Monitoring Of Wastewater Treatment Plants Research Articles

Emergence of Carbapenemase Genes in Gram-Negative Bacteria Isolated from the Wastewater Treatment Plant in A Coruña, Spain.

Wastewater treatment plants (WWTPs) are recognized as important niches of antibiotic-resistant bacteria that can be easily spread to the environment. In this study, we collected wastewater samples from the WWTP of A Coruña (NW Spain) from April 2020 to February 2022 to evaluate the presence of Gram-negative bacteria harboring carbapenemase genes. Bacteria isolated from wastewater were classified and their antimicrobial profiles were determined. In total, 252 Gram-negative bacteria carrying various carbapenemase genes were described. Whole-genome sequencing was conducted on 55 selected carbapenemase producing isolates using Oxford Nanopore technology. This study revealed the presence of a significant population of bacteria carrying carbapenemase genes in WWTP, which constitutes a public health problem due to their risk of dissemination to the environment. This emphasizes the usefulness of WWTP monitoring for combating antibiotic resistance. Data revealed the presence of different types of sequences harboring carbapenemase genes, such as blaKPC-2, blaGES-5, blaGES-6, blaIMP-11, blaIMP-28, blaOXA-24, blaOXA-48, blaOXA-58, blaOXA-217, and blaVIM-2. Importantly, the presence of the blaKPC-2 gene in wastewater, several months before any clinical case was detected in University Hospital of A Coruña, suggests that wastewater-based epidemiology can be used as an early warning system for the surveillance of antibiotic-resistant bacteria.

Enhanced data-driven monitoring of wastewater treatment plants using the Kolmogorov–Smirnov test

Wastewater treatment plants (WWTPs) are indispensable facilities that play a pivotal role in safeguarding public health, protecting the environment, and supporting economic development by efficiently treating and managing wastewater.

Surveillance of COVID-19 and influenza A(H1N1) prevalence in China via medicine-based wastewater biomarkers

The simultaneous monitoring of individual or multiple diseases can be achieved by selecting therapeutic medicines used to treat the primary symptoms of the condition as biomarkers in wastewater. This study proposes a novel approach to monitor the prevalence of COVID-19 and influenza A (H1N1) by selecting nine medicines to serve as biomarkers, including three antipyretics, three antivirals, and three cough suppressants. To verify our approach, wastewater samples were collected from seventeen urban and five rural wastewater treatment plants (WWTPs) in a Chinese city over a period of one year. The use of antipyretics increased notably during the COVID-19 pandemic, while the consumption of antivirals for influenza A (H1N1) rose in the post-COVID-19 pandemic period, indicating a minor spike in the occurrence of influenza A (H1N1) after the COVID-19 pandemic. Fever is a significant symptom of COVID-19 and can serve as a reliable indicator of disease prevalence. Our research found that the prevalence of COVID-19 in urban areas was significantly higher (at 78.5 %, 95 % CI: 73.4 % - 83.9 %) than in rural areas (with a prevalence of 48.1 %, 95 % CI: 42.4 % - 53.8 %). The prevalence of COVID-19 in urban areas in this study was consistent with the data reported by the Chinese center for Disease Control and Prevention (82.4 %). Continuous monitoring of WWTPs in urban areas with fluctuating populations and complex demographics can provide early disease warning. Our results demonstrate the feasibility of evaluating community disease prevalence by selecting major therapeutic medicines as biomarkers in wastewater.

A mathematical model to predict the performance of the secondary clarifier of a municipal wastewater treatment plants

The activated sludge process for urban wastewater treatment has a high purification performance and reliability, especially for organic pollution. However, it presents several operational insufficiencies, such as sludge control, pollution degradation monitoring, and liquid/solid separation problems in the biological reactor/secondary clarifier couple. This paper presents an analysis of a secondary clarifier of wastewater treatment plant (WWTP) via an activated sludge, which could be used for the plant optimization. The models of the settling velocity and the secondary clarifier based on the sludge volume index (SVI) are addressed. We have proposed these models to explain the settling velocity activities and illustrate the relationship between the settleability characteristics. This research aims to develop and predict a one-dimensional model (1-D model) based on an exhaustive experimental study performed on the WWTP and online information, which are the total suspended solids (TSS), the SVI, and the influent characteristics. The results of the novel model for the secondary clarifiers integrating the empirical settling velocity equation presented a good agreement and a better fit with our experimental results, with an error of approximately zero. This simulation is a perfect complement to the management solution and the hydraulic overload monitoring of WWTPs.

