{"id":4936,"date":"2025-11-16T14:20:21","date_gmt":"2025-11-16T14:20:21","guid":{"rendered":"https:\/\/izvestiyakbncran.ru\/?page_id=4936"},"modified":"2026-06-02T13:56:26","modified_gmt":"2026-06-02T12:56:26","slug":"27-5-11-en","status":"publish","type":"page","link":"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-5-11-en\/","title":{"rendered":"27.5.11 En"},"content":{"rendered":"\n

Cloud-based ecosystem of cognitive automation for integrated management of the CIP processes in brewing<\/strong><\/h1>\n\n\n\n

A.S. Maksimov, V.S. Artemyev, L.S. Mangusheva, Zh.V. Meksheneva<\/strong><\/p>\n\n\n\n

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Abstract<\/strong><\/strong>: <\/em>The paper presents a cloud\u2013edge cognitive architecture for managing brewery CIP processes. The system is based on a ResNet-CNN and Transformer ensemble operating within an active learning loop and integrated with multi-sensor monitoring ATP bioluminescence, IR fluorescence, and biofilm optical density. Edge nodes provide instant anomaly detection and local control, while the cloud level performs predictive optimization and model retraining. Pilot trials demonstrated reductions in reagent consumption by 29%, water usage by 22%, and energy use by 18%, along with a decrease in control latency to 140 ms and an increase in predictive accuracy to R\u00b2 = 0.92, accompanied by a 37% reduction in false alarms. The architecture ensures compliance with sanitary standards and enables a proactive paradigm for CIP cycle management.
Aim<\/strong>. The aim of the study is to develop a cloud\u2013edge ecosystem capable of reducing decision latency in CIP processes to less than 150 ms, cutting resource consumption, and enhancing sanitary reliability under the conditions of high variability in brewing recipes and technological parameters.
Methods<\/strong>. The methodological foundation relied on the theory of distributed multi-agent systems and the principles of active learning. The dataset included 48,000 fouling profiles, incorporating ATP bioluminescence, IR fluorescence, and biofilm optical density. At the edge level, signal preprocessing is performed, an autoencoder generates compact embeddings, and a GRU-based classifier detects anomalies with a reaction time of less than 40 ms. At the cloud level, a hybrid ResNet-CNN and Transformer model predicts cleaning depth and optimizes CIP cycle parameters. SHAP values and Grad-CAM are used to ensure interpretability of decisions. System validation is conducted in accordance with ISO and GOST standards on metrology, cybersecurity, and sanitary compliance.
Results<\/strong>. The experiments confirm stable real-time operation of the ecosystem and compliance with regulatory requirements. Average consumption of cleaning agents is reduced by 29%, water usage by 22%, and energy demand by 18%. Control latency decreased to 140 ms, while predictive accuracy reached R\u00b2 = 0.92. The system demonstrates a 37% reduction in false alarms and full fault tolerance under partial data loss. Economic analysis shows a 24.7% reduction in operating costs and a payback period of less than eight months.
Conclusions<\/strong>. The developed cloud\u2013edge cognitive architecture enables the transition of CIP processes from static operation to proactive control. The combination of fast edge modules and predictive cloud models ensures both resource efficiency and strict sanitary compliance.<\/p>\n\n\n\n

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Keywords<\/strong><\/em><\/strong>:<\/em> cognitive automation, CIP, active learning, OPC UA, reagent optimization, industrial IoT<\/p>\n\n\n\n

For citation<\/strong>.<\/strong> Maksimov A.S., Artemyev V.S., Mangusheva L.S., Meksheneva Zh.V. Cloud-based ecosystem of cognitive automation for integrated management of the CIP processes in brewing. News of the Kabardino-Balkarian Scientific Center of RAS<\/em>. 2025. Vol. 27. No. 5. Pp. 143\u2013158. DOI: 10.35330\/1991-6639-2025-27-5-143-158<\/p>\n\n\n\n

