A novel biomass fluidized bed gasification model coupled with machine learning and CFD simulation
This work addresses the need for better simulation tools in biomass energy conversion, though it appears incremental as it integrates existing techniques.
The authors tackled the challenge of predicting biomass fluidized bed gasification by developing a model that combines machine learning and computational fluid dynamics, resulting in improved prediction accuracy and computational efficiency for the thermochemical reaction process.
A coupling model of biomass fluidized bed gasification based on machine learning and computational fluid dynamics is proposed to improve the prediction accuracy and computational efficiency of complex thermochemical reaction process. By constructing a high-quality data set based on experimental data and high fidelity simulation results, the agent model used to describe the characteristics of reaction kinetics was trained and embedded into the computational fluid dynamics (CFD) framework to realize the real-time update of reaction rate and composition evolution.