Abstract:Municipal solid waste incineration is increasingly central to urban waste management, yet its sustainability benefit depends on controlling carbon emissions and multiple air pollutants under highly heterogeneous operating conditions. Current data-driven models are often accurate within individual plants but are difficult to transfer across facilities, limiting their value for scalable emission-control strategies. Here we show that multi-site emission behaviour can be represented through transferable system-level structures when physical constraints, operating-regime heterogeneity and carbon--pollutant coupling are jointly considered. We develop a physics-informed transfer learning framework built on a carbon--pollutant mixture-of-experts model, which combines regime-dependent expert routing with conservation-based regularization and a carbon--pollutant synergistic index for integrated risk evaluation. Across 13 municipal solid waste incineration plants, the model captured both pollutant-specific emissions and system-level risk, achieving source-domain average pollutant $R^2$ values of 0.668--0.904 and CPSI $R^2$ values of 0.666--0.970. After transfer from a reference facility to 12 target plants, average pollutant $R^2$ remained between 0.661 and 0.842, while CPSI retained comparable transferability ($R^2$ = 0.610--0.841). Expert-utilization patterns further indicate that adaptation occurs through structured re-weighting of operating regimes rather than complete model re-learning. By extending the learned representation into an interpretable digital twin, this framework provides a route from emission prediction to regime-aware operational navigation, supporting scalable carbon--pollutant synergistic control across heterogeneous waste-to-energy systems.
| Comments: | Supplementary materials will be released after the final version is finalized |
| Subjects: | Machine Learning (cs.LG); Chemical Physics (physics.chem-ph); Data Analysis, Statistics and Probability (physics.data-an) |
| Cite as: | arXiv:2604.26571 [cs.LG] |
| (or arXiv:2604.26571v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26571 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Jun Chen [view email]
[v1]
Wed, 29 Apr 2026 11:54:18 UTC (12,341 KB)