<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">O. Kadri</style></author><author><style face="normal" font="default" size="100%">A. Abdelhadi</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Optimizing Milk Pasteurization Diagnosis Through Deep Q-Networks and Digital Twin Technology</style></title><secondary-title><style face="normal" font="default" size="100%">International Journal of Web Services Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2024</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://www.igi-global.com/article/optimizing-milk-pasteurization-diagnosis-through-deep-q-networks-and-digital-twin-technology/366586</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">21</style></volume><pages><style face="normal" font="default" size="100%">1-22</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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.</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record></records></xml>