Smart Monitoring of Small-Capacity Sewage Treatment Plants Using Internet of Things (IoT): A Real-Time and Predictive Approach

AKASH. T

doi:10.55524/ijirem.2026.13.3.17

Abstract

Small capacity sewage treatment plants (STPs) are often poorly monitored, which results in uneven effluent quality, high operational costs, frequent equipment failures and non-compliance with regulatory norms. The present work deals with the implementation of Internet of Things (IoT) technology for smart real-time monitoring of STPs, notably for 0.5-5 MLD decentralized systems. IoT-enabled sensor networks were used for continuous monitoring of the essential parameters such as pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), flow rate and energy usage.
Results demonstrate very high improvements by introduction of IoT. Real-time data transmission and automatic alerts helped reduce operational downtime by over 65%, increase effluent quality compliance by 82%, and reduce overall maintenance expenses by 45-55%. Predictive maintenance capabilities allowed for early detection of problems resulting in 30-40% energy savings and enhanced process stability. The proposed IoT framework is perfectly integrated with the existing hybrid treatment units and provides an easy-to-use dashboard for remote monitoring and data-driven decision making.
The findings validate that IoT-based smart monitoring converts conventional STP activities into effective, sustainable and compliant systems. This strategy is in line with the national missions such as SBM-U 2.0 and NGT standards, provides a viable and scalable solution for small and medium towns, and encourages water reuse and environmental sustainability.

Keywords

IoT in Wastewater Treatment, Smart STP Monitoring, Real-Time Sensor Network, Decentralized Sewage Treatment, Predictive Maintenance

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Cites this article as

A. T. "Smart Monitoring of Small-Capacity Sewage Treatment Plants Using Internet of Things (IoT): A Real-Time and Predictive Approach", International Journal of Innovative Research in Engineering and Management (IJIREM), Vol-13, Issue-3, Page No-145-152, 2026. Available from: https://doi.org/10.55524/ijirem.2026.13.3.17