(Antibiotic-Resistant) E. coli in the Dutch-German Vecht Catchment─Monitoring and Modeling.

Fecally contaminated waters can be a source for human infections. We investigated the occurrence of fecal indicator bacteria (E. coli) and antibiotic-resistant E. coli, namely, extended spectrum beta-lactamase (ESBL)-producing E. coli (ESBL-EC) and carbapenemase-producing E. coli (CP-EC) in the Dutch-German transboundary catchment of the Vecht River. Over the course of one year, bacterial concentrations were monitored in wastewater treatment plant (WWTP) influents and effluents and in surface waters with and without WWTP influence. Subsequently, the GREAT-ER model was adopted for the prediction of (antibiotic-resistant) E. coli concentrations. The model was parametrized and evaluated for two distinct scenarios (average flow scenario, dry summer scenario). Statistical analysis of WWTP monitoring data revealed a significantly higher (factor 2) proportion of ESBL-EC among E. coli in German compared to Dutch WWTPs. CP-EC were present in 43% of influent samples. The modeling approach yielded spatially accurate descriptions of microbial concentrations for the average flow scenario. Predicted E. coli concentrations exceed the threshold value of the Bathing Water Directive for a good bathing water quality at less than 10% of potential swimming sites in both scenarios. During a single swimming event up to 61 CFU of ESBL-EC and less than 1 CFU of CP-EC could be taken up by ingestion.

Implementing Early Warning Systems in WWTP. An investigation with cost-effective LED-VIS spectroscopy-based genetic algorithms

Measuring how the pollution load evolves in real time along sewer networks is key for proper management of water resources and protecting the environment. The technique of molecular spectroscopy for water characterization has increasingly widespread use, as it is a non-invasive technique that leads to the correlation of the physical-chemical conditions of wastewater with spectroscopic surrogates by a series of mathematical estimation models. In the present research work, different symbolic regression models obtained with evolutive genetic algorithms are evaluated for the estimation of chemical oxygen demand (COD); five-day biochemical oxygen demand (BOD5); total suspended solids (TSS); total phosphorus (TP); and total nitrogen (TN), from the spectral response of samples measured between 380 and 700 nm and without the use of chemicals or pre-treatment. Around 650 wastewater samples were used in the campaign, from 43 different wastewater treatment plants (WWTP) in which both, raw/influent and treated/effluent, were examined through 18 models composed of Classical Genetic Algorithm (CGA), the Age-Layered Population Structure (ALPS), and Offspring Selection (OS) by mean of HeuristicLab software, to make a comparison among them and to determine which models and wavelengths are most suitable for the correlation. Models are proposed considering both raw and treated samples together (15) and only with tertiary treated wastewater reclaimed for agriculture irrigation effluent (3). The Pearson correlation coefficients were in the range of 67–91% for the test data in the case of the combined models. The results conform the first steps for a real-time monitoring of WWTP.

Improving the quality of wastewater treatment plant monitoring by adopting proper sampling strategies and data processing criteria

Monitoring is a crucial operation for plant management. However, proper sampling procedures and data processing criteria are not always adopted. Wastewater treatment plants work under dynamic conditions, which poses a challenge for a correct performance assessment. The aim of this work is to analyse some important aspects of wastewater sampling and data processing, to identify case by case methods which should to be adopted in order to obtain reliable and consistent information on plant performance. The study was conducted through simulations and real data analyses. It turned out that: a) the preferable 24-hour composite sampling procedure is the flow-proportional mode; in addition, the required sampling frequency (i.e. the number of sub-samples to be taken to make the 24-h composite sample) increases as the percentage of population discontinuously discharging the monitored substance decreases; b) a Variability Index was defined to help find the minimum sampling frequency (i.e. the number of 24-h composite samples per year) for the calculation of annual mass flows with an acceptable uncertainty; and c) criteria were proposed for the identification of pseudo-steady state periods needed to calculate reliable mass balances and plant performance indicators.

