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AI Control for Pasteurized Soft-Boiled Eggs.

Primož Podržaj¹, Dominik Kozjek¹, Gašper Škulj¹

¹Laboratory for Mechatronics, Production Systems and Automation (LAMPA), Faculty of Mechanical Engineering, University of Ljubljana, 1000 Ljubljana, Slovenia.

Foods (Basel, Switzerland)

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Summary

This study introduces reinforcement learning (RL) for precise thermal control in food processing, specifically for soft-boiled eggs. The Deep Q-Network (DQN) algorithm enhances pasteurization and cooking accuracy, outperforming traditional methods.

Keywords:

AI temperature control artificial intelligence machine learning pasteurized soft-boiled eggs robust reinforcement learning

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Area of Science:

Food Science and Technology
Artificial Intelligence
Process Control Engineering

Background:

Precise temperature control is critical for food processing, especially for pasteurization and cooking of items like soft-boiled eggs.
Traditional control methods, such as fuzzy logic, struggle with varying loads and environmental conditions in thermal processes.
Limitations in existing control systems necessitate advanced approaches for consistent food quality.

Purpose of the Study:

To develop and evaluate a novel thermal process control system for soft-boiled eggs using artificial intelligence.
To address the limitations of traditional controllers in managing precise temperature requirements for pasteurization and cooking.
To demonstrate the superiority of reinforcement learning techniques over conventional methods in food thermal processing.

Main Methods:

Implementation of a reinforcement learning (RL) agent, specifically the Deep Q-Network (DQN) algorithm.
Training the RL agent within a simulated environment to optimize thermal process management.
Testing the RL-based controller's adaptability to variations in heat capacity, initial conditions, and environmental factors.

Main Results:

The RL-based controller achieved significantly improved temperature regulation accuracy compared to traditional methods.
The system demonstrated robust adaptation to diverse process conditions, ensuring consistent pasteurization and cooking quality.
Experimental validation confirmed the enhanced performance and reliability of the proposed AI-driven control strategy.

Conclusions:

Reinforcement learning, particularly DQN, offers a powerful and adaptive solution for complex thermal process control in the food industry.
The developed RL system provides superior temperature management for soft-boiled egg production, enhancing product consistency and quality.
This research highlights the potential of AI applications to revolutionize industrial food processing, improving efficiency and control.