Optimizing Milk Pasteurization Diagnosis Through Deep Q-Networks and Digital Twin Technology

Abstract

Industrial diagnostic systems play an important role in food manufacturing by ensuring rapid detection of defective components and precise identification of systemic dysfunction. This article proposes a diagnostic model for the pasteurization process to enhance dairy production systems. The authors found that, when a breakdown occurs, the acquisition system stops providing necessary data for diagnostics. To solve this problem, the authors used digital twin (DT) engineering to generate missing values and build a learning model based on reinforcement learning (RL). The effectiveness of this approach was validated through implementation at Aures Batna Dairy, a prominent player in Algeria's dairy industry. Experiments demonstrated the superior efficiency of this method; its precision surpassed that of traditional data imputation techniques by a significant margin.