Modelling and management principles of combined granulated feed production through information-analytical systems
Article Sidebar
Issue Vol. 16 No. 3 (2026)
-
Deep learning approach for automated skin cancer detection with comparative analysis of different batch sizes in dermatological image classification
Abini M.A.5-13
-
Modelling and management principles of combined granulated feed production through information-analytical systems
Mahil Mammadov, Tural Mammadli14-22
-
An intelligent IoT-based automatic control system for energy-efficient grain drying
Ainur Rustemova, Maria Yukhymchuk, Vladyslav Lesko, Marat Orynbet, Yurii Ivanov23-30
-
Design and experimental validation of an IoT-RPA-based smart refrigerator inventory system
Lyudmila Samchuk, Yuliia Povstiana, Nataliia Lishchyna, Mykyta Ponomarenko31-38
-
Intelligent soil monitoring system for sustainable agriculture using TinyML and ultra-low power wireless sensor networks
Muhammad Ovais Akhter39-48
-
Modelling the operation of a dynamic queue management system in supermarkets
Larysa Gumeniuk, Volodymyr Lotysh, Pavlo Humeniuk49-58
-
Experimental validation of error compensation techniques for fibre-optic gyroscopes in semi-natural conditions
Nurzhigit Smailov, Ainur Kuttybayeva, Yerlan Tashtay, Zhandos Dosbayev, Aruzhan Nazarova, Beibarys Sekenov, Aida Serbayeva, Akezhan Sabibolda59-63
-
Simulation of the operation of a four-channel ultrasonic flowmeter with identical signal trajectories in acoustic channels
Yosyp Bilynsky, Andrii Stetsenko64-68
-
Etching line with integrated electrochemical regeneration for copper recovery and sludge reduction
Anatoliy Nester, Vadim Romanuke, Tetiana Yakovyshyna69-76
-
Method for HCI users identification based on their "polyfactor portraits" of perception subjectivization of HCI object
Andrii Pukach, Vasyl Teslyuk, Artem Kazarian77-87
-
Enhancing diagnostic systems for analysing and controlling operating modes in electrical distribution networks
Igor Khomenko, Ruslan Lozhkin, Andrii Shkrebela, Oleksandr Miroshnyk, Anatolii Sereda, Taras Shchur, Mariana Bohach88-94
-
A modular monolith architecture for high-availability electric vehicle charging station management software
Volodymyr Horshkov, Nataliia Lishchyna, Andrii Yashchuk, Olena Surynovych, Valerii Lishchyna95-102
-
Investigation of a multilevel inverter based on IGBT transistors for power supply of a tethered UAV with telemetry-based monitoring of power parameters
Kyrmyzy Taissariyeva, Kuanysh Muslimov, Gulim Jobalayeva, Ingkar Issakozhayeva, Zhansaya Ayapbergen103-107
-
Dynamic obstacle avoidance for UAVs using fuzzy logic control and neural network
Anton Makohonov, Ivan Marynych108-113
-
Hybrid simulation and experimental framework for real-time fault detection in PV boost converters using fuzzy logic and LoRa connectivity
Oussama Sait, Mabrouk Khemliche, Samia Latreche, Sait Belkacem, Hamza Khemliche114-122
-
Benchmarking machine learning algorithms for high-fidelity power forecasting in utility-scale PV plants
Ahmed Saidi, Abdelghani Draoui, Touhami Abdelouahed123-128
-
Study of the dynamic variation in the security level of an information protection system
Olha Saliieva, Yurii Yaremchuk129-133
-
A new hyperchaotic generator: circuit realization and analysis
Volodymyr Rusyn, Petro Kindrachuk, Bogdan Markovych134-138
-
Strategies for ensuring functional stability of a complex sensor network based on the research of the dynamics of the behavior of evolutionary equations
Valentyn Sobchuk, Yurii Kravchenko, Mykhaylo Sharapov, Oleksandr Laptiev, Andrii Sobchuk139-143
-
Development of robust DVB-T2 OFDM transmission: BER assessment and channel impairment mitigation for reliable high-definition broadcasting
Olarewaju Peter Ayeoribe144-149
-
Real-time network anomaly detection in O-RAN using deep learning on streaming big data
Satya Sumanth Vanapalli, Rajesh Polepogu, Parish Venkata Kumar K, Vijayasankar Anumala, Vinodh Babu Panguluri, Lakshmi Narayana Jammula, Brahmaiah Madamanchi, Sravani Duvvu, Bhanusree Nanduri, Syam Sundar Musinala150-158
-
Technical predictive analysis of big data for Apache Spark-based resource planning
