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Author |
Balemans, S.; Vlaeminck, S.E.; Torfs, E.; Hartog, L.; Zaharova, L.; Rehman, U.; Nopens, I. |
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Title |
The impact of local hydrodynamics on high-rate activated sludge flocculation in laboratory and full-scale reactors |
Type |
A1 Journal article |
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Year  |
2020 |
Publication |
Processes |
Abbreviated Journal |
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Volume |
8 |
Issue |
2 |
Pages |
131-18 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
High rate activated sludge (HRAS) processes have a high potential for carbon and energy recovery from sewage, yet they suffer frequently from poor settleability due to flocculation issues. The process of flocculation is generally optimized using jar tests. However, detailed jar hydrodynamics are often unknown, and average quantities are used, which can significantly differ from the local conditions. The presented work combined experimental and numerical data to investigate the impact of local hydrodynamics on HRAS flocculation for two different jar test configurations (i.e., radial vs. axial impellers at different impeller velocities) and compared the hydrodynamics in these jar tests to those in a representative section of a full scale reactor using computational fluid dynamics (CFD). The analysis showed that the flocculation performance was highly influenced by the impeller type and its speed. The axial impeller appeared to be more appropriate for floc formation over a range of impeller speeds as it produced a more homogeneous distribution of local velocity gradients compared to the radial impeller. In contrast, the radial impeller generated larger volumes (%) of high velocity gradients in which floc breakage may occur. Comparison to local velocity gradients in a full scale system showed that also here, high velocity gradients occurred in the region around the impeller, which might significantly hamper the HRAS flocculation process. As such, this study showed that a model based approach was necessary to translate lab scale results to full scale. These new insights can help improve future experimental setups and reactor design for improved HRAS flocculation. |
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Wos |
000521167900088 |
Publication Date |
2020-01-24 |
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ISSN |
2227-9717 |
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UA library record; WoS full record; WoS citing articles |
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Notes |
; This research was funded by Research Foundation Flanders (FWO SB Grant 1.S.705.18N). ; |
Approved |
Most recent IF: NA |
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Call Number |
UA @ admin @ c:irua:165420 |
Serial |
6543 |
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Author |
Decostere, B.; Coppens, J.; Vervaeren, H.; Vlaeminck, S.E.; De Gelder, L.; Boon, N.; Nopens, I.; Van Hulle, S.W.H. |
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Title |
Kinetic exploration of intracellular nitrate storage in marine microalgae |
Type |
A1 Journal article |
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Year |
2017 |
Publication |
Journal of environmental science and health : part A: toxic/hazardous substances and environmental engineering |
Abbreviated Journal |
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Volume |
52 |
Issue |
14 |
Pages |
1303-1311 |
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Keywords |
A1 Journal article; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
In this study, a recently developed model accounting for intracellular nitrate storage kinetics was thoroughly studied to understand and compare the storage capacity of Phaeodactylum tricornutum and Amphora coffeaeformis. In the first stage the identifiability of the biokinetic parameters was examined. Next, the kinetic model was calibrated for both microalgal species based on experimental observations during batch growth experiments. Two kinetic parameters were calibrated, namely the maximum specific growth rate (mu(max)) and the nitrate storage rate (k(sto)). A significant difference was observed for the nitrate storage rate between both species. For P. tricornutum, the nitrate storage rate was much higher (k(sto) = 0.036m(3) g(-1) DW d(-1)) compared to A. coffeaeformis (k(sto) = 0.0004m(3) g(-1) DW d(-1)). This suggests that P. tricornutum has a more efficient nitrate uptake ability and intracellular nitrate storage capacity and also indicates the need for determination of k(sto) in order to quantify nitrate storage. |
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000415634300004 |
Publication Date |
2017-09-26 |
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1093-4529; 1532-4117 |
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UA library record; WoS full record |
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no |
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Call Number |
UA @ admin @ c:irua:147467 |
Serial |
8137 |
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Author |
Seuntjens, D.; Bundervoet, B.L.M.; Mollen, H.; De Mulder, C.; Wypkema, E.; Verliefde, A.; Nopens, I.; Colsen, J.G.M.; Vlaeminck, S.E. |
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Title |
Energy efficient treatment of A-stage effluent : pilot-scale experiences with short-cut nitrogen removal |
Type |
A1 Journal article |
