Academic Journal
Unveiling Hydrogen Fluoride Emission Mechanisms in Municipal Solid Waste Incineration Using a Machine Learning Approach.
| Τίτλος: | Unveiling Hydrogen Fluoride Emission Mechanisms in Municipal Solid Waste Incineration Using a Machine Learning Approach. |
|---|---|
| Συγγραφείς: | Feng X; Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province/Hainan Provincial Academician Team Innovation Center/International Joint Research Center for the Control and Prevention of Environmental Pollution on Tropical Islands of Hainan Province/School of Environment Science and Engineering/School of Computer Science and Technology, Hainan University, Haikou 570228, China., Liu L; Everbright Environmental Energy (Danzhou) Co., Ltd, No.1 Guangda Environmental Protection Avenue, Nada Town, Danzhou 571754, China., Li J; Everbright Environmental Energy (Danzhou) Co., Ltd, No.1 Guangda Environmental Protection Avenue, Nada Town, Danzhou 571754, China., Ye M; School of Information Engineering, Shanghai Maritime University, Shanghai 201306, China., Mašek O; UK Biochar Research Centre, School of GeoSciences, University of Edinburgh, Alexander Crum Brown Road, Edinburgh EH9 3FF, U.K., Gouda S; Agricultural and Biosystems Engineering Department, Faculty of Agriculture, Benha University, Benha 13736, Egypt., Mohamed ElSayed Ali I; Agricultural and Biosystems Engineering Department, Faculty of Agriculture, Benha University, Benha 13736, Egypt., Chang K; Institute of Environmental Engineering, National Sun Yat-Sen University, Taiwan 80424, China., Wang X; Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province/Hainan Provincial Academician Team Innovation Center/International Joint Research Center for the Control and Prevention of Environmental Pollution on Tropical Islands of Hainan Province/School of Environment Science and Engineering/School of Computer Science and Technology, Hainan University, Haikou 570228, China., Huang Q; Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province/Hainan Provincial Academician Team Innovation Center/International Joint Research Center for the Control and Prevention of Environmental Pollution on Tropical Islands of Hainan Province/School of Environment Science and Engineering/School of Computer Science and Technology, Hainan University, Haikou 570228, China. |
| Πηγή: | Environmental science & technology [Environ Sci Technol] 2026 Jun 09; Vol. 60 (22), pp. 15901-15914. Date of Electronic Publication: 2026 May 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: American Chemical Society Country of Publication: United States NLM ID: 0213155 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1520-5851 (Electronic) Linking ISSN: 0013936X NLM ISO Abbreviation: Environ Sci Technol Subsets: MEDLINE |
| Imprint Name(s): | Publication: Washington DC : American Chemical Society Original Publication: Easton, Pa. : American Chemical Society, c1967- |
| Ιατρικοί όροι (MeSH): | Boosting Machine Learning Algorithms* , Incineration*, Air Pollutants ; China ; Solid Waste |
| Περίληψη: | Hydrogen fluoride (HF) emissions from municipal solid waste incineration (MSWI) pose significant environmental and health risks. However, their complex formation mechanisms remain poorly understood. This study presents an integrated machine learning framework combining XGBoost for HF prediction, SHAP for feature interpretation, structural equation modeling (SEM) for mechanistic analysis, generalized additive models (GAMs) for threshold identification, and self-adaptive nondominated sorting genetic algorithm II (SA-NSGA-II) for multiparameter optimization. Using over 150,000 high-frequency (5 s interval) sensor records from a waste-to-energy plant in Hainan Province, China (June 1-10, 2024), the XGBoost model showed the best performance among the evaluated models (R2 = 0.755, RMSE = 0.041 mg/m3, MAE = 0.031 mg/m3) via 5-fold cross-validation. SHAP analysis identified flue gas temperatures─especially the second flue right side (10.97%) and first flue top (10.19%)─as dominant factors. SEM confirmed the grate incineration zone as the primary HF source (path coefficient = 1.058, p < 0.001). GAM identified location-specific critical temperature thresholds for HF emission control, specifically 767 °C at the upper second flue gas pass, 875 °C at the first flue top, and 212 °C at the low-temperature economizer inlet. SA-NSGA-II optimization, validated with June 11 data, reduced HF emissions in 89.74% of cases, achieving a 17.61% average reduction (0.1176 mg/m3). This framework advances mechanistic understanding and provides data-driven strategies for sustainable MSWI operation and pollution mitigation. |
| Contributed Indexing: | Keywords: Emission Control Strategies; Hydrogen fluoride (HF); Machine learning; Municipal solid waste incineration (MSWI); Structural Equation Modeling (SEM) |
| Substance Nomenclature: | 0 (Air Pollutants) 0 (Solid Waste) |
| Entry Date(s): | Date Created: 20260528 Date Completed: 20260612 Latest Revision: 20260624 |
| Update Code: | 20260625 |
| DOI: | 10.1021/acs.est.6c00686 |
| PMID: | 42207555 |
| Βάση Δεδομένων: | MEDLINE |
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