识别感应炉中的最佳实践熔化模式：一种使用时间序列 KMeans 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
AI总结：
本文提出一种基于时间序列 K-means 聚类与多准则决策的数据驱动方法，通过分析感应炉温度曲线识别出最佳熔化模式簇，实施后使电力成本降低 8.6%，有效提升了能源效率。
摘要：
提高工业生产过程中的能源效率对于竞争力和遵守气候政策至关重要。本文引入了一种数据驱动方法来识别感应炉中的最佳熔化模式。通过时间序列 K-means 聚类，可以根据温度曲线将熔化模式分类为不同的簇。使用肘部法，识别出 12 个簇，代表了熔化模式的范围。为每个簇建立了熔化时间、比能性能和碳成本等性能参数，指示了炉效率和环境影响。利用包括简单加性加权、乘法指数加权、逼近理想解排序法、改进的 TOPSIS 和 VlseKriterijumska Optimizacija I Kompromisno Resenje 在内的多准则决策方法来确定最佳实践簇。研究成功识别出性能最佳的簇。实施最佳实践操作使电力成本降低了 8.6%，凸显了铸造厂的潜在节能空间。
英文摘要：
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.
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