IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i7p3216-d1628133.html

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/7/3216/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/7/3216/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Huang, Zhen & Wang, Xiao-jie & Ren, Xuan, 2024. "Kinetic study of sesame stalk pyrolysis by thermogravimetric analysis," Renewable Energy, Elsevier, vol. 222(C).
    2. 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).
    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).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Djandja, Oraléou Sangué & Salami, Adekunlé Akim & Wang, Zhi-Cong & Duo, Jia & Yin, Lin-Xin & Duan, Pei-Gao, 2022. "Random forest-based modeling for insights on phosphorus content in hydrochar produced from hydrothermal carbonization of sewage sludge," Energy, Elsevier, vol. 245(C).
    2. Teimouri, Zahra & Abatzoglou, Nicolas & Dalai, Ajay K., 2024. "A novel machine learning framework for designing high-performance catalysts for production of clean liquid fuels through Fischer-Tropsch synthesis," Energy, Elsevier, vol. 289(C).
    3. Kim, Jun Young & Kim, Dongjae & Li, Zezhong John & Dariva, Claudio & Cao, Yankai & Ellis, Naoko, 2023. "Predicting and optimizing syngas production from fluidized bed biomass gasifiers: A machine learning approach," Energy, Elsevier, vol. 263(PC).
    4. Mehdi Dasineh & Amir Ghaderi & Mohammad Bagherzadeh & Mohammad Ahmadi & Alban Kuriqi, 2021. "Prediction of Hydraulic Jumps on a Triangular Bed Roughness Using Numerical Modeling and Soft Computing Methods," Mathematics, MDPI, vol. 9(23), pages 1-24, December.
    5. Torii, André Jacomel & Novotny, Antonio André, 2021. "A priori error estimates for local reliability-based sensitivity analysis with Monte Carlo Simulation," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
    6. Wojciech Jerzak & Bin Li & Dennys Correia da Silva & Glauber Cruz, 2025. "Direct Air Capture Using Pyrolysis and Gasification Chars: Key Findings and Future Research Needs," Energies, MDPI, vol. 18(15), pages 1-42, August.
    7. Li, Chunxing & Wang, Yu & Xie, Shengyu & Wang, Ruming & Sheng, Hu & Yang, Hongmin & Yuan, Zengwei, 2024. "Synergistic treatment of sewage sludge and food waste digestate residues for efficient energy recovery and biochar preparation by hydrothermal pretreatment, anaerobic digestion, and pyrolysis," Applied Energy, Elsevier, vol. 364(C).
    8. Fan, Yongsheng & Qin, Changsheng & Zhao, Keyu & Xiong, Yonglian & Shi, Yunxi, 2025. "Conversion of lignin with polystyrene into high-value aromatics through co-pyrolysis and post-plasma refining," Energy, Elsevier, vol. 318(C).
    9. Jung, WoongHee & Taflanidis, Alexandros A., 2023. "Efficient global sensitivity analysis for high-dimensional outputs combining data-driven probability models and dimensionality reduction," Reliability Engineering and System Safety, Elsevier, vol. 231(C).
    10. Li, Dinghao & Wang, Zhi & Liu, Yulong & Shi, Haobo & Wan, Yanbo & Shi, Qingdong, 2025. "Impact of surface water and groundwater changes on the growth condition of desert riparian forests," Agricultural Water Management, Elsevier, vol. 319(C).
    11. Ascher, Simon & Watson, Ian & You, Siming, 2022. "Machine learning methods for modelling the gasification and pyrolysis of biomass and waste," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    12. Pilowsky, Julia A. & Manica, Andrea & Brown, Stuart & Rahbek, Carsten & Fordham, Damien A., 2022. "Simulations of human migration into North America are more sensitive to demography than choice of palaeoclimate model," Ecological Modelling, Elsevier, vol. 473(C).
    13. Yue Guo & Xin Zheng & Wei Wei & Yuancheng He & Xiang Peng & Fei Zhao & Hailong Wu & Wenxin Bi & Hongyang Yan & Xiaohan Ren, 2025. "Construction of Multi-Sample Public Building Carbon Emission Database Model Based on Energy Activity Data," Energies, MDPI, vol. 18(14), pages 1-25, July.
    14. Guan, Xuefei, 2026. "The moment quadrature method for global sensitivity analysis," Reliability Engineering and System Safety, Elsevier, vol. 265(PA).
    15. Xiang Peng & Xiaoqing Xu & Jiquan Li & Shaofei Jiang, 2021. "A Sampling-Based Sensitivity Analysis Method Considering the Uncertainties of Input Variables and Their Distribution Parameters," Mathematics, MDPI, vol. 9(10), pages 1-18, May.
    16. Lei, Hongxuan & Liu, Pan & Cheng, Qian & Xu, Huan & Liu, Weibo & Zheng, Yalian & Chen, Xiangding & Zhou, Yong, 2024. "Frequency, duration, severity of energy drought and its propagation in hydro-wind-photovoltaic complementary systems," Renewable Energy, Elsevier, vol. 230(C).
    17. Chen, Xuyong & Xu, Zhifeng & Wu, Yushun & Wu, Qiaoyun, 2023. "Heuristic algorithms for reliability estimation based on breadth-first search of a grid tree," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    18. Cheng, Xuekun & Hu, Mengchen & Keski-Saari, Sarita & Zhu, Xudan & Zhou, Yufeng & Berninger, Frank & Zhou, Guomo, 2025. "Integrating economic and ecological drivers in forest product yield forecasting: A Finnish case study," Forest Policy and Economics, Elsevier, vol. 178(C).
    19. Ai, Zejian & Luo, Song & Xu, Zhengyong & Cao, Jianbing & Leng, Lijian & Li, Hailong, 2024. "Prediction and optimization design of porous structure properties of biomass-derived biochar using machine learning methods," Energy, Elsevier, vol. 313(C).
    20. Cosenza, Bartolomeo & Picone, Antonio & Volpe, Maurizio & Messineo, Antonio, 2025. "Improving hydrothermal carbonization prediction by machine learning: towards a more accurate and less expensive process modelling through data augmentation," Energy, Elsevier, vol. 333(C).

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:3216-:d:1628133. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.