Energy performance analysis of continuous processes using surrogate models

Highlights

• Influencing factors are identified for the evaluation of the energy performance.

• The best observed operation is determined, which reveals energy savings potentials.

• A verification of performance improvements is facilitated by comprehensive baselines.

• The data-driven modeling approach is shown to be efficient in the industrial context.

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.