Intelligent sensor validation for sustainable influent quality monitoring in wastewater treatment plants using stacked denoising autoencoders

Wastewater treatment plants (WWTPs) influent conditions can dramatically affect a treatment unit's state and effluent quality. WWTP sensors may record faulty measurements due to abnormal events or the malfunction of the system, leading to serious problems in the system's operation and the violation of effluent discharge standards. Therefore, automatic fault detection and faulty data reconciliation are crucial for an efficient and stable WWTP monitoring. In this study, a holistic framework for sensor validation of WWTP influent conditions is presented considering the non-linearity, measurement noise, and complexity of the WWTP's data. A stacked denoising autoencoder (SDAE) model is proposed to detect, identify, and reconcile faulty data based on data from a real WWTP in South Korea. The proposed SDAE architecture presented a detection rate (DR) between 74% and 98%. The faulty sensor was identified using an SDAE-based sensor validity index (SVI). Data reconciliation showed that the SDAE was the most suitable reconciliation method based on the root mean square error (RMSE) for total nitrogen (TN) influent conditions of 4.04 mg N/L. Finally, faulty, noisy, and reconciled measurements were evaluated in a WWTP model to determine the proposed method's resilience potential.

Evaluation of Metagenomic-Enabled Antibiotic Resistance Surveillance at a Conventional Wastewater Treatment Plant.

Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antimicrobials, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens and thus are a key point of interest for antibiotic resistance surveillance. WWTP monitoring has the potential to inform with respect to the antibiotic resistance status of the community served as well as the potential for ARGs to escape treatment. However, there is lack of agreement regarding suitable sampling frequencies and monitoring targets to facilitate comparison within and among individual WWTPs. The objective of this study was to comprehensively evaluate patterns in metagenomic-derived indicators of antibiotic resistance through various stages of treatment at a conventional WWTP for the purpose of informing local monitoring approaches that are also informative for global comparison. Relative abundance of total ARGs decreased by ∼50% from the influent to the effluent, with each sampling location defined by a unique resistome (i.e., total ARG) composition. However, 90% of the ARGs found in the effluent were also detected in the influent, while the effluent ARG-pathogen taxonomic linkage patterns identified in assembled metagenomes were more similar to patterns in regional clinical surveillance data than the patterns identified in the influent. Analysis of core and discriminatory resistomes and general ARG trends across the eight sampling events (i.e., tendency to be removed, increase, decrease, or be found in the effluent only), along with quantification of ARGs of clinical concern, aided in identifying candidate ARGs for surveillance. Relative resistome risk characterization further provided a comprehensive metric for predicting the relative mobility of ARGs and likelihood of being carried in pathogens and can help to prioritize where to focus future monitoring and mitigation. Most antibiotics that were subject to regional resistance testing were also found in the WWTP, with the total antibiotic load decreasing by ∼40–50%, but no strong correlations were found between antibiotics and corresponding ARGs. Overall, this study provides insight into how metagenomic data can be collected and analyzed for surveillance of antibiotic resistance at WWTPs, suggesting that effluent is a beneficial monitoring point with relevance both to the local clinical condition and for assessing efficacy of wastewater treatment in reducing risk of disseminating antibiotic resistance.

