Incorporating AI and IoT in Automated Membrane Monitoring Systems

Abstract

The integration of Artificial Intelligence and the Internet of Things in automated membrane monitoring systems offers a transformative approach toward enhancing operational efficiency, predictive maintenance, and sustainability in industrial applications. This study proposes a comprehensive framework for real-time monitoring and optimization of membrane systems using IoT-enabled sensors and AI-based models. IoT sensors were deployed to continuously capture critical parameters such as pressure, flow rate, temperature, and turbidity, while AI models, including Long Short-Term Memory networks and autoencoders, were developed for anomaly detection and predictive maintenance. The system achieved an anomaly detection accuracy of 98.3%, reducing false positive rates to 1.7%, and extended the mean time between failures (MTBF) by 50%, from 14 to 21 days. Optimization algorithms increase permeate output by 12.5% and reduce energy consumption by 9.3%, contributing to operational cost savings of $18,000 annually for a mid-scale industrial plant. These results point out the reliability, scalability, and economic feasibility of the system and serve as a basis for the development of intelligent, sustainable, and efficient membrane operations. The findings of this work show the potential of AI-IoT integration in enhancing industrial monitoring systems and setting a structure for future research and applications.