Dissolved and colloidal organic nitrogen removal from wastewater treatment plants effluents and reject waters using physical–chemical processes

2014 ◽  
Vol 70 (3) ◽  
pp. 561-568 ◽  
Author(s):  
K. Czerwionka ◽  
J. Makinia
Keyword(s):  
Activated Sludge ◽  
Organic Nitrogen ◽  
Wastewater Treatment Plants ◽  
Exchange Process ◽  
Cation Exchange Resin ◽  
Chemical Processes ◽  
Biological Nutrient Removal ◽  
Reject Water ◽  
Physical Chemical ◽  
Low Efficiency

Four physical–chemical processes were compared in terms of the efficiencies of dissolved and colloidal organic nitrogen (DON and CON) removal from the secondary effluents (SE) and reject water from full-scale biological nutrient removal activated sludge systems. Adsorption on activated carbon was most efficient and allowed removal from the SE of up to 80% and 100% of DON and CON, respectively. High efficiencies of DON removal from SE (up to 55%) were also obtained when using coagulation with iron(III) chloride and calcium hydroxide at final pH = 11.0–11.5. The efficiency of DON removal from thickening waste activated sludge (TWAS) reject water, obtained using coagulation with iron(III) chloride, was comparable with the efficiency for the SE. The efficiency of this process with regard to the sludge digester liquors (SDL) was significantly higher, i.e., 65–70% for both DON and CON. The ion exchange process with strongly acidic cation exchange resin (without pH correction) resulted in a relatively small efficiency of DON removal (<15%), and negligible efficiency of CON removal (<10%). Furthermore, ultrafiltration (0.015 μm) of SE and TWAS reject water resulted in a relatively low efficiency of DON removal (10–13% and 10–20% respectively). Ultrafiltration was found to be more effective for DON removal from SDL (41–68%).

Model based evaluation of plant improvement strategies for biological nutrient removal

1999 ◽  
Vol 39 (4) ◽  
pp. 45-53 ◽  
Author(s):  
H. M. van Veldhuizen ◽  
M. C. M. van Loosdrecht ◽  
F. A. Brandse
Keyword(s):  
Activated Sludge ◽  
Wastewater Treatment Plants ◽  
P Uptake ◽  
Biological Nutrient Removal ◽  
Adequate Description ◽  
Control Scheme ◽  
Detailed Simulation ◽  
High Fraction ◽  
Biokinetic Parameters ◽  
P Removal

An activated sludge model for biological N- and P-removal was developed, which describes anoxic and aerobic P-uptake based on bacterial metabolism. This model was tested in practice on two wastewater treatment plants, which are BCFS®-processes, which contain activated sludge with a high fraction of denitrifying P-removing bacteria (DPB's). The model appeared to be able to give an adequate description of the performance of these treatment plants under different conditions. If the process parameters are well defined almost no calibration of the biokinetic parameters was necessary. In the simulation of Dalfsen wwtp, which has a complex control scheme, it was possible to give an adequate simulation of the control actions and the concentration profiles in a rather simple way, showing that detailed simulation of these controllers was not necessary. With the calibrated model it was possible to analyse bottlenecks and give suggestions for upgrading of the concerned treatments plants. The simulation results were used in decisions on investments.

Benchmark simulation model no 2: general protocol and exploratory case studies

2007 ◽  
Vol 56 (8) ◽  
pp. 67-78 ◽  
Author(s):  
U. Jeppsson ◽  
M.-N. Pons ◽  
I. Nopens ◽  
J. Alex ◽  
J.B. Copp ◽  
...  
Keyword(s):  
Activated Sludge ◽  
Simulation Model ◽  
Wastewater Treatment Plants ◽  
Control Strategies ◽  
Integrated Control ◽  
Ring Test ◽  
Anaerobic Sludge ◽  
Reject Water ◽  
Whole Plant ◽  
Wide Range

