AI-Enabled Hybrid MBBR–MBR System With ANN–RF-Based Iot Monitoring For Sustainable Urban Wastewater Reuse

Abstract

Background: Rapid urbanization and freshwater scarcity have increased the need for intelligent and sustainable wastewater treatment technologies for urban water reuse applications.

Methods: This study proposes an AI-Enabled Hybrid MBBR–MBR Intelligent Wastewater Treatment Framework (AHMIWF) integrating Moving Bed Biofilm Reactor (MBBR), Membrane Bioreactor (MBR), IoT-based monitoring, and machine learning techniques for advanced wastewater management. The hybrid system combines biofilm-assisted biodegradation and membrane filtration to improve pollutant removal efficiency and effluent quality. An ESP32-based IoT architecture equipped with pH, turbidity, dissolved oxygen, temperature, conductivity, and water level sensors enables continuous real-time monitoring and automated data acquisition. The collected data are transmitted to a cloud platform for remote supervision and process control. Artificial Neural Network (ANN) and Random Forest (RF) algorithms were implemented to predict and optimize critical wastewater parameters, including BOD, COD, TSS, turbidity, and dissolved oxygen levels.

Results: Experimental evaluation demonstrated significant reductions in organic pollutants, suspended solids, and microbial contaminants, confirming the effectiveness of the proposed framework.

Conclusion: The developed AHMIWF system offers an energy-efficient, compact, and automated solution for sustainable urban wastewater reuse and intelligent water resource management.