高炉煤气流分布及组分预测研究

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中图分类号：TF53 文献标志码：A 文章编号：1003-5168（2025）07-0088-04

DOI：10.19968/j.cnki.hnkj.1003-5168.2025.07.017

Study and Prediction of Blast Furnace Gas Flow Distribution and Compositon

CAO Shengfu （International Institute for Innovation，Jiangxi Universityof Scienceand Technology，Nanchang 330000， China）

Abstract：[Purposes] This study explores the impact of particle size on blast furnace gas flow distribution and develops a machine learning model for rapid prediction.[Methods] The research uses a coupled CFD-DEM approach to simulate gas flow and particle dynamics， while employing machine learning to create a predictive model for eficient gas flow forecasting.[Findings] The results show that larger particle sizes shift gas flow from the periphery to the center.The developed machine learning model was able to predict the internal gas distribution quickly and accurately.[Conclusions] The conclusions drawn from this study provide practical recommendations for industrial operations and offer a novel approach for constructing efficient predictive models for blast fumace gas flow distribution.

Keywords：blast furnace gas flow distribution; particle size; CFD-DEM coupling;machine learning; predictive model

0 引言

煤气流分布对高炉炼铁效率和质量至关重要。（剩余3029字）