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 and in-situ analysis 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.

中文翻译：

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通过高光谱反射率回归模型表征钢包炉渣以优化二次冶金工艺


在炼钢过程中，严密控制炉渣的演变与控制钢的成分一样重要。然而，迄今为止，还没有工业综合技术可以让我们对炉渣的化学成分进行快速原位分析，就像用光学发射光谱仪对钢进行分析一样。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，基于回归算法，以数学方式映射炉渣的光谱反射率与其实际成分，误差低于 10%。已经提出了专门设计的标准化和校准步骤，以便我们能够使用来自不同位置的数据进行全局模型训练。这使我们能够通过输入热力学平衡优化模型来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限度地降低了达到目标钢材质量的成本。该系统已在安赛乐米塔尔的多个工厂进行了验证，每吨钢液可节省 0.71 _ 的工艺成本。