Automated System to Optimize the Process and Energy Consumption for Sewage Treatment Plant Based on Gas Emission by Using Sensors and IoT

Sewage Treatment Plants (STPs) play a crucial role in environmental sustainability and public health by removing pollutants from wastewater. Traditional STPs suffer from high energy consumption, operational inefficiencies, and a lack of real-time monitoring, leading to increased energy consumption, unassured water quality, and potential environmental pollution. This study presents a custom-designed Internet of Things (IoT)- enabled automated system to optimize energy usage and treatment efficiency in STPs by monitoring gas emissions, including carbon monoxide, carbon dioxide, ammonia, methane, and hydrogen sulfide. The system explores gas emission patterns during water treatment and maps emission levels during the process to assess treatment completion. It integrates advanced sensors, microcontrollers, and cloud-based data management for real-time tracking and process optimization. Experimental validation across two STPs demonstrates significant energy savings, reducing aerator run times by 45 minutes per cycle —a 15% reduction compared to manual operation —while maintaining satisfactory effluent quality. Additionally, the system enhances operational safety by providing automated alerts for hazardous gas levels. The findings indicate that IoT-based automation can revolutionize wastewater treatment, making it more cost-effective, energy-efficient, and environmentally sustainable.