Abstract
In recent decades, development of computational approaches to theoretically estimate the surface tension of various types of ionic liquids (ILs) has allowed the scientists to appropriately arrange new industrial-based processes with higher cost-effectiveness compared to experimental methods. In this paper three artificial intelligence (AI)-based predictive models have been developed to measure the surface tension two prevalent ILs including 1,3-dimethylimidazolium methylsulfate and 1-ethyl-3-methylimidazolium ethylsulfate. To create prediction models in this study, we employed Lasso, K Nearest Neighbor (KNN), and Linear Regression (LR) as basis models of a bagging regression ensemble. However, the dataset used has 45 input features and one output feature (surface tension). To find most important and useful input features, we also require a feature selection mechanism. We used ant colony optimization as a search strategy to discover the best feature combination for this purpose. Finally, the models were tested using various standard metrics. Bagging LR, Bagging Lasso, and Bagging KNN illustrate R-squared of 0.934, 0.915, and 0.977. Bagging KNN has been selected as the main model in this investigation because it had less mistakes and a higher R-squared. (c) 2022 Elsevier B.V. All rights reserved.