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

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Abstract

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.
OriginalsprogEngelsk
TitelEnergy Informatics : Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part I
RedaktørerBo Nørregaard Jørgensen, Luiz Carlos Pereira da Silva, Zheng Ma
ForlagSpringer
Publikationsdatodec. 2023
Sider271–288
ISBN (Elektronisk)978-3-031-48649-4
DOI
StatusUdgivet - dec. 2023
BegivenhedEnergy Informatics.Academy Conference 2023 - Unicamp campus, São Paulo , Brasilien
Varighed: 6. dec. 20238. dec. 2023
Konferencens nummer: 3
https://www.energyinformatics.academy/eia-2023-conference

Konference

KonferenceEnergy Informatics.Academy Conference 2023
Nummer3
LokationUnicamp campus
Land/OmrådeBrasilien
BySão Paulo
Periode06/12/202308/12/2023
Internetadresse
NavnLecture Notes in Computer Science
Vol/bind14467
ISSN0302-9743

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