Over a decade ago, the concept of objectively evaluating the performance of control strategies by simulating them using a standard model implementation was introduced for activated sludge wastewater treatment plants. The resulting Benchmark Simulation Model No 1 (BSM1) has been the basis for a significant new development that is reported on here: Rather than only evaluating control strategies at the level of the activated sludge unit (bioreactors and secondary clarifier) the new BSM2 now allows the evaluation of control strategies at the level of the whole plant, including primary clarifier and sludge treatment with anaerobic sludge digestion. In this contribution, the decisions that have been made over the past three years regarding the models used within the BSM2 are presented and argued, with particular emphasis on the ADM1 description of the digester, the interfaces between activated sludge and digester models, the included temperature dependencies and the reject water storage. BSM2-implementations are now available in a wide range of simulation platforms and a ring test has verified their proper implementation, consistent with the BSM2 definition. This guarantees that users can focus on the control strategy evaluation rather than on modelling issues. Finally, for illustration, twelve simple operational strategies have been implemented in BSM2 and their performance evaluated. Results show that it is an interesting control engineering challenge to further improve the performance of the BSM2 plant (which is the whole idea behind benchmarking) and that integrated control (i.e. acting at different places in the whole plant) is certainly worthwhile to achieve overall improvement.

Biological nitrification and denitrification of opto-electronic industrial wastewater

2003 ◽  
Vol 48 (8) ◽  
pp. 27-34 ◽  
Author(s):  
T.-K. Chen ◽  
C.-H. Ni ◽  
J.-N. Chen
Keyword(s):  
Wastewater Treatment ◽  
Industrial Wastewater ◽  
Organic Nitrogen ◽  
Wastewater Treatment Plants ◽  
Biological Nutrient Removal ◽  
Nitrogen And Phosphorus ◽  
Mixed Liquor ◽  
The Past ◽  
Two Factors ◽  
Recycle Rate

Development and application of biological nutrient removal processes accelerated significantly over the past decade due to more stringent nutrients (nitrogen and phosphorus) discharge limits being imposed on wastewater treatment plants. The opto-electronic industry has developed very fast over the past decade in the world. The wastewater often contains a significant quantity of organic nitrogen compounds and has a ratio of over 95% in organic nitrogen (Org-N) to total nitrogen (T-N). In this study, a 2-stage Anoxic/Aerobic pre-denitrification process was established and the efficiency of wastewater treatment was evaluated. Wastewater from an actual LCD-plant was obtained as the sample for looking into the feasibility of opto-electronic industrial wastewater treatment. Hydraulic retention time (HRT) and mixed liquor recycle rate (MLR) were controlled independently to distinguish between the effects of these two factors. Under suitable HRT and mixed liquor recycle ratio, effluents of NH4-N, NOx-N and COD can fall below 20 mg/l, 30 mg/l and 80 mg/l.

Distillery wastes as external carbon sources for denitrification in municipal wastewater treatment plants

2012 ◽  
Vol 65 (9) ◽  
pp. 1583-1590 ◽  
Author(s):  
K. Czerwionka ◽  
J. Makinia ◽  
M. Kaszubowska ◽  
J. Majtacz ◽  
M. Angowski
Keyword(s):  
Wastewater Treatment ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Carbon Sources ◽  
Biological Nutrient Removal ◽  
Yield Coefficient ◽  
Municipal Wastewater Treatment ◽  
Reject Water ◽  
Fusel Oils ◽  
By Products

In this study, by-products from alcohol production were examined in terms of their potential application as external carbon sources for enhancing denitrification in biological nutrient removal systems. Three types of batch tests were used to compare the effects of the distillery by-products, such as fusel oil, syrup and reject water, on the non-acclimated activated sludge. Much higher nitrate utilization rates (NURs) were observed for the latter two carbon sources. In the conventional NUR measurements (one-phase experiments), the observed NURs with syrup and reject water were 3.2–3.3 g N/(kg VSS h) compared with 1.0 g N/(kg VSS h) obtained for fusel oils from two different distilleries. When the carbon sources were added at the beginning of the anoxic phase preceded by an anaerobic phase (two-phase experiments), the NURs were 4.2 g N/(kg VSS h) (syrup and reject water) and 2.4–2.7 g N/(kg VSS h) (fusel oils). The heterotrophic yield coefficient, determined based on the conventional OUR measurements, varied in a relatively narrow range (0.72–0.79 g COD/g COD) for all the examined carbon sources. Due to advantageous composition (much higher COD concentrations and COD/N ratios), fusel is a preferred carbon source for practical handling in full-scale wastewater treatment plants.

