高爐煤氣流分布及組分預測研究

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中圖分類號：TF53 文獻標志碼：A 文章編號：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

煤氣流分布對高爐煉鐵效率和質(zhì)量至關(guān)重要。（剩余3029字）