WHAT IS HIDDEN BEHIND ACTIVATED SLUDGE SUPERNATANT? FLUORESCENT STAINING AND LASER GRANULOMETRY INVESTIGATION SUPPORTED BY MACHINE LEARNING

Studies on the biological composition of activated sludge flocs in the operating conditions of wastewater treatment plants are generally limited to the estimation of respiratory activity or to the analysis of images made with optical microscopy. The results of these studies indirectly provide information on the microbiological composition. To date, molecular methods, although very promising, have not found a wider application in operational monitoring of wastewater treatment plants. In this paper, the supernatant was under discussion as a potential source of sampling for analyzing microbial quality changes. The results of 270 activated sludge and treated wastewater samples showed that the smallest flocs leaching to the outflow constitute a group of microorganisms that is most numerous. The studies carried out using the fluorescence in situ hybridization method have shown that the microorganisms responsible for the nitrification processes occur both in activated sludge and supernatant. Image analysis of microorganisms from activated sludge and supernatant stained with Live/DEAD reagent indicate that microflocs and bacteria in the outer flocs, which are relatively loosely attached to the flocculent matrix are more exposed to external factors. The results suggest that it is advisable to find information about the condition of the whole community especially for group of particles in the supernatant. In addition, the authors recommend using machine learning methods to evaluate predicting anomalies in biological composition of activated sludge flocs.

Evaluating contamination impact of wastewater irrigation to soils in Zahedan, Iran.

With respect to water crisis, using urban wastewater to irrigate urban green spaces is an upcoming strategy. The pollution of lands irrigated with wastewater is one of the upcoming challenges. The main purpose of the present study was to investigate contamination indices of soils irrigated with wastewater. In this study, field experimental method in statistical form of totally random blocks with three reiterations for considered treatments was employed. To this end, one piece of three-hectare grass irrigated with wastewater and one piece of land irrigated with well water (control sample) were selected out of the green spaces of Zahedan refinery. Then, 30 compound samples were randomly taken from the depth of 0-50cm of three lands' soil. The refinery wastewater outlet and wall water were also sampled. Physicochemical and biological features of wastewater, wall water and soil samples irrigated with the refinery wastewater and well water were measured according to the standard methods. The obtained data were analyzed using MSTAT-C software. Based on the obtained results, the modified degree of contamination (mCd) of highly and lowly used elements in the considered soils in the experiment was less than 1, indicating a low-contamination status. PLI index of the mentioned elements in the surface soil of grass irrigated with wastewater was higher than 1 and by wall water was less than 1. The concentration of sulfur in surface soil of the two treatments under investigation was in a highly severe enrichment factor (EF ≥ 50). In addition, the concentration of nitrogen, magnesium, zinc and manganese in the surface soil of palm trees and that of manganese, copper and manganese in the surface soil of the grass irrigated with wastewater had a high enrichment factor (5 ≤ EF < 25). Geoaccumulation index (Igeo) of sulfur in the surface soil of the pieces irrigated with wall water (control) and that of potassium in the surface soil of the grass irrigated with wastewater were in no-contamination or average-contamination statuses (0 < Igeo ≤ 1). Other elements were in non-contaminated status (Igeo ≤ 0) in terms of Igeo. The results of investigation of soil contamination indices showed that the soil contamination status of the study area due to wastewater irrigation was low to medium. Therefore, periodic and more accurate monitoring of wastewater treatment plant of Zahedan can be used from this wastewater for landscapes irrigation.

Assessment of efficiency of a decentralized wastewater treatment plant at Mzuzu University, Mzuzu, Malawi

