论文标题
一种可解释的机器学习方法
An interpretable machine learning approach for ferroalloys consumptions
论文作者
论文摘要
本文致力于一种实用方法，用于用铁合金消费建模和优化。我们考虑基于传感器的历史数据分析选择最佳过程控制参数的问题。我们开发了方法，该方法可以预测化学反应的结果并为铁合金的消费建议提供建议。我们方法的主要特征是简单的解释和抗噪声。我们的方法基于K-均值聚类算法，决策树和线性回归。该方法的主要思想是确定过程类似的情况。为此，我们建议使用基于K-Means的数据集聚类算法和分类算法来确定群集。该算法也可以应用于各种技术过程，在本文中，我们证明了其在冶金中的应用。为了测试所提出的方法的应用，我们使用它来优化基本氧气炉中的铁合金消耗，以便在钢包炉中完成钢。选择给定钢等级的最小必需元素含量作为预测模型的目标变量，以及将要添加到熔体中的元素的含量作为优化变量。关键词：聚类，机器学习，线性回归，钢材制造，优化，梯度提升，人工智能，决策树，推荐服务
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
