1
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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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2
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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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3
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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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4
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Application of atomic electrostatic potential descriptors for predicting the eco-toxicity of ionic liquids towards leukemia rat cell line. Chem Eng Sci 2022. [DOI: 10.1016/j.ces.2022.117941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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5
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Asensio-Delgado S, Pardo F, Zarca G, Urtiaga A. Machine learning for predicting the solubility of high-GWP fluorinated refrigerants in ionic liquids. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120472] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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6
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Bîrgăuanu I, Danu M, Lisa C, Leon F, Curteanu S, Ibanescu C, Lisa G. Viscosity Deviation Modeling for Binary and Ternary Mixtures of Benzyl Alcohol-N-Hexanol-Water. MATERIALS (BASEL, SWITZERLAND) 2022; 15:5699. [PMID: 36013833 PMCID: PMC9413247 DOI: 10.3390/ma15165699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/28/2022] [Revised: 08/16/2022] [Accepted: 08/17/2022] [Indexed: 06/15/2023]
Abstract
Knowing the thermodynamic and transport properties of liquid systems is very important in engineering for the development of theoretical models and for the design of new technologies. Models that allow accurate predictions of thermodynamic and transport properties are needed in chemical engineering calculations involving fluid, heat, and mass transfer. In this study, the modeling of viscosity deviation for binary and ternary systems containing benzyl alcohol, n-hexanol, and water, less studied in the literature, was carried out using Redlich and Kister (R-L) models, multiple linear regression (MLR) models and artificial neural networks (ANN). The viscosity of the binary and ternary systems was experimentally determined at the following temperatures: 293.15, 303.15, 313.15, and 323.15 K. Viscosity deviation was calculated and then correlated with mole fractions, normalized temperature, and refractive index. The neural model that led to the best performance in the testing and validation stages contains 4 neurons in the input layer, 12 neurons in the hidden layer, and one neuron in the output layer. In the testing stage for this model, the standard deviation is 0.0067, and the correlation coefficient is 0.999. In the validation stage, a deviation of 0.0226 and a correlation coefficient of 0.996 were obtained. The MLR model led to worse results than those obtained with the neural model and also with the R-L models. The standard deviation for this model is 0.099, and the correlation coefficient is 0.898. Its advantage over the R-L type models is that the influence of both composition and temperature are included in a single equation.
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Affiliation(s)
- Iuliana Bîrgăuanu
- Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, Gheorghe Asachi Technical University, 73 Prof. Dr. Doc. D. Mangeron Street, 700050 Iasi, Romania
| | - Maricel Danu
- Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, Gheorghe Asachi Technical University, 73 Prof. Dr. Doc. D. Mangeron Street, 700050 Iasi, Romania
- Petru Poni Institute of Macromolecular Chemistry of Iaşi, 41A Aleea Gr. Ghica Voda, 700487 Iasi, Romania
| | - Cătălin Lisa
- Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, Gheorghe Asachi Technical University, 73 Prof. Dr. Doc. D. Mangeron Street, 700050 Iasi, Romania
| | - Florin Leon
- Faculty of Automatic Control and Computer Engineering, Gheorghe Asachi Technical University, 27A Prof. Dr. Doc. D. Mangeron Street, 700050 Iasi, Romania
| | - Silvia Curteanu
- Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, Gheorghe Asachi Technical University, 73 Prof. Dr. Doc. D. Mangeron Street, 700050 Iasi, Romania
| | - Constanta Ibanescu
- Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, Gheorghe Asachi Technical University, 73 Prof. Dr. Doc. D. Mangeron Street, 700050 Iasi, Romania
| | - Gabriela Lisa
- Faculty of Chemical Engineering and Environmental Protection “Cristofor Simionescu”, Gheorghe Asachi Technical University, 73 Prof. Dr. Doc. D. Mangeron Street, 700050 Iasi, Romania
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7
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Zheng W, Ma Z, Sun W, Zhao L. Target High‐efficiency Ionic Liquids to Promote
H
2
SO
4
‐catalyzed
C4
Alkylation by Machine Learning. AIChE J 2022. [DOI: 10.1002/aic.17698] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Affiliation(s)
- Weizhong Zheng
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Zhihong Ma
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Weizhen Sun
