Prediction of Peracetic Acid Disinfection Performance for Secondary Municipal Wastewater Treatment Using Artificial Neural Networks

Newhart, Kathryn B.; Goldman-Torres, J.E.; Freedman, Dan; Wisdom, K Blair; Hering, Amanda S.; Cath, Tzahi Y.

doi:10.1021/acsestwater.0c00095

Cited by 35 publications

(18 citation statements)

References 39 publications

(62 reference statements)

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“…An early application of ANNs in wastewater treatment demonstrated the superiority of neural networks compared to conventional kinetic models of microbial inactivation during disinfection [145]. In the past quarter-century, there was an increase in the application of ANN to a myriad of contexts, including wastewater process control [146,147], constituent monitoring [148], treatment performance [149,150], and virus disinfection [151] or removal [152] to deal with scaling challenges associated with multi-dimensional data. Yet, applications of such data-driven models to assess viral risk are lacking.…”

Section: Modeling Of Infectious Viruses Using Artificial Neural Networkmentioning

confidence: 99%

Integrating Virus Monitoring Strategies for Safe Non-Potable Water Reuse

Jiang

Bischel

Goel

et al. 2022

Water

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Wastewater reclamation and reuse have the potential to supplement water supplies, offering resiliency in times of drought and helping to 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, especially viral pathogen exposure, limit the widespread implementation of water reuse. Cost-effective, real-time methods to measure or indicate the viral quality of recycled water would do much to instill greater confidence in the practice. This manuscript discusses advancements in monitoring and modeling viral health risks in the context of water reuse. First, we describe 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 outline 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 will be ready to identify and implement viral surrogates for health risk reduction in the next decade. Incorporating modeling with monitoring data would likely result in a more robust assessment of water reuse risk.

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Section: Modeling Of Infectious Viruses Using Artificial Neural Networkmentioning

confidence: 99%

Integrating Virus Monitoring Strategies for Safe Non-Potable Water Reuse

Jiang

Bischel

Goel

et al. 2022

Water

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“…e data collection scheme used uses a multithreaded approach to perform data collection tasks and improves the processing performance of data collection by operating in a distributed cloud cluster, ensuring throughput and stability of data collection, and providing a certain degree of scalability. e data acquisition adopts a dynamic and direct processing model, unlike the traditional store-and-process model, without first completing data accumulation and landing [16]. A partitioned associative task queue structure is used to improve the throughput rate of data production and consumption, enabling data acquisition tasks to be performed in real time.…”

Section: Model Construction Of Improved Rnn For Strategic Hr Decision-makingmentioning

confidence: 99%

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

Zhang

et al. 2022

Journal of Mathematics

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In this paper, RNN (Recurrent Neural Network) algorithm is used to conduct an in-depth analysis of HR strategic decision-making and an HR strategic decision model is constructed for simulation. The four evaluation dimensions of index screening are extracted, the generalized gray correlation analysis is applied to screen the initial selection indexes of HR strategic decision-making, and then the HR strategic decision-making index system of power supply enterprises is constructed. On this basis, the applicability of the BP neural network in HR strategic decision-making is analysed and demonstrated, a BP neural network-based HR strategic decision-making model for power supply enterprises is constructed, and the rationality of the model is illustrated through the model training and testing. Finally, an empirical study is conducted with S power supply company as an example to illustrate the operability of the BP network model for HR strategic decision-making. The results of this paper provide a scientific basis for human resource decision-making in power supply enterprises and provide theoretical support for promoting the healthy development of power supply enterprises. Improving the level of human resource management can make fuller use of human resources and realize the strategic goals of the enterprise. A strategy for optimizing the training of hybrid convolutional neural networks is proposed, using an exponential linear unit activation function to solve the problem of neuron dead zones, a hybrid pooling strategy is used to improve the problem of feature information loss in maximum pooling processing, and an improved cross-entropy loss function is used to solve the problem of insufficient learning of difficult classification samples. The optimization of the model training process is finally completed, and the recommendation quality is improved. The Bayesian probability distribution table is learned to be filled in the expert data set; finally, the accuracy and effectiveness of the model are tested on the experimental platform. The results of the experiments show that the established Bayesian model beats the platform’s built-in intelligent method with a 78.2% win rate. That is, the model can make intelligent recommendations for strategies in staffed and unmanned platform decision-making and command-and-control combat units to execute tactical actions to achieve the best operational effectiveness.

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“…The entity tags associated with imaginative visuals and the entity tags associated with image descriptions are first associated with WordNet resources and DBpedia resources, respectively [ 19 ]. This ensures good scalability of the data.…”

Section: Analysis Of a Digital Neural Network Model For Carbon Assets...mentioning

confidence: 99%

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

Liu

Zhang

2022

Computational Intelligence and Neuroscience

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In this paper, a multimodal knowledge mapping approach is used to digitize enterprise carbon assets, and a corresponding neural network model is designed for use in the practical process. Rich textual entity labels associated with images are obtained using an entity annotation system. A topology-based data fusion method is also designed based on the hierarchical relationship between WordNet and DBpedia to fuse the knowledge obtained from image visualization and text description mining. Existing neural network-based entity linking methods ignore the semantic gap between the context of sequential entity denotative items and the context of graph-structured entities, thus affecting the accuracy of entity linking. It is observed that the importance of words in the context of entity denotative items is different, and the importance of content in the entity context is also different. To solve the above problems, this paper proposes an entity linking method that combines a common attention mechanism with a graph convolutional neural network. Secondly, based on the basic theory of value assessment, the characteristics of classical asset valuation methods and their inapplicability to the valuation of carbon assets are analyzed, and thus the real option valuation method and its two classical models are introduced; after demonstrating the real option characteristics of carbon assets of power enterprise projects, a real option model-based carbon asset valuation model for power enterprise projects is constructed and its applicability is verified with case studies. Through analyzing the current situation and problems of carbon asset valuation work in power enterprises, targeted practical suggestions are put forward to further strengthen and enhance the carbon asset valuation work in power enterprises in the future.

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Prediction of Peracetic Acid Disinfection Performance for Secondary Municipal Wastewater Treatment Using Artificial Neural Networks

Cited by 35 publications

References 39 publications

Integrating Virus Monitoring Strategies for Safe Non-Potable Water Reuse

Integrating Virus Monitoring Strategies for Safe Non-Potable Water Reuse

Construction and Simulation of a Strategic HR Decision Model Based on Recurrent Neural Network

A Neural Network Model for Digitizing Enterprise Carbon Assets Based on Multimodal Knowledge Mapping

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