{"volume":5,"type":"conference","date_created":"2025-12-22T13:13:37Z","language":[{"iso":"eng"}],"doi":"10.1145/3777518.3777520","status":"public","author":[{"last_name":"Zapata Gonzalez","full_name":"Zapata Gonzalez, David Ricardo","first_name":"David Ricardo","id":"105506"},{"last_name":"Brennig","full_name":"Brennig, Katharina","id":"51905","first_name":"Katharina"},{"first_name":"Kay","last_name":"Benkert","full_name":"Benkert, Kay"},{"first_name":"Florian","id":"88614","full_name":"Schlosser, Florian","last_name":"Schlosser"},{"first_name":"Oliver","id":"72849","full_name":"Müller, Oliver","last_name":"Müller"}],"oa":"1","publication":"ACM SIGEnergy Energy Informatics Review","main_file_link":[{"open_access":"1","url":"https://dl.acm.org/doi/abs/10.1145/3777518.3777520"}],"issue":"3","year":"2025","_id":"63397","citation":{"bibtex":"@inproceedings{Zapata Gonzalez_Brennig_Benkert_Schlosser_Müller_2025, title={Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling}, volume={5}, DOI={<a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>}, number={3}, booktitle={ACM SIGEnergy Energy Informatics Review}, publisher={Association for Computing Machinery (ACM)}, author={Zapata Gonzalez, David Ricardo and Brennig, Katharina and Benkert, Kay and Schlosser, Florian and Müller, Oliver}, year={2025}, pages={19–31} }","mla":"Zapata Gonzalez, David Ricardo, et al. “Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling.” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 5, no. 3, Association for Computing Machinery (ACM), 2025, pp. 19–31, doi:<a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>.","ieee":"D. R. Zapata Gonzalez, K. Brennig, K. Benkert, F. Schlosser, and O. Müller, “Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling,” in <i>ACM SIGEnergy Energy Informatics Review</i>, 2025, vol. 5, no. 3, pp. 19–31, doi: <a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>.","apa":"Zapata Gonzalez, D. R., Brennig, K., Benkert, K., Schlosser, F., &#38; Müller, O. (2025). Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling. <i>ACM SIGEnergy Energy Informatics Review</i>, <i>5</i>(3), 19–31. <a href=\"https://doi.org/10.1145/3777518.3777520\">https://doi.org/10.1145/3777518.3777520</a>","chicago":"Zapata Gonzalez, David Ricardo, Katharina Brennig, Kay Benkert, Florian Schlosser, and Oliver Müller. “Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling.” In <i>ACM SIGEnergy Energy Informatics Review</i>, 5:19–31. Association for Computing Machinery (ACM), 2025. <a href=\"https://doi.org/10.1145/3777518.3777520\">https://doi.org/10.1145/3777518.3777520</a>.","short":"D.R. Zapata Gonzalez, K. Brennig, K. Benkert, F. Schlosser, O. Müller, in: ACM SIGEnergy Energy Informatics Review, Association for Computing Machinery (ACM), 2025, pp. 19–31.","ama":"Zapata Gonzalez DR, Brennig K, Benkert K, Schlosser F, Müller O. Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling. In: <i>ACM SIGEnergy Energy Informatics Review</i>. Vol 5. Association for Computing Machinery (ACM); 2025:19-31. doi:<a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>"},"publication_identifier":{"issn":["2770-5331","2770-5331"]},"intvolume":"         5","date_updated":"2025-12-22T13:15:08Z","abstract":[{"lang":"eng","text":"Decarbonizing industrial process heat is a crucial step in mitigating climate change. While Process Mining (PM) has gained traction in sustainability research—such as optimizing production scheduling to reduce energy use or accounting for carbon footprints—it has largely overlooked the challenges and opportunities related to thermal energy, accounting for 66% of total energy demand in industrial processes. At the same time, Heat Integration (HI) is an established engineering discipline focused on maximizing the efficiency of thermal energy systems. However, HI traditionally relies on static or incomplete data about energy demands, limiting its effectiveness and accuracy. In this paper, we propose a novel framework that combines PM and HI to enable data-driven, process- and product-centric modeling of industrial energy demands. By integrating event logs and thermal energy data, our approach allows for a fine-grained analysis of heat demand patterns corresponding to specific process activities and product variants. We demonstrate the applicability and advantages of the framework by simulating a pharmaceutical manufacturing process and evaluating energy demands and heat recovery potentials. Our findings show that our PM-enabled HI framework provides more accurate and actionable insights into the temporal and product-specific variation of thermal energy demands. By capturing the causal relationships between process activities, product characteristics, and energy consumption, our approach enables improved analysis, planning, and optimization for heat recovery and process decarbonization. This integration of PM and HI expands the analytical tools for both disciplines and contributes to advancing the sustainable transformation of industrial processes."}],"publication_status":"published","publisher":"Association for Computing Machinery (ACM)","user_id":"105506","page":"19-31","title":"Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling"}