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The intelligent steelmaking system for converter integrating neural network and artificial intelligence deep learning technology is developed by taking the 1^# converter of Baotou Steel as research object aiming at such problems as dependence of manual experiences, low intelligence as well as insufficient stability of production efficiency and quality of liquid steel for traditional converter steelmaking. The real-time judgment of converting state, accurate prediction of end point carbon and temperature as well as intelligent collaborative regulation and control of oxygen supply-oxygen lance position are realized by integrating such multi-source heterogeneous data as vision of flame at converter mouth, flue gas analysis and audio testing as well as establishing the dual-drive model of “mechanism-data” combining with such algorithms as the CNN, Bi-LSTM and reinforcement learning. The industrial tests showed that the dual hit rates of end point carbon and temperature for converter were increased to over 90%, smelting cycle of converter was shortened by 2 min as well as cost per ton of steel was reduced by CNY 1~2 Yuan with the system so that the transformation of converter operations from “experience driven” to “data driven” is effectively promoted.

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针对传统转炉炼钢依赖人工经验、智能化程度低、生产效率与钢水质量稳定性不足等问题，以包钢1号转炉为研究对象，开发了一套融合神经网络与人工智能深度学习技术的转炉智慧炼钢系统。系统通过整合炉口火焰视觉、烟气分析、音频检测等多源异构数据，构建“机理-数据”双驱动模型，结合CNN、Bi-LSTM、强化学习等算法，实现吹炼状态实时判识、终点碳和温度精准预测及供氧-枪位智能协同调控。工业试验表明，该系统使转炉终点碳和温度双命中率提升至90%以上，转炉冶炼周期缩短2 min，吨钢降本1~2元，有效推动了转炉炼钢操作从“经验驱动”向“数据驱动”的转型。

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张胤（1985-），男，河北省张家口市人，工程师，现从事炼钢工艺研究工作.

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张胤（1985-），男，河北省张家口市人，工程师，现从事炼钢工艺研究工作.

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