Data-driven best-practice for energy-efficient operation of industrial processes

Projekter: ProjektForskning



This project aims to identify and evaluating best-practice guidelines for energy-efficient operation of industrial processes, and this project is expected to reduce the energy use in the manufacturing and process industry by 5-10% depending on the industrial process.
To achieve the goals, this project plans to address the following design and development aspects. Firstly, an IoT-enabled edge-computing infrastructure with energy smart metering and sensors is developed for onsite process monitoring, data collection, and validation. Secondly, a cloud platform with machine learning features is developed for energy consumption profiling and forecasting, energy flexibility potential analysis, production performance insights, and best practice evaluation with benchmarking and recommendation. The delivered platform will allow the manufacturing and process industry to automatically collect and analyses energy consumption and production data to determine best operation practice for minimizing energy use and CO2e emissions while ensuring product quality and process throughput.
Kort titelDECODE
Effektiv start/slut dato01/09/202231/08/2025


  • FellowMind (Projektpartner)
  • Inuatek A/S (Projektpartner)
  • Vald. Birn (Projektpartner)
  • Brødrene Hartmann A/S (Projektpartner)
  • Kongskilde Industries A/S (Projektpartner)
  • Lyras A/S (Projektpartner)
  • Kyocera Unimecro A/S (Projektpartner)
  • Lactosan A/S (Projektpartner)
  • Nordex Food A/S (Projektpartner)
  • Mammen Mejerier A/S (Projektpartner)
  • SDU Center for Energy Informatics (leder)


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