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R<\/strong>eferences<\/summary>\n
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  2. Chikina T.A., Prokhorova E.V. Sanitary treatment of process lines in brewery production. In The Role of Agricultural Science in Ensuring Food Security: Proceedings of the International Scientific and Practical Conference. Melitopol, June 21, 2024. Melitopol: Melitopol’skiy gosudarstvennyy universitet. 2024. Pp. 200\u2013206.<\/li>\n\n\n\n
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    «Nauchno-izdatel’skiy tsentr «Vestnik nauki». 2020. Pp. 91\u201397. (In Russian)<\/li>\n\n\n\n
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  8. Ageev O.V., Lizorkina O.A., Samoylova N.V. Analysis of methodological principles for modeling flexible food systems. Bulletin of Science and Education of the North-West of Russia. No. 9(2). Pp. 7\u201324. (In Russian)<\/li>\n\n\n\n
  9. Romanova A.G., Abramova I.M., Medrish M.E. et al. Carbohydrate composition as an indicator of authenticity and quality of whiskey and aged grain distillates. Beer and Beverages. No. 3. Pp. 21\u201325. DOI: 10.52653\/PIN.2024.03.04. (In Russian)<\/li>\n<\/ol>\n<\/details>\n\n\n\n
    Information about the author<\/strong>s<\/summary>\n
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    Aleksey S. Maksimov<\/strong>, Candidate of Technical Sciences, Professor of the Department of Informatics and Computer Engineering for Food Production, Russian Biotechnological University (ROSBIOTECH);
    11 Volokolamskoye shosse, Moscow, 125080, Russia;
    maksimov@mgupp.ru, SPIN-code: 7284-7751
    Victor S. Artemyev<\/strong>, Senior Lecturer, Department of Computer Science, Plekhanov Russian University of Economics;
    36 Stremyannyy lane, Moscow, 115054, Russia;
    Artemev.vs@rea.ru, ORCID: https:\/\/orcid.org\/0000-0002-0860-6328, SPIN-code: 8912-5825
    Lyalya S. Mangusheva<\/strong>, Associate Professor of the Department of Computer Science, Plekhanov Russian University of Economics;
    36 Stremyannyy lane, Moscow, 115054, Russia;
    klyalya80@mail.ru, ORCID: https:\/\/orcid.org\/0000-0002-2331-8308
    Zhanna V. Meksheneva<\/strong>, Candidate of Economic Sciences, Associate Professor, Head of the Department of Applied Mathematics, Synergy University;
    80B Leningradskiy prospekt, Moscow, 125315, Russia;
    zhmeksheneva@synergy.ru, ORCID: https:\/\/orcid.org\/0000-0002-1716-7857, SPIN-code: 5187-4859<\/p>\n\n\n\n

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    Cloud-based ecosystem of cognitive automation for integrated management of the CIP processes in brewing A.S. Maksimov, V.S. Artemyev, L.S. Mangusheva, Zh.V. Meksheneva Abstract: The paper presents a cloud\u2013edge cognitive architecture for managing brewery CIP processes. The system is based on a ResNet-CNN and Transformer ensemble operating within an active learning loop and integrated with multi-sensor […]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"wp-custom-template-home","meta":{"footnotes":""},"class_list":["post-4936","page","type-page","status-publish","hentry"],"yoast_head":"\n27.5.11 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/izvestiyakbncran.ru\/index.php\/en\/27-5-11-en\/\" \/>\n<meta property=\"og:locale\" content=\"ru_RU\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"27.5.11 En - \u0418\u0417\u0412\u0415\u0421\u0422\u0418\u042f \u041a\u0410\u0411\u0410\u0420\u0414\u0418\u041d\u041e-\u0411\u0410\u041b\u041a\u0410\u0420\u0421\u041a\u041e\u0413\u041e \u041d\u0410\u0423\u0427\u041d\u041e\u0413\u041e \u0426\u0415\u041d\u0422\u0420\u0410 \u0420\u0410\u041d\u00bb\" \/>\n<meta property=\"og:description\" content=\"Cloud-based ecosystem of cognitive automation for integrated management of the CIP processes in brewing A.S. 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Maksimov, V.S. Artemyev, L.S. Mangusheva, Zh.V. Meksheneva Abstract: The paper presents a cloud\u2013edge cognitive architecture for managing brewery CIP processes. 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