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

Run Zhou, Qing Gao, Qiuju Wang and Guoren Xu ()
Additional contact information
Run Zhou: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Qing Gao: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Qiuju Wang: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
Guoren Xu: College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China

Sustainability, 2025, vol. 17, issue 7, 1-20

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 R 2 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.

Keywords: waste salts; machine learning; pyrolysis optimization (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/7/3216/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/7/3216/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:7:p:3216-:d:1628133

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().