Thermal food processing, which includes drying and cooking, is essential for preserving and sterilizing our food. However, it's not very energy-efficient, often using more energy than necessary.Improving the efficiency of this process could significantly reduce carbon emissions, benefiting our environment.However, the challenge is that this food processing method is subject to many variables. For example, changing the type of food being processed or the equipment used can disrupt the entire system, requiring it to be recalibrated or even rebuilt. Furthermore, certain essential data, like the exact moisture content in food during processing, is tricky to measure in real-time, which complicates optimization efforts.This project aims to revolutionize the way we manage these challenges. By creating a Cyber-Physical System (CPS), we should be ableto manage and account for both the variables we can measure and those we can't. This is done by building flexible models that can be quickly adjusted or combined with others, making the process more adaptable.Moreover, while there are detailed simulations available that can help predict the outcomes of certain inputs, these simulations take a lot of computational power and time. This project proposes an innovative solution: creating a simplified version of these simulations (Reduced-Order Modeling, ROM) which is faster but still reasonably accurate.The ultimate goal is to make thermal food processing smarter and more energy efficient. By using these new models and systems, webelieves they can optimize the processing conditions in real-time. The models and systems will be validatedusing specialized equipment at Michigan State University, focusing on how well the systemscan handle drying food with hot air.In summary, this project is about making our food processing greener and more efficient. It combines knowledge from food science, engineering, and technology to make a difference in the industry and, ultimately, our environment.