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
| | - Ling Zhao
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering East China University of Science and Technology Shanghai China
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8
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Chen Y, Peng B, Kontogeorgis GM, Liang X. Machine learning for the prediction of viscosity of ionic liquid–water mixtures. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.118546] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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9
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Hajihosseinloo A, Salahinejad M, Rofouei MK, Ghasemi JB. Exploratory and machine learning analysis of the stability constants of HgII- triazene ligands complexes. MAIN GROUP CHEMISTRY 2021. [DOI: 10.3233/mgc-210130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Knowing stability constants for the complexes HgII with extracting ligands is very important from environmental and therapeutic standpoints. Since the selectivity of ligands can be stated by the stability constants of cation–ligand complexes, quantitative structure–property relationship (QSPR) investigations on binding constant of HgII complexes were done. Experimental data of the stability constants in ML2 complexation of HgII and synthesized triazene ligands were used to construct and develop QSPR models. Support vector machine (SVM) and multiple linear regression (MLR) have been employed to create the QSPR models. The final model showed squared correlation coefficient of 0.917 and the standard error of calibration (SEC) value of 0.141 log K units. The proposed model presented accurate prediction with the Leave-One-Out cross validation ( Q LOO 2 = 0.756) and validated using Y-randomization and external test set. Statistical results demonstrated that the proposed models had suitable goodness of fit, predictive ability, and robustness. The results revealed the importance of charge effects and topological properties of ligand in HgII - triazene complexation.
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Affiliation(s)
| | - Maryam Salahinejad
- Maryam Salahinejad, Nuclear Science and Technology Research Institute, Tehran, Iran
| | | | - Jahan B. Ghasemi
- Department of Chemistry, Faculty of Science, University of Tehran, Tehran, Iran
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10
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Chen Y, Meng X, Cai Y, Liang X, Kontogeorgis GM. Optimal Aqueous Biphasic Systems Design for the Recovery of Ionic Liquids. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c03341] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Yuqiu Chen
- Department of Chemical and Biochemical Engineering, Technical University of Denmark DK-2800 Lyngby, Denmark
| | - Xianglei Meng
- Beijing Key Laboratory of Ionic Liquids Clean Process, CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase ComplexSystems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Yingjun Cai
- Beijing Key Laboratory of Ionic Liquids Clean Process, CAS Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase ComplexSystems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaodong Liang
- Department of Chemical and Biochemical Engineering, Technical University of Denmark DK-2800 Lyngby, Denmark
| | - Georgios M. Kontogeorgis
- Department of Chemical and Biochemical Engineering, Technical University of Denmark DK-2800 Lyngby, Denmark
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11
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Song F, Xiao Y, An S, Wan R, Xu Y, Peng C, Liu H. Prediction of Infinite Dilution Molar Conductivity for Unconventional Ions: A Quantitative Structure–Property Relationship Study. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c03019] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Fan Song
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yongjun Xiao
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Shuhao An
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Ren Wan
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Yingjie Xu
- Department of Chemistry, Shaoxing University, Shaoxing 312000, China
| | - Changjun Peng
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Honglai Liu
- School of Chemistry & Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
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12
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Zhang X, Wang J, Song Z, Zhou T. Data-Driven Ionic Liquid Design for CO 2 Capture: Molecular Structure Optimization and DFT Verification. Ind Eng Chem Res 2021. [DOI: 10.1021/acs.iecr.1c01384] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Affiliation(s)
- Xiang Zhang
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, Magdeburg D-39106, Germany
| | - Jingwen Wang
- Academy of Building Energy Efficiency, School of Civil Engineering, Guangzhou University, Guangzhou 510006, China
| | - Zhen Song
- Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, Magdeburg D-39106, Germany
| | - Teng Zhou
- Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, Magdeburg D-39106, Germany
