New Conference Paper at the International Electronic Conference on Processes: Processes System Innovation (ECP) [24.05.22]
Christian Krupitzer from the Department of Food Informatics is author of the peer-reviewed conference Paper "DigiFoodTwin: Digital Biophysical Twins combined with Machine Learning for optimized Food Processing" at the International Electronic Conference on Processes: Processes System Innovation (ECP).
The publication "DigiFoodTwin: Digital Biophysical Twins combined with Machine Learning for optimized Food Processing" by Christian Krupitzer (Department of Food Informatics, University of Hohenheim) was accepted at the International Electronic Conference on Processes: Processes System Innovation (peer-reviewed). The International Electronic Conference on Processes: Processes System Innovation provides an advanced forum for new development, challenges, and opportunities in process systems engineering.
Production processes must allow high flexibility and adaptivity to ensure the food supply. This includes to react on disruptions in the supply with ingredients as well as varying quality of ingredients, e.g., seasonal fluctuations of raw material quality. Digital twins are know from Industry 4.0 as a method to model, simulate, and optimize processes. In this vision paper, we describe the concept of a digital food twin. Due to the variability of this raw materials, such a digital twin has to take into account not only the processing steps but also the chemical, physical, or microbiological properties that change the food independently from the processing. We propose a model-based learning and reasoning loop, which is known from self-aware computing (SeAC) systems in the so called learn-reason-action loop (LRA-M loop), for modeling the input for the LRA-M loop of the food production not as a pure knowledge database, but data which is generated by simulations of the bio-chemical and physical properties of food. This work presents a conceptual framework on how to include data provided by a digital food twin into a self-aware food processing system to respond to fluctuating raw material quality and to secure food supply and discusses the applicability of the concept.
The publication is available at MDPI Engineering Proceedings.
