Fault Detection and Monitoring Using Multiscale Principal Component Analysis at a Sewage Treatment Plant

2014 ◽  
Vol 70 (3) ◽  
Author(s):  
Siti Nur Suhaila Mirin ◽  
Norhaliza Abdul Wahab
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Principal Component ◽  
Component Analysis ◽  
False Alarms ◽  
Sewage Treatment Plants ◽  
Hotelling’S T2

Safety, environmental regulations, the cost of maintenance and the operation of sewage treatment plants are some of the many reasons researchers have carried out countless research studies into fault detection and monitoring over the years. Conventional principal component analysis (PCA) in particular has been used in the field of fault detection, where the technique is able to separate useful information from multivariate data. However, conventional PCA can only be used on data that has a constant mean, which is rare in sewage treatment plants. Consequently, the success of combining wavelet and conventional PCA has attracted many researchers to apply it to fault detection where the wavelet is capable of separating data into several time scales. The separated data will be approximated to a constant mean. In addition, the conventional PCA only captures the correlation across the data, unlike multiscale PCA (MSPCA) which captures the correlation within the data and across the data. Therefore, in this work, MSPCA is introduced to improve the performance of PCA in fault detection. The objective of this paper is to reduce false alarms that exist in PCA fault detection and monitoring. Data from the Bunus sewage treatment plant (Bunus STP) is used and analysed using conventional PCA with Hotelling’s T2 and the squared prediction error (SPE). MSPCA with Hotelling’s T2 and SPE is used to improve the efficiency of fault detection and monitoring performance in conventional PCA. Therefore, MSPCA is successful in improving conventional PCA in fault detection and monitoring by reducing false alarms. 

scholarly journals Effluent quality assessment of sewage treatment plant using principal component analysis and cluster analysis

2021 ◽  
Vol 27 (4) ◽  
pp. 79-95
Author(s):  
Saad Uffi AL-Kordy ◽  
Dr. Basim Hussein Khudair
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Principal Component ◽  
Treatment Method ◽  
Component Analysis ◽  
Sewage Water ◽  
Quality Parameters

Sewage water is a mixture of water and solids added to water for various uses, so it needs to be treated to meet local or global standards for environmentally friendly waste production. The present study aimed to analyze the new Maaymyrh sewage treatment plant's quality parameters statistically at Hilla city. The plant is designed to serve 500,000 populations, and it is operating on a biological treatment method (Activated Sludge Process) with an average wastewater inflow of 107,000m3/day. Wastewater data were collected daily by the Mayoralty of Hilla from November 2019 to June 2020 from the influent and effluent in the (STP) new in Maaymyrh for five water quality standards, such as (BOD5), (COD), (TSS), (TP) and (TN). The results showed that the removal efficiency was 88%, 75%, 94%, 57%, and 77%, respectively. The results of the cluster analysis (CA) showed the formation of clusters in four stages and then gave the final shape consisting of two groups. At the same time, two influencing factors were extracted in the principal component analysis (PCA). The effluent's final quality (an average of eight consecutive months) complies with the stringent regulations proposed in the Iraqi Quality Requirements.

Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP

2011 ◽  
Vol 64 (8) ◽  
pp. 1661-1667 ◽  
Author(s):  
Magda Ruiz ◽  
Gürkan Sin ◽  
Xavier Berjaga ◽  
Jesús Colprim ◽  
Sebastià Puig ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Treatment Plant ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Case Based Reasoning ◽  
Process Data ◽  
Predictive Diagnosis ◽  
Case Based

The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation.

Changes of microbial indices of water quality in the Vistula and Brda rivers as a result of sewage treatment plant operation

2008 ◽  
Vol 37 (2) ◽  
Author(s):  
Maciej Walczak
Keyword(s):  
Water Quality ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Sewage Treatment Plants ◽  
Plant Operation ◽  
Treated Sewage ◽  
Microbiological Parameters ◽  
Microbial Indices

Changes of microbial indices of water quality in the Vistula and Brda rivers as a result of sewage treatment plant operationThis paper reports the results of studies of microbiological changes in the water quality of the Vistula and Brda rivers after the opening of sewage treatment plants in Bydgoszcz. The study involved determining the microbiological parameters of water quality. Based on the results obtained, it was found that the quality of the water in both rivers had improved decidedly after the opening of the plants, although an increased number of individual groups of microorganisms was found at the treated sewage outlet from one of the plants.

