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Establishment of Neural Network Prediction Model for Terminative Temperature Based on Grey Theory in Hot Metal Pretreatment

ZHANG Hui-ning , XU An-jun , CUI Jian , HE Dong-feng , TIAN Nai-yuan

钢铁研究学报(英文版)

In order to improve the accuracy of model for terminative temperature in steelmaking, it is necessary to predict and control before decarburization. Thus, an optimization neural network model of terminative temperature in the process of dephosphorization by laying correlative degree weights to all input factors related was used. Then simulation experiment of model newly established is conducted utilizing 210 data from a domestic steel plant. The results show that hit rate arrives at 5645% when error is within plus or minus 5%, and the value is 100% when within ±10%. Comparing to the traditional neural network prediction model, the accuracy almost increases by 6839%.Thus, the simulation prediction fits the real perfectly, which accounts for that neural network model for terminative temperature based on grey theory can reflect accurately the practice in dephosphorization. Naturally, this method is effective and practicable.

关键词: grey theory , correlation degree , dephosphorization , terminative temperature , neural network model

应用COMI炼钢工艺控制转炉脱磷基础研究

吕明，朱荣，毕秀荣，魏宁，汪灿荣，柯建祥

钢铁

基于转炉炼钢过程脱磷的热力学分析和计算，以控制转炉冶炼过程脱磷期温度为出发点，提出一种利用CO2气体代替部分O2进行吹炼的转炉炼钢新工艺，即COMI炼钢工艺。研究发现：COMI炼钢工艺能有效控制转炉熔池温度，降低半钢和一倒钢液磷含量，同时可有效减少炉渣铁损，为转炉高效脱磷提供了一种新思路。

关键词: 转炉 , dephosphorization , steelmaking process , carbon dioxide

转炉生产低磷钢的脱磷反应热力学

刘锟 , 刘浏 , 何平 , 崔阳 , 朱国森 , 李海波

钢铁

为实现磷质量分数小于0.010%的低磷钢批量生产，系统研究了转炉脱磷反应热力学。分析了影响转炉渣-金间磷分配比LP的主要因素，研究了P2O5活度系数和脱磷反应氧分压的定量确定方式，以及碳、磷选择性氧化问题。研究结果表明：LP主要受氧分压、P2O5活度系数和温度的影响；P2O5活度系数采用修正的柯热乌罗夫规则离子溶液模型计算较为准确；脱磷反应氧分压受炉渣氧分压控制，炉渣氧分压主要取决于钢中碳含量、炉渣碱度和温度。对传统复吹转炉生产磷质量分数小于0.010%低磷钢的工艺条件是：终渣碱度w(CaO)/w(SiO2)≥3.0，终渣w(MgO)≤9.0%，终点碳w(［C］)≤0.065%，终点温度控制在1873～1923K范围。

关键词: 转炉 , low phosphorous steel , dephosphorization , thermodynamics , regular solution model

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