scholarly journals Treatment of Slaughterhouse Wastewater in a Sequencing Batch Reactor: Performance Evaluation and Biodegradation Kinetics

2015 ◽  
pp. 107-136
Keyword(s):  
Performance Evaluation ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Biodegradation Kinetics ◽  
Reactor Performance ◽  
Slaughterhouse Wastewater

scholarly journals Treatment of Slaughter House Wastewater in a Sequencing Batch Reactor: Performance Evaluation and Biodegradation Kinetics

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Pradyut Kundu ◽  
Anupam Debsarkar ◽  
Somnath Mukherjee
Keyword(s):  
Performance Evaluation ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Ammonia Nitrogen ◽  
Blood Protein ◽  
Reactor Performance ◽  
Operating Cycle ◽  
Slaughterhouse Wastewater ◽  
Carbon And Nitrogen ◽  
Sequential Mode

Slaughterhouse wastewater contains diluted blood, protein, fat, and suspended solids, as a result the organic and nutrient concentration in this wastewater is vary high and the residues are partially solubilized, leading to a highly contaminating effect in riverbeds and other water bodies if the same is let off untreated. The performance of a laboratory-scale Sequencing Batch Reactor (SBR) has been investigated in aerobic-anoxic sequential mode for simultaneous removal of organic carbon and nitrogen from slaughterhouse wastewater. The reactor was operated under three different variations of aerobic-anoxic sequence, namely, (4+4), (5+3), and (3+5) hr. of total react period with two different sets of influent soluble COD (SCOD) and ammonia nitrogen (NH4+-N) level1000±50 mg/L, and90±10 mg/L,1000±50 mg/L and180±10 mg/L, respectively. It was observed that from 86 to 95% of SCOD removal is accomplished at the end of 8.0 hr of total react period. In case of (4+4) aerobic-anoxic operating cycle, a reasonable degree of nitrification 90.12 and 74.75% corresponding to initialNH4+-N value of 96.58 and 176.85 mg/L, respectively, were achieved. The biokinetic coefficients (k,Ks,Y,kd) were also determined for performance evaluation of SBR for scaling full-scale reactor in future operation.

Effect of heterotrophic growth on nitritation/anammox in a single sequencing batch reactor

2008 ◽  
Vol 58 (2) ◽  
pp. 277-284 ◽  
Author(s):  
Kai M. Udert ◽  
Elija Kind ◽  
Mieke Teunissen ◽  
Sarina Jenni ◽  
Tove A. Larsen
Keyword(s):  
Nitric Oxide ◽  
Nitrogen Removal ◽  
Sequencing Batch Reactor ◽  
Heterotrophic Bacteria ◽  
Batch Reactor ◽  
Organic Substrate ◽  
Anammox Bacteria ◽  
Heterotrophic Growth ◽  
Reactor Performance ◽  
Heterotrophic Denitrification

The combination of nitritation and autotrophic denitrification (anammox) in a single sequencing batch reactor (SBR) is an energy efficient process for nitrogen removal from high-strength ammonia wastewaters. So far, the process has been successfully applied to digester supernatant. However, the process could also be suitable to treat source-separated urine, which has very high ammonium and organic substrate concentrations (up to 8,200 gN/m3 and 10,000 gCOD/m3). In this study, reactor performance was tested for digester supernatant and diluted source-separated urine. Ammonium concentrations in both solutions were similar (between 611 and 642 gN/m3), thus reactor performance could be directly compared. Differences were mainly due to higher activity of heterotrophic bacteria in urine. Nitrogen removal was slightly higher for source-separated urine, because heterotrophic bacteria denitrified the nitrate that was produced by anammox bacteria. In spite of higher heterotrophic growth with source-separated urine, calculated sludge concentrations at steady state were higher with digester supernatant due to accumulation of inert particulate organic matter from the influent. Although the sludge concentrations are less problematic for source-separated urine, process instabilities are more likely, because lower pH values are reached and heterotrophic denitrification can cause sudden increases of nitrite concentrations and/or nitric oxide. Both compounds inhibit aerobic ammonium oxidizing bacteria, heterotrophic bacteria and, most importantly, anammox bacteria. Nitrite and nitric oxide production by heterotrophic denitrification must be better understood to optimize nitritation/anammox for source-separated urine.

scholarly journals Artificial Neural Network Modeling for Biological Removal of Organic Carbon and Nitrogen from Slaughterhouse Wastewater in a Sequencing Batch Reactor

2013 ◽  
Vol 2013 ◽  
pp. 1-15 ◽  
Author(s):  
Pradyut Kundu ◽  
Anupam Debsarkar ◽  
Somnath Mukherjee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Sequencing Batch Reactor ◽  
Batch Reactor ◽  
Experimental Results ◽  
Neural Network Modeling ◽  
Data Sets ◽  
Slaughterhouse Wastewater ◽  
Ann Models ◽  
Artificial Neural

The present paper deals with treatment of slaughterhouse wastewater by conducting a laboratory scale sequencing batch reactor (SBR) with different input characterized samples, and the experimental results are explored for the formulation of feedforward backpropagation artificial neural network (ANN) to predict combined removal efficiency of chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The reactor was operated under three different combinations of aerobic-anoxic sequence, namely, (4 + 4), (5 + 3), and (5 + 4) hour of total react period with influent COD and NH4+-N level of 2000 ± 100 mg/L and 120 ± 10 mg/L, respectively. ANN modeling was carried out using neural network tools, with Levenberg-Marquardt training algorithm. Various trials were examined for training of three types of ANN models (Models “A,” “B,” and “C”) using number of neurons in the hidden layer varying from 2 to 30. All together 29, data sets were used for each three types of model for which 15 data sets were used for training, 7 data sets for validation, and 7 data sets for testing. The experimental results were used for testing and validation of three types of ANN models. Three ANN models (Models “A,” “B,” and “C”) were trained and tested reasonably well to predict COD and NH4+-N removal efficiently with 3.33% experimental error.

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