- Process Systems Engineering, Otto-von-Guericke University Magdeburg, Universitätsplatz 2, Magdeburg D-39106, Germany
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13
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Nordness O, Kelkar P, Lyu Y, Baldea M, Stadtherr MA, Brennecke JF. Predicting thermophysical properties of dialkylimidazolium ionic liquids from sigma profiles. J Mol Liq 2021. [DOI: 10.1016/j.molliq.2021.116019] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
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14
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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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15
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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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16
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Abstract
In addition to proper physicochemical properties, low toxicity is also desirable when seeking suitable ionic liquids (ILs) for specific applications. In this context, machine learning (ML) models were developed to predict the IL toxicity in leukemia rat cell line (IPC-81) based on an extended experimental dataset. Following a systematic procedure including framework construction, hyper-parameter optimization, model training, and evaluation, the feedforward neural network (FNN) and support vector machine (SVM) algorithms were adopted to predict the toxicity of ILs directly from their molecular structures. Based on the ML structures optimized by the five-fold cross validation, two ML models were established and evaluated using IL structural descriptors as inputs. It was observed that both models exhibited high predictive accuracy, with the SVM model observed to be slightly better than the FNN model. For the SVM model, the determination coefficients were 0.9289 and 0.9202 for the training and test sets, respectively. The satisfactory predictive performance and generalization ability make our models useful for the computer-aided molecular design (CAMD) of environmentally friendly ILs.
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17
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Li H, Dai Q, Yang M, Li F, Liu X, Zhou M, Qian X. Heavy metals in submicronic particulate matter (PM 1) from a Chinese metropolitan city predicted by machine learning models. CHEMOSPHERE 2020; 261:127571. [PMID: 32721685 PMCID: PMC7340598 DOI: 10.1016/j.chemosphere.2020.127571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2020] [Revised: 06/28/2020] [Accepted: 06/29/2020] [Indexed: 05/04/2023]
Abstract
The aim of this study was to establish a method for predicting heavy metal concentrations in PM1 (aerosol particles with an aerodynamic diameter ≤ 1.0 μm) based on back propagation artificial neural network (BP-ANN) and support vector machine (SVM) methods. The annual average PM1 concentration was 26.31 μg/m3 (range: 7.00-73.40 μg/m3). The concentrations of most metals were higher in winter and lower in autumn and summer. Mn and Ni had the highest noncarcinogenic risk, and Cr the highest carcinogenic risk. The hazard index was below safe limit, and the integrated carcinogenic risk was less than precautionary value. There were no obvious differences in the simulation performances of BP-ANN and SVM models. However, in both models many elements had better simulation effects when input variables were atmospheric pollutants (SO2, NO2, CO, O3 and PM2.5) rather than PM1 and meteorological factors (temperature, relative humidity, atmospheric pressure and wind speed). Models performed better for Pb, Tl and Zn, as evidenced by training R and test R values consistently >0.85, whereas their performances for Ti and V were relatively poor. Predicted results by the fully trained models showed atmospheric heavy metal pollution was heavier in December and January and lighter in August and July of 2019. For the period covering the COVID-19 outbreak in China, from January to March 2020, most of the predicted element concentrations were lower than in 2018 and 2019, and the concentrations of nearly all metals were lowest during the nationwide implementation of countermeasures taken against the pandemic.
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Affiliation(s)
- Huiming Li
- School of Environment, Nanjing Normal University, Nanjing, 210023, China
| | - Qian'ying Dai
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Meng Yang
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China; Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Fengying Li
- Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China; Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control, School of Environmental Science and Engineering, Nanjing University of Information Science & Technology, Nanjing, 210044, China
| | - Xuemei Liu
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Mengfan Zhou
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China
| | - Xin Qian
- State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing, 210023, China; Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology, Nanjing, 210044, China.
