Development of a novel physics-informed machine learning model for advanced thermochemical waste conversion
A physics-informed machine learning (ML) model, which incorporates the conservation of carbon mass, was developed to predict the product gas yield and composition for indirect gasification of waste in a fluidized bed. A dataset was compiled from experimental data of an in-house reactor, encompassing...
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Chemical Engineering Journal Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666821124001169 |
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