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Motivation

Industrial companies are increasingly challenged by rising energy costs while also having to meet ambitious emission targets. Key levers to address these challenges are improving energy efficiency and increasing energy flexibility in production and energy supply. Previous research projects such as KI4ETA (KI4ETA – Production Management, Technology and Machine Tools – TU Darmstadt) and PHI-Factory (PHI-Factory – Production Management, Technology and Machine Tools – TU Darmstadt) have already investigated production planning and supply systems, but largely treated them separately. In both areas, significant savings potentials were identified, reaching up to 15 % in production and up to 50 % in supply systems.

Despite these successes, it has become evident that the isolated optimization of individual subsystems is insufficient to meet the growing complexity of industrial energy systems. In particular, the interactions between production and technical supply systems have not yet been comprehensively explored, although additional cost reduction potential is expected in this area. At the same time, the high energy consumption in industry and data centers highlights the relevance of integrated, system-wide approaches.

Against this background, the ENIPRO research project addresses this gap by focusing on the integrated consideration of production and energy systems.

Objectives

The aim of the ENIPRO research project is to develop, implement, and validate an integrated optimization solution for industrial production and energy systems. For this purpose, a software-based methodology is being developed that holistically links production planning, supply systems, and energy-related forecasts and makes them applicable in real operations.

The focus lies on practical implementation in industrial applications as well as validation in the large-scale demonstrator ETA Factory. The developed approaches are intended to enable forward-looking and flexible operations that specifically enhance both energy efficiency and energy flexibility. In addition, solutions are being developed to improve energy demand forecasting and to support energy-oriented production decisions.

The results will subsequently be transferred to various industries and disseminated into both industrial practice and the scientific community.

Approaches

The ENIPRO project is structured into seven interconnected subprojects and follows a holistic, data-driven approach to the integrated optimization of production and energy systems. At the outset, barriers and success factors are identified, while technical and organizational requirements of the application partners are collected and system boundaries are defined. Subsequently, real industrial assets are connected via IoT gateways to a central data platform, enabling data processing as well as the operation of forecasting, simulation, and optimization applications.

Building on this, time series data are analyzed using statistical methods and machine learning techniques to generate reliable forecasts. These insights are then used to model cyber-physical production systems, which serve as the basis for simulation-based optimization. In the next phase, the developed approaches are implemented and tested in real industrial operations. Finally, scalable software components are developed to facilitate the transfer and application of the solutions across different industrial contexts.

Acknowlegement

The joint research project ENIPRO is funded by the German Federal Ministry for Economic Affairs and Energy (BMWE). We are grateful for the opportunity to work on this project.

Funding source

Project sponsor

Consortium partners

Associated partners