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18
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Tu W, Zeng S, Zhang X, He X, Liu L, Zhang S, Zhang X. Strategy Combining Free Volume Theory and Fragment Contribution Corresponding State Method for Predicting Viscosities of Ionic Liquids. Ind Eng Chem Res 2019. [DOI: 10.1021/acs.iecr.8b06255] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Wenhui Tu
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
- School of Chemistry and Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Shaojuan Zeng
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaochun Zhang
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Xuezhong He
- Department of Chemical Engineering, Norwegian University of Science and Technology, NO-7491, Trondheim, Norway
| | - Lei Liu
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Suojiang Zhang
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
- School of Chemistry and Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiangping Zhang
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
- School of Chemistry and Chemical Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
- Dalian National Laboratory for Clean Energy, Dalian 116023, China
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19
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Forte E, Jirasek F, Bortz M, Burger J, Vrabec J, Hasse H. Digitalization in Thermodynamics. CHEM-ING-TECH 2019. [DOI: 10.1002/cite.201800056] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Affiliation(s)
- Esther Forte
- University of Kaiserslautern; Laboratory of Engineering Thermodynamics (LTD); Erwin-Schrödinger-Straße 44 67663 Kaiserslautern Germany
- Evonik Technology & Infrastructure GmbH; Rodenbacher Chaussee 4 63457 Hanau-Wolfgang Germany
| | - Fabian Jirasek
- University of Kaiserslautern; Laboratory of Engineering Thermodynamics (LTD); Erwin-Schrödinger-Straße 44 67663 Kaiserslautern Germany
| | - Michael Bortz
- Fraunhofer Institute for Industrial Mathematics (ITWM); Fraunhofer-Platz 1 67663 Kaiserslautern Germany
| | - Jakob Burger
- Technical University of Munich; Campus Straubing for Biotechnology and Sustainability; Chair of Chemical Process Engineering; Schulgasse 16 94315 Straubing Germany
| | - Jadran Vrabec
- Technical University Berlin; Thermodynamics and Process Engineering; Ernst-Reuter-Platz 1 10587 Berlin Germany
| | - Hans Hasse
- University of Kaiserslautern; Laboratory of Engineering Thermodynamics (LTD); Erwin-Schrödinger-Straße 44 67663 Kaiserslautern Germany
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20
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Relationship between hydrogen bond and viscosity for a series of pyridinium ionic liquids: Molecular dynamics and quantum chemistry. J Mol Liq 2018. [DOI: 10.1016/j.molliq.2018.01.121] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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21
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Kliemann C, Kleiber M, Müller K. Rheological Behavior of Mixtures of Ionic Liquids with Water. Chem Eng Technol 2018. [DOI: 10.1002/ceat.201600413] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Christian Kliemann
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Institute of Separation Science and Technology; Egerlandstrasse 3 91058 Erlangen Germany
| | - Michael Kleiber
- thyssenkrupp Industrial Solutions AG; Friedrich-Uhde-Strasse 2 65812 Bad Soden Germany
| | - Karsten Müller
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU); Institute of Separation Science and Technology; Egerlandstrasse 3 91058 Erlangen Germany
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22
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Kang X, Zhao Z, Qian J, Muhammad Afzal R. Predicting the Viscosity of Ionic Liquids by the ELM Intelligence Algorithm. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b02722] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Xuejing Kang
- College
of Materials and Chemical Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
| | - Zhijun Zhao
- State
Key Laboratory of Chemical Engineering, Department of Chemical Engineering, Tsinghua University, Beijing 100084, China
| | - Jianguo Qian
- Beijing
Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory
of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
| | - Raja Muhammad Afzal
- Beijing
Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory
of Multiphase Complex Systems, Institute of Process Engineering, Chinese Academy of Sciences, Beijing 100190, China
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23
