Process analytics to predict arc loss in an electric arc furnace**

冶炼是冶金过程中的一种能源密集型操作，即在电弧炉等设备中将矿石精炼成贱金属，电弧炉可以吸收高达80兆瓦的电力。熔炉运行的中断，如电弧的损失，可能会导致与生产时间损失和能源效率降低相关的昂贵成本。在这个项目中，我们的目标是结合过程分析、机器学习和大量的历史数据来开发一种能够预测炉弧损失的推断传感器。具体地说，我们的目标是研究上游冶金和电气变量的模式，以便开发一种监督学习分类模型，该模型可以在弧损发生的高概率时通知操作员。然后，操作员可以使用这些信息采取纠正措施并执行预测性维护，以确保保持稳定的运行和持续的生产。

Smelting is an energy intensive operation in metallurgical processes where ore is refined into base metal in units such as an electric arc furnace that can draw up to 80MW of power. Disruptions to the furnace operation, such as loss of the electric arc, can result in expensive costs associated with lost production time and reduced energy efficiency. In this project we aim to combine process analytics, machine learning and large amounts of historical data to develop an inferential sensor that can predict loss of the furnace arc. Specifically, our goal is to study patterns in upstream metallurgical and electrical variables in order to develop a supervised learning classification model that can inform operators when there is a high probability of the onset of arc loss. Operators can then use this information to take corrective actions and perform predictive maintenance to ensure that stable operation and constant production is maintained.