  This paper is devoted to a practical method for ferroalloys consumption
modeling and optimization. We consider the problem of selecting the optimal
process control parameters based on the analysis of historical data from
sensors. We developed approach, which predicts results of chemical reactions
and give ferroalloys consumption recommendation. The main features of our
method are easy interpretation and noise resistance. Our approach is based on
k-means clustering algorithm, decision trees and linear regression. The main
idea of the method is to identify situations where processes go similarly. For
this, we propose using a k-means based dataset clustering algorithm and a
classification algorithm to determine the cluster. This algorithm can be also
applied to various technological processes, in this article, we demonstrate its
application in metallurgy. To test the application of the proposed method, we
used it to optimize ferroalloys consumption in Basic Oxygen Furnace steelmaking
when finishing steel in a ladle furnace. The minimum required element content
for a given steel grade was selected as the predictive model's target variable,
and the required amount of the element to be added to the melt as the optimized
variable. Keywords: Clustering, Machine Learning, Linear Regression,
Steelmaking, Optimization, Gradient Boosting, Artificial Intelligence, Decision
Trees, Recommendation services

Machine Learning