Real-time fault detection and isolation in biological wastewater treatment plants

2009 ◽  
Vol 60 (11) ◽  
pp. 2949-2961 ◽  
Author(s):  
F. Baggiani ◽  
S. Marsili-Libelli
Keyword(s):  
Wastewater Treatment ◽  
Fault Detection ◽  
Real Time ◽  
Fault Detection And Isolation ◽  
Large Set ◽  
Data Set ◽  
Plant Operation ◽  
Pca Algorithm ◽  
Operational Data

Automatic fault detection is becoming increasingly important in wastewater treatment plant operation, given the stringent treatment standards and the need to protect the investment costs from the potential damage of an unchecked fault propagating through the plant. This paper describes the development of a real-time Fault Detection and Isolation (FDI) system based on an adaptive Principal Component Analysis (PCA) algorithm, used to compare the current plant operation with a correct performance model based on a reference data set and the output of three ion-specific sensors (Hach-Lange gmbh, Düsseldorf, Germany): two Nitratax® NOx UV sensors, in the denitrification tank and downstream of the oxidation tanks, where an Amtax® ammonium-N sensor was also installed. The algorithm was initially developed in the Matlab environment and then ported into the LabView 8.20 (National Instruments, Austin, TX, USA) platform for real-time operation using a compact Field Point®, a Programmable Automation Controller by National Instruments. The FDI was tested with a large set of operational data with 1 min sampling time from August 2007 through May 2008 from a full-scale plant. After describing the real-time version of the PCA algorithm, this was tested with nine months of operational data which were sequentially processes by the algorithm in order to simulate an on-line operation. The FDI performance was assessed by organizing the sequential data in two differing moving windows: a short-horizon window to test the response to single malfunctions and a longer time-horizon to simulate multiple unrepaired failures. In both cases the algorithm performance was very satisfactory, with a 100% failure detection in the short window case, which decreased to 84% in the long window setting. The short-window performance was very effective in isolating sensor failures and short duration disturbances such as spikes, whereas the long term horizon provided accurate detection of long-term drifts and proved robust enough to allow for some delay in failure recovery. The system robustness is based on the use of multiple statistics which proved instrumental in discriminating among the various causes of malfunctioning.

scholarly journals Two years of near real-time chemical composition of submicron aerosols in the region of Paris using an Aerosol Chemical Speciation Monitor (ACSM) and a multi-wavelength Aethalometer

2015 ◽  
Vol 15 (6) ◽  
pp. 2985-3005 ◽  
Author(s):  
J.-E. Petit ◽  
O. Favez ◽  
J. Sciare ◽  
V. Crenn ◽  
R. Sarda-Estève ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Real Time ◽  
Chemical Speciation ◽  
Chemical Properties ◽  
Temporal Variations ◽  
Mitigation Strategies ◽  
Chemical Information ◽  
Data Set ◽  
Pollution Episodes

Abstract. Aerosol mass spectrometer (AMS) measurements have been successfully used towards a better understanding of non-refractory submicron (PM1) aerosol chemical properties based on short-term campaigns. The recently developed Aerosol Chemical Speciation Monitor (ACSM) has been designed to deliver quite similar artifact-free chemical information but for low cost, and to perform robust monitoring over long-term periods. When deployed in parallel with real-time black carbon (BC) measurements, the combined data set allows for a quasi-comprehensive description of the whole PM1 fraction in near real time. Here we present 2-year long ACSM and BC data sets, between mid-2011 and mid-2013, obtained at the French atmospheric SIRTA supersite that is representative of background PM levels of the region of Paris. This large data set shows intense and time-limited (a few hours) pollution events observed during wintertime in the region of Paris, pointing to local carbonaceous emissions (mainly combustion sources). A non-parametric wind regression analysis was performed on this 2-year data set for the major PM1 constituents (organic matter, nitrate, sulfate and source apportioned BC) and ammonia in order to better refine their geographical origins and assess local/regional/advected contributions whose information is mandatory for efficient mitigation strategies. While ammonium sulfate typically shows a clear advected pattern, ammonium nitrate partially displays a similar feature, but, less expectedly, it also exhibits a significant contribution of regional and local emissions. The contribution of regional background organic aerosols (OA) is significant in spring and summer, while a more pronounced local origin is evidenced during wintertime, whose pattern is also observed for BC originating from domestic wood burning. Using time-resolved ACSM and BC information, seasonally differentiated weekly diurnal profiles of these constituents were investigated and helped to identify the main parameters controlling their temporal variations (sources, meteorological parameters). Finally, a careful investigation of all the major pollution episodes observed over the region of Paris between 2011 and 2013 was performed and classified in terms of chemical composition and the BC-to-sulfate ratio used here as a proxy of the local/regional/advected contribution of PM. In conclusion, these first 2-year quality-controlled measurements of ACSM clearly demonstrate their great potential to monitor on a long-term basis aerosol sources and their geographical origin and provide strategic information in near real time during pollution episodes. They also support the capacity of the ACSM to be proposed as a robust and credible alternative to filter-based sampling techniques for long-term monitoring strategies.

