20.500.12556/RUL-173355 AI control for pasteurized soft-boiled eggs This paper presents a novel approach to thermal process control in the food industry, specifically targeting the pasteurization and cooking of soft-boiled eggs. The unique challenge of this process lies in the precise temperature control required, as pasteurization and cooking must occur within a narrow temperature range. Traditional control methods, such as fuzzy logic controllers, have proven insufficient due to their limitations in handling varying loads and environmental conditions. To address these challenges, we propose the integration of robust reinforcement learning (RL) techniques, particularly the utilization of the Deep Q-Network (DQN) algorithm. Our approach involves training an RL agent in a simulated environment to manage the thermal process with high accuracy. The RL-based system adapts to different heat capacities, initial conditions, and environmental variations, demonstrating superior performance over traditional methods. Experimental results indicate that the RL-based controller significantly improves temperature regulation accuracy, ensuring consistent pasteurization and cooking quality. This study opens new avenues for the application of artificial intelligence in industrial food processing, highlighting the potential for RL algorithms to enhance process control and efficiency. AI temperature control artificial intelligence machine learning pasteurized soft-boiled eggs robust reinforcement learning true false true Angleški jezik Ni določen Članek v reviji 2025-09-16 14:03:16 2025-09-16 14:03:21 2025-11-08 03:42:16 0000-00-00 00:00:00 2025 0 0 Str. 1-26 iss. 18, 3171 Vol. 14 2025 0000-00-00 Zaloznikova Objavljeno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 641:004.896 2304-8158 10.3390/foods14183171 249147395 https://www.mdpi.com/2304-8158/14/18/3171 1 f88f84a2-92f4-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=217741 Fakulteta za strojništvo 0 0 0