The Effect of Aeration Rate on COD Removal from High Salinity Wastewater in SBR Process

Chang, Gong Fa; Hong, Wei; Bian, Xiao; Liu, Bo; Tan, Xin; Su, Ying

doi:10.4028/www.scientific.net/amm.522-524.605

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…Increasing the aeration amount has led to the outlet COD decrease. This is because, the increase in aeration amount, increases the growth of microorganisms and microalgae as well as their activity, which in turn reduces the amount of COD effluent 40 . However, this effect is not significant for aeration flow‐rates higher than 50 L/min.…”

Section: Results and Discusionmentioning

confidence: 99%

“…This is because, the increase in aeration amount, increases the growth of microorganisms and microalgae as well as their activity, which in turn reduces the amount of COD effluent. 40 However, this effect is not significant for aeration flow-rates higher than 50 L/min. Since more aeration will result in an increase in the costs and in some cases to cellular break and thus decrease in the growth of the microorganisms, the optimum aeration flowrate has been considered 50 L/min in this research.…”

Section: Effect Of Aeration Flowratementioning

confidence: 96%

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Evaluating the performance of a sequencing batch reactor for sanitary wastewater treatment using artificial neural network

Yazdani

Khoshhal

Mousavi

2020

Env Prog and Sustain Energy

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In this work, the performance of a sequencing batch reactor (SBR) was studied for treating sanitary wastewater of Yazd power plant, Iran. For this purpose, at the first, a pilot system was designed, installed, and started up. Then the effects of retention time, pH, temperature, influent chemical oxygen demand (COD) concentration, and air flow rate were investigated on the effluent concentration of COD. In SBR reactor used in the Yazd power plant, the microalga was not used for the wastewater treatment. In this case, the COD effluent output was, at the best conditions, approximately 92 mg/L. In the studied SBR system, we used the Chlorella vulgaris microalgae and microorganisms, simultaneously. In this case, the COD level reached 34 mg/L. An artificial neural network (ANN) was developed by applying Levenberg-Marquardt training algorithm to predict the effluent concentration of COD. The optimum conditions were obtained at pH = 8, temperature of 30 C, influent COD concentration of 600 mg/L, and air flow rate of 50 L/min. ANN predicted results were in good agreement with the experimental data with a validation coefficient of determination (R 2) and validation mean square error of 0.962 and 0.0015, respectively.

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Section: Results and Discusionmentioning

confidence: 99%

Section: Effect Of Aeration Flowratementioning

confidence: 96%

Evaluating the performance of a sequencing batch reactor for sanitary wastewater treatment using artificial neural network

Yazdani

Khoshhal

Mousavi

2020

Env Prog and Sustain Energy

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show abstract

“…When SBRs are used for purification, aeration becomes very important in COD removal efficiency. However, there has been limited research on the influence of this parameter on the COD removal rate (Chang et al 2014). The desired wastewater with an input COD of 600 mg/l, pH equal to 8, and temperature of 35 °C was placed in different aeration conditions.…”

Section: The Effect Of Aeration On the Performance Of The Filtration ...mentioning

confidence: 99%

A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae

Jery

Noreen

Isam³

et al. 2023

Appl Water Sci

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By using microorganisms and the microalgae Chlorella vulgaris in conjunction with sequencing batch reactors (SBRs), the performance of a wastewater treatment facility was studied. For this purpose, the effect of pH, temperature, $${\mathrm{COD}}_{\mathrm{inlet}}$$ COD inlet , and air flowrate on COD removal rate and residual was investigated. A single-factorial optimization method is utilized to optimize the amount of COD removal, and the best result is obtained with a pH of 8, $${\mathrm{COD}}_{\mathrm{inlet}}=600\, \mathrm{mg}/\mathrm{l}$$ COD inlet = 600 mg / l , and an airflow rate of 55 l/min. Under optimal conditions, the amount of residual COD in the effluent reached 36 $$\mathrm{mg}/\mathrm{l}$$ mg / l , showing an augmentation in the efficiency of the desired system. Moreover, empirical correlations are proposed for double-factorial optimization of residual COD and COD removal. Also, a multilayer perceptron artificial neural network is proposed to model the process and predict the residual COD concentration. The useful technique of hyperparameter tuning is utilized to obtain the best result for the predictions. All the effective parameters, including the number of hidden layers, neurons, epochs, and batch size, are adjusted. Data from the experiments agreed well with the artificial neural network modeling results. For this modeling, the values of the correlation coefficient ($${R}^{2}$$ R 2 ) and mean absolute error (MAE) were obtained as 0.98 and 2%, respectively.

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C/N ratio effect on oily wastewater treatment using column type SBR: machine learning prediction and metagenomics study

Khan,

Majumder,

Singh

et al. 2024

Sci Rep

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The Effect of Aeration Rate on COD Removal from High Salinity Wastewater in SBR Process

Cited by 3 publications

References 3 publications

Evaluating the performance of a sequencing batch reactor for sanitary wastewater treatment using artificial neural network

Evaluating the performance of a sequencing batch reactor for sanitary wastewater treatment using artificial neural network

A novel experimental and machine learning model to remove COD in a batch reactor equipped with microalgae

C/N ratio effect on oily wastewater treatment using column type SBR: machine learning prediction and metagenomics study

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