Energy performance analysis of continuous processes using surrogate models

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

Energy, 2019, vol. 183, issue C, 776-787

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.

Keywords: Energy performance indicators; Energy baseline; Energy management systems; Surrogate models; Process monitoring (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:183:y:2019:i:c:p:776-787

DOI: 10.1016/j.energy.2019.05.176

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