Advancing multi-site emission control: A physics-informed transfer learning framework with mixture of experts for carbon-pollutant synergy
arXiv:2604.26571v2 Announce Type: replace Abstract: Municipal solid waste incineration (MSWI) converts urban waste to energy but simultaneously emits carbon dioxide, carbon monoxide and multiple regulated air pollutants whose formation is tightly coupled within a single combustion system. Controlling these emissions across a network of diverse facilities poses a fundamentally different challenge from optimising a single plant: data-driven models trained at one site capture local statistical patterns that rarely survive transfer to another, because they lack the physical constraints and regime-
The increasing scale of urban waste and energy demands, coupled with stricter environmental regulations, necessitates more sophisticated and scalable emission control solutions.
This research provides a framework for more efficient and adaptable emission control across distributed industrial sites, directly impacting environmental quality and operational costs for critical infrastructure.
Current site-specific AI models for emissions control are replaced by physics-informed transfer learning models that are more robust and generalizable across diverse facilities.
- · Environmental engineering firms
- · Smart city developers
- · AI/ML providers for industrial applications
- · Waste-to-energy plant operators
- · Providers of non-adaptive, single-site optimisation software
- · Regions with poor emission control due to technical challenges
- · Traditional combustion efficiency consultants
Improved air quality and reduced carbon footprint from industrial waste processing.
Increased economic viability and social acceptance of waste-to-energy solutions globally due to better environmental performance.
New regulations and industry standards emerge that mandate multi-site, physics-informed AI for environmental controls.
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Read at arXiv cs.LG