A novel biomass fluidized bed gasification model coupled with machine learning and CFD simulation

Key Points

The proposed model enhances prediction accuracy during biomass gasification by integrating machine learning and CFD simulation.
A high-quality dataset constructed from experimental data and simulations was pivotal in training the agent model.
Integrating reaction kinetics within the CFD framework allows for real-time updates of reaction rates and composition changes.
This innovative approach may facilitate operational efficiency in thermochemical processes involving biomass.

Abstract

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.

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Cite This Study

Chun Wang (Sun,) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff261f https://doi.org/https://doi.org/10.48550/arxiv.2509.06056