acePhysics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework
Accurate prediction of ash fusion temperatures (AFTs) is crucial for ensuring the operational efficiency and safety of solid-fuel boilers and gasifiers. However, conventional machine learning methods typically treat each characteristic temperature as an independent prediction target, resulting in temperature inversions that violate the required physical ordering of AFTs. This study aimed to develop a Categorical Chain Differential framework for the coupled and physically consistent prediction of the four AFTs ac…
Physics-informed categorical chain differential model improved coupled prediction of four ash fusion temperatures, reducing deformation temperature error and eliminating physically impossible temperature inversions to support boiler safety and slagging-risk management.
Evidence
- Peer-reviewedBioresource Technology2026-09-11
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Truvace Impact Record TRV-2026-1065, v1: “Physics-informed machine learning for universal ash fusion temperatures prediction: A novel categorical chain differential framework.” Truvace, 2026-09-13. /record/TRV-2026-1065 (accessed at citation time). sha256 ea9f11df4fd00280…
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