Modeling energy efficiency in industrial plants: A novel diagnostic approach

John William Vásquez Capacho, Carlos Gustavo Perez-Zuñiga, Adalberto Ospino-Castro

Research output: Contribution to journalArticlepeer-review

Abstract

In industrial plants, diagnosing energy efficiency issues is essential to achieve sustainable operations and reduce costs. This paper introduces a novel diagnostic approach using advanced modeling techniques to identify inefficiencies in energy consumption within industrial environments. The proposed method uses discrete event analysis to detect and characterize abnormal energy usage patterns, providing a systematic framework for diagnosing performance issues in complex systems. Two case studies involving high-performance computing (HPC) systems illustrate the practical application of the approach, showcasing its ability to uncover critical inefficiencies and inform energy management strategies. The research addresses a significant gap in current methodologies by providing a detailed diagnostic tool customized to the unique challenges of industrial energy management. This study paves the way for future research into advanced diagnostic techniques, strengthening the importance of precise and actionable information on energy use for industrial stakeholders.

Original languageEnglish
Article number109777
JournalEngineering Applications of Artificial Intelligence
Volume142
DOIs
StatePublished - 15 Feb 2025

Keywords

  • Artificial intelligence
  • Diagnosis
  • Discrete-time systems
  • Energy efficiency
  • Industry 4.0

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