Multi-dimensional data interpretation breakthrough enables non-invasive defective filter identification in water treatment facilities

Researchers have developed a revolutionary non-invasive method combining 3D laser scanning technology and sensor data time-series analysis for identifying defective water treatment filters, achieving significant reductions in inspection time and labor costs while eliminating operational disruptions. Published in Smart Construction, this breakthrough has the potential to transform water treatment facility maintenance, ensuring safer and more efficient water processing by detecting subsurface structural defects through surface geometric changes and operational anomalies.

Water treatment filters serve as the final physical barrier for removing suspended solids and pathogens, making their structural integrity crucial for effective water treatment and public health protection. However, traditional filter inspection methods require manual disruption of filter media, are time-consuming and labor-intensive, involving at least two personnel for several hours, and risk overlooking defects due to limited sampling areas. Addressing this challenge, Dr. Pengkun Liu and Professor Pingbo Tang from Carnegie Mellon University, in collaboration with researchers from Circular Water Solution LLC, has developed a state-of-the-art multi-dimensional data interpretation framework that revolutionizes water treatment filter monitoring without operational interruptions.

"This framework design marks a critical advancement in water treatment facility maintenance," explains Professor Pingbo Tang. "Our central hypothesis is that when subsurface structural irregularities disrupt normal backwash operations and flow patterns, they produce both visible surface deformations and distinctive anomalies in sensor data, enabling non-invasive defect detection."

The newly developed framework utilizes upside-down installed 3D laser scanners to capture high-precision geometric changes on filter media surfaces before and after backwash processes. Lead researcher Pengkun Liu emphasizes, "The integration of geometric feature analysis—including roughness, curvature, omnivariance, and planarity—with time-series sensor data significantly enhances detection accuracy while eliminating the need for invasive media disruption."

A major challenge in filter inspection has been the inability to detect subsurface defects like uneven gravel support beds, mud ball formation, or underdrain blockages without disrupting operations. The team addressed this by developing a comprehensive four-module architecture: data acquisition through 3D laser scanning and SCADA sensor monitoring, geometric analysis using advanced clustering methods, time-series analysis of operational parameters, and fusion diagnosis validated by computational fluid dynamics simulations.

The framework, tested extensively at the Shenango Water Treatment Plant in Pennsylvania across six filter units, underwent rigorous validation over multiple operational cycles. Led by Jinghua Xiao from Circular Water Solution, the team demonstrated that the system effectively identifies abnormal filters through surface elevation irregularities, geometric feature variations, and operational parameter anomalies. Their measurements confirmed that defective filters exhibit distinctive patterns: surface elevation differences, higher geometric feature values, longer backwash durations, elevated turbidity levels, and reduced water production rates.

In practical applications, the framework successfully identified Filter 2 as defective, showing consistent surface bulging patterns across multiple scans, abnormal geometric characteristics, and suboptimal operational performance. These findings were validated through CFD simulations, which confirmed that subsurface defects like mud balls or dead zones disrupt uniform flow distribution from bottom drainage pipes, causing uneven surface deformations. The repeatability tests demonstrated high consistency in defect detection, proving the system's reliability in real-world water treatment settings.

"This framework has the potential to significantly impact the development of smart water treatment systems and infrastructure monitoring applications," says co-researcher Jinghua Xiao. "Its non-invasive nature and comprehensive multi-modal approach could lead to safer, more cost-effective, and more reliable maintenance practices for water treatment facilities worldwide."

In addition to water treatment applications, the techniques developed in this framework could inspire innovations in other infrastructure monitoring settings requiring precise subsurface defect detection, such as advanced pipeline inspection, industrial filtration systems, and civil infrastructure health monitoring.

The multi-dimensional data interpretation framework achieves exceptional performance through its innovative combination of 3D geometric analysis and sensor data fusion. It operates effectively with millimeter-level precision in surface change detection and real-time operational parameter monitoring, offering both geometric and time-series anomaly detection capabilities. "This approach establishes quantitative relationships between surface irregularities and subsurface defects, setting a new standard for non-invasive infrastructure inspection," notes Professor Pingbo Tang.

While the team acknowledges the need for expanding the dataset to more water treatment facilities and developing real-time monitoring systems, this study represents a critical step toward more efficient, safer, and more reliable water treatment operations. Future research directions include integrating 3D LiDAR sensors directly into filter structures for continuous monitoring and developing adaptive backwash control systems based on real-time surface geometry analysis.

This paper ”Multi-dimensional data interpretation for defective filter identification” was published in Smart Construction.
Liu P, Xiao J, Tang P. Multi-dimensional data interpretation for defective filter identification. Smart Constr. 2025(2):0014, https://doi.org/10.55092/sc20250014.