Simulation of TCM-based ionic liquids density behavior in a mixture with ethanol using machine learning approaches

• Accurate modeling of density behavior of TCM-based ionic liquids (ILs) mixed with ethanol. • Sensitivity analysis via Monte Carlo simulations. • CNN emerging as the top-performing estimator. The density behavior of TCM-based ionic liquids (ILs) mixed with ethanol forms the focus of this investigation, highlighting their distinctive physicochemical characteristics and industrial relevance. This research utilizes a suite of sophisticated machine learning methods, encompassing K-Nearest Neighbors (KNN), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), Decision Tree (DT), Adaptive Boosting (AdaBoost), Artificial Neural Networks (ANN), Random Forest (RF), and Ensemble Learning (EL) to model and forecast density behavior with high precision. To ensure each predictive model operated at its highest capability, the tuning of their internal hyperparameters was carried out through the Coupled Simulated Annealing (CSA) optimization strategy. This global search method was chosen for its ability to efficiently traverse complex parameter spaces and avoid premature convergence. The learning algorithms were trained and validated on a curated experimental dataset consisting of 426 density observations, of which approximately eighty percent (341 samples) were dedicated to model training, while the remaining portion (85 samples) served as an independent test set for evaluating generalization performance. The study covers three TCM-based ionic liquids: [C1C2IM][TCM], [C1C2PYR][TCM], and [C1C2MOR][TCM], in mixture with ethanol, across varying mole fractions and temperatures. The modeling leveraged key input features including ionic liquid type, molar mass (g/mol), mole fraction, and temperature (K) which are crucial determinants of the density behavior. The Monte Carlo–based sensitivity evaluation revealed that the ionic liquid mole fraction exerted the strongest effect on the system, with IL type, molar mass, and temperature contributing in descending order of influence. Prior to developing any of the predictive models, the complete dataset was thoroughly examined to verify its consistency, reliability, and overall suitability for machine-learning–based analysis. After this verification stage, the trained models were benchmarked using multiple statistical criteria. Among all evaluated approaches, the convolutional neural network demonstrated the most superior predictive capability, reflected in its minimal RMSE values, near-unity R² scores, and the lowest AARE percentages in both the training and independent testing evaluations. These findings clearly confirm the remarkable ability of machine learning techniques particularly convolutional neural networks to precisely predict the density of mixtures containing TCM-based ionic liquids and ethanol. This method provides a strong, efficient, and economical substitute for conventional experimental methodologies, empowering scientists to predict density characteristics with enhanced assurance and decreased experimental effort.