Spatial and Temporal Water Quality Trends in Severn Sound, Georgian Bay, since the Introduction of Phosphorus Control Guidelines: Nutrients and Phytoplankton, 1973 to 1991

1995 ◽  
Vol 30 (4) ◽  
pp. 565-592 ◽  
Author(s):  
A.F. Gemza
Keyword(s):  
Total Phosphorus ◽  
Sewage Treatment ◽  
Sewage Treatment Plant ◽  
Treatment Plant ◽  
Soluble Reactive Phosphorus ◽  
Euphotic Zone ◽  
Water Clarity ◽  
Sewage Treatment Plants ◽  
Order Of Magnitude ◽  
Phosphorus Levels

Abstract Severn Sound continues to exhibit signs of eutrophication despite initial identification of the problem in 1969 and the construction of several sewage treatment plants since then. In general, improvements in trophic state indicators have been marginal, suggesting that the sewage treatment plants have had limited success in controlling phosphorus concentrations. These discharges likely contributed to the increased total phosphorus levels and consequently the higher phytoplankton densities of the nearshore waters. Phytoplankton biovolumes were on average one order of magnitude higher than in the open waters of Lake Huron with mean summer biovolumes as high as 8.0 mm/L. Algal biovolumes were most dense in Penetang Bay, which experienced limited exchange with the main waters of the sound. No significant long-term trends were observed. Water clarity was declining significantly, however, at a rate of -0.60 to -0.78 m/year throughout the sound except in Sturgeon Bay. Total phosphorus levels were highly variable from year to year; however, concentrations from a 20-year perspective were declining in the open waters at a rate of 0.70 µg/L/year, but response was limited in nearshore areas. In Sturgeon Bay, mean annual euphotic zone total phosphorus as well as soluble reactive phosphorus levels declined by as much as 50% following the construction of a sewage treatment plant with tertiary treatment. Phytoplankton genera typical of eutrophic waters continued to dominate the algal assemblage but members indicative of mesotrophic conditions have become apparent in some areas of the sound.

scholarly journals Monitoring a Reverse Osmosis Process with Kernel Principal Component Analysis: A Preliminary Approach

2021 ◽  
Vol 11 (14) ◽  
pp. 6370
Author(s):  
Elena Quatrini ◽  
Francesco Costantino ◽  
David Mba ◽  
Xiaochuan Li ◽  
Tat-Hean Gan
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Water Purification ◽  
Principal Component ◽  
Temporal Correlation ◽  
Component Analysis ◽  
Kernel Principal Component Analysis ◽  
Original Dataset ◽  
Monitoring Process ◽  
Machine Health

The water purification process is becoming increasingly important to ensure the continuity and quality of subsequent production processes, and it is particularly relevant in pharmaceutical contexts. However, in this context, the difficulties arising during the monitoring process are manifold. On the one hand, the monitoring process reveals various discontinuities due to different characteristics of the input water. On the other hand, the monitoring process is discontinuous and random itself, thus not guaranteeing continuity of the parameters and hindering a straightforward analysis. Consequently, further research on water purification processes is paramount to identify the most suitable techniques able to guarantee good performance. Against this background, this paper proposes an application of kernel principal component analysis for fault detection in a process with the above-mentioned characteristics. Based on the temporal variability of the process, the paper suggests the use of past and future matrices as input for fault detection as an alternative to the original dataset. In this manner, the temporal correlation between process parameters and machine health is accounted for. The proposed approach confirms the possibility of obtaining very good monitoring results in the analyzed context.

Batch process monitoring based on global enhanced multiple neighborhoods preserving embedding

2021 ◽  
pp. 014233122110447
Author(s):  
Hongjuan Yao ◽  
Xiaoqiang Zhao ◽  
Wei Li ◽  
Yongyong Hui
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Objective Function ◽  
Principal Component ◽  
Batch Process ◽  
Component Analysis ◽  
Support Vector ◽  
Support Vector Data Description ◽  
Order Information ◽  
Multiple Neighborhoods

Batch process generally has varying dynamic characteristic that causes low fault detection rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This paper proposes a global enhanced multiple neighborhoods preserving embedding based fault detection strategy for dynamic batch process. Firstly, the angle neighbor is defined and selected to compensate for the insufficient expression for the spatial similarity of samples only by using the distance neighbor, and the time neighbor is introduced to describe the time correlations between samples. These three types of neighbors can fully characterize the similarity of the samples in time and space. Secondly, considering the minimum reconstruction error and the order information of three types of neighbors, an enhanced objective function is constructed to prevent the loss of order information when neighborhood preserving embedding (NPE) calculates the reconstruction weights. Furthermore, the enhanced objective function and a global objective function are organically combined to extract both global and local features, to describe process dynamics and visualize process data in a low-dimensional space. Finally, a monitoring index based on support vector data description is constructed to eliminate adverse effects of non-Gaussian data for monitoring performance. The advantages of the proposed method over principal component analysis, neighborhood preserving embedding, dynamic principal component analysis and time NPE are demonstrated by a numerical example and the penicillin fermentation process simulation.

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