Domain knowledge based explainable feature construction method and its application in ironmaking process

Abstract

Data driven industrial modeling has been comprehensively studied for its high modeling accuracy. However, the unexplainable characteristic of data-driven modeling hinders workers from understanding the model and controlling process, and further holds back its application in industrial process. In order to solve this problem, we propose a genetic algorithm based method to construct interpretable features for industrial modeling in this paper. This model adopts the framework similar to genetic algorithm, but redefines the populations as features to adapt to the task of feature construction. The populations are evaluated by fitness function with punishment term to ensure the constructed features are concise. By using different genetic mutation and crossover operators, the proposed framework has the ability to combine domain knowledge to handle the characteristics of data, such as nonlinear, dynamic and time lag. The proposed method is experimented on the silicon content prediction task in ironmaking process, which is a classical process industry, achieving high accuracy.