DIGITAL TWIN OF THE MEAT STORAGE MONITORING PROCESS: MODEL AND SIMULATION

Abstract

This paper presents the concept and mathematical implementation of a digital twin for modeling the meat storage process aimed at simulating quality dynamics within the cold chain. The developed system integrates a virtual sensor model for temperature and humidity monitoring, a numerical simulation module describing physicochemical and microbiological transformations, and a software architecture for data integration, processing, and visualization. The mathematical framework incorporates the kinetics of psychrotrophic microflora growth, protein and lipid degradation, moisture loss, coupled heat and mass transfer, and the impact of temperature fluctuations and environmental variability. Scenario-based modeling of normal storage conditions and cold chain disruptions is also provided.

A series of virtual experiments was conducted using literature and reference data for model parameterization and validation. The obtained results confirm that the proposed digital twin is capable of accurately reproducing meat spoilage dynamics, predicting quality changes, and estimating shelf life under different temperature–humidity regimes. The model enables the identification of critical risk points, assessment of short-term temperature abuse, optimization of storage and transportation conditions, and reduction of product losses.

The proposed approach can be applied to the development of intelligent food quality monitoring systems, decision-support tools for cold chain logistics, and adaptive inventory management. Integration of the digital twin with real-time sensor data provides opportunities for predictive analytics, early spoilage detection, and automated control of storage parameters. The research findings have practical relevance for the meat processing industry, refrigerated logistics, and food safety management systems, contributing to improved product quality, reduced waste, and enhanced operational efficiency.