Evaluation of Effective Operational Cycle Time and Bioprocess Parameters in a Sequential Batch Reactor for Efficient Organic and Nutrient Removal from Domestic Sewage

2015 ◽  
Keyword(s):  
Nutrient Removal ◽  
Cycle Time ◽  
Batch Reactor ◽  
Domestic Sewage ◽  
Sequential Batch Reactor ◽  
Operational Cycle

scholarly journals SIMULTANEOUS CARBON AND NUTRIENT REMOVAL FROM DAIRY WASTEWATER IN SEQUENCING BATCH REACTOR (SBR)

2015 ◽  
Vol 17 (3) ◽  
pp. 628-636 ◽  
Keyword(s):  
Nutrient Removal ◽  
Cycle Time ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Dairy Wastewater ◽  
Treatment Efficiency ◽  
Cycle Times ◽  
Operational Cycle ◽  
Working Volume ◽  
The Cost

<div> <p>Sequencing batch reactor (SBR) is a time-oriented wastewater treatment (WWT) system in a single reactor with flow and energy input according to the predetermined operational cycle time. The treatment efficiency of SBR varies with the duration of the cycle time, which affects the reactor size and hence the cost of WWT plant. This paper presents an experimental study in a bench scale SBR model with a working volume of 15 L with an onjective to determine&nbsp; the optimum cycle time for simultaneous removal of carbon and nutrient from the dairy wastewater. Using the equalized dairy wastewater experiments with four cycle times of 8 h, 6 h, 4 h and 2 h were conducted and the effluent concentrations were compared to the effluent standards. In conclusion, the data suggest the SBR process with 6 h cycle time as the optimum cycle time for treating dairy wastewater for simultaneous carbon and nutrient removal.</p> </div> <p>&nbsp;</p>

Modelling and Evaluation of Sequential Batch Reactor Using Artificial Neural Network

2017 ◽  
Vol 7 (3) ◽  
pp. 1620
Author(s):  
Norjannah Hazali ◽  
Norhaliza Abdul Wahab ◽  
Syahira Ibrahim
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Nutrient Removal ◽  
Batch Reactor ◽  
Granular Sludge ◽  
Aerobic Granular Sludge ◽  
Sequential Batch Reactor ◽  
Radial Basis

The main objective of wastewater treatment plant is to release safe effluent not only to human health but also to the natural environment. An aerobic granular sludge technology is used for nutrient removal of wastewater treatment process using sequential batch reactor system. The nature of the process is highly complex and nonlinear makes the prediction of biological treatment is difficult to achieve. To study the nonlinear dynamic of aerobic granular sludge, high temperature real data at 40˚C were used to model sequential batch reactor using artificial neural network. In this work, the radial basis function neural network for modelling of nutrient removal process was studied. The network was optimized with self-organizing radial basis function neural network which adjusted the network structure size during learning phase. Performance of both network were evaluated and compared and the simulation results showed that the best prediction of the model was given by self-organizing radial basis function neural network.

Cyclic sequential batch reactor: nitrogen and phosphorus removal from domestic sewage

2020 ◽  
Vol 190 ◽  
pp. 71-79
Author(s):  
Rodrigo de Freitas Bueno ◽  
Thiago Andrade ◽  
Júlia Kersul Faria ◽  
Vitor Silva Liduino
Keyword(s):  
Phosphorus Removal ◽  
Batch Reactor ◽  
Domestic Sewage ◽  
Nitrogen And Phosphorus ◽  
Nitrogen And Phosphorus Removal ◽  
Sequential Batch Reactor

Aerobic sequential batch reactor for domestic sewage treatment: parametric optimization and kinetics studies

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Neela Acharya ◽  
Vijay Kumar ◽  
Vandana Gupta ◽  
Chandrakant Thakur ◽  
Parmesh Kumar Chaudhari
Keyword(s):  
Water Quality ◽  
Batch Reactor ◽  
Sewage Treatment ◽  
Oxygen Demand ◽  
Domestic Sewage ◽  
Initial Value ◽  
Sequential Batch Reactor ◽  
Treated Water ◽  
Second Stage ◽  
Study Process

Abstract Domestic sewage (DS) was first treated in aerobic sequential batch reactor (SBR). In order to increase the treated water quality, DS from SBR was further treated using electrocoagulation (EC) and Ion exchange (IE) process. In the SBR study, process parameters such as hydraulic retention time (HRT) and reactor fill time (t f ) was optimized at various volume exchange ratio (VER) of 0.534, 0.4, 0.266, and 0.133. The best HRT and t f were observed to be 0.78 day (d) and 2 h, respectively, providing 72.37% chemical oxygen demand (COD) reduction (initial value of COD = 270 mg/dm3). Kinetics of biodegradation in SBR was also studied. The second stage treatment was performed in EC reactor at 1 ampere (A) current for 30 min electrolysis time (t R). EC reactor, further reduced COD and biological oxygen demand (BOD) up to 72 and 21 mg/dm3 from its average initial COD and BOD of 94 and 23 mg/dm3, respectively. Second stage treatment in IE process reduced hardness, sulphate, and phosphate up to 15, 0.05, and 0.13 mg/dm3 from its initial value 350, 5.48 and 1.16 mg/dm3, respectively. The treated water can be used as potable water after disinfection as its water quality is near to river water.

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