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Sensor validation and reconciliation for a partial nitrification process

(2006) WATER SCIENCE AND TECHNOLOGY. 53(4-5). p.513-521
Author
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Abstract
Wastewater treatment plants (WWTP) are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function. In this paper, a structured residual approach with maximum sensitivity (SRAMS) based on the redundancy of the measurements is used to detect, identify and reconstruct single and multiple sensor faults in a single reactor for high activity ammonia removal over nitrite (SHARON) process. SRAMS is based on inferences, which are insensitive to the faults in the sensor of interest and sensitive to faults in the other sensors. It is used for four types of sensor failure detection: bias, drift, complete failure and precision degradation. The application of sensor validation shows that single and multiple sensor faults can be detected and that the fault magnitude and fault type can be estimated by the reconstruction scheme. This sensor validation method is not limited by the type or application of the considered sensors. The methodology can thus easily be applied for sensor surveillance of other continuously measuring sensors and analysers.
Keywords
RECONSTRUCTION, OPERATION, FAULTY SENSORS, WASTE-WATER, IDENTIFICATION, PRINCIPAL COMPONENT ANALYSIS, smart sensor, sensor validation and reconciliation, data reconstruction, fault detection and identification, nitrogen removal, DIAGNOSIS, REACTOR, ANAMMOX

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Chicago
Yoo, CK, Kris Villez, IB Lee, Stijn Van Hulle, and Peter A Vanrolleghem. 2006. “Sensor Validation and Reconciliation for a Partial Nitrification Process.” Water Science and Technology 53 (4-5): 513–521.
APA
Yoo, CK, Villez, K., Lee, I., Van Hulle, S., & Vanrolleghem, P. A. (2006). Sensor validation and reconciliation for a partial nitrification process. WATER SCIENCE AND TECHNOLOGY, 53(4-5), 513–521. Presented at the 2nd IWA International Conference on Instrumentation, Control and Automation for Water and Wastewater Treatment and Transport Systems.
Vancouver
1.
Yoo C, Villez K, Lee I, Van Hulle S, Vanrolleghem PA. Sensor validation and reconciliation for a partial nitrification process. WATER SCIENCE AND TECHNOLOGY. 2006;53(4-5):513–21.
MLA
Yoo, CK, Kris Villez, IB Lee, et al. “Sensor Validation and Reconciliation for a Partial Nitrification Process.” WATER SCIENCE AND TECHNOLOGY 53.4-5 (2006): 513–521. Print.
@article{714754,
  abstract     = {Wastewater treatment plants (WWTP) are notorious for poor data quality and sensor reliability due to the hostile environment in which the measurement equipment has to function. In this paper, a structured residual approach with maximum sensitivity (SRAMS) based on the redundancy of the measurements is used to detect, identify and reconstruct single and multiple sensor faults in a single reactor for high activity ammonia removal over nitrite (SHARON) process. SRAMS is based on inferences, which are insensitive to the faults in the sensor of interest and sensitive to faults in the other sensors. It is used for four types of sensor failure detection: bias, drift, complete failure and precision degradation. The application of sensor validation shows that single and multiple sensor faults can be detected and that the fault magnitude and fault type can be estimated by the reconstruction scheme. This sensor validation method is not limited by the type or application of the considered sensors. The methodology can thus easily be applied for sensor surveillance of other continuously measuring sensors and analysers.},
  author       = {Yoo, CK and Villez, Kris and Lee, IB and Van Hulle, Stijn and Vanrolleghem, Peter A},
  issn         = {0273-1223},
  journal      = {WATER SCIENCE AND TECHNOLOGY},
  language     = {eng},
  location     = {Busan, South Korea},
  number       = {4-5},
  pages        = {513--521},
  title        = {Sensor validation and reconciliation for a partial nitrification process},
  url          = {http://dx.doi.org/10.2166/wst.2006.155},
  volume       = {53},
  year         = {2006},
}

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