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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
|
| Series: | Chemical Engineering Journal Advances |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666821124001169 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | 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 a wide range of feedstocks characteristics (biomass to plastics) and process conditions, which served as input for the model. Four data-driven models were trained and evaluated, with the XGBoost model having the best predictive accuracy (RMSE = 1.1 & R2 = 0.99) and being adapted for the physics-informed model. The optimum physics contribution was 30 % (70 % data contribution) to maintain predictive accuracy (RMSE = 2.7 & R2 = 0.95) and improve carbon closure. Feedstock properties were shown to have a higher feature importance compared to the operating conditions. The developed physics-informed model demonstrated the potential of ML models for the modelling of gasification of various waste streams. This is a promising first step towards improving data-driven ML models for application to thermochemical systems. |
|---|---|
| ISSN: | 2666-8211 |