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首页> 外文期刊>Industrial Informatics, IEEE Transactions on >Ladle Furnace Slag Characterization Through Hyperspectral Reflectance Regression Model for Secondary Metallurgy Process Optimization
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Ladle Furnace Slag Characterization Through Hyperspectral Reflectance Regression Model for Secondary Metallurgy Process Optimization

机译：高光谱反射率回归模型表征钢包炉渣用于二次冶金工艺优化

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摘要

In steelmaking process, close control of slag evolution is as important as control of steel composition. However, to date, there are no industrially consolidated techniques that allow us fast andnin-situnanalysis of the chemical composition of the slag, as in the case of steel with optical emission spectrometer spectrometers. In this work, a method to analyze spectral reflectance of ladle furnace slag samples to estimate their composition is proposed. This method does not require sample preprocessing and is based on a regression algorithm that mathematically maps the spectral reflectance of the slag with its actual composition with errors lower than 10%. Specifically designed normalization and calibration steps have been proposed to allow us a global model training with data from different locations. This allows us real-time monitoring of the thermo-dynamical state of the steel process by feeding a thermodynamic equilibrium optimization model. The optimizer minimizes the cost to reach the target steel quality with lower energy and additive costs. The system has been validated on several ArcelorMittal locations achieving process savings of 0.71 € per liquid steel tons.
机译：在炼钢过程中，严格控制炉渣的释放与控制钢成分同样重要。但是，到目前为止，还没有任何工业整合的技术可以使我们快速地运行n 矿渣化学成分的原位分析，例如采用光发射光谱仪的钢。在这项工作中，提出了一种分析钢包炉渣样品的光谱反射率以估计其成分的方法。该方法不需要样品预处理，它基于一种回归算法，该算法可数学绘制出炉渣的光谱反射率及其实际组成，其误差低于10％。已经提出了专门设计的归一化和校准步骤，以允许我们对来自不同位置的数据进行全局模型训练。通过输入热力学平衡优化模型，我们可以实时监控钢铁过程的热力学状态。优化器以较低的能源和添加剂成本最大程度地降低了达到目标钢质量的成本。该系统已在多个安赛乐米塔尔工厂进行了验证，每吨液态钢可节省0.71欧元的加工成本。

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