Improving energy efficiency in industrial production processes is crucial for
competitiveness, and compliance with climate policies. This paper introduces a
data-driven approach to identify optimal melting patterns in induction
furnaces. Through time-series K-means clustering the melting patterns could be
classified into distinct clusters based on temperature profiles. Using the
elbow method, 12 clusters were identified, representing the range of melting
patterns. Performance parameters such as melting time, energy-specific
performance, and carbon cost were established for each cluster, indicating
furnace efficiency and environmental impact. Multiple criteria decision-making
methods including Simple Additive Weighting, Multiplicative Exponential
Weighting, Technique for Order of Preference by Similarity to Ideal Solution,
modified TOPSIS, and VlseKriterijumska Optimizacija I Kompromisno Resenje were
utilized to determine the best-practice cluster. The study successfully
identified the cluster with the best performance. Implementing the best
practice operation resulted in an 8.6 % reduction in electricity costs,
highlighting the potential energy savings in the foundry.

通过数据驱动方法识别感应炉的最佳熔化模式，采用时间序列 K-means 聚类对温度曲线进行分类，通过多种多标准决策方法确定最佳实践集群，研究成功地识别到具有最佳性能的集群，实施最佳操作可以降低 8.6％的电费，凸显铸造行业的节能潜力。

感应炉中最佳熔化模式的识别：一种基于时间序列 K 均值聚类和多准则决策的数据驱动方法

Identifying Best Practice Melting Patterns in Induction Furnaces: A  Data-Driven Approach Using Time Series KMeans Clustering and Multi-Criteria  Decision Making