Model-based measurement error detection of a coagulant dosage control system
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Online instruments are widely used in wastewater treatment plants and drinking water treatment plants for process monitor and control. Although maintenances of online sensors are important routine works, potential measurement errors of online sensors are challenging not only monitoring of coagulation process but also the coagulant dosage control system, what this paper is focusing on. In order to estimate and detect the potential measurement errors, this paper proposes a concept of model-based measurement error detection. Relying on the model of the outlet software senor, the differences between simulations and measurements of outlet turbidity can be used as indicator of inlet measurement errors. Based on the concept, this paper enables to quantify the measurement errors and build up a novel detection method. In addition, the paper compares the proposed detection method with a traditional method—the normal variation range. The results show that the proposed method has a better efficiency to detect the measurement error.
KeywordsError detection Model Coagulation Online sensors Normal distribution
The authors appreciate the assistance provided by NRA WWTP and DOSCON Co Ltd. (www.doscon.no) for providing access to the multi-parameter-based dosing control system.
- Edward N, Charles F (2014) Essentials of testing and assessment: a practical guide to counselors, social workers and psychologists, 3rd edn. Brooks Cole, CaliforniaGoogle Scholar
- Liu W, Ratnaweera H (2016) Improvement of multi-parameter based feed-forward coagulant dosing control systems with Feed-back functionalities. Water Sci Technol 56:67–78Google Scholar
- Liu W, Ratnaweera, H, Song HP (2013) Better treatment efficiencies and process economies with real-time coagulant dosing control. In: 11th IWA conference on instrumentation control and automation, Narbonne, FranceGoogle Scholar
- Rathnaweera S (2010) Modelling and optimization of wastewater coagulation process. PhD thesis, Norwegian University of Life Sciences, Aas, NorwayGoogle Scholar
- Robinson RB, Chris D, Odom K (2005) Identifying outliers in correlated water quality data. Environ Eng Sci 131:651–657Google Scholar
- Taylor JR (1999) An introduction to error analysis: the study of uncertainties in physical measurements. University Science Books, LondonGoogle Scholar
- Yates DS, David SM, Daren SS (2008) The practice of statistics, 3rd edn. Freeman, New YorkGoogle Scholar