  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.
