Dynamic Recipe Adjustment in Industrial Processes: Exploring Reinforcement Learning Approaches

Authors

Tarun Parmar

Abstract

Reinforcement learning (RL) has emerged as a promising approach for optimizing recipes and manufacturing processes in various industries. This review explores the application of RL techniques for dynamic recipe adjustment and discusses the key concepts, algorithms, and challenges. RL fundamentals, including Q-learning, policy gradients, and actor-critic methods, are reviewed, explaining how these algorithms can model recipes as RL environments. Potential state representations, action spaces, and reward functions are examined, considering factors such as ingredient quantities, process parameters, and product quality metrics. Challenges in implementing RL for recipe optimization were addressed, including sample efficiency, safety constraints, interpretability, and generalization. Case studies of food production and chemical processes were analyzed by comparing RL-based approaches with traditional control methods. Future research directions are discussed, highlighting the potential of hybrid approaches combining RL with human expertise, multi-objective optimization, transfer learning, and improved exploration strategies. The review concludes by emphasizing the broader impacts of RL on manufacturing and production industries, discussing the potential for increased efficiency, reduced waste, and improved product quality. By providing a comprehensive overview of RL applications for dynamic recipe adjustment, this review aims to inspire further research and development in this field, ultimately contributing to the advancement of intelligent and adaptive manufacturing processes.

Keywords

Reinforcement Learning (RL), Recipe Optimization, Manufacturing Processes, Q-Learning, Policy Gradients, Actor-Critic Methods, Reward Functions

Dynamic Recipe Adjustment in Industrial Processes: Exploring Reinforcement Learning Approaches. Tarun Parmar. 2025. IJIRCT, Volume 11, Issue 1. Pages 1-7. https://www.ijirct.org/viewPaper.php?paperId=2501114