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

2025-09-07

AI Summary

To address the low prediction accuracy and high computational cost in modeling thermochemical reactions during biomass fluidized-bed gasification, this study proposes a dynamic modeling framework coupling machine learning with computational fluid dynamics (CFD). By integrating experimental data with high-fidelity simulation results, a high-quality training dataset is constructed to develop surrogate models for reaction rates and species evolution—models directly embeddable into CFD solvers. This enables data-driven reconstruction and real-time updating of the chemical submodel. The approach overcomes conventional kinetic models’ reliance on mechanistic simplifications and stiff numerical solvers, enhancing dynamic adaptability while preserving physical consistency. Results demonstrate over 35% reduction in prediction error for key gasification parameters—including H₂ and CO concentrations and temperature distribution—and approximately 80% decrease in single-case simulation time. This work establishes a novel paradigm for high-fidelity, low-cost, and real-time controllable digital twins of gasification processes.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Cognitive Modeling & Cognitive Systems: Computational Creativity
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Improving prediction accuracy of biomass gasification thermochemical reactions
• Enhancing computational efficiency for fluidized bed simulation processes
• Integrating machine learning with CFD for real-time reaction updates

Innovation

• Coupling machine learning with CFD simulation
• Training agent model on experimental dataset
• Embedding real-time reaction kinetics update