Dataset Information

Application of the CANARY event detection software for real-time performance monitoring of decentralized water reuse systems.

ABSTRACT: Real-time monitoring of water reuse systems ensures the production of high quality water to protect human health at the point-of-use. In this study, several online real-time sensors were utilized to monitor effluent from a wastewater fed laboratory-scale membrane bioreactor (MBR) under natural and simulated failure conditions. These simulated failures included adding reactor mixed liquor to emulate a membrane breach, and spiking MS2 bacteriophage into the reactor to create a high viral load, which might be observed during an outbreak. The CANARY event detection software was used to analyze sensor data and report changes in water quality that might be indicative of poor system behavior. During simulated failure conditions, CANARY reported 20 alarms, accurately detecting each failure. During natural operating conditions, 219 alarms were produced and 189 were attributed to known events (e.g., system and sensor maintenance). The remaining alarms (23) during natural operating conditions were considered to have an unknown cause. However, 13 of those had signal deviations similar to known events, but could not be definitively linked to a source. The results of this study suggest that real-time monitoring in conjunction with CANARY analysis may be useful as an early warning system for monitoring the effluent of water reuse systems, and may help to quickly identify treatment malfunctions or other abnormal conditions.

SUBMITTER: Leow A

PROVIDER: S-EPMC7787991 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Application of the CANARY event detection software for real-time performance monitoring of decentralized water reuse systems.

Leow Aaron A Burkhardt Jonathan J Platten William E WE Zimmerman Brian B Brinkman Nichole E NE Turner Anne A Murray Regan R Sorial George G Garland Jay J

Environmental science : water research & technology 20170101 2

Real-time monitoring of water reuse systems ensures the production of high quality water to protect human health at the point-of-use. In this study, several online real-time sensors were utilized to monitor effluent from a wastewater fed laboratory-scale membrane bioreactor (MBR) under natural and simulated failure conditions. These simulated failures included adding reactor mixed liquor to emulate a membrane breach, and spiking MS2 bacteriophage into the reactor to create a high viral load, whi ...[more]

PMID: 33425368

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Project description:Wastewater reclamation and reuse have the potential to supplement water supplies, offering resiliency in times of drought and helping meet increased water demands associated with population growth. Non-potable water reuse represents the largest potential reuse market. Yet economic constraints for new water reuse infrastructure and safety concerns due to microbial water quality, and especially viral pathogen exposure, limit widespread implementation of water reuse. Cost-effective, real-time methods to measure or indicate viral quality of recycled water would do much to instill greater confidence in the practice. This manuscript discusses advancements in monitoring and modeling of viral health risks in the context of water reuse. First, we describe the current wastewater reclamation processes and treatment technologies with an emphasis on virus removal. Second, we review technologies for the measurement of viruses, both culture- and molecular-based, along with their advantages and disadvantages. We introduce promising viral surrogates and specific pathogenic viruses that can serve as indicators of viral risk for water reuse. We suggest metagenomic analyses for viral screening and flow cytometry for quantification of virus-like particles as new approaches to complement more traditional methods. Third, we describe modeling to assess health risks through quantitative microbial risk assessments (QMRAs), the most common strategy to couple data on virus concentrations with human exposure scenarios. We then explore the potential of artificial neural networks (ANNs) to incorporate suites of data from wastewater treatment processes, water quality parameters, and viral surrogates. We recommend ANNs as a means to utilize existing water quality data, alongside new complementary measures of viral quality, to achieve cost-effective strategies to assess risks associated with infectious human viruses in recycled water. Given the review, we conclude that technologies are ready for identifying and implementing viral surrogates for health risk reduction in the next decade. Incorporating modeling with monitoring data would likely result in more robust assessment of water reuse risk.

Project description:Real-time monitoring of the brain is potentially valuable for performance monitoring, communication, training or rehabilitation. In natural situations, the brain performs a complex mix of various sensory, motor or cognitive functions. Thus, real-time brain monitoring would be most valuable if (a) it could decode information from multiple brain systems simultaneously, and (b) this decoding of each brain system were robust to variations in the activity of other (unrelated) brain systems. Previous studies showed that it is possible to decode some information from different brain systems in retrospect and/or in isolation. In our study, we set out to determine whether it is possible to simultaneously decode important information about a user from different brain systems in real time, and to evaluate the impact of concurrent activity in different brain systems on decoding performance.We study these questions using electrocorticographic signals recorded in humans. We first document procedures for generating stable decoding models given little training data, and then report their use for offline and for real-time decoding from 12 subjects (six for offline parameter optimization, six for online experimentation). The subjects engage in tasks that involve movement intention, movement execution and auditory functions, separately, and then simultaneously. Main Results: Our real-time results demonstrate that our system can identify intention and movement periods in single trials with an accuracy of 80.4% and 86.8%, respectively (where 50% would be expected by chance). Simultaneously, the decoding of the power envelope of an auditory stimulus resulted in an average correlation coefficient of 0.37 between the actual and decoded power envelopes. These decoders were trained separately and executed simultaneously in real time.This study yielded the first demonstration that it is possible to decode simultaneously the functional activity of multiple independent brain systems. Our comparison of univariate and multivariate decoding strategies, and our analysis of the influence of their decoding parameters, provides benchmarks and guidelines for future research on this topic.

Dataset Information

Application of the CANARY event detection software for real-time performance monitoring of decentralized water reuse systems.

Publications

Application of the CANARY event detection software for real-time performance monitoring of decentralized water reuse systems.

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