Low-Cost IoT Monitoring of Energy-Relevant Variables in an Industrial Compressed-Air System: Field Deployment in a Colombian SME

Industrial compressed-air systems require continuous operational visibility because pressure regulation, load cycling, thermal conditions, and pneumatic demand affect energy performance. This study develops and field-evaluates a low-cost Internet of Things (IoT) monitoring architecture for a 10 hp lubricated rotary-screw compressor installed in a Colombian microenterprise. The platform combines an ESP32 microcontroller with a PZEM-004T electrical module, an HK1100C pressure sensor, two DS18B20 temperature sensors, a MySQL database, and Power BI visualization. The retained field campaign contains 1014~records across four valid acquisition days between 14 and 18 October 2025. Comparisons with reference instruments under the tested operating conditions yielded mean deviations of 1.4 V, 1.1 A, 1.0 °C, and 4.8 psi for voltage, current, inlet temperature, and pressure, respectively. These comparisons provide an empirical characterization of the monitored channels but do not constitute complete metrological validation of the sensing system. The field data reveal cyclic pressure behavior and pronounced electrical transitions associated with compressor loading. For a representative active-operation subset on 16 October, the mean Estimated Net Receiver-Inventory Rate was 0.0198 m3/s and corresponding exploratory power-to-inventory-rate was 432.2 kW/(m3/s), close in scale to the nominal reference specific power of approximately 416.7 kW/(m3/s). Thermodynamic indicators were calculated using isentropic and isothermal compression relations and pressure-volume-temperature corrections. Because airflow was not independently measured and the electrical channel was not validated with a traceable true-RMS power quality analyzer, these derived indicators are interpreted as exploratory rather than certified or standard-compliant performance measurements. The architecture provides an accessible pathway for persistent energy-relevant and condition monitoring in resource-constrained industrial environments.