Integrated mechanistic and data-driven model for food process optimization
A digital twin tool for food process optimization, combining mechanistic and data-driven modeling to simulate and optimize food manufacturing processes in real-time.
Overview
The University of Tennessee Food Engineering laboratory has developed a pioneering digital twin tool designed to optimize food manufacturing processes. This tool integrates mechanistic models with data-driven machine learning approaches to simulate and enhance processes such as mixing, heating, extrusion, and drying. By leveraging extensive datasets from previous product developments, this tool can accommodate the complex interactions unique to each manufacturer's environment, offering real-time process adjustments and scenario evaluations.
Technical specifications
Key features:
- Mechanistic Modeling: Employs physics-based simulations to mimic food processing environments.
- Data-Driven Insights: Utilizes large datasets and machine learning to enhance process accuracy and efficiency.
- AI Capabilities: Offers real-time adjustments and predictive scenarios for manufacturing processes.
- Versatile Applications: Suitable for microwave heating, radiofrequency processing, bioreactor mixing, and more.
Technology readiness level
Currently at Technology Readiness Level 5, the model has been validated for its core capabilities and is preparing for further on-site testing and fine-tuning in collaboration with industry sponsors.
About University of Tennessee, Knoxville
UT offers a partnership model that reduces risk, accelerates time-to-value, and supports long-term growth strategies for industry—providing an attractive platform for companies seeking to formalize collaboration and establish a long-term presence in the East Tennessee region. By aligning institutional investment with regional and national priorities, UT provides a stable and effective platform for industry collaboration, technology deployment, and sustained economic impact.
delivered to your inbox.