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A modified scaled variable reduced coordinate (SVRC)-quantitative structure property relationship (QSPR) model for predicting liquid viscosity of pure organic compounds. KOREAN J CHEM ENG 2017. [DOI: 10.1007/s11814-017-0173-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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24
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Charte F, Romero I, Pérez-Godoy MD, Rivera AJ, Castro E. Comparative analysis of data mining and response surface methodology predictive models for enzymatic hydrolysis of pretreated olive tree biomass. Comput Chem Eng 2017. [DOI: 10.1016/j.compchemeng.2017.02.008] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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25
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Zhao Y, Gani R, Afzal RM, Zhang X, Zhang S. Ionic liquids for absorption and separation of gases: An extensive database and a systematic screening method. AIChE J 2017. [DOI: 10.1002/aic.15618] [Citation(s) in RCA: 62] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Affiliation(s)
- Yongsheng Zhao
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process; Institute of Process Engineering, Chinese Academy of Sciences; Beijing 100190 China
- School of Chemistry and Chemical Engineering; University of Chinese Academy of Sciences; Beijing 100049 China
| | - Rafiqul Gani
- Dept. of Chemical & Biochemical Engineering; Technical University of Denmark; Kgs. Lyngby DK 2800 Denmark
| | - Raja Muhammad Afzal
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process; Institute of Process Engineering, Chinese Academy of Sciences; Beijing 100190 China
| | - Xiangping Zhang
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process; Institute of Process Engineering, Chinese Academy of Sciences; Beijing 100190 China
| | - Suojiang Zhang
- Key Laboratory of Green Process and Engineering, State Key Laboratory of Multiphase Complex Systems, Beijing Key Laboratory of Ionic Liquids Clean Process; Institute of Process Engineering, Chinese Academy of Sciences; Beijing 100190 China
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26
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Usman M, Huang H, Li J, Hillestad M, Deng L. Optimization and Characterization of an Amino Acid Ionic Liquid and Polyethylene Glycol Blend Solvent for Precombustion CO2 Capture: Experiments and Model Fitting. Ind Eng Chem Res 2016. [DOI: 10.1021/acs.iecr.6b02457] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Muhammad Usman
- Department
of Chemical Engineering, Norwegian University of Science and Technology, Sem Sælandsvei 4, 7491 Trondheim, Norway
| | - Huang Huang
- Department
of Chemical Engineering, Norwegian University of Science and Technology, Sem Sælandsvei 4, 7491 Trondheim, Norway
| | - Jun Li
- Shanghai Research Institute of Petrochemical Technology, 1658 Pudong Beilu, Shanghai 201208, China
| | - Magne Hillestad
- Department
of Chemical Engineering, Norwegian University of Science and Technology, Sem Sælandsvei 4, 7491 Trondheim, Norway
| | - Liyuan Deng
- Department
of Chemical Engineering, Norwegian University of Science and Technology, Sem Sælandsvei 4, 7491 Trondheim, Norway
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27
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Red water treatment by photodegradation process in presence of modified TiO2 nanoparticles and validation of treatment efficiency by MLR technique. JOURNAL OF THE IRANIAN CHEMICAL SOCIETY 2016. [DOI: 10.1007/s13738-016-0945-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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28
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Zhao Y, Gao J, Huang Y, Afzal RM, Zhang X, Zhang S. Predicting H2S solubility in ionic liquids by the quantitative structure–property relationship method using Sσ-profile molecular descriptors. RSC Adv 2016. [DOI: 10.1039/c6ra15429h] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Predicting hydrogen sulfide (H2S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization.
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Affiliation(s)
- Yongsheng Zhao
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
| | - Jubao Gao
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
| | - Ying Huang
- Process Simulation Technology Department
- China HuanQiu Contracting & Engineering Corporation
- Beijing 100012
- China
| | - Raja Muhammad Afzal
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
| | - Xiangping Zhang
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
| | - Suojiang Zhang
- Beijing Key Laboratory of Ionic Liquids Clean Process
- State Key Laboratory of Multiphase Complex Systems
- Institute of Process Engineering
- Chinese Academy of Sciences
- Beijing 100190
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