Modeling surface tension of ionic liquids by chemical structure-intelligence based models

Mousavi, Seyed-Pezhman; Atashrouz, Saeid; Amar, Menad Nait; Hadavimoghaddam, Fahimeh; Mohammadi, Mohammad-Reza; Hemmati-Sarapardeh, Abdolhossein; Mohaddespour, Ahmad

doi:10.1016/j.molliq.2021.116961

Cited by 26 publications

(16 citation statements)

References 72 publications

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“…The sensitivity analysis was performed to evaluate the magnitude of the impacts of all input parameters on the As(V) removal predicted by the LightGBM model 66 . The value of the relevancy factor (r) determines the extent of each input parameter’s effect on the As(V) adsorption 67 . The factor “r” can be a negative or positive value.…”

Section: Resultsmentioning

confidence: 99%

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

Abdi

Mazloom²

2022

Sci Rep

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Arsenic in drinking water is a serious threat for human health due to its toxic nature and therefore, its eliminating is highly necessary. In this study, the ability of different novel and robust machine learning (ML) approaches, including Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting, Gradient Boosting Decision Tree, and Random Forest was implemented to predict the adsorptive removal of arsenate [As(V)] from wastewater over 13 different metal–organic frameworks (MOFs). A large experimental dataset was collected under various conditions. The adsorbent dosage, contact time, initial arsenic concentration, adsorbent surface area, temperature, solution pH, and the presence of anions were considered as input variables, and adsorptive removal of As(V) was selected as the output of the models. The developed models were evaluated using various statistical criteria. The obtained results indicated that the LightGBM model provided the most accurate and reliable response to predict As(V) adsorption by MOFs and possesses R2, RMSE, STD, and AAPRE (%) of 0.9958, 2.0688, 0.0628, and 2.88, respectively. The expected trends of As(V) removal with increasing initial concentration, solution pH, temperature, and coexistence of anions were predicted reasonably by the LightGBM model. Sensitivity analysis revealed that the adsorption process adversely relates to the initial As(V) concentration and directly depends on the MOFs surface area and dosage. This study proves that ML approaches are capable to manage complicated problems with large datasets and can be affordable alternatives for expensive and time-consuming experimental wastewater treatment processes.

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Section: Resultsmentioning

confidence: 99%

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

Abdi

Mazloom²

2022

Sci Rep

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“…The LSSVM which was suggested by Suykens and Vandewalle [33, 34] is considered as an alternative to the supervised SVM learning method proposed by Vapnik in the 1990s used for classification and regression to analyze data and identify patterns [35, 36]. This new version replaces the convex quadratic programming and inequality constraints of the original SVM by solving a linear set of equations and instead using equality constraints in the LSSVM method, regression error is applied to the optimization settings.…”

Section: Methodsmentioning

confidence: 99%

“…This new version replaces the convex quadratic programming and inequality constraints of the original SVM by solving a linear set of equations and instead using equality constraints in the LSSVM method, regression error is applied to the optimization settings. In fact, in SVM algorithms, the regression error is minimized in the learning phase whereas in LSSVM methods, it is mathematically defined and solved [36].…”

Section: Methodsmentioning

confidence: 99%

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Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ‐profile descriptors

Benmouloud

Si‐Moussa

Benkortbi

2022

Int J of Quantum Chemistry

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Ionic liquids (IL) are a new class of liquids considered as green solvents; less toxic, less flammable, and less polluting which retain their liquid state over wide temperature ranges and are considered alternatives to volatile organic solvents. The surface tension of IL-organic solvent mixtures plays an important role in the design and development of many industrial processes. This work investigated the capability and feasibility of four ANN model topologies ("trainbr, logsig"; "trainbr, tansig"; "trainlm, logsig"; "trainlm, tansig"), a PSO-SVM model, and an LSSVM model to predict the surface tension of binary systems containing IL. For this purpose, 1623 data points corresponding to the experimental surface tension values of binary mixtures containing IL were collected from the literature. The surface tension values were between 18.9 and 72.7 mN m À1 . The temperature, the composition in mole fraction of IL (X IL ), descriptors based on the sigma profiles, relating to the H-bond donor and to the H-bond acceptor character, the anion, the cation and the solvent were used as input variables of the model in order to differentiate the different compounds involved in the binary systems. A comparison of the experimental and the predicted values in terms of several statistical metrics showed good agreement, however, the prediction (trainbr, logsig) was better than the other approaches with an overall average absolute relative deviation of .8466% and a mean square error of .4952. These results are very encouraging for future projects modeling other physical and chemical properties of ILs.

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“…It is important to mention that the present study's IL property database is significantly larger than those reported previously in the literature and covers a large range of anion and cation structural diversity for model development. [27][28][29]31 Detailed information on the IL properties, anions, cations, temperature, and pressures is provided in the Supporting Information (Tables S3 and S4) along with the corresponding references.…”

Section: ■ Introductionmentioning

confidence: 99%

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

Mohan

Smith

Demerdash

et al. 2023

ACS Sustainable Chem. Eng.

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Ionic liquids (ILs) have unique solvent properties and have thus garnered significant interest. However, exhaustive experimental determination of the physicochemical properties of ILs is unrealistic due to the large structural diversity of anions and cations, their high cost, the requirements of elevated temperature and pressure, and the time required. To circumvent these experimental costs, computational approaches to accurately calculate these properties have emerged. In the present study, we present a demonstration of two machine learning (ML) models for the prediction of two critical IL physical properties, the surface tension and the speed of sound, across a wide range of temperatures and pressures. The models make use of molecular descriptors derived from the COSMO-RS, a quantum chemical-based model. The ML models show excellent agreement with experimental observations, with an R 2 value of 0.96–0.99 and RMSE of 1.71 mN/m and 16.12 m/s for the surface tension and speed of sound, respectively. This work paves the way for the development of COSMO-RS-informed ML models for the prediction of IL properties which can help to further optimize and accelerate technology development for ILs.

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Modeling surface tension of ionic liquids by chemical structure-intelligence based models

Cited by 26 publications

References 72 publications

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

Machine learning approaches for predicting arsenic adsorption from water using porous metal–organic frameworks

Machine learning approach for the prediction of surface tension of binary mixtures containing ionic liquids using σ‐profile descriptors

Quantum Chemistry-Driven Machine Learning Approach for the Prediction of the Surface Tension and Speed of Sound in Ionic Liquids

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