Modular process plants, with standardized, scalable designs, enable rapid deployment and adaptation to market changes in industries like chemicals and pharmaceuticals, offering higher capital gains through increased flexibility but requiring faster time-to-market solutions due to their shorter lifespans [1]. If modular plants (MPs) are designed energy efficiently [2], they can play a significant role in reducing the carbon footprint of industrial processes. MPs are capable of optimizing energy usage, which is crucial for minimizing operational costs and environmental impact [3].
In order to come as close as possible to optimum efficiency in planning and operation when using Process Equipment Assemblies (PEAs) in MPs, a transparent evaluation with regard to energy use, time, and reuse based on Energy Key Performance Indicators (eKPIs) [4] should be developed and integrated. As absolute indicators alone do not suffice for the performance evaluation of MPs, an Energy Baseline is essential for monitoring energy usage [5]; by comparing current operations with the baseline, improvement potential is identified, enabling real-time energy monitoring and enhanced plant operation [4]. However, major challenges in developing the energy baseline include the limited availability of historical or statistical data [6], as well as constraints related to the complexity, time requirements, and high computational cost posed by mechanistic models [7].
This study focuses on the design of the energy management for a Reactor PEA, where the key contributors towards the Energy Baseline are process energy consumption and heat loss due to inefficiencies. Since MPs face challenges in the distribution of knowledge and information between vendor and owner/operator, the study discusses the conception of a distributed energy management system and the integration of available information on the PEA-level. One key issue is the lack of readily available data on necessary performance parameters. For example, equipment datasheets do not directly provide the Overall Heat Transfer Coefficient (OHTC), which is essential for evaluating energy efficiency. To address this, empirical equations and available data are used to estimate the OHTC, allowing for a more accurate assessment of energy consumption and heat loss in modular reactors. Once an energy baseline is established, it is utilized within the energy management system for comparison with current measurements for PEA-level eKPIs and will help identify opportunities to enhance PEA efficiency.
In future work, this energy management concept for individual PEAs will be integrated into a distributed energy management system for MPs including eKPIs for both PEA and plant levels.

References
[1] “Modular Plants Flexible chemical production by modularization and standardization – status quo and future trends,” 2017. [Online]. Available: https://api.semanticscholar.org/CorpusID:41463729
[2] “CN WG 4.17 Energy Efficiency.” Accessed: Feb. 13, 2025. [Online]. Available: https://www.namur.net/china/en/working-groups/cn-wg-417-energy-efficiency.html
[3] “ENPRO.” Accessed: Feb. 13, 2025. [Online]. Available: http://enpro-initiative.de/enpro/en/
[4] “ISO 50006:2023(en), Energy management systems — Evaluating energy performance using energy performance indicators and energy baselines.” Accessed: Jul. 04, 2024. [Online]. Available: https://www.iso.org/obp/ui/#iso:std:iso:50006:ed-2:v1:en
[5] B. Beisheim, M. Kalliski, D. Ackerschott, S. Engell, and S. Krämer, “Real-Time Performance Indicators for Energy and Resource Efficiency in Continuous and Batch Processing,” in Resource Efficiency of Processing Plants, 2018, pp. 81–127. doi: 10.1002/9783527804153.ch5.
[6] P. Wetterich, M. Kuhr, and P. Pelz, “Model-Based Condition Monitoring of Modular Process Plants,” Processes, vol. 11, p. 2733, Sep. 2023, doi: 10.3390/pr11092733.
[7] S. Paul, R. P. Srikar, S. M. Rao, and P. Kumar, “Surrogate model based multi-objective optimisation of supercritical CO2 ejectors,” The Journal of Supercritical Fluids, vol. 218, p. 106493, Apr. 2025, doi: 10.1016/j.supflu.2024.106493.