一种可解释的铁合金消耗机器学习方法
An interpretable machine learning approach for ferroalloys consumptions
摘要：
研究了一种铁合金消耗建模与优化的实用方法。我们考虑的问题，选择最佳的过程控制参数的基础上分析的历史数据从传感器。提出了一种预测化学反应结果并给出铁合金消费建议的方法。我们的方法的主要特点是易于解释和抗噪声。我们的方法是基于k-均值聚类算法，决策树和线性回归。该方法的主要思想是识别流程相似的情况。为此，我们提出使用基于k-均值的数据集聚类算法和分类算法来确定聚类。该算法也可以应用于各种工艺过程，本文中，我们展示了它在冶金中的应用。为了测试所提出的方法的应用，我们使用它来优化铁合金消耗在碱性氧气炉炼钢时，完成在钢包炉钢。对于给定的钢种，所需的最小元素含量被选为预测模型的目标变量，并且要添加到熔体中的元素的所需量被选为优化变量。保留字：聚类，机器学习，线性回归，炼钢，优化，梯度提升，人工智能，决策树，推荐服务
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
