Development of a novel physics-informed machine learning model for advanced thermochemical waste conversion
article
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.
TNO Identifier
1003786
Source
Chemical Engineering Journal Advances, 21, pp. 1-16.
Pages
1-16