Framework for AI-Driven Predictive Maintenance in IoT-Enabled Water Treatment Plants to Minimize Downtime and Improve Efficiency - Ayodeji Idowu Taiwo; Lawani Raymond Isi; Michael Okereke; Oludayo Sofoluwe; Gilbert Isaac Tokunbo Olugbemi; Nkese Amos Essien (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
This paper explores the development of an AI-driven predictive maintenance framework tailored for IoT-enabled water treatment plants to minimize downtime and improve operational efficiency. Water treatment plants play a critical role in ensuring the availability of clean water, yet traditional maintenance practices often result in equipment failures, inefficiencies, and increased costs. By integrating IoT technologies, such as sensors and real-time monitoring systems, with advanced AI applications, including machine learning and predictive analytics, this framework shifts maintenance strategies from reactive to proactive. The proposed framework emphasizes seamless data collection, real-time analysis, anomaly detection, and automated responses to mitigate potential issues before they escalate. Key challenges, such as data quality, cost constraints, cybersecurity, and regulatory compliance, are analyzed alongside emerging AI algorithms, edge computing, and cloud integration trends. The paper concludes by offering practical recommendations for implementing the framework, including phased adoption, workforce development, and strengthened cybersecurity measures. This innovative approach significantly improves plant reliability, resource optimization, and long-term sustainability.
Keywords: Predictive Maintenance; IoT-Enabled Water Treatment; Artificial Intelligence; Real-Time Monitoring; Operational Efficiency; Cybersecurity