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Energy performance analysis of continuous processes using surrogate models

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  • Beisheim, Benedikt
  • Rahimi-Adli, Keivan
  • Krämer, Stefan
  • Engell, Sebastian

Abstract

Energy intensity is a commonly used key performance indicator (KPI) for the energy performance of production processes and often serves as an Energy Performance Indicator (EnPI). The energy performance of a process depends on a variety of factors like capacity utilization, ambient temperature and operational performance. Understanding the influence of these factors on the relevant KPI or EnPI helps to distinguish between influenceable and non-influenceable contributions and to identify the improvement potential. By describing the best historically observed performance as a function of the non-influenceable factors, valuable information on the efficiency of the current operation of a plant and the improvement potential is provided to plant managers and operators. In this contribution, a method is proposed to identify a surrogate performance model for the attainable energy performance considering the relevant factors. The modeling method is based solely on the evaluation of historical process data and employs a novel combination of known surrogate modeling techniques using clustering, model fitting and model simplification by backward elimination. The method is applied to real process data of a large industrial production plant and the use of the model for process performance monitoring and reporting in accordance with energy management system requirements is illustrated and discussed.

Suggested Citation

  • Beisheim, Benedikt & Rahimi-Adli, Keivan & Krämer, Stefan & Engell, Sebastian, 2019. "Energy performance analysis of continuous processes using surrogate models," Energy, Elsevier, vol. 183(C), pages 776-787.
    • Handle: RePEc:eee:energy:v:183:y:2019:i:c:p:776-787
    DOI: 10.1016/j.energy.2019.05.176
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    References listed on IDEAS

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    1. Saygin, D. & Patel, M.K. & Worrell, E. & Tam, C. & Gielen, D.J., 2011. "Potential of best practice technology to improve energy efficiency in the global chemical and petrochemical sector," Energy, Elsevier, vol. 36(9), pages 5779-5790.
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    Cited by:

    1. Keivan Rahimi-Adli & Egidio Leo & Benedikt Beisheim & Sebastian Engell, 2021. "Optimisation of the Operation of an Industrial Power Plant under Steam Demand Uncertainty," Energies, MDPI, vol. 14(21), pages 1-28, November.
    2. Marta Daroń & Monika Górska, 2023. "Relationships between Selected Quality Tools and Energy Efficiency in Production Processes," Energies, MDPI, vol. 16(13), pages 1-20, June.
    3. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
    4. Beisheim, Benedikt & Krämer, Stefan & Engell, Sebastian, 2020. "Hierarchical aggregation of energy performance indicators in continuous production processes," Applied Energy, Elsevier, vol. 264(C).
    5. Panjapornpon, Chanin & Bardeeniz, Santi & Hussain, Mohamed Azlan, 2023. "Improving energy efficiency prediction under aberrant measurement using deep compensation networks: A case study of petrochemical process," Energy, Elsevier, vol. 263(PC).

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