Ai-driven digital twin platform for predictive process optimization in food manufacturing
A hybrid multi-agent digital twin platform leveraging AI and physics-based simulations for real-time process optimization in food manufacturing. Enhances product quality and sustainability by predicting and adapting to process variations.
Overview
The AI-driven digital twin platform offers a revolutionary approach to optimizing food manufacturing processes. By integrating physics-based simulations with AI-driven predictive modeling and adaptive control, this solution enhances the precision and adaptability of key manufacturing steps such as mixing, heating, extrusion, and drying. The platform employs intelligent agents that learn from live sensor data to predict and regulate product traits such as texture, moisture, and color, allowing manufacturers to improve product quality and consistency while reducing trial-and-error and fostering sustainable production.
Technical specifications
The platform is a hybrid multi-agent system, combining thermomechanical modeling with data-driven learning. It utilizes live sensor data, soft-sensing algorithms, and feedback controllers to ensure real-time adaptive process control. Key features include:
- Modular design for seamless integration with existing production lines
- Physics-Informed Neural Networks, LSTM, and Bayesian optimization for sensor calibration and prediction validation
- Plug-and-play framework enabling "what-if" simulations and cross-line optimization
- Real-time adaptive control for improved yield and energy efficiency
Technology readiness level
This digital twin platform is at technology readiness level 8, having undergone pilot-scale validation with live sensor feedback for adaptive control. The system is ready for industrial deployment with proven capabilities in enhancing production efficiency and quality.
About New Mexico Institute of Mining and Technology
New Mexico Tech is a STEM-focused public research university in Socorro with an applied, field-scale research profile. Its Energetic Materials Research and Testing Center runs expansive test ranges adjacent to campus for research, evaluation, and training. The Petroleum Recovery Research Center and the New Mexico Bureau of Geology and Mineral Resources provide industry-facing labs, data, and services, while a co-op program links employers to student talent. Research is supported by competitive federal funding from agencies such as the National Science Foundation, Department of Energy, and Department of Defense. An Office of Innovation Commercialization and a university research park support IP, licensing, and startup formation.
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