Limited wastewater treatment contributes to water quality degradation of water bodies in Malawi, Lunyangwa River (LR) in Mzuzu is no exceptional. There is lack of comprehensive and regular monitoring of wastewater treatment plants and surface water quality in Malawi mainly due to inadequate funding and laboratory facilities. This study assessed overall performance of Mzuzu University Wastewater Treatment Plant (MUWWTP) and its impacts on receiving waters namely the Luwinga stream (LS) that drains into LR. Representative duplicate grab samples (n = 40) were collected from eight sampling points from the treatment stagess of MUWWTP, upstream (control) and downstream of LS in five sampling campaigns from January to April 2019. Onsite tests were carried out for pH, temperature, Electrical Conductivity (EC), and Dissolved Oxygen (DO), whereas Total Suspended Solids (TSS), Total Solids (TS), Biochemical Oxygen Demand (BOD5), Chemical Oxygen Demand (COD), Nitrate (NO3−), Ammonium (NH4+), and Total Phosphorus (TP) were analyzed at the Government Central Water Laboratory in accordance with the American Public Health Association (APHA) Standards of 2014. Except pH (9.39), the rest of the parameters were within Malawi Effluent Standards (MS691:2005) and Environmental Protection Agency (EPA) Effluent Guidelines of 2014TDS (118.8 mg/L); DO (6.57 mg/L); TSS (18 mg/L); BOD5 (4.5 mg/L); COD (51.4 mg/L); NO3− (0.01 mg/L); TP (0.11 mg/L) and NH4+ (0.003 mg/L). Furthermore, MUWWTP effluent did not impact stream water quality as evidenced by the recommended scores with respect to surface water quality regulation and Water Quality Index. There is need to regularly monitor effluent and stream water quality.

Characteristics of DOM in 14 AAO processes of municipal wastewater treatment plants

The characteristics of dissolved organic matter (DOM) such as chemical composition, molecular weight (MW) distribution and hydrophobic/hydrophilic distribution can affect wastewater treatment efficiency, effluent quality and ecological risk. Fluorescence spectroscopy could provide a quick estimate of DOM characteristics during the monitoring of wastewater treatment plants (WWTPs). In this study, the characteristic and quantitative correlation of DOM from 14 anaerobic-anoxic-oxic (AAO) processes of WWTPs located in different provinces (municipalities) of China were investigated. The results showed that DOM of MW <1 kDa was the largest group of DOM in influent and secondary effluent, and DOM removal increased as the MW increased. Hydrophilic (HPI) fraction and hydrophobic acid (HPO-A) comprised the major portion of DOM in influent and secondary effluent and exhibited the lowest rate of removal. In addition, DOM concentrations in the northern provinces were higher than in the southern provinces, which were related to the water quality, economy and population. There were positive correlations between specific fluorescence intensity (SFI) and the MW <1 kDa, 1–5 kDa and <10 kDa fractions. The smaller the molecular weight, the better the correlation. Strong positive correlations between regional fluorescence proportion (fi) and HPI were found. SFI and fi may be explored as potential indicators of the MW fractions and the hydrophobic/hydrophilic distribution of DOM in AAO processes WWTPs.

Seasonal influences on the use of genetic markers as performance indicators for small wastewater treatment plants

The development of microbial source tracking methods has resulted in an array of genetic faecal markers for assessing human health risks posed from surface water pollution. However, their use as performance metrics at wastewater treatment plants (WWTPs) has not been explored extensively. Here we compared three Bacteroides (HF183, HumM2, AllBac) and two E. coli (H8, RodA) genetic markers for summer and winter performance monitoring at twelve small rural (<250 PE) and three larger WWTPs in NE England. Small WWTPs are of interest because they are poorly understood and their impact on surface water quality may be underestimated. Overall, genetic marker data showed significant differences in treatment performance at smaller versus larger WWTPs. For example, effluent abundances of HF183 and HumM2 were significantly higher in smaller systems (p = 0.003 for HumM2; p = 0.02 for HF183). Genetic markers also showed significant differences in performance between seasons (p < 0.01, n = 120), with human-specific markers (i.e., HF183, HumM2, H8) being generally better for summer WWTP monitoring. In contrast, Bacteroides markers were much more suitable for winter monitoring, possibly because the E. coli markers are less sensitive to differences in temperature and sunlight conditions. Overall, Bacteroides markers best described WWTP treatment performance across all samples, although seasonal differences suggest caution is needed when markers are used for performance monitoring. Genetic markers definitely provide rapid and new information about WWTP performance, but more spatially diverse studies are needed to refine their use for routine WWTP monitoring.

