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Peer-reviewedCC BYToxics

Machine Learning Prediction of NOx Emissions from Sewage Sludge Incineration.

  • Suwan Wei1,2
  • Guanghua Wu3
  • Jinli Zhou3
  • Zhigang Jiang3
  • Hong Li3
  • Zhenyi Qian3
  • Ping Lv3
  • Xiaoqian Wang3
  • Efeng Ma3
  • Jun Chu1,2
  • Junliang Wang2
  • Lian Fan3
1 more authors
  • Min Wu1,2,4,5
5 affiliations
  1. 1College of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou 310014, China.
  2. 2Shaoxing Research Institute, Zhejiang University of Technology, Shaoxing 312000, China.
  3. 3Zhejiang Zheneng Binhai Environmental Protection Energy Co., Ltd., Shaoxing 312000, China.
  4. 4Zhejiang Carbon Neutral Innovation Institute, Zhejiang University of Technology, Hangzhou 310014, China.
  5. 5International Science and Technology Cooperation Base in Carbon Emission Reduction and Monitoring Authorized by Zhejiang Provincial Department of Science and Technology, Hangzhou 310014, China.
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Abstract

Accurate prediction of NOx emissions is critical for optimizing selective non-catalytic reduction systems in circulating fluidized bed boilers co-firing sewage sludge and coal. This study develops a data-driven framework by using operational records. A multidimensional feature engineering scheme incorporating auto-regressive, combustion-related, denitration-related features is constructed. Six models-linear regression, random forest, XGBoost, backpropagation neural network, tuned XGBoost, and a stacking ensemble-are systematically compared. SHAP analysis reveals that the model correctly learns the key physicochemical mechanisms, including the temperature window effect and the reducing role of ammonia. The results provide a reliable predictive basis for precise ammonia injection control under fluctuating sludge incineration conditions.

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Cite

BibTeXRIS
BibTeX
@article{wei2026machine,
  title = {{Machine Learning Prediction of NOx Emissions from Sewage Sludge Incineration.}},
  author = {Suwan Wei and Guanghua Wu and Jinli Zhou and Zhigang Jiang and Hong Li and Zhenyi Qian and Ping Lv and Xiaoqian Wang and Efeng Ma and Jun Chu and Junliang Wang and Lian Fan and Min Wu},
  journal = {Toxics},
  year = {2026},
  month = sep,
  doi = {10.3390/toxics14100877},
  url = {https://europepmc.org/article/MED/42931664},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Machine Learning Prediction of NOx Emissions from Sewage Sludge Incineration.
AU  - Suwan Wei
AU  - Guanghua Wu
AU  - Jinli Zhou
AU  - Zhigang Jiang
AU  - Hong Li
AU  - Zhenyi Qian
AU  - Ping Lv
AU  - Xiaoqian Wang
AU  - Efeng Ma
AU  - Jun Chu
AU  - Junliang Wang
AU  - Lian Fan
AU  - Min Wu
PY  - 2026
DA  - 2026/09/30
JO  - Toxics
DO  - 10.3390/toxics14100877
UR  - https://europepmc.org/article/MED/42931664
AB  - Accurate prediction of NOx emissions is critical for optimizing selective non-catalytic reduction systems in circulating fluidized bed boilers co-firing sewage sludge and coal. This study develops a data-driven framework by using operational records. A multidimensional feature engineering scheme incorporating auto-regressive, combustion-related, denitration-related features is constructed. Six models-linear regression, random forest, XGBoost, backpropagation neural network, tuned XGBoost, and a stacking ensemble-are systematically compared. SHAP analysis reveals that the model correctly learns the key physicochemical mechanisms, including the temperature window effect and the reducing role of ammonia. The results provide a reliable predictive basis for precise ammonia injection control under fluctuating sludge incineration conditions.
SN  - 2305-6304
ER  -