IoT-Enabled Automated Drainage Clog Detection and Removal System for Uninterrupted Flow in Food Processing Industries
DOI:
https://doi.org/10.46610/IJAITCI.2026.v02i02.004Keywords:
Arduino Uno, Clog Detection, Drainage Monitoring, Flow Sensor, Industry 4.0, IoT, Predictive Maintenance, Pressure SensorAbstract
Drainage clogging in food processing industries poses significant challenges to operational efficiency, hygiene maintenance, and environmental sustainability. Conventional clog detection and removal methods, including manual inspection and chemical cleaning, are labor-intensive, time-consuming, and may lead to pipeline degradation and environmental pollution. This study presents an IoT-enabled Automated Drainage Clog Detection and Removal System designed to ensure uninterrupted wastewater flow in industrial environments. The proposed system integrates an Arduino Uno microcontroller with pressure and flow sensors to continuously monitor pipeline conditions in real time. Abnormal variations in flow rate and pressure are used to identify the occurrence of blockages. Upon clog detection, relay-controlled DC motors activate a mechanical cleaning mechanism consisting of brushes and flow reversal units to remove obstructions autonomously. Bluetooth Low Energy (BLE) communication facilitates remote monitoring and instant alert generation through mobile devices, enhancing system accessibility and reducing maintenance efforts. The system also incorporates an LCD interface for local status visualization and an SMPS for reliable power management. Experimental evaluation demonstrated a clog detection accuracy exceeding 95%, with most blockages cleared within 30 seconds, significantly reducing downtime compared to conventional approaches. By eliminating chemical cleaners and minimizing human intervention, the proposed solution promotes sustainable wastewater management while aligning with Industry 4.0 principles. Furthermore, the system offers scalability for diverse industrial applications and provides a foundation for future integration of artificial intelligence and predictive maintenance techniques. The developed prototype presents a cost-effective, eco-friendly, and intelligent approach to modern drainage management in food processing industries.
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