Bakhshali Bakhtiyarov, Aynur Jabiyeva, Ulkar Musavi159-166
-
Conversion of voxel models into polygonal meshes with ensuring structural integrity and editability
Semen Duvanov, Iryna Baranova167-172
-
Fuzzy Delphi method for identifying key parameters in the optimization of intelligent control systems for oil refining processes
Kamala Aliyeva173-179
-
Method of determining the grounds for relating data to official information and the degree of restriction access "For official use"
Yurii Dreis, Oleh Harasymchuk180-183
-
Model and tools for content generation and publication based on large language models
Artem Kazarian, Vasyl Teslyuk, Andrii Pukach184-190
-
Evaluation of the usability of web services with interactive maps
Lukasz Sendecki, Sergiusz Skalski, Mariusz Dzienkowski191-197
-
Fuzzy model for assessing digital security literacy across population groups with different social profiles
Volodymyr Polishchuk, Vasyl Sehlianyk, Inna Polishchuk, Andrii Shafar198-203
-
An intelligent module for productivity assessment of remote employees in working time monitoring systems
Aigul Moldakalykova, Maria Yukhymchuk, Gulzhan Kashaganova, Vladyslav Lesko, Yurii Ivanov, Natalia Sachaniuk-Kavets’ka204-209
-
Hybrid machine learning framework for forecasting and evaluating university rankings
Nurzhigit Smailov, Nursultan Kuldeyev, Akezhan Sabibolda, Raigul Ustemirova210-216
Archives
-
Vol. 16 No. 3
2026-09-30 30
-
Vol. 16 No. 2
2026-06-30 27
-
Vol. 16 No. 1
2026-03-30 27
-
Vol. 15 No. 4
2025-12-20 27
-
Vol. 15 No. 3
2025-09-30 24
-
Vol. 15 No. 2
2025-06-27 24
-
Vol. 15 No. 1
2025-03-31 26
-
Vol. 14 No. 4
2024-12-21 25
-
Vol. 14 No. 3
2024-09-30 24
-
Vol. 14 No. 2
2024-06-30 24
-
Vol. 14 No. 1
2024-03-31 23
-
Vol. 13 No. 4
2023-12-20 24
-
Vol. 13 No. 3
2023-09-30 25
-
Vol. 13 No. 2
2023-06-30 14
-
Vol. 13 No. 1
2023-03-31 12
-
Vol. 12 No. 4
2022-12-30 16
-
Vol. 12 No. 3
2022-09-30 15
-
Vol. 12 No. 2
2022-06-30 16
-
Vol. 12 No. 1
2022-03-31 9
Main Article Content
Authors
Abstract
The production of granulated compound feed in livestock enterprises is a complex technological process that requires precise monitoring of physical and chemical parameters. Ensuring efficiency and quality in this process demands advanced modelling and management approaches. The study aims to develop a methodology for modelling compound feed production through information-analytical systems and to define the principles of process management. The research is based on a systematic review of literature studies. After applying relevance and thematic selection criteria, 16 sources were retained for the final review and included in the reference list. The methodology combines literature review, comparative analysis, and synthesis. The analysis shows that effective modelling of feed production requires hybrid applications of deterministic, statistical, neural network, and fuzzy logic approaches. Information-analytical systems must adopt a multi-level architecture covering field operations, process monitoring, production management, and business planning. Artificial intelligence technologies – particularly machine learning, deep learning, and reinforcement learning – demonstrate high efficiency in quality prediction, anomaly detection, and the development of optimal management strategies. According to the comparative synthesis of the reviewed literature, the application of information-analytical systems can reduce raw material loss by 28–35%, decrease product quality variation by 58–62%, optimize energy consumption by 18–24%, increase production productivity by 20–30%, reduce deviation from plan by 67–75%, shorten technical downtime duration by 30–40%, and decrease quality control labour expenditure by 57–60%.Conclusion. For Azerbaijan, it is recommended to design systems that account for regional raw material bases, climatic conditions, and the technical capacity of local enterprises. The study confirms that the digital transformation of the feed industry is a multifaceted challenge requiring coordinated efforts among academic institutions, industrial enterprises, and governmental bodies.
