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

Daniel Anthony Howard; Bo Nørregaard Jørgensen; Zheng Grace Ma

doi:10.1007/978-3-031-48649-4_16

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

Daniel Anthony Howard, Bo Nørregaard Jørgensen, Zheng Grace Ma^*

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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.

Original language	English
Title of host publication	Energy Informatics : Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part I
Editors	Bo Nørregaard Jørgensen, Luiz Carlos Pereira da Silva, Zheng Ma
Publisher	Springer
Publication date	Dec 2023
Pages	271–288
ISBN (Electronic)	978-3-031-48649-4
DOIs	https://doi.org/10.1007/978-3-031-48649-4_16
Publication status	Published - Dec 2023
Event	Energy Informatics.Academy Conference 2023 - Unicamp campus, São Paulo , Brazil Duration: 6. Dec 2023 → 8. Dec 2023 Conference number: 3 https://www.energyinformatics.academy/eia-2023-conference

Conference

Conference	Energy Informatics.Academy Conference 2023
Number	3
Location	Unicamp campus
Country/Territory	Brazil
City	São Paulo
Period	06/12/2023 → 08/12/2023
Internet address	https://www.energyinformatics.academy/eia-2023-conference

Series	Lecture Notes in Computer Science
Volume	14467
ISSN	0302-9743

Documents & Links

10.1007/978-3-031-48649-4_16

https://arxiv.org/abs/2401.04751Licence: CC BY-NC-ND

Embargoed Document

Open Access Version
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Embargo ends: 02/12/2024

1 Ph.D. thesis

Generic Digital Twin Development Framework for Enhancing Energy Efficiency and Flexibility in Industrial Production Processes
Howard, D. A., 16. Oct 2023, Syddansk Universitet. Det Tekniske Fakultet. 274 p.
Research output: Thesis › Ph.D. thesis

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Howard, D. A., Jørgensen, B. N., & Ma, Z. G. (2023). Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-Criteria Decision Making. In B. N. Jørgensen, L. C. Pereira da Silva, & Z. Ma (Eds.), Energy Informatics: Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part I (pp. 271–288). Springer. https://doi.org/10.1007/978-3-031-48649-4_16

Howard, Daniel Anthony ; Jørgensen, Bo Nørregaard ; Ma, Zheng Grace. / Identifying Best Practice Melting Patterns in Induction Furnaces : A Data-Driven Approach Using Time Series K-Means Clustering and Multi-Criteria Decision Making. Energy Informatics: Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part I. editor / Bo Nørregaard Jørgensen ; Luiz Carlos Pereira da Silva ; Zheng Ma. Springer, 2023. pp. 271–288 (Lecture Notes in Computer Science, Vol. 14467).

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

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.",

author = "Howard, {Daniel Anthony} and J{\o}rgensen, {Bo N{\o}rregaard} and Ma, {Zheng Grace}",

year = "2023",

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language = "English",

series = "Lecture Notes in Computer Science",

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editor = "J{\o}rgensen, {Bo N{\o}rregaard} and {Pereira da Silva}, { Luiz Carlos} and Zheng Ma",

booktitle = "Energy Informatics",

address = "Germany",

note = "Energy Informatics.Academy Conference 2023, EI.A 2023 ; Conference date: 06-12-2023 Through 08-12-2023",

url = "https://www.energyinformatics.academy/eia-2023-conference",

}

Howard, DA, Jørgensen, BN & Ma, ZG 2023, Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-Criteria Decision Making. in BN Jørgensen, LC Pereira da Silva & Z Ma (eds), Energy Informatics: Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part I. Springer, Lecture Notes in Computer Science, vol. 14467, pp. 271–288, Energy Informatics.Academy Conference 2023, São Paulo , Brazil, 06/12/2023. https://doi.org/10.1007/978-3-031-48649-4_16

Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-Criteria Decision Making. / Howard, Daniel Anthony; Jørgensen, Bo Nørregaard ; Ma, Zheng Grace.
Energy Informatics: Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part I. ed. / Bo Nørregaard Jørgensen; Luiz Carlos Pereira da Silva; Zheng Ma. Springer, 2023. p. 271–288 (Lecture Notes in Computer Science, Vol. 14467).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Identifying Best Practice Melting Patterns in Induction Furnaces

T2 - Energy Informatics.Academy Conference 2023

AU - Howard, Daniel Anthony

AU - Jørgensen, Bo Nørregaard

AU - Ma, Zheng Grace

N1 - Conference code: 3

PY - 2023/12

Y1 - 2023/12

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-031-48649-4_16

DO - 10.1007/978-3-031-48649-4_16

M3 - Article in proceedings

T3 - Lecture Notes in Computer Science

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EP - 288

BT - Energy Informatics

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ER -

Howard DA, Jørgensen BN , Ma ZG. Identifying Best Practice Melting Patterns in Induction Furnaces: A Data-Driven Approach Using Time Series K-Means Clustering and Multi-Criteria Decision Making. In Jørgensen BN, Pereira da Silva LC, Ma Z, editors, Energy Informatics: Third Energy Informatics Academy Conference, EI.A 2023, Campinas, Brazil, December 6–8, 2023, Proceedings, Part I. Springer. 2023. p. 271–288. (Lecture Notes in Computer Science, Vol. 14467). doi: 10.1007/978-3-031-48649-4_16

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

Abstract

Conference

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Related research output

Generic Digital Twin Development Framework for Enhancing Energy Efficiency and Flexibility in Industrial Production Processes

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