Fault Detection and Isolation in a Spiral-Wound Reverse Osmosis (RO) Desalination Plant

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• 作者：Xavier Pascual ; Han Gu ; Alex Bartman ; Aihua Zhu ; Anditya Rahardianto ; Jaume Giralt ; Robert Rallo ; Panagiotis D. Christofides ; Yoram Cohen
• 刊名：Industrial & Engineering Chemistry Research
• 出版年：2014
• 出版时间：February 26, 2014
• 年：2014
• 卷：53
• 期：8
• 页码：3257-3271
• 全文大小：770K
• 年卷期：v.53,no.8(February 26, 2014)
• ISSN：1520-5045

文摘

Sensor fault detection and isolation (SFDI) approaches, based on support vector regression (SVR) plant sensor models and self-organizing-map (SOM) analysis, were investigated for application to reverse osmosis (RO) desalination plant operation. SFDI-SVR and SFDI-SOM were assessed using operational data from a small spiral-wound RO pilot plant and synthetic faulty data generated as perturbations relative to normal plant operational data. SFDI-SVR was achieved without false negative (FN) detections for sensor deviations of |10%| and FN detections of, at the most, |5%|, and for sensor deviations of |4%| at sensor fault detection (FD) thresholds of up to |4%|. False positive (FP) detections were almost invariant, with respect to sensor FD, being |5%| for sensor deviations of |5%|. Corrections of faulty sensor readings were within SVR model accuracy (AARE < 1%) for SFDI-SVR and |5%| for SFDI-SOM. Although SFDI-SOM has lower detection accuracy, it requires a single overall plant model (or SOM), while providing pictorial representation of plant operation and depiction of faulty operational trajectories.