title: Digital Twins for Autonomous Thermal Food Processing
Creators: name: Kannapinn, Maximilian
Publisher: name: UNSPECIFIED
Year: 2023

description: Developing sustainable and efficient food processing technologies is paramount to reducing greenhouse gas emissions and food waste. Autonomous food processing technologies, driven by artificial intelligence, offer a promising solution for achieving these goals while maintaining high-quality standards. In this context, digital twins have emerged as a powerful tool to model and optimize complex systems, providing accurate predictions of the system behavior to allow optimization of the process variables live during operation. This work proposes a software framework that combines multi-physical, conjugate simulations and data-driven reduced-order modeling to develop physics-based, data-driven digital twins for autonomous thermal food processing. The framework is independent of the modeling approach and simulation software and aims at the immediate application in the industry. Physics-based, data-driven digital twins are highly accurate and fast-solving virtual replications of a physical product or process, giving information on the current processing variables that cannot easily or feasibly be measured during operation. Generally, the concept of digital twins stands for a paradigm shift in computational engineering. In past decades, knowledge gained from simulations remained in the hands of highly skilled product and process development experts. Digital twins attempt to provide access to this knowledge even during operation to enable potential process autonomy. This work demonstrates how multi-physical simulation models of realistic size and dimension form the basis for physics-based, data-driven digital twins. To model thermal food processing inside a convection oven, non-isothermal flow and thermal radiation are coupled with mechanistic food processing models in one setup. This approach captures the couplings between the food process variables and the heat transfer mechanisms much better than heat- and mass-transfer-coefficient-based modeling approaches that still dominate within food science. A challenge for modern-day computational engineering is that computing power does not keep pace with the progressively increasing complexity and computational cost of multi-physical simulation models. So far, real-time simulations of such models are not feasible, especially when the models should be executed on low-end processors. In this work, neural-ODEs, a novel data-driven reduced-order modeling technique, is applied to generate an accurate and fast-solving surrogate of simulation models, which also exhibits low computational cost. The resulting reduced-order models are stored in an encrypted container format to protect the developer’s intellectual property when deployed in the final appliance. The container files are executed at the device level without cluster, edge or cloud computing. For the presented generation of a digital twin for a convection oven, fewer errors are caused by the reduced-order models than by the underlying food processing models. As coupled, multi-physical models of realistic dimensions still require considerable solution times on modern cluster PCs, generating many data sets for data-driven reduced-order modeling is not economically feasible. This work proposes an efficient design of experiments that enables data-driven reduced-order modeling with only one-to-two training data sets. Finally, the performance of fast-solving and highly accurate digital twins is demonstrated within a model predictive control algorithm. The latter autonomously handles two scenarios during thermal food processing in a convection oven.