Blast furnace modeling and control is one of the important problems in the industrial field, and the black-box model is an effective mean to describe the complex blast furnace system. In practice, there are often different learning targets, such as safety and energy saving in industrial applications, depending on the application. For this reason, this paper proposes a framework to design a domain knowledge integrated classification model that yields a classifier for industrial application. Our knowledge incorporated learning scheme allows the users to create a classifier that identifies "important samples" (whose misclassifications can lead to severe consequences) more correctly, while keeping the proper precision of classifying the remaining samples. The effectiveness of the proposed method has been verified by two real blast furnace datasets, which guides the operators to utilize their prior experience for controlling the blast furnace systems better.

翻译：高炉建模与控制是工业领域的重要问题之一，而黑箱模型是描述复杂高炉系统的有效手段。实际应用中，由于工业场景的不同，往往存在不同的学习目标，例如安全性和节能性。为此，本文提出了一种融合领域知识的分类模型设计框架，能够生成适用于工业应用的分类器。我们提出的知识融合学习方案允许用户构建一个分类器，该分类器能够更准确地识别“重要样本”（其误分类可能导致严重后果），同时保持对剩余样本的分类精度。通过两个真实高炉数据集验证了所提方法的有效性，该方法可指导操作人员利用先验经验更好地控制高炉系统。