Development of novel computational models based on artificial intelligence technique to predict the viscosity of ionic liquids-water mixtures

Ran, Longyi; Wang, Zheng; Yang, Bing; Amiri-Margavi, Alireza; Alshahrani, Najim

doi:10.1016/j.csite.2024.104076

Case Studies in Thermal Engineering

2024

DOI: 10.1016/j.csite.2024.104076

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Development of novel computational models based on artificial intelligence technique to predict the viscosity of ionic liquids-water mixtures

Longyi Ran,

Zheng Wang,

Bing Yang

et al.

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Cited by 3 publications

References 17 publications

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Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

Talebi,

Haeri Boroujeni,

Razi

2024

Iran J Comput Sci

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Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

Talebi,

Haeri Boroujeni,

Razi

2024

Iran J Comput Sci

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Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition

Han

2025

Case Studies in Thermal Engineering

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Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures

Huwaimel,

Alanazi,

Alanazi

et al. 2024

Sci Rep

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This research article presents a thorough and all-encompassing examination of predictive models utilized in the estimation of viscosity for ionic liquid solutions. The study focuses on crucial input parameters, namely the type of cation, the type of anion, the temperature (measured in Kelvin), and the concentration of the ionic liquid (expressed in mol%). This study assesses three influential machine learning algorithms that are based on the Decision Tree methodology. These algorithms include Random Forest (RF), Gradient Boosting (GB), and XGBoost (XGB). Furthermore, the study incorporates the use of Glowworm Swarm Optimization (GSO) for hyper-parameter optimization, thereby further elevating the efficacy of the models. The results obtained from the evaluation showcase the exceptional predictive capabilities of the models, with Random Forest (RF) achieving an impressive R 2 value of 0.9971, Gradient Boosting (GB) attaining an R 2 value of 0.9916, and XGBoost (XGB) yielding an R 2 value of 0.9911. In addition to the R 2 metric, the study also presents other performance metrics, such as RMSE and MAPE, for each model. This comprehensive assessment of accuracy further solidifies the credibility and effectiveness of the models employed in the estimation of viscosity for ionic liquid solutions.

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Development of novel computational models based on artificial intelligence technique to predict the viscosity of ionic liquids-water mixtures

Cited by 3 publications

References 17 publications

Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

Integrating random regret minimization-based discrete choice models with mixed integer linear programming for revenue optimization

Theoretical and computational investigations on estimation of viscosity of ionic liquids for green adsorbent: Effect of temperature and composition

Computational models based on machine learning and validation for predicting ionic liquids viscosity in mixtures

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