Case studies in real-time fault isolation in a decentralized wastewater treatment facility

Klanderman, Molly C.; Newhart, Kathryn B.; Cath, Tzahi Y.; Hering, Amanda S.

doi:10.1016/j.jwpe.2020.101556

Cited by 7 publications

(10 citation statements)

References 52 publications

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“…In fused lasso, the difference between adjacent coefficients are penalized as opposed to the coefficients themselves (Hastie et al, 2015;Tibshirani et al, 2005). An example of fused lasso in WWTP for fault isolation can be found in Klanderman et al (2020). A variation of lasso that is useful in the biological sciences is group lasso.…”

Section: Lassomentioning

confidence: 99%

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

2022

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The water sector is in the middle of a paradigm shift from focusing on treatment and meeting discharge permit limits to integrated operation that also enables a circular water economy via water reuse, resource recovery, and system level planning and operation. While the sector has gone through different stages of such revolution, from improving energy efficiency to recovering renewable energy and resources, when it comes to the next step of achieving carbon neutrality or negative emission, it falls behind other infrastructure sectors such as energy and transportation. The water sector carries tremendous potential to decarbonize, from technological advancements, to operational optimization, to policy and behavioural changes. This book aims to fill an important gap for different stakeholders to gain knowledge and skills in this area and equip the water community to further decarbonize the industry and build a carbon-free society and economy. The book goes beyond technology overviews, rather it aims to provide a system level blueprint for decarbonization. It can be a reference book and textbook for graduate students, researchers, practitioners, consultants and policy makers, and it will provide practical guidance for stakeholders to analyse and implement decarbonization measures in their professions. ISBN: 9781789061789 (Paperback) ISBN: 9781789061796 (eBook) ISBN: 9781789061802 (ePUB)

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Section: Lassomentioning

confidence: 99%

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

2022

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“…MSPM PCA-based methods used without any adjustments are referred to as static PCA, but many adjustments to PCA have been proposed to improve fault detection performance . These include dynamic PCA, , adaptive PCA, , and adaptive-dynamic PCA (AD-PCA). ,, Dynamic PCA accounts for autocorrelation in the data by including lags of detrended monitoring variables, adaptive PCA uses a rolling window approach to account for nonstationarity, and AD-PCA incorporates both adjustments, making it suited for monitoring multivariate autocorrelated data over long periods of time.…”

Section: Introductionmentioning

confidence: 99%

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process

Grimm,

Branch,

Thompson

et al. 2024

ACS EST Eng.

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As water treatment technology has improved, the amount of available process data has substantially increased, making real-time, data-driven fault detection a reality. One shortcoming of the fault detection literature is that methods are usually evaluated by comparing their performance on hand-picked, short-term case studies, which yields no insight into long-term performance. In this work, we first evaluate multiple statistical and machine learning approaches for detrending process data. Then, we evaluate the performance of a PCA-based fault detection approach, applied to the detrended data, to monitor influent water quality, filtrate quality, and membrane fouling of an ultrafiltration membrane system for indirect potable reuse. Based on two short case studies, the adaptive lasso detrending method is selected, and the performance of the multivariate approach is evaluated over more than a year. The method is tested for different sets of three critical tuning parameters, and we find that for long-term, autonomous monitoring to be successful, these parameters should be carefully evaluated. However, in comparison with industry standards of simpler, univariate monitoring or daily pressure decay tests, multivariate monitoring produces substantial benefits in long-term testing.

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“…However, these multivariate fault detection methods reduce an operator’s ability to identify the specific variables that contribute to a fault and can be challenging to integrate with existing utility monitoring approaches. , Furthermore, when the training period is periodically updated, fault detection can be insensitive to long and slow drift faults . Efforts have been made to solve these problems, such as isolating variables associated with the fault through penalized regression , and integrating knowledge-based strategies such as fuzzy logic or decision trees . While improvements have been made, these methods still require increased cost and complexity.…”

Section: Introductionmentioning

confidence: 99%

“…1,8 Furthermore, when the training period is periodically updated, fault detection can be insensitive to long and slow drift faults. 10 Efforts have been made to solve these problems, such as isolating variables associated with the fault through penalized regression 11,12 and integrating knowledge-based strategies such as fuzzy logic or decision trees. 13 While improvements have been made, these methods still require increased cost and complexity.…”

Section: Introductionmentioning

confidence: 99%

Functional Data Analysis Approach for Detecting Faults in Cyclic Water and Wastewater Treatment Processes

2023

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Early and effective fault detection in water and wastewater treatment plants is important to maintain water quality and prevent process disruptions. Some faults, such as spike faults, are easily detected with traditional fault detection methods that identify extreme values, while other faults, such as drift faults, are difficult to identify due to their slowly changing behavior. In addition, proposed methods should assist operator decision making with straightforward interpretability. This paper applies a method in functional data analysis (FDA) for fault detection to drift faults observed in a sequencing batch membrane bioreactor and a closed-circuit reverse osmosis system. FDA enables analysis of cyclic data, which are curves or functions produced by a system with repetitive behavior over a period of time or space. Unusual functions can be identified by comparing statistics describing each function's shape and magnitude to a reference set. In addition, functional plots visually supplement alarm results to assist operators. In this paper, we modify a retrospective FDA outlier detection method to handle the nonstationary, real-time setting required for tracking cyclic water and wastewater process data. In two case studies, we demonstrate its ability to identify drifts faults in early stages as well as a shift fault.

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Case studies in real-time fault isolation in a decentralized wastewater treatment facility

Cited by 7 publications

References 52 publications

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

Pathways to Water Sector Decarbonization, Carbon Capture and Utilization

Long-Term Statistical Process Monitoring of an Ultrafiltration Water Treatment Process

Functional Data Analysis Approach for Detecting Faults in Cyclic Water and Wastewater Treatment Processes

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