Exploration of the antibiotic resistome in a wastewater treatment plant by a nine-year longitudinal metagenomic study

The spread of antibiotic resistance genes (ARGs) is a growing global problem. Activated sludge (AS) in wastewater treatment plants (WWTPs) has been proposed as a hotspot for ARGs. However, few studies have been conducted to uncover the temporal dynamics of the resistome of AS in WWTPs by long-term longitudinal sampling. In this study, we quantified ARGs and identified their host microbiome in a Hong Kong WWTP in 97 monthly AS samples spanning 9 years. Throughout this analysis, we demonstrated that both the abundance and structures of the resistome changed significantly every two to three years, implying that there was a successive selection of resistomes in the AS system over the study period. The detection of genes of antibiotic-resistant pathogens that are emerging major threats to public health in the AS samples, including mcr, CRE (carbapenem-resistant Enterobacteriaceae) and MRSA (methicillin-resistant Staphylococcus aureus)-related genes, highlight the role of WWTPs as reservoirs of ARGs. In addition, the core resistome (abundant and persistent genes) in AS were found to overlap with those in other ecosystems such as urban sewage, livestock feces, and fishpond sediments, revealing the broad dissemination of ARGs in WWTPs and other environments. Annual variation of resistomes were explained via structural equation modeling (SEM), which deciphered the structural linkages of determining factors such as the operational parameters, microbial community composition and horizontal gene transfer (HGT). Specifically, potentially relevant antibiotic resistance bacteria (ARBs) were explored and discussed based on assembly-based analyses and network correlations. Moreover, consistent with the clear relationship between resistomes and mobile genetic elements (MGEs), it was found that there was a relatively high potential for gene exchange in AS in comparison with soil genomes, which could be explained by the engineering features of WWTPs. Based on these findings, longitudinal monitoring of WWTPs is warranted for risk assessment to reveal emerging ARGs, resistome evolution, correlations with ARBs, and the potential for spread in downstream environments and concomitant exposure risks for humans.

Rapid BOD assessment with a microbial array coupled to a neural machine learning system

The domestic usage of water generates approximately 310 km3 of wastewater worldwide (2015, AQUASTAT, Food and Agriculture Organization of United Nations). This sewage contains an important organic load due to the use of this water; this organic load is characterized using a standard method, namely, the biological oxygen demand measurement (BOD5). The BOD5 provides information about the biodegradable organic load (standard ISO 5815). However, this measurement protocol is very time-consuming (5 days) and may produce variability in approximately 20% of results mainly due to variation in the environmental inocula.To remedy these limitations, this work proposes an innovative concept relying on the implementation of a set of rigorously selected bacterial strains. This publication depicts the different steps used in this study, from bio-indicator selection to validation with real wastewater samples. The results obtained in the final step show a strong correlation between the developed approach and the reference method (ISO 5815) with a correlation rate of approximately 0.9. In addition, the optimization of the experimental conditions and the use of controlled strains (8 selected strains) allow significant reduction in the duration of the BOD5 analysis, with only 3 h required for the proposed method versus 5 days for the reference method. This technological breakthrough should simplify the monitoring of wastewater treatment plants and provide quicker, easier and more coherent control in terms of the treatment time.

Optimized Forecast Components-SVM-Based Fault Diagnosis With Applications for Wastewater Treatment

Process monitoring of wastewater treatment plant (WWTP) is a challenging industrial problem, due to its exposure to the hostile working environment and significant disturbances. This paper proposed a novel fault diagnosis method, termed as optimization forecast components-support vector machine (OFC-SVM). The method firstly improved the forecastable component analysis (ForeCA) for feature extraction. Secondly, in order to further enhance the method, the quadratic Grid Search (GS) algorithm is utilized to optimize the parameters of the proposed method. Thirdly, to properly evaluate the method performance, a new evaluation index is proposed, named Pre Alarm Rate (PAR), aiming to achieve the quantitative trade-off between false alarm rate (FAR) and missed alarm rate(MAR). Then, the new ROC curve can be further derived by PAR. Finally, the performance of OFC-SVM is strictly compared with other five methods as well as validated by a Monte Carlo model and a full-scale WWTP.

