高炉铁水[Si]含量预测控制模型的设计与实现_电子资料文库 关键词:铁水硅[Si]含量;BP神经网络;专家系统;预测控制 [gap=548]Key Words: Hot metal Si-content, BP Neural network, Expert System, Prediction control
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铁水含硅量预测 prediction for silicon content of hot metal
The predicting and controlling model of silicon content in hot metal tapped from blast furnace is composed of following components or sub-models: ANNs prediction model, Expert model and interface.
高炉铁水硅含量预报、控制神经网络、专家系统主要由神经网络模块、专家系统模块以及人机接口所组成。
参考来源 - 基于神经网络和专家系统的铁水硅含量预报、控制软件研究·2,447,543篇论文数据，部分数据来源于NoteExpress
同时也讨论了喷煤、风温、铁水硅含量及装料制度对焦比的影响。
The factors for affecting coke rate were discussed such as coal injection, blast temperature, silicon content and charging patterns.
南京钢铁集团公司高炉炉渣中氧化铝含量高达19%，因此炉渣流动性变差，高炉被迫采用高温操作，铁水硅含量偏高，影响了高炉的强化。
Alumina content in Nanjing Steel blast furnace slag is up to 19%, which results in worse slag fluidity, higher furnace operating temperature and higher silicon content of hot metal.
文章分别用多元线性回归方法和神经元网络方法对铁水的硅含量进行预测。
This paper presents the prediction of Si content of molten iron by adopting methods of multivariate linear regression and neural networks.
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