scholarly journals Quality control of wastewater treatment operational data by continuous mass balancing: dealing with missing measurements and delayed outputs

2015 ◽  
Vol 50 (3) ◽  
pp. 228-239
Author(s):  
A. Spindler ◽  
J. Krampe
Keyword(s):  
Wastewater Treatment ◽  
Wastewater Treatment Plants ◽  
Oxygen Demand ◽  
Mass Conservation ◽  
Missing Measurements ◽  
Daily Data ◽  
Continuous Mass ◽  
Mass Balancing ◽  
Operational Data

Continuous mass balancing defines a new standard in data quality validation. Likewise relying on the principles of mass conservation it outperforms long-term static mass balancing approaches because faults in data can be assigned to their time of occurrence. This research was carried out with practical application to routine operational data in mind and two major aspects are investigated to make this application feasible. Sludge concentrations of typically balanced components (chemical oxygen demand, total nitrogen, total phosphate) are not routinely measured in wastewater treatment plants. Therefore they need to be determined from alternative, more frequent measurements such as total suspended solids. To provide the necessary statistical basis for such determination, monthly sludge sampling was found sufficient. Further, contrary to long-term static mass balancing, the effects of delay between input and output loads must not be neglected in continuous mass balancing based on daily data. While a storage/release approach did not give the desired results, the consideration of hydraulic retention (first-order flow dynamics) fundamentally improved the performance of the proposed method.

Mahalanobis Taguchi System (MTS) as a Prognostics Tool for Rolling Element Bearing Failures

2010 ◽  
Vol 132 (5) ◽  
Author(s):  
Ahmet Soylemezoglu ◽  
S. Jagannathan ◽  
Can Saygin
Keyword(s):  
Fault Detection ◽  
Real Time ◽  
Operating Conditions ◽  
Fault Detection And Isolation ◽  
Numerical Control ◽  
Rolling Element Bearing ◽  
Time To Failure ◽  
Cnc Machine ◽  
Bearing Failures ◽  
Rolling Element

In this paper, a novel Mahalanobis–Taguchi system (MTS)-based fault detection, isolation, and prognostics scheme is presented. The proposed data-driven scheme utilizes the Mahalanobis distance (MD)-based fault clustering and the progression of MD values over time. MD thresholds derived from the clustering analysis are used for fault detection and isolation. When a fault is detected, the prognostics scheme, which monitors the progression of the MD values, is initiated. Then, using a linear approximation, time to failure is estimated. The performance of the scheme has been validated via experiments performed on rolling element bearings inside the spindle headstock of a microcomputer numerical control (CNC) machine testbed. The bearings have been instrumented with vibration and temperature sensors and experiments involving healthy and various types of faulty operating conditions have been performed. The experiments show that the proposed approach renders satisfactory results for bearing fault detection, isolation, and prognostics. Overall, the proposed solution provides a reliable multivariate analysis and real-time decision making tool that (1) presents a single tool for fault detection, isolation, and prognosis, eliminating the need to develop each separately and (2) offers a systematic way to determine the key features, thus reducing analysis overhead. In addition, the MTS-based scheme is process independent and can easily be implemented on wireless motes and deployed for real-time monitoring, diagnostics, and prognostics in a wide variety of industrial environments.

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