Use of chemometric analyses to assess biological wastewater treatment plants by protozoa and metazoa monitoring.

Protozoa and metazoa biota communities in biological wastewater treatment plants (WWTP) are known to be dependent of both the plant type (oxidation ditch, trickling filter, conventional activated sludge, among others) and the working operational conditions (incoming effluent characteristics, toxics presence, organic load, aeration, hydraulic and sludge retention times, nitrification occurrence, etc.). Thus, for analogous WWTP operating in equivalent operating conditions, similar protozoa and metazoa communities can be found. Indeed, the protozoa and metazoa biota monitoring can be considered a quite useful tool for assessing the functioning of biological WWTP. Furthermore, the use of chemometric techniques in WWTP monitoring is becoming widespread to enlighten interrelationships within the plant, especially when a large collection of data can be obtained. In the current study, the protozoa and metazoa communities of three different types of WWTP, comprising one oxidation ditch, four trickling filters, and three conventional activated sludge plants, were monitored. For that purpose, metazoa, as well as the main protozoa groups (flagellates, free-swimming, crawling and sessile ciliates, and testate amoeba) were determined in terms of contents and relative abundance. The collected data was further processed by chemometric techniques, such as cross-correlation, principal components, multivariate ANOVA, and decision trees analyses, allowing to successfully identify, and characterize, the different studied WWTP, and thus, being able to help monitoring and diagnosing operational problems.

Monitoring of wastewater treatment plants using improved univariate statistical technique

Proper operation of the wastewater treatment plants (WWTPs) is crucial in order to maintain the sought effectiveness and desirable water quality. Therefore, the objective of this paper is to develop univariate statistical technique that aims at enhancing the monitoring of wastewater treatment plants using an improved particle filtering (IPF)-based multiscale optimized exponentially weighted moving average chart (MS-OEWMA). The advantages of the developed technique are fivefold: (i) estimate a nonlinear state variables of WWTPs using IPF technique. The IPF method yields an optimum choice of the sampling distribution, which also accounts for the observed data; (ii) use the dynamical multiscale representation to extract accurate deterministic features and decorrelate autocorrelated measurements. (iii) Develop an optimized EWMA (OEWMA) based on the best selection of smoothing parameter (λ) and control width L; (iv) combine the advantages of state estimation technique with MS-OEWMA chart to improve the fault detection in WWTP systems; and (v) investigate the effect of fault types (offset or bias, variance and drift) and fault sizes on the fault detection performances. The developed technique is validated using simulated COST wastewater treatment BSM1 model. The BSM1, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor faults disturbances in a wastewater treatment plant. The detection results are evaluated using three fault detection criteria: missed detection rate (MDR), false alarm rate (FAR) and average run length (ARL1).

Monitoring of Wastewater Treatment Plants using Improved Univariate Statistical Strategy

In this paper, we propose an enhanced monitoring of wastewater treatment plant (WWTP) using state estimation-based fault detection strategies. The WWTP state estimation problem is performed using particle filter (PF) technique. The PF has shown good improvement and provides a significant advantage over extended Kalman filter (EKF), unscented Kalman filter (UKF) techniques and can be applied to nonlinear models with non-Gaussian errors. The fault detection phase is achieved using a novel chart called multiscale exponentially weighted moving average chart (MS-EWMA). The new chart allows to optimize the smoothing parameter (λ) and control width L of EWMA chart to deal with the dynamic nature of WWTPs. It enables also to extract accurate deterministic features and decorrelate autocorrelated measurements using dynamical multiscale representation. The fault detection performance is studied using simulated COST wastewater treatment BSM1 model. The BSM1, provided by the IWA Task Group on Benchmarking of Control Strategies, is a simulation platform that allows for creating sensor faults disturbances in a wastewater treatment plant. The developed technique is used to enhance fault detection of the BSM1 system through monitoring some of the key variables involved in this model. The results demonstrate the effectiveness of the proposed PF-based MS-optimized EWMA method over EWMA and Shewhart charts.