Modelagem e simulação de um sistema inteligente para controle de dosagem da pós-cloração em estações de tratamento de água

Dias, Cleber Gustavo; Librantz, André Felipe Henriques; Santos, Fábio Cosme Rodrigues dos

doi:10.1590/s1413-41522020173961

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…The architecture of an ANN is represented in Figure 1. The equations that make up the structure of the represented networks are Equations ( 1) and ( 2) [17].…”

Section: A the Ann Methodsmentioning

confidence: 99%

Application of Analytic Hierarchy Process Considering Artificial Neural Network and ARIMA for Selecting a Chemical Waste Plant

Bornéo¹,

Franco²,

Orlandin³

et al. 2021

SCI

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This study defines the best model for a chemical waste plant where the Artificial Neural Network (ANN) and the Integrated Auto Regressive Moving Average Model (ARIMA) were applied as tools for predicting future maintenance cost data. These methods were applied together considering the criteria as follows: plant size, process cost, treatment flexibility, environmental safety and maintenance cost. For this, a decision-making model was developed using the Hierarchical Analysis Method (AHP) with which the company can decide from three alternatives of waste plant models. As a result, the recommendation and solution provide by the multicriteria method was the choice of the alternative 3 of a waste center. This solution indicated the best alternative considering the criteria selected by the company and also the data from RNA and ARIMA In this case, the model presented an index above 70% both in the final aggregation and in the sensitivity analysis.

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“…The architecture of an ANN is represented in Figure 1. The equations that make up the structure of the represented networks are Equations ( 1) and ( 2) [17].…”

Section: A the Ann Methodsmentioning

confidence: 99%

Application of Analytic Hierarchy Process Considering Artificial Neural Network and ARIMA for Selecting a Chemical Waste Plant

Bornéo¹,

Franco²,

Orlandin³

et al. 2021

SCI

View full text Add to dashboard Cite

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Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

Ding,

Sun,

Lin

et al. 2024

Water Research

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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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Modelagem e simulação de um sistema inteligente para controle de dosagem da pós-cloração em estações de tratamento de água

Cited by 3 publications

References 24 publications

Application of Analytic Hierarchy Process Considering Artificial Neural Network and ARIMA for Selecting a Chemical Waste Plant

Application of Analytic Hierarchy Process Considering Artificial Neural Network and ARIMA for Selecting a Chemical Waste Plant

Application of artificial intelligence in (waste)water disinfection: Emphasizing the regulation of disinfection by-products formation and residues prediction

Integrating Virus Monitoring Strategies for Safe Non-Potable Water Reuse

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