A data-driven predictive model for solubility: A case study of the NaCl-Na2SO4-H2O system

Abstract

Accurate prediction of solubility data in the Sodium Chloride-Sodium Sulfate-Water system is essential. It provides theoretical support for salt lake resource development and wastewater treatment technologies. This study proposes an innovative solubility prediction approach. It addresses the limitations of traditional thermodynamic models. This is particularly important when experimental data from various sources contain inconsistencies. Our approach combines the Weighted Local Outlier Factor technique for anomaly detection with a Deep Ensemble Neural Network architecture. This methodology effectively removes local outliers while preserving data distribution integrity, and integrates multiple neural network sub-models to comprehensively capture system features while minimizing individual model biases. Experimental validation demonstrates exceptional prediction performance across temperatures from −20 °C to 150 °C, achieving a coefficient of determination of 0.989 after Bayesian hyperparameter optimization. This data-driven approach provides more accurate and universally applicable solubility predictions than conventional thermodynamic models, offering theoretical guidance for industrial applications in salt lake resource utilization, separation process optimization, and environmental salt management systems.