When
Monday, April 20, 2026, at 10:00 a.m.
Mohamed Shafae
Assistant Professor
Systems & Industrial Engineering
University of Arizona
"Toward Autonomous and Self-Correcting Water Treatment: Data Science Foundations for Ultrafiltration Monitoring"
Harshbarger 118A-A1
ABSTRACT: Ultrafiltration is a key technology for water treatment and reuse, but its operation is challenging because membrane fouling, cleaning cycles, control settings and changing source-water conditions interact in complex and time-varying ways. This seminar presents our broader effort to build the data science foundation for more autonomous and self-correcting water treatment systems using an engineering-scale ultrafiltration platform. We combine long-horizon operational sensor data with environmental and weather information, develop cycle-level representations of system behavior and use exploratory and functional data analysis to better understand filtration dynamics, cleaning effectiveness, seasonal variability and anomalous operating patterns. The talk will highlight how these insights support monitoring, productivity prediction and future decision-support capabilities for more adaptive and intelligent water treatment operations.
BIOSKETCH: Mohamed Shafae is an assistant professor in the Department of Systems and Industrial Engineering at The University of Arizona, where he directs the Cyber-Physical Manufacturing Systems (CyPhyMan) Lab. Shafae earned his PhD and MS degrees in industrial engineering from Virginia Tech in 2018, after completing his MSc and BSc degrees from Alexandria University in Egypt. His research lies at the intersection of Artificial Intelligence, advanced manufacturing and cyber-physical systems. Shafae has led and contributed to several multi-institutional research initiatives, supported by NASA, the U.S. Army Corps of Engineers and Arizona's Technology and Research Initiative Fund, where he has helped develop machine learning methods for monitoring and quality assurance of metal additive manufacturing in aerospace applications; cyber-physical systems security tools for risk assessment, prevention, detection and mitigation; and AI-driven autonomous water treatment and reuse systems for decentralized resilient infrastructure. He has also collaborated on applied research projects with a wide range of industry leaders, including IBM, Honeywell, Lockheed Martin and Roche.