Keywords:
Sustainable Development Goal (SDG)
- Zero hunger
- Industry, Innovation, Technology and Infrastructure
References
[1] Goncharov, A. V., Butyaikin, V. V., Kochetkova, E. V., Korotkiy, A. A., & Bugaeva, A. V. (2022). Modernization of the automated control system for compound feed transfer. IOP Conference Series: Earth and Environmental Science, 949(1), 012061. https://doi.org/10.1088/1755-1315/949/1/012061 DOI: https://doi.org/10.1088/1755-1315/949/1/012061
[2] Mammadov, M. I. (2025). Study of feed granulation process based on system analysis – justification of optimization criteria. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, 15(3), 138–142. https://doi.org/10.35784/iapgos.6967 DOI: https://doi.org/10.35784/iapgos.6967
[3] Mancini, M., Mircoli, A., Potena, D., Diamantini, C., Duca, D., & Toscano, G. (2020). Prediction of pellet quality through machine learning techniques and near-infrared spectroscopy. Computers & Industrial Engineering, 147, 106566. https://doi.org/10.1016/j.cie.2020.106566 DOI: https://doi.org/10.1016/j.cie.2020.106566
[4] Məmmədov, Q. Y., Hümbətov, H. S., Hüseynov, A. R., & Məmmədov, V. Ə. (2020). Yem istehsalı. Star. http://dspace.khazar.org/handle/20.500.12323/4962
[5] Pérez-Marı́n, D. C., Garrido-Varo, A., Guerrero-Ginel, J. E., & Gómez-Cabrera, A. (2004). Near-infrared reflectance spectroscopy (NIRS) for the mandatory labelling of compound feedingstuffs: Chemical composition and open-declaration. Animal Feed Science and Technology, 116(3–4), 333–349. https://doi.org/10.1016/j.anifeedsci.2004.05.002 DOI: https://doi.org/10.1016/j.anifeedsci.2004.05.002
[6] Schroeder, B., Andretta, I., Kipper, M., Franceschi, C. H., & Remus, A. (2020). Empirical modelling the quality of pelleted feed for broilers and pigs. Animal Feed Science and Technology, 265, 114522. https://doi.org/10.1016/j.anifeedsci.2020.114522 DOI: https://doi.org/10.1016/j.anifeedsci.2020.114522
[7] Schwenzer, M., Ay, M., Bergs, T., & Abel, D. (2021). Review on model predictive control: An engineering perspective. The International Journal of Advanced Manufacturing Technology, 117(5–6), 1327–1349. https://doi.org/10.1007/s00170-021-07682-3 DOI: https://doi.org/10.1007/s00170-021-07682-3
[8] Shojaeinasab, A., Charter, T., Jalayer, M., Khadivi, M., Ogunfowora, O., Raiyani, N., Yaghoubi, M., & Najjaran, H. (2022). Intelligent manufacturing execution systems: A systematic review. Journal of Manufacturing Systems, 62, 503–522. https://doi.org/10.1016/j.jmsy.2022.01.004 DOI: https://doi.org/10.1016/j.jmsy.2022.01.004
[9] Sun, W., Wang, Y., He, H., & Sun, Y. (2023). Analysis of densification mechanisms of feed pelleting. Biosystems Engineering, 234, 92–107. https://doi.org/10.1016/j.biosystemseng.2023.08.017 DOI: https://doi.org/10.1016/j.biosystemseng.2023.08.017
[10] van der Poel, A. F. B., Abdollahi, M. R., Cheng, H., Colovic, R., Den Hartog, L. A., Miladinovic, D., Page, G., Sijssens, K., Smillie, J. F., Thomas, M., Wang, W., Yu, P., & Hendriks, W. H. (2020). Future directions of animal feed technology research to meet the challenges of a changing world. Animal Feed Science and Technology, 270, 114692. https://doi.org/10.1016/j.anifeedsci.2020.114692 DOI: https://doi.org/10.1016/j.anifeedsci.2020.114692
[11] Verdouw, C., Tekinerdogan, B., Beulens, A., & Wolfert, S. (2021). Digital twins in smart farming. Agricultural Systems, 189, 103046. https://doi.org/10.1016/j.agsy.2020.103046 DOI: https://doi.org/10.1016/j.agsy.2020.103046
[12] Yang, A., Zhuansun, Y., Shi, Y., Liu, H., Chen, Y., & Li, R. (2021). IoT System for Pellet Proportioning Based on BAS Intelligent Recommendation Model. IEEE Transactions on Industrial Informatics, 17(2), 934–942. https://doi.org/10.1109/TII.2019.2960600 DOI: https://doi.org/10.1109/TII.2019.2960600
[13] You, J., Tulpan, D., Malpass, M. C., & Ellis, J. L. (2022). Using machine learning regression models to predict the pellet quality of pelleted feeds. Animal Feed Science and Technology, 293, 115443. https://doi.org/10.1016/j.anifeedsci.2022.115443 DOI: https://doi.org/10.1016/j.anifeedsci.2022.115443
Article Details
Abstract views: 10
Downloads: 7

