Improvement of the thickening and dewatering characteristics of activated sludge by electroflotation (EF)

2005 ◽  
Vol 52 (10-11) ◽  
pp. 219-226 ◽  
Author(s):  
Y.G. Choi ◽  
H.S. Kim ◽  
Y.H. Park ◽  
S.H. Jeong ◽  
D.H. Son ◽  
...  
Keyword(s):  
Water Quality ◽  
Activated Sludge ◽  
Solid Content ◽  
Laboratory Scale ◽  
Volume Reduction ◽  
Air Bubbles ◽  
Effluent Water ◽  
Gravity Thickening

The performances of electroflotation (EF) on the thickening of activated sludge were investigated using laboratory scale batch flotation reactors. Four activated sludges including bulking sludges were tested. After 30minutes of EF operation, 57–84% of sludge volume reduction could be achieved by EF, while only about 1.5–14% could be obtained by gravity thickening for the same period. After thickening the effluent water quality in terms of TCOD, SS, and turbidity was improved by EF operation for all sludge samples. In addition, the EF thickened sludge showed much better dewaterability both in SRF and cake solid content. It is induced that the air bubbles entrapped in the thickened sludge play a key role in the observed dewaterbility improvement.

Filamentous sludge bulking control by nano zero-valent iron in activated sludge treatment systems

2014 ◽  
Vol 16 (12) ◽  
pp. 2721-2728 ◽  
Author(s):  
Shengnan Xu ◽  
Minghao Sun ◽  
Chiqian Zhang ◽  
Rao Surampalli ◽  
Zhiqiang Hu
Keyword(s):  
Water Quality ◽  
Activated Sludge ◽  
Zero Valent Iron ◽  
Effluent Water ◽  
Sludge Treatment ◽  
Sludge Bulking ◽  
Nano Zero Valent Iron

Sludge bulking causes loss of biomass in the effluent and deterioration of effluent water quality.

Predictive performance modeling of Habesha brewery wastewater treatment plant using artificial neural networks

2019 ◽  
Author(s):  
Chem Int
Keyword(s):  
Neural Networks ◽  
Water Quality ◽  
Wastewater Treatment ◽  
Artificial Neural Networks ◽  
Performance Modeling ◽  
Predictive Performance ◽  
Mathematical Tool ◽  
Effluent Water ◽  
Artificial Neural

Recently, process control in wastewater treatment plants (WWTPs) is, mostly accomplished through examining the quality of the water effluent and adjusting the processes through the operator’s experience. This practice is inefficient, costly and slow in control response. A better control of WTPs can be achieved by developing a robust mathematical tool for performance prediction. Due to their high accuracy and quite promising application in the field of engineering, Artificial Neural Networks (ANNs) are attracting attention in the domain of WWTP predictive performance modeling. This work focuses on applying ANN with a feed-forward, back propagation learning paradigm to predict the effluent water quality of the Habesha brewery WTP. Data of influent and effluent water quality covering approximately an 11-month period (May 2016 to March 2017) were used to develop, calibrate and validate the models. The study proves that ANN can predict the effluent water quality parameters with a correlation coefficient (R) between the observed and predicted output values reaching up to 0.969. Model architecture of 3-21-3 for pH and TN, and 1-76-1 for COD were selected as optimum topologies for predicting the Habesha Brewery WTP performance. The linear correlation between predicted and target outputs for the optimal model architectures described above were 0.9201 and 0.9692, respectively.

Experiment of Polyester Wastewater Treatment by Aerobic Membrane Bioreactor and an Engineering Application

2012 ◽  
Vol 518-523 ◽  
pp. 2130-2137
Author(s):  
Si Hao Lv ◽  
Hui Chang ◽  
Zhi Hui Liang ◽  
Yan Yan Zeng ◽  
Hong Bo Fan
Keyword(s):  
Water Quality ◽  
Pilot Study ◽  
Membrane Bioreactor ◽  
Suspended Solids ◽  
Loading Rate ◽  
Engineering Application ◽  
Full Scale ◽  
Effluent Water ◽  
Small Influence ◽  
Volumetric Loading

A pilot study was carried out to evaluate the application of aerobic submerged MBR in treating polyester wastewater and a full scale system in which MBR was adopted as the key aspect was put forward to treat the polyester wastewater of Zhuhai Yuhua Polyester co., Ltd., China. The pilot study revealed that HRT could be affected by the influent COD, and a HRT larger than 36h was proposed for treating polyester wastewater by MBR. Volumetric loading rate and sludge loading rate ranged in 0.7-2.0 kgCOD/m3•d and 0.09~0.24 kgCOD/ kgMLSS•d respectively were of small influence on the COD removal. DO in the range of 2.5-4.0 mg/L seemed to be optimal for avoiding limitations due to oxygen concentration and creating an effective turbulence. The modified full scale system which includes anaerobic digesting tank and MBR followed by a stabilization pond was verified to be effective to treat the polyester wastewater in the latest two years. The results illustrated that the effluent water quality could meet the discharge limits of water pollutants (DB 44/26-2001) which was established in Guangdong, China. Observed sludge yield of the system fluctuated between 0.10-0.18 gMLSS/gCOD and averaged at 0.137 gMLSS/gCOD. The membrane permeate at around 10 L/m2h, TMP of less than 0.7bar, and total mixed liquor suspended solids (MLSS) between 6-7g/L were suitable to operate the MBR.

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