Machine learning-based prediction of paracetamol solubility and CO₂ density in supercritical systems using artificial rabbits optimization.

The accurate prediction of solubility and solvent properties in supercritical CO₂ systems remains a critical challenge in pharmaceutical process design due to the nonlinear and coupled effects of temperature and pressure. This study proposes a novel artificial intelligence-based modeling framework for predicting solvent density and paracetamol mole fraction under supercritical conditions using temperature and pressure as input variables. Three regression models-Multilayer Perceptron (MLP), Support Vector Regression (SVR), and Tweedie Regression (TDR)-were developed and systematically optimized using the Artificial Rabbits Optimization (ARO) algorithm. Unlike conventional single-model studies, this study provides a comparative and optimization-driven evaluation of both nonlinear machine learning models and statistically grounded regression methods under identical conditions. The results demonstrate that the ARO-optimized MLP model achieves superior predictive performance for both solvent density (R² = 0.99898) and mole fraction (R² = 0.96555), outperforming SVR and TDR models across all evaluation metrics. The study further reveals clear nonlinear dependencies of solubility and density on pressure and temperature, which are effectively captured through data-driven modeling and visualized via contour-based response surfaces. The main innovation of this work lies in the integration of a metaheuristic optimization strategy (ARO) with multiple regression paradigms to establish a unified and systematic framework for supercritical solubility prediction. This approach provides both high predictive accuracy and interpretable process insights, supporting early-stage optimization of pharmaceutical manufacturing in supercritical CO₂ environments.