Machine Learning Optimization of Waste Salt Pyrolysis: Predicting Organic Pollutant Removal and Mass Loss

Abstract

Pyrolysis presents a promising solution for the complete purification and recycling of waste salt. However, the presence of organic pollutants in waste salts significantly hinders their practical application, owing to their diverse sources and strong resistance to degradation. This study developed predictive models for the removal of organic pollutants from waste salt using three machine learning techniques: Random Forest (RF), Support Vector Machine, and Artificial Neural Network. The models were evaluated based on the total organic carbon (TOC) removal rate and the mass loss rate, with the RF model demonstrating high accuracy, achieving R2 values of 0.97 and 0.99, respectively. Feature engineering revealed that the contribution of salt components was minimal, rendering them redundant. Feature importance analysis identified temperature as the most critical factor for TOC removal, while moisture content and carbon and nitrogen content were key determinants of mass loss. Partial dependence plots further elucidated the individual and interactive effects of these variables. The model was validated using both the literature data and laboratory experiments, and a user interface was developed using the Python GUI library. This study provides novel insights into the pyrolysis process of waste salt and establishes a foundation for optimizing its application.