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

Author

Listed:
  • Run Zhou

    (College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
    These authors contributed equally to this work.)

  • Qing Gao

    (College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
    These authors contributed equally to this work.)

  • 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)

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.

Suggested Citation

  • Run Zhou & Qing Gao & Qiuju Wang & Guoren Xu, 2025. "Machine Learning Optimization of Waste Salt Pyrolysis: Predicting Organic Pollutant Removal and Mass Loss," Sustainability, MDPI, vol. 17(7), pages 1-20, April.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3216-:d:1628133
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    References listed on IDEAS

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    1. Antoniadis, Anestis & Lambert-Lacroix, Sophie & Poggi, Jean-Michel, 2021. "Random forests for global sensitivity analysis: A selective review," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    2. Huang, Zhen & Wang, Xiao-jie & Ren, Xuan, 2024. "Kinetic study of sesame stalk pyrolysis by thermogravimetric analysis," Renewable Energy, Elsevier, vol. 222(C).
    3. Luke Cullen & Andrea Marinoni & Jonathan Cullen, 2024. "Machine learning for gap‐filling in greenhouse gas emissions databases," Journal of Industrial Ecology, Yale University, vol. 28(4), pages 636-647, August.
    4. Li, Jie & Pan, Lanjia & Suvarna, Manu & Tong, Yen Wah & Wang, Xiaonan, 2020. "Fuel properties of hydrochar and pyrochar: Prediction and exploration with machine learning," Applied Energy, Elsevier, vol. 269(C).
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