&lt;b&gt;Artificial neural networks to control chlorine dosing in a water treatment plant

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

doi:10.4025/actascitechnol.v40i1.37275

Cited by 12 publications

(4 citation statements)

References 33 publications

(37 reference statements)

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“…Como alternativas para solucionar problemas na concentração do cloro, temos: monitorização com maior frequência da cloração, ajuste dos equipamentos dosadores de cloro ou até mesmo substituí-los por equipamentos automáticos. Também deve-se determinar a concentração de CRL nos produtos de sanitização por meio de análises laboratoriais, bem como da água a ser tratada (BLOKKER et al, 2014;LIBRANTZ et al, 2018).…”

Section: Saúde E Ambienteunclassified

Monitoramento Da Qualidade Da Água Em Hospitais: Um Estudo Descritivo Longitudinal Para Detecção De Pontos Críticos

Paixão

Silva

Gouvêa

et al. 2022

ICSA

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Em ambiente hospitalar a água é utilizada para as mais diferentes finalidades, como: consumo, processos de higienização e hemodiálise. O abastecimento regular e o monitoramento frequente da qualidade da água são fundamentais para garantir a segurança das atividades desenvolvidas. A Portaria de Consolidação n° 5 de 28 de setembro de 2017, do Ministério da Saúde, regulamenta os requisitos sensoriais, químicos e microbiológicos de potabilidade da água. No presente trabalho foi realizado um estudo longitudinal do monitoramento da qualidade da água utilizada em dois hospitais, identificando os seus principais pontos críticos. Para isso, foram analisadas 126 amostras de água coletadas nos anos de 2016 a 2019, das quais 80 foram provenientes do hospital A e 46 do hospital B. Ao longo dos quatro anos de estudo, observou-se variação na quantidade de amostras em desacordo com a legislação vigente. Também foi verificado que as amostras apresentaram inconformidades principalmente nas análises de cloro residual livre, 11,3% e 19,6%, e contagem de bactérias heterotróficas, 5,0% e 2,2%, nos hospitais A e B, respectivamente. Com isso, verifica-se a importância no monitoramento da qualidade da água em ambientes hospitalares, visando o cuidado à saúde dos funcionários e, em especial, dos pacientes. Palavras-chave: Cloro Residual. Controle de Qualidade. Padrão de Potabilidade da Água.

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Section: Saúde E Ambienteunclassified

Monitoramento Da Qualidade Da Água Em Hospitais: Um Estudo Descritivo Longitudinal Para Detecção De Pontos Críticos

Paixão

Silva

Gouvêa

et al. 2022

ICSA

View full text Add to dashboard Cite

show abstract

“…Another strategy presented in [23] uses a cascaded PI to control chlorination in contact tanks with long downtimes. As alternative strategies to PID algorithms, [24] presents a control algorithm in which the variability of demand is addressed through the use of neural networks for a drinking water plant with a nominal flow rate of 0.9 m 3 /s. Reference [25] addresses the problem of chlorination control by means of a predictive control strategy using a controller in a dynamic matrix configuration.…”

Section: A Related Literaturementioning

confidence: 99%

Fuzzy Gain Scheduling and Feed-Forward Control for Drinking Water Treatment Plants (DWTP) Chlorination Process

et al. 2020

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Chlorination in drinking water treatment plants (DWTP) is the final process applied to water before it is sent to storage tanks in the supply network for subsequent human consumption. An excessive dosage of chlorine or, conversely, too small a dosage, may breach existing legal regulations on mandatory limits. Furthermore, excessive amounts generate an unnecessary cost in terms of chlorine and, collaterally, problems due to an increase in the maximum permitted amounts of such by-products as trihalomethanes, which are carcinogenic compounds for humans. In DWTP where there is no significant variability in the quality of the water to be treated, a type of control that is proportional to the flow rate in the effluent can have fully satisfactory results. However, in a control strategy applied when there are inherently long delays in the process, variability in the quality of the water to be treated and considerable variations in flow, a proportional type of control does not tend to work and an alternative type is needed. This article presents the strategy and results of a control method that combines a feed-forward system with gain scheduling in a (Proportional-Integral) PI control. The control system design was validated beforehand by simulation and then applied to a real DWTP, producing satisfactory experimental results.INDEX TERMS Chlorination process, drinking water treatment plants, fuzzy control, gain scheduling.

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“…[37] ANNs were also used to maintain free chlorine concentrations in another water plant in Sao Paulo, Brazil. [38] After creating simulations of the constant fluctuations in chlorine demand resulting from natural causes, the water treatment plant incorporated a system of devices to correctly dose the water supply with the required concentration of free chlorine.…”

Section: Introductionmentioning

confidence: 99%

Modeling Biodegradable Free Chlorine Sensor Performance Using Artificial Neural Networks

Siddiqui,

Deen

2023

Adv Materials Technologies

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Electrochemical sensors are used to measure target analytes in water, meat, fruits, or vegetables, to ensure their safety, security, and integrity for human use. In this paper, a solution‐based fabrication process is presented for a biodegradable electrochemical free chlorine sensor using asparagine that is functionalized onto graphene oxide (GO). An ink solution of the GO functionalized with asparagine is synthesized, and then deposited onto a screen‐printed carbon electrode (SPCE) using a spin coater. The sensor shows high a sensitivity of 0.30 µA ppm−1 over a linear range of 0–8 ppm with a hysteresis‐limited resolution of 0.2 ppm after achieving a steady state at 50 s. From the development and testing of the free chlorine sensor, over 9000 datapoints are collected and used for training an artificial neural network (ANN) model to quantify and characterize the factors affecting the free chlorine sensor's performance, and model its degradation characteristics. The model reports a low mean absolute error (MAE) of 0.1603 and a high model accuracy with a Pearson correlation coefficient (PCC) of 0.9950, demonstrating that these degradation characteristics can be modeled and be used to compensate the degraded performance characteristics of the free chlorine sensors.

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<b>Artificial neural networks to control chlorine dosing in a water treatment plant

Cited by 12 publications

References 33 publications

Monitoramento Da Qualidade Da Água Em Hospitais: Um Estudo Descritivo Longitudinal Para Detecção De Pontos Críticos

Monitoramento Da Qualidade Da Água Em Hospitais: Um Estudo Descritivo Longitudinal Para Detecção De Pontos Críticos

Fuzzy Gain Scheduling and Feed-Forward Control for Drinking Water Treatment Plants (DWTP) Chlorination Process

Modeling Biodegradable Free Chlorine Sensor Performance Using Artificial Neural Networks

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