1
|
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.
Collapse
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.
| |
Collapse
|
2
|
Chew AK, Sender M, Kaplan Z, Chandrasekaran A, Chief Elk J, Browning AR, Kwak HS, Halls MD, Afzal MAF. Advancing material property prediction: using physics-informed machine learning models for viscosity. J Cheminform 2024; 16:31. [PMID: 38486289 PMCID: PMC10938832 DOI: 10.1186/s13321-024-00820-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 02/27/2024] [Indexed: 03/18/2024] Open
Abstract
In materials science, accurately computing properties like viscosity, melting point, and glass transition temperatures solely through physics-based models is challenging. Data-driven machine learning (ML) also poses challenges in constructing ML models, especially in the material science domain where data is limited. To address this, we integrate physics-informed descriptors from molecular dynamics (MD) simulations to enhance the accuracy and interpretability of ML models. Our current study focuses on accurately predicting viscosity in liquid systems using MD descriptors. In this work, we curated a comprehensive dataset of over 4000 small organic molecules' viscosities from scientific literature, publications, and online databases. This dataset enabled us to develop quantitative structure-property relationships (QSPR) consisting of descriptor-based and graph neural network models to predict temperature-dependent viscosities for a wide range of viscosities. The QSPR models reveal that including MD descriptors improves the prediction of experimental viscosities, particularly at the small data set scale of fewer than a thousand data points. Furthermore, feature importance tools reveal that intermolecular interactions captured by MD descriptors are most important for viscosity predictions. Finally, the QSPR models can accurately capture the inverse relationship between viscosity and temperature for six battery-relevant solvents, some of which were not included in the original data set. Our research highlights the effectiveness of incorporating MD descriptors into QSPR models, which leads to improved accuracy for properties that are difficult to predict when using physics-based models alone or when limited data is available.
Collapse
|
3
|
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.
Collapse
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
| |
Collapse
|
4
|
Zhou T, Gui C, Sun L, Hu Y, Lyu H, Wang Z, Song Z, Yu G. Energy Applications of Ionic Liquids: Recent Developments and Future Prospects. Chem Rev 2023; 123:12170-12253. [PMID: 37879045 DOI: 10.1021/acs.chemrev.3c00391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2023]
Abstract
Ionic liquids (ILs) consisting entirely of ions exhibit many fascinating and tunable properties, making them promising functional materials for a large number of energy-related applications. For example, ILs have been employed as electrolytes for electrochemical energy storage and conversion, as heat transfer fluids and phase-change materials for thermal energy transfer and storage, as solvents and/or catalysts for CO2 capture, CO2 conversion, biomass treatment and biofuel extraction, and as high-energy propellants for aerospace applications. This paper provides an extensive overview on the various energy applications of ILs and offers some thinking and viewpoints on the current challenges and emerging opportunities in each area. The basic fundamentals (structures and properties) of ILs are first introduced. Then, motivations and successful applications of ILs in the energy field are concisely outlined. Later, a detailed review of recent representative works in each area is provided. For each application, the role of ILs and their associated benefits are elaborated. Research trends and insights into the selection of ILs to achieve improved performance are analyzed as well. Challenges and future opportunities are pointed out before the paper is concluded.
Collapse
Affiliation(s)
- Teng Zhou
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR 999077, China
- HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Futian, Shenzhen 518048, China
| | - Chengmin Gui
- State Key Laboratory of Chemical Resource Engineering, Beijing University of Chemical Technology, Beijing 100029, China
| | - Longgang Sun
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
| | - Yongxin Hu
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
| | - Hao Lyu
- Sustainable Energy and Environment Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China
| | - Zihao Wang
- Department for Process Systems Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstr. 1, D-39106 Magdeburg, Germany
| | - Zhen Song
- State Key Laboratory of Chemical Engineering, School of Chemical Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Gangqiang Yu
- Faculty of Environment and Life, Beijing University of Technology, 100 Ping Le Yuan, Chaoyang District, Beijing 100124, China
| |
Collapse
|
5
|
Artificial neural network modeling on the polymer-electrolyte aqueous two-phase systems involving biomolecules. Sep Purif Technol 2022. [DOI: 10.1016/j.seppur.2022.122624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
|
6
|
Effect of hydrated shell layers on surface tension of electrolyte solutions: Insights from interpretable machine learning. J Mol Liq 2022. [DOI: 10.1016/j.molliq.2022.120887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|