SmartCool-IoT: An Experimental IoT-Based Monitoring and Treatment System for Mitigating Biofouling and Thermal Instability in Large Coolant Storage Tanks

Introduction/Objective: Large underground coolant tanks in industrial and data-center environments frequently suffer from microbial contamination, thermal instability, and excessive energy consumption. Conventional mitigation strategies—such as periodic chemical dosing or installing centralized Building Management Systems (BMS)—are often effective but prohibitively expensive and unsuitable for small- and mid-sized facilities. This study presents SmartCool-IoT, a low-cost, IoT-enabled monitoring and treatment framework designed to address these challenges using real-time sensing, adaptive control, and automated microbial suppression. Methods: The system employs an ESP32 microcontroller interfaced with temperature, flow, pressure, turbidity, ORP, and pH sensors, along with UV-C disinfection, cartridge filtration, biocide dosing, and variable-frequency pumping. Real-time data acquisition and control were visualized via an IoT dashboard. Experimental evaluation was conducted on a 5×7 ft prototype tank (~3000 liters) over eight weeks, comparing SmartCool-IoT with baseline manual operation. Baseline refers to operation without automated sensing, UV–biocide coordination, or adaptive pump regulation. Results: SmartCool-IoT achieved notable improvements: ~70% temperature-stability enhancement (±1.3 °C), ~85% microbial-growth reduction, turbidity maintained below 5 NTU, ORP stabilized at 680–720 mV, and 12–13% reduction in pump-energy consumption. Effectiveness metrics were normalized against baseline manual-operation values. Discussion: The findings demonstrate that microbial suppression, thermal stability, and energy efficiency can be achieved without costly BMS installations. The ESP32-based architecture showed autonomy, scalability, and adaptability across diverse industrial conditions. Conclusion: SmartCool-IoT offers a scalable, low-cost solution for coolant-quality management, with strong potential for patentable deployment in industrial and data-center systems. Future work includes AI-driven anomaly detection and digital-twin integration..