Identifying Best Practice Melting Patterns in Induction Furnaces: A
  Data-Driven Approach Using Time Series KMeans Clustering and Multi-Criteria
  Decision Making
Daniel Anthony Howard Bo Nørregaard Jørgensen Zheng Ma
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.