转底炉直接还原多尺度耦合行为与介尺度优化机制

The Rotary Hearth Furnace Directly Restores the Multi-Scale Coupling Behavior and Mesoscale Optimization Mechanism

摘要: 针对传统转底炉直接还原炼铁传热效率低、还原效果不均的关键技术瓶颈，本文融合介尺度科学与智能化技术开展多尺度耦合及介尺度结构优化研究。通过热重–吊篮双路线实验，结合混沌特征识别与功率谱分析，构建高磷铁矿碳热还原三维扩散模型，建立介尺度结构与冶炼指标的多维度关联体系。研究识别含碳球团双“∞”混沌吸引子，揭示纯氧化铁与含CaO体系还原机制差异，明确介尺度动态演化与宏观性能的定量关联规律。经全周期验证与靶向调控，转底炉辐射传热效率达62%、热量利用率78%，为钢铁行业低碳智能转型与低品位铁矿高效利用提供技术参考。

Abstract: To tackle the key technical bottlenecks—low heat transfer efficiency and uneven reduction in traditional rotary hearth furnace direct reduction ironmaking, this study combines mesoscale science and intelligent technologies for multi-scale coupling and mesostructure optimization. By integrating thermogravimetric and hanging basket dual tests with chaotic feature recognition and power spectrum analysis, a 3D diffusion model for carbothermic reduction of high-phosphorus iron ore is built, and a multi-dimensional correlation system between mesoscale structures and smelting performance is established. This research discovers the double “∞” chaotic attractor of carbon-bearing pellets, reveals reduction mechanism differences between pure iron oxide and CaO-bearing ore systems, and clarifies the quantitative relationship between dynamic mesoscale evolution and macroscopic properties. After full-cycle verification and targeted regulation, the radiant heat transfer efficiency hits 62% and heat utilization efficiency reaches 78%. The findings offer solid technical support for low-carbon intelligent upgrading of the steel industry and efficient exploitation of low-grade iron ore resources.