Sistema IoT para la gestión de variables de proceso en ambientes de refrigeración de la industria cárnica

Abstract

The food industry faces the challenge of ensuring product quality and safety throughout the supply chain, particularly in cold storage processes. Within this context, the meat industry requires highly efficient refrigeration systems, as parameters like temperature and relative humidity directly impact product safety and shelf life (Pardo Martínez & Cotte Poveda, 2022). However, the lack of real-time monitoring mechanisms and energy optimization strategies has led to operational inefficiencies and risks of product spoilage (Ramírez-Faz et al., 2020). To address this issue, an IoT system was developed and implemented to manage and control critical variables in meat industry refrigeration systems. The primary goal is to optimize product preservation through continuous monitoring of temperature, relative humidity, and energy consumption, enabling data-driven decisions to ensure food safety (Díaz-Ruiz et al., 2019). The methodology involved integrating low-cost sensors connected to IoT devices, which transmit real-time data to a VPS server using the MQTT (Message Queuing Telemetry Transport) protocol. This infrastructure enables data visualization and analysis via the gAiA web platform, along with automated alerts for storage condition deviations (Wang et al., 2021). Predictive algorithms were also incorporated to identify energy consumption patterns and their impact on refrigeration efficiency (Han et al., 2021). Results demonstrate that this IoT system reduces energy consumption by 5%-10% without compromising storage quality. Additionally, data traceability supports preventive maintenance strategies and resource optimization, enhancing sector sustainability (Onoufriou et al., 2019). Due to its low cost and ease of implementation, this solution is a viable alternative for large-scale adoption in the meat industry, strengthening food security and reducing the environmental impact of refrigeration (Moran, 2024).