AI-Driven Smart Membrane Filtration System for Sustainable Industrial Wastewater Recovery and Resource Optimization

Abstract

The growing demand for sustainable industrial operations and increasing freshwater scarcity have intensified the need for advanced wastewater recovery technologies capable of improving water reuse efficiency and reducing environmental impact. This study presents an artificial intelligence-driven smart membrane filtration system for sustainable industrial wastewater recovery and resource optimization under dynamic operating conditions. The proposed framework integrates membrane separation technology with real-time monitoring, intelligent process control, and machine learning-based predictive analytics to enhance contaminant removal efficiency and optimize operational performance. A comprehensive experimental and simulation-based investigation was conducted to evaluate the influence of transmembrane pressure, feedwater composition, flow rate, membrane fouling behavior, temperature, and cleaning cycles on filtration efficiency and system stability. Advanced AI algorithms were employed to predict membrane fouling trends, optimize filtration parameters, and enable adaptive operational control for minimizing energy consumption and maximizing water recovery. The membrane system demonstrated significant improvements in pollutant removal efficiency, permeate quality, and operational reliability compared to conventional wastewater treatment approaches. The intelligent monitoring framework also enabled rapid detection of process anomalies, reduced membrane cleaning frequency, and improved long-term membrane lifespan through predictive maintenance strategies. Comparative analysis revealed substantial reductions in operational costs, energy demand, and wastewater discharge volume under optimized AI-assisted conditions. Furthermore, the integration of resource recovery mechanisms supported circular water management and sustainable industrial production practices.