@article {97383,
	title = {Optimizing Milk Pasteurization Diagnosis Through Deep Q-Networks and Digital Twin Technology},
	journal = {International Journal of Web Services Research},
	volume = {21},
	number = {1},
	year = {2024},
	pages = {1-22},
	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{\textquoteright}s dairy industry. Experiments demonstrated the superior efficiency of this method; its precision surpassed that of traditional data imputation techniques by a significant margin.},
	url = {https://www.igi-global.com/article/optimizing-milk-pasteurization-diagnosis-through-deep-q-networks-and-digital-twin-technology/366586},
	author = {O. Kadri and A. Abdelhadi}
}
