sonochemiluminescence experiments validated the model.
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Affiliation(s)
- Dalma Schieppati
- Department of Chemical Engineering, École Polytechnique Montréal, C.P. 6079, Succ. CV, Montréal H3C 3A7, Québec, Canada
| | - Mood Mohan
- Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Bioscience Division and Center for Molecular Biophysics, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
| | - Bruno Blais
- Department of Chemical Engineering, École Polytechnique Montréal, C.P. 6079, Succ. CV, Montréal H3C 3A7, Québec, Canada
| | - Kobra Fattahi
- Department of Chemical Engineering, École Polytechnique Montréal, C.P. 6079, Succ. CV, Montréal H3C 3A7, Québec, Canada
| | - Gregory S Patience
- Department of Chemical Engineering, École Polytechnique Montréal, C.P. 6079, Succ. CV, Montréal H3C 3A7, Québec, Canada
| | - Blake A Simmons
- Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA; Biological Systems and Engineering Division, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, CA 94720, USA
| | - Seema Singh
- Deconstruction Division, Joint BioEnergy Institute, 5885 Hollis Street, Emeryville, CA 94608, USA
| | - Daria C Boffito
- Department of Chemical Engineering, École Polytechnique Montréal, C.P. 6079, Succ. CV, Montréal H3C 3A7, Québec, Canada.
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6
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Iftakher A, Monjur MS, Hasan MMF. An Overview of Computer‐aided Molecular and Process Design. CHEM-ING-TECH 2023. [DOI: 10.1002/cite.202200172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Affiliation(s)
- Ashfaq Iftakher
- Texas A&M University Artie McFerrin Department of Chemical Engineering 100 Spence St. TX 77843-3122 College Station USA
| | - Mohammed Sadaf Monjur
- Texas A&M University Artie McFerrin Department of Chemical Engineering 100 Spence St. TX 77843-3122 College Station USA
| | - M. M. Faruque Hasan
- Texas A&M University Artie McFerrin Department of Chemical Engineering 100 Spence St. TX 77843-3122 College Station USA
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7
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Viscosity prediction of ionic liquids using NLR and SVM approaches. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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8
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A REVIEW OF GROUP CONTRIBUTION MODELS TO CALCULATE THERMODYNAMIC PROPERTIES OF IONIC LIQUIDS FOR PROCESS SYSTEMS ENGINEERING. Chem Eng Res Des 2022. [DOI: 10.1016/j.cherd.2022.07.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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9
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Yu L, Hou X, Ren G, Wu K, He C. Viscosity model of deep eutectic solvents from group contribution method. AIChE J 2022. [DOI: 10.1002/aic.17744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Affiliation(s)
- Liu‐Ying Yu
- Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering Zhejiang University Hangzhou China
- Institute of Zhejiang University‐Quzhou Quzhou China
| | - Xiao‐Jing Hou
- Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering Zhejiang University Hangzhou China
- Institute of Zhejiang University‐Quzhou Quzhou China
| | - Gao‐Peng Ren
- Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering Zhejiang University Hangzhou China
| | - Ke‐Jun Wu
- Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering Zhejiang University Hangzhou China
- Institute of Zhejiang University‐Quzhou Quzhou China
- School of Chemical and Process Engineering University of Leeds Leeds UK
| | - Chao‐Hong He
- Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, College of Chemical and Biological Engineering Zhejiang University Hangzhou China
- Institute of Zhejiang University‐Quzhou Quzhou China
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10
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Mital DK, Nancarrow P, Zeinab S, Jabbar NA, Ibrahim TH, Khamis MI, Taha A. Group Contribution Estimation of Ionic Liquid Melting Points: Critical Evaluation and Refinement of Existing Models. Molecules 2021; 26:2454. [PMID: 33922374 PMCID: PMC8122861 DOI: 10.3390/molecules26092454] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Revised: 04/19/2021] [Accepted: 04/20/2021] [Indexed: 11/17/2022] Open
Abstract
While several group contribution method (GCM) models have been developed in recent years for the prediction of ionic liquid (IL) properties, some challenges exist in their effective application. Firstly, the models have been developed and tested based on different datasets; therefore, direct comparison based on reported statistical measures is not reliable. Secondly, many of the existing models are limited in the range of ILs for which they can be used due to the lack of functional group parameters. In this paper, we examine two of the most diverse GCMs for the estimation of IL melting point; a key property in the selection and design of ILs for materials and energy applications. A comprehensive database consisting of over 1300 data points for 933 unique ILs, has been compiled and used to critically evaluate the two GCMs. One of the GCMs has been refined by introducing new functional groups and reparametrized to give improved performance for melting point estimation over a wider range of ILs. This work will aid in the targeted design of ILs for materials and energy applications.
