Abstract
Energy Baselines (EnBs) provide a reference for evaluating Energy Key Performance Indicators (eKPIs), and their establishment is mandated under ISO 50001. Since eKPIs are typically defined per functional unit, such as product, recipe or recipe phase, EnBs should not be averaged across heterogeneous operating conditions but instead be defined in a context-specific manner. This requires detailed mechanistic models or sufficiently rich operational data for statistical approaches, both of which are often unavailable in highly flexible, semi-continuous production systems.This paper proposes a four-stage framework for the automated generation of surrogate EnBs to address this gap. In the first stage, the relevant training data space is defined, including non-influenceable variables (e.g., equipment deviations), design parameters (e.g., material properties), and adaptable recipe parameters (e.g., operating conditions and control actions). In the second stage, these parameters are systematically varied to automatically generate a labelled dataset representing process dynamics and energy behavior. The third stage trains and evaluates candidate surrogate models using the PRESTO recommendation framework, followed by a global sensitivity analysis based on Sobol indices to identify the dominant drivers of energy performance.The framework is demonstrated using a stirred tank reactor at laboratory (2 L) scale. The results show that Random Forest surrogates provide reliable performance across a wide operating range and that recipe parameters, particularly mixer rotational speed, dominate energy efficiency. Future work will address scalability to other equipment types and reactive processes.