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Year |
2016 |
Publication |
Water science and technology |
Abbreviated Journal |
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Volume |
73 |
Issue |
9 |
Pages |
2150-2158 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Sustainable Energy, Air and Water Technology (DuEL) |
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000376285300013 |
Publication Date |
2016-02-09 |
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ISSN |
0273-1223; 1996-9732 |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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no |
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Call Number |
UA @ admin @ c:irua:130442 |
Serial |
7908 |
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Author |
Van Hoey, S.; Nopens, I.; van der Kwast, J.; Seuntjens, P. |
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Title |
Dynamic identifiability analysis-based model structure evaluation considering rating curve uncertainty |
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A1 Journal article |
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Year |
2015 |
Publication |
Journal of hydrologic engineering |
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Volume |
20 |
Issue |
5 |
Pages |
04014072 |
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Keywords |
A1 Journal article; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
When applying hydrological models, different sources of uncertainty are present, and evaluations of model performances should take these into account to assess model outcomes correctly. Furthermore, uncertainty in the discharge observations complicates the model identification, both in terms of model structure and parameterization. In this paper, the authors compare two different lumped model structures (PDM and NAM) considering uncertainty coming from the rating curve. Limits of acceptability for the model simulations were determined based on derived uncertainty bounds of the discharge observations. The authors applied the DYNamic Identifiability Approach (DYNIA) to identify structural failure of both models and to evaluate the configuration of their structures. In general, similar model performances are observed. However, the model structures tend to behave differently in the course of time, as revealed by the DYNIA approach. Based on the analyses performed, the probability based soil storage representation of the PDM model outperforms the NAM structure. The incorporation of the observation error did not prevent the DYNIA analysis to identify potential model structural deficiencies that are limiting the representation of the seasonal variation, primarily indicated by shifting regions of parameter identifiability. As such, the proposed approach is able to indicate where deficiencies are found and model improvement is needed. |
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000353995400002 |
Publication Date |
2014-03-06 |
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ISSN |
1084-0699 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Notes |
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no |
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Call Number |
UA @ admin @ c:irua:126056 |
Serial |
7829 |
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Author |
Seuntjens, D.; Bundervoet, B.L.M.; Mollen, H.; De Mulder, C.; Wypkema, E.; Verliefde, A.; Nopens, I.; Colsen, J.G.M.; Vlaeminck, S.E. |
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Title |
Energy efficient treatment of A-stage effluent : pilot-scale experiences with short-cut nitrogen removal |
Type |
P3 Proceeding |
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Year |
2015 |
Publication |
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Volume |
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Issue |
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Pages |
10 p.
T2 - IWA Nutrient Removal and Recovery 2015: |
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Keywords |
P3 Proceeding; Engineering sciences. Technology; Sustainable Energy, Air and Water Technology (DuEL) |
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UA library record |
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no |
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Call Number |
UA @ admin @ c:irua:151146 |
Serial |
7909 |
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Author |
Van Hoey, S.; Seuntjens, P.; van der Kwast, J.; Nopens, I. |
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Title |
A qualitative model structure sensitivity analysis method to support model selection |
Type |
A1 Journal article |
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Year |
2014 |
Publication |
Journal of hydrology |
Abbreviated Journal |
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Volume |
519 |
Issue |
D |
Pages |
3426-3435 |
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Keywords |
A1 Journal article; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
The selection and identification of a suitable hydrological model structure is a more challenging task than fitting parameters of a fixed model structure to reproduce a measured hydrograph. The suitable model structure is highly dependent on various criteria, i.e. the modeling objective, the characteristics and the scale of the system under investigation and the available data. Flexible environments for model building are available, but need to be assisted by proper diagnostic tools for model structure selection. This paper