scholarly journals Causes and remedies for filamentous foaming in activated sludge treatment plant

2014 ◽  
Vol 16 (4) ◽  
pp. 762-772 ◽  
Keyword(s):  
Activated Sludge ◽  
Municipal Wastewater ◽  
Wastewater Treatment Plants ◽  
Treatment Plant ◽  
Mycolic Acid ◽  
Foam Formation ◽  
Activated Sludge Process ◽  
Polymer Addition ◽  
Physical Chemical ◽  
Biological Methods

<p>This paper reviews the problem of foaming associated with the activated sludge process and its control using various physical, chemical and biological methods. Activated sludge process is widely used for treatment of every type of wastewater like industrial, domestic and municipal wastewater. This process is driven by a complex microbial population, among which some mycolic acid containing bacteria leads to the stable foam formation which ultimately results in poor efficiency of the plants and leading to major environmental, operational, and health problems. A number of researches provide the evidences of foaming in wastewater treatment plants and its control using physical, chemical and biological methods. Current approaches for controlling foam includes operational adjustments, additional structures, controlling dissolved oxygen levels, water sprays, steam application, polymer addition, chlorination and a novel and ecofriendly approach that is treatment of filamentous bacteria with the specific phages. A detailed study of all methods is presented and collectively described in this review paper for a better understanding of the foam controlling strategies.&nbsp;</p>

Modeling organic nitrogen conversions in activated sludge bioreactors

2011 ◽  
Vol 63 (7) ◽  
pp. 1418-1426 ◽  
Author(s):  
Jacek Makinia ◽  
Krishna Pagilla ◽  
Krzysztof Czerwionka ◽  
H. David Stensel
Keyword(s):  
Activated Sludge ◽  
Organic Nitrogen ◽  
Field Measurements ◽  
Biological Nutrient Removal ◽  
Organic N ◽  
Determine Parameter ◽  
Dynamic Variations ◽  
Organic Fractions ◽  
Process Measurements ◽  
Hydrolysis Of

For biological nutrient removal (BNR) systems designed to maximize nitrogen removal, the effluent total nitrogen (TN) concentration may range from 2.0 to 4.0 g N/m3 with about 25–50% in the form of organic nitrogen (ON). In this study, current approaches to modeling organic N conversions (separate processes vs. constant contents of organic fractions) were compared. A new conceptual model of ON conversions was developed and combined with Activated Sludge Model No. 2d (ASM2d). The model addresses a new insight into the processes of ammonification, biomass decay and hydrolysis of particulate and colloidal ON (PON and CON, respectively). Three major ON fractions incorporated are defined as dissolved (DON) (&lt;0.1 µm), CON (0.1–1.2 µm) and PON (41.2 µm). Each major fraction was further divided into two sub-fractions – biodegradable and non-biodegradable. Experimental data were collected during field measurements and lab experiments conducted at the ‘‘Wschod’’ WWTP (570,000 PE) in Gdansk (Poland). The accurate steady-state predictions of DON and CON profiles were possible by varying ammonification and hydrolysis rates under different electron acceptor conditions. With the same model parameter set, the behaviors of both inorganic N forms (NH4-N, NOX-N) and ON forms (DON, CON) in the batch experiments were predicted. The challenges to accurately simulate and predict effluent ON levels from BNR systems are due to analytical methods of direct ON measurement (replacing TKN) and lack of large enough database (in-process measurements, dynamic variations of the ON concentrations) which can be used to determine parameter value ranges.

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