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Affiliation(s)
- Dhruve Kumar Mital
- Department of Chemical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (D.K.M.); (S.Z.); (N.A.J.); (T.H.I.); (A.T.)
| | - Paul Nancarrow
- Department of Chemical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (D.K.M.); (S.Z.); (N.A.J.); (T.H.I.); (A.T.)
| | - Samira Zeinab
- Department of Chemical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (D.K.M.); (S.Z.); (N.A.J.); (T.H.I.); (A.T.)
| | - Nabil Abdel Jabbar
- Department of Chemical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (D.K.M.); (S.Z.); (N.A.J.); (T.H.I.); (A.T.)
| | - Taleb Hassan Ibrahim
- Department of Chemical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (D.K.M.); (S.Z.); (N.A.J.); (T.H.I.); (A.T.)
| | - Mustafa I. Khamis
- Department of Biology, Chemistry and Environmental Sciences, American University of Sharjah, Sharjah 26666, United Arab Emirates;
| | - Alnoman Taha
- Department of Chemical Engineering, American University of Sharjah, Sharjah 26666, United Arab Emirates; (D.K.M.); (S.Z.); (N.A.J.); (T.H.I.); (A.T.)
- Department of Chemical Engineering, University of Birmingham, SW Campus, Birmingham B15 2TT, UK
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11
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Zhang S, Jia Q, Yan F, Xia S, Wang Q. Evaluating the properties of ionic liquid at variable temperatures and pressures by quantitative structure–property relationship (QSPR). Chem Eng Sci 2021. [DOI: 10.1016/j.ces.2020.116326] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Nanvakenari S, Ghasemi M, Movagharnejad K. Viscosity prediction of hydrocarbon binary mixture using an artificial neural network-group contribution method. CHEMICAL PRODUCT AND PROCESS MODELING 2021. [DOI: 10.1515/cppm-2020-0084] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Abstract
In this study, the viscosity of hydrocarbon binary mixtures has been predicted with an artificial neural network and a group contribution method (ANN-GCM) by utilizing various training algorithm including Scaled Conjugate Gradient (SCG), Levenberg-Marquardt (LM), Resilient back Propagation (RP), and Gradient Descent with variable learning rate back propagation (GDX). Moreover, different transfer functions such as Tan-sigmoid (tansig), Log-sigmoid (logsig), and purelin were investigated in hidden and output layer and their effects on network precision were estimated. Accordingly, 796 experimental data points of viscosity of hydrocarbon binary mixture were collected from the literature for a wide range of operating parameters. The temperature, pressure, mole fraction, molecular weight, and structural group of the system were selected as the independent input parameters. The statistical analysis results with R
2 = 0.99 revealed a small value for Average absolute relative deviation (AARD) of 1.288 and Mean square error (MSE) of 0.001018 by comparing the ANN predicted data with experimental data. Neural network configuration was also optimized. Based on the results, the network with one hidden layer and 27 neurons with the Levenberg-Marquardt training algorithm and tansig transfer function for hidden layer along with purelin transfer function for output layer constituted the best network structure. Further, the weights and bias were optimized to minimize the error. Then, the obtained results of the present study were compared with the data from some previous methods. The results suggested that this work can predict the viscosity of hydrocarbon binary mixture with better AARD. In general, the results indicated that combining ANN and GCM model is capable to predict the viscosity of hydrocarbon binary mixtures with a good accuracy.
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Affiliation(s)
- Sara Nanvakenari
- Faculty of Chemical Engineering , Babol Noshirvani University of Technology , Babol , Mazandaran , Iran
| | - Mitra Ghasemi
- Faculty of Chemical Engineering , Babol Noshirvani University of Technology , Babol , Mazandaran , Iran
| | - Kamyar Movagharnejad
- Faculty of Chemical Engineering , Babol Noshirvani University of Technology , Babol , Mazandaran , Iran
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Koi ZK, Yahya WZN, Kurnia KA. Prediction of ionic conductivity of imidazolium-based ionic liquids at different temperatures using multiple linear regression and support vector machine algorithms. NEW J CHEM 2021. [DOI: 10.1039/d1nj01831k] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
The conductivity of various imidazolium-based ILs has been predicted via QSPR approach using MLR and SVM regression coupled with stepwise model-building. This will aid the screening of suitable ILs with desired conductivity for specific applications.
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Affiliation(s)
- Zi Kang Koi
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | - Wan Zaireen Nisa Yahya
- Department of Chemical Engineering, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
- Center of Research in Ionic Liquids, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Perak Darul Ridzuan, Malaysia
| | - Kiki Adi Kurnia
- Department of Chemical Engineering, Faculty of Industrial Technology, Institut Teknologi Bandung, Jl. Ganesha No. 10, Bandung 40132, Indonesia
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14
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Bouarab AF, Harvey JP, Robelin C. Viscosity models for ionic liquids and their mixtures. Phys Chem Chem Phys 2021; 23:733-752. [DOI: 10.1039/d0cp05787h] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Review of principles and limitations of viscosity models for ionic liquids and their mixtures focusing on the use of inappropriate mixing rules for molten salts.
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Affiliation(s)
- Anya F. Bouarab
- Centre for Research in Computational Thermochemistry (CRCT)
- Department of Chemical Engineering
- Polytechnique Montréal
- Montréal
- Canada
| | - Jean-Philippe Harvey
- Centre for Research in Computational Thermochemistry (CRCT)
- Department of Chemical Engineering
- Polytechnique Montréal
- Montréal
- Canada
| | - Christian Robelin
- Centre for Research in Computational Thermochemistry (CRCT)
- Department of Chemical Engineering
- Polytechnique Montréal
- Montréal
- Canada
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15
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Viscosity of Ionic Liquids: Application of the Eyring's Theory and a Committee Machine Intelligent System. Molecules 2020; 26:molecules26010156. [PMID: 33396329 PMCID: PMC7795042 DOI: 10.3390/molecules26010156] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 12/05/2020] [Accepted: 12/15/2020] [Indexed: 11/17/2022] Open
Abstract
Accurate determination of the physicochemical characteristics of ionic liquids (ILs), especially viscosity, at widespread operating conditions is of a vital role for various fields. In this study, the viscosity of pure ILs is modeled using three approaches: (I) a simple group contribution method based on temperature, pressure, boiling temperature, acentric factor, molecular weight, critical temperature, critical pressure, and critical volume; (II) a model based on thermodynamic properties, pressure, and temperature; and (III) a model based on chemical structure, pressure, and temperature. Furthermore, Eyring’s absolute rate theory is used to predict viscosity based on boiling temperature and temperature. To develop Model (I), a simple correlation was applied, while for Models (II) and (III), smart approaches such as multilayer perceptron networks optimized by a Levenberg–Marquardt algorithm (MLP-LMA) and Bayesian Regularization (MLP-BR), decision tree (DT), and least square support vector machine optimized by bat algorithm (BAT-LSSVM) were utilized to establish robust and accurate predictive paradigms. These approaches were implemented using a large database consisting of 2813 experimental viscosity points from 45 different ILs under an extensive range of pressure and temperature. Afterward, the four most accurate models were selected to construct a committee machine intelligent system (CMIS). Eyring’s theory’s results to predict the viscosity demonstrated that although the theory is not precise, its simplicity is still beneficial. The proposed CMIS model provides the most precise responses with an absolute average relative deviation (AARD) of less than 4% for predicting the viscosity of ILs based on Model (II) and (III). Lastly, the applicability domain of the CMIS model and the quality of experimental data were assessed through the Leverage statistical method. It is concluded that intelligent-based predictive models are powerful alternatives for time-consuming and expensive experimental processes of the ILs viscosity measurement.
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Abstract
Since their conception, ionic liquids (ILs) have been investigated for an extensive range of applications including in solvent chemistry, catalysis, and electrochemistry. This is due to their designation as designer solvents, whereby the physiochemical properties of an IL can be tuned for specific applications. This has led to significant research activity both by academia and industry from the 1990s, accelerating research in many fields and leading to the filing of numerous patents. However, while ILs have received great interest in the patent literature, only a limited number of processes are known to have been commercialised. This review aims to provide a perspective on the successful commercialisation of IL-based processes, to date, and the advantages and disadvantages associated with the use of ILs in industry.
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17
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Chemmangattuvalappil NG. Development of solvent design methodologies using computer-aided molecular design tools. Curr Opin Chem Eng 2020. [DOI: 10.1016/j.coche.2019.11.005] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
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18
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Zohari N, Fareghi-Alamdari R, Sheibani N. Model development and design criteria of hypergolic imidazolium ionic liquids from ignition delay time and viscosity viewpoints. NEW J CHEM 2020. [DOI: 10.1039/d0nj00521e] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
The relationships between ID time, viscosity and molecular structure of hypergolic imidazolium ILs are discussed to specify ideal structural characteristics.
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Affiliation(s)
- Narges Zohari
- Faculty of Chemistry and Chemical Engineering
- Malek-Ashtar University of Technology
- Tehran
- Iran
| | - Reza Fareghi-Alamdari
- Faculty of Chemistry and Chemical Engineering
- Malek-Ashtar University of Technology
- Tehran
- Iran
| | - Nasser Sheibani
- Faculty of Chemistry and Chemical Engineering
- Malek-Ashtar University of Technology
- Tehran
- Iran
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19
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Paduszyński K. Extensive Databases and Group Contribution QSPRs of Ionic Liquids Properties. 2. Viscosity. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.9b03150] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Kamil Paduszyński
- Department of Physical Chemistry, Faculty of Chemistry, Warsaw University of Technology, Noakowskiego 3, 00-664 Warsaw, Poland
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20
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Gani R. Group contribution-based property estimation methods: advances and perspectives. Curr Opin Chem Eng 2019. [DOI: 10.1016/j.coche.2019.04.007] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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21
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Koi ZK, Yahya WZN, Abu Talip RA, Kurnia KA. Prediction of the viscosity of imidazolium-based ionic liquids at different temperatures using the quantitative structure property relationship approach. NEW J CHEM 2019. [DOI: 10.1039/c9nj03436f] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A multilinear relationship between the viscosity and interaction energies using a stepwise model-building approach was applied to generate the correlation model.
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Affiliation(s)
- Zi Kang Koi
- Department of Chemical Engineering
- Universiti Teknologi PETRONAS
- Perak Darul Ridzuan
- Malaysia
| | - Wan Zaireen Nisa Yahya
- Department of Chemical Engineering
- Universiti Teknologi PETRONAS
- Perak Darul Ridzuan
- Malaysia
- Center of Research in Ionic Liquids
| | | | - Kiki Adi Kurnia
- Faculty of Fisheries and Marines
- Universitas Airlangga
- Surabaya
- Indonesia
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22
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Yan F, He W, Jia Q, Wang Q, Xia S, Ma P. Prediction of ionic liquids viscosity at variable temperatures and pressures. Chem Eng Sci 2018. [DOI: 10.1016/j.ces.2018.03.044] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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23
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Macías-Salinas R. A viscosity model for ionic liquids based on the Eyring's theory and a cubic EoS. J Mol Liq 2018. [DOI: 10.1016/j.molliq.2018.04.048] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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24
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Macías-Salinas R. Viscosity Modeling of Ionic Liquids Using the Friction Theory and a Simple Cubic Equation of State. Ind Eng Chem Res 2018. [DOI: 10.1021/acs.iecr.7b04252] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Ricardo Macías-Salinas
- ESIQIE-SEPI, Departamento
de Ingeniería Química, Instituto Politécnico Nacional, Ciudad
de México 07738, Mexico
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25
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Baghban A, Kardani MN, Habibzadeh S. Prediction viscosity of ionic liquids using a hybrid LSSVM and group contribution method. J Mol Liq 2017. [DOI: 10.1016/j.molliq.2017.04.019] [Citation(s) in RCA: 50] [Impact Index Per Article: 7.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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26
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Roosta A, Bardool R. A Simple Correlation for Estimating the Viscosity of Pure Ionic Liquids and Their Binary Mixtures. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b00532] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Affiliation(s)
- Aliakbar Roosta
- Chemical Engineering, Oil
and Gas Department, Shiraz University of Technology, Shiraz 13876-71557, Iran
| | - Roghayeh Bardool
- Chemical Engineering, Oil
and Gas Department, Shiraz University of Technology, Shiraz 13876-71557, Iran
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27
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Haghbakhsh R, Parvaneh K, Shariati A. Viscosities of Pure Ionic Liquids Using Combinations of Free Volume Theory or Friction Theory with the Cubic, the Cubic Plus Association, and the Perturbed-Chain Statistical Associating Fluid Theory Equations of State at High Pressures. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.6b04193] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Reza Haghbakhsh
- Natural Gas Engineering Department,
School of Chemical and Petroleum Engineering, Shiraz University, Mollasadra Avenue, Shiraz 71345, Iran
| | - Khalil Parvaneh
- Natural Gas Engineering Department,
School of Chemical and Petroleum Engineering, Shiraz University, Mollasadra Avenue, Shiraz 71345, Iran
| | - Alireza Shariati
- Natural Gas Engineering Department,
School of Chemical and Petroleum Engineering, Shiraz University, Mollasadra Avenue, Shiraz 71345, Iran
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28
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New levulinic acid-based deep eutectic solvents: Synthesis and physicochemical property determination. J Mol Liq 2016. [DOI: 10.1016/j.molliq.2016.07.039] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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29
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Zhao Y, Zhang X, Deng L, Zhang S. Prediction of viscosity of imidazolium-based ionic liquids using MLR and SVM algorithms. Comput Chem Eng 2016. [DOI: 10.1016/j.compchemeng.2016.04.035] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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Sattari M, Kamari A, Hashemi H, Mohammadi AH, Ramjugernath D. A group contribution model for prediction of the viscosity with temperature dependency for fluorine-containing ionic liquids. J Fluor Chem 2016. [DOI: 10.1016/j.jfluchem.2016.04.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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31
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Farahipour R, Mehrkesh A, Karunanithi AT. A systematic screening methodology towards exploration of ionic liquids for CO2 capture processes. Chem Eng Sci 2016. [DOI: 10.1016/j.ces.2015.12.015] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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32
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Haghbakhsh R, Raeissi S. Two simple correlations to predict viscosities of pure and aqueous solutions of ionic liquids. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2015.08.036] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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33
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Lazzús JA, Pulgar-Villarroel G. A group contribution method to estimate the viscosity of ionic liquids at different temperatures. J Mol Liq 2015. [DOI: 10.1016/j.molliq.2015.05.030] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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34
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Shen G, Held C, Mikkola JP, Lu X, Ji X. Modeling the Viscosity of Ionic Liquids with the Electrolyte Perturbed-Chain Statistical Association Fluid Theory. Ind Eng Chem Res 2014. [DOI: 10.1021/ie503485h] [Citation(s) in RCA: 27] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Affiliation(s)
- Gulou Shen
- Division
of Energy Science/Energy Engineering, Luleå University of Technology, 97187 Luleå, Sweden
- State
Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University, Nanjing 210009, P. R. China
| | - Christoph Held
- Laboratory
of Thermodynamics, Department of Biochemical and Chemical Engineering, TU Dortmund, Emil-Figge-Str. 70, 44227 Dortmund, Germany
| | - Jyri-Pekka Mikkola
- Technical
Chemistry, Department of Chemistry, Chemical-Biological Centre, Umeå University, 90187 Umeå, Sweden
- Industrial Chemistry & Reaction Engineering, Process Chemistry Centre, Åbo Akademi University, Biskopsgatan 8, 20500 Åbo-Turku, Finland
| | - Xiaohua Lu
- State
Key Laboratory of Materials-Oriented Chemical Engineering, Nanjing Tech University, Nanjing 210009, P. R. China
| | - Xiaoyan Ji
- Division
of Energy Science/Energy Engineering, Luleå University of Technology, 97187 Luleå, Sweden
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35
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Hosseinzadeh M, Hemmati-Sarapardeh A. Toward a predictive model for estimating viscosity of ternary mixtures containing ionic liquids. J Mol Liq 2014. [DOI: 10.1016/j.molliq.2014.10.033] [Citation(s) in RCA: 96] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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36
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Yan F, Lartey M, Jariwala K, Bowser S, Damodaran K, Albenze E, Luebke DR, Nulwala HB, Smit B, Haranczyk M. Toward a Materials Genome Approach for Ionic Liquids: Synthesis Guided by Ab Initio Property Maps. J Phys Chem B 2014; 118:13609-20. [DOI: 10.1021/jp506972w] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Fangyong Yan
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Michael Lartey
- National Energy Technology Laboratory, P.O. Box
10940, Pittsburgh, Pennsylvania 15236, United States
| | - Kuldeep Jariwala
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Sage Bowser
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
| | - Krishnan Damodaran
- Department
of Chemistry, University of Pittsburgh, 219 Parkman Avenue, Pittsburgh, Pennsylvania 15260, United States
| | - Erik Albenze
- National Energy Technology Laboratory, P.O. Box
10940, Pittsburgh, Pennsylvania 15236, United States
- URS Corporation, P.O. Box 618, South
Park, Pennsylvania 15129, United States
