INTELLIGENT PRODUCTION LINE CONTROL SYSTEMS IN THE FOOD INDUSTRY BASED ON MACHINE LEARNING
DOI:
https://doi.org/10.32782/tnv-tech.2025.1.40Keywords:
production automation, predictive analytics, cyber-physical systems, quality control, digital transformationAbstract
The possibilities of introducing intelligent production process control systems in the food industry, taking into account modern challenges and automation needs, are investigated. The relevance of the study is due to the need to increase the efficiency of production lines, reduce costs, improve quality control and compliance with international food safety standards, in particular ISO 22000, HACCP, BRC Global Standard, FSSC 22000 and IFS. It has been established that traditional approaches to automation do not provide sufficient flexibility and adaptability to changes in technological parameters and market conditions, which necessitates the implementation of systems based on machine learning, predictive analytics, and IoT.The purpose of the study is to develop approaches to automation of production lines in the food industry by integrating machine learning, robotics, and IoT to improve the efficiency of production processes. The methodological basis is based on analytical and comparative research methods that allowed us to evaluate the effectiveness of existing automation approaches, as well as modeling an adaptive control system.The results of the study confirm that the integration of IoT sensors, machine learning algorithms, and predictive analysis can significantly improve the accuracy of monitoring and control of production processes. The main problems of implementing such systems are identified, including the high cost of modernization, the complexity of integration with existing infrastructure, the need for qualified personnel and compliance with regulatory requirements. It is proved that the gradual introduction of intelligent systems using predictive analytics to monitor the technical condition of equipment and prevent failures can reduce risks, minimize production losses and improve product quality.Prospects for further research include the development of deep learning algorithms to improve the accuracy of the analysis of production parameters, as well as the study of the economic aspects of implementing such technologies in various segments of the food industry.
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