| | - David R. Luebke
- National Energy Technology Laboratory, P.O. Box
10940, Pittsburgh, Pennsylvania 15236, United States
| | - Hunaid B. Nulwala
- National Energy Technology Laboratory, P.O. Box
10940, Pittsburgh, Pennsylvania 15236, United States
- Department
of Chemistry, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Berend Smit
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Department
of Chemical and Biomolecular Engineering, University of California, Berkeley, California 94720, United States
- Institut des Sciences et Ingénierie Chimiques, Ecole Polytechnique Fédérale de Lausanne (EPFL), CH-1015 Lausanne, Switzerland
| | - Maciej Haranczyk
- Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
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37
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Zhao Y, Wang J, Jiang H, Hu Y. Study on the thermodynamic properties of ether-functionalized imidazolium-based ionic liquids. J Mol Liq 2014. [DOI: 10.1016/j.molliq.2014.04.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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38
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Dharaskar SA, Wasewar KL, Varma MN, Shende DZ, Yoo CK. Extractive Desulfurization of Liquid Fuels by Energy Efficient Green Thiazolium based Ionic Liquids. Ind Eng Chem Res 2014. [DOI: 10.1021/ie501108w] [Citation(s) in RCA: 38] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Swapnil A. Dharaskar
- Advance
Separation and Analytical Laboratory (ASAL), Department of Chemical
Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, MS 440010, India
| | - Kailas L. Wasewar
- Advance
Separation and Analytical Laboratory (ASAL), Department of Chemical
Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, MS 440010, India
| | - Mahesh N. Varma
- Advance
Separation and Analytical Laboratory (ASAL), Department of Chemical
Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, MS 440010, India
| | - Diwakar Z. Shende
- Advance
Separation and Analytical Laboratory (ASAL), Department of Chemical
Engineering, Visvesvaraya National Institute of Technology (VNIT), Nagpur, MS 440010, India
| | - Chang Kyoo Yoo
- Environmental Management & Systems Engineering Lab (EMSEL), Dept. of Environmental Science and Engineering, College of Engineering, Kyung Hee University, Seocheon-dong 1, Giheung-gu, Yongin-Si, Gyeonggi-Do 446-701, Korea
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39
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Paduszyński K, Domańska U. Viscosity of Ionic Liquids: An Extensive Database and a New Group Contribution Model Based on a Feed-Forward Artificial Neural Network. J Chem Inf Model 2014; 54:1311-24. [DOI: 10.1021/ci500206u] [Citation(s) in RCA: 165] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Kamil Paduszyński
- Department
of Physical Chemistry,
Faculty of Chemistry, Warsaw University of Technology, Noakowskiego
3, 00-664 Warsaw, Poland
| | - Urszula Domańska
- Department
of Physical Chemistry,
Faculty of Chemistry, Warsaw University of Technology, Noakowskiego
3, 00-664 Warsaw, Poland
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40
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Chen QL, Wu KJ, He CH. Thermal Conductivity of Ionic Liquids at Atmospheric Pressure: Database, Analysis, and Prediction Using a Topological Index Method. Ind Eng Chem Res 2014. [DOI: 10.1021/ie403500w] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Affiliation(s)
- Qiao-Li Chen
- State Key Laboratory
of Chemical Engineering, Department of Chemical and Biological
Engineering, Zhejiang University, Hangzhou 310027, China
| | - Ke-Jun Wu
- State Key Laboratory
of Chemical Engineering, Department of Chemical and Biological
Engineering, Zhejiang University, Hangzhou 310027, China
| | - Chao-Hong He
- State Key Laboratory
of Chemical Engineering, Department of Chemical and Biological
Engineering, Zhejiang University, Hangzhou 310027, China
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41
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Development of a LSSVM-GC model for estimating the electrical conductivity of ionic liquids. Chem Eng Res Des 2014. [DOI: 10.1016/j.cherd.2013.06.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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42
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Atilhan M, Jacquemin J, Rooney D, Khraisheh M, Aparicio S. Viscous Behavior of Imidazolium-Based Ionic Liquids. Ind Eng Chem Res 2013. [DOI: 10.1021/ie403065u] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Mert Atilhan
- Department
of Chemical Engineering, Qatar University, Doha 2173, Qatar
| | - Johan Jacquemin
- School
of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast, BT7 1NN, Northern Ireland, U.K
| | - David Rooney
- School
of Chemistry and Chemical Engineering, Queen’s University Belfast, Belfast, BT7 1NN, Northern Ireland, U.K
| | - Majeda Khraisheh
- Department
of Chemical Engineering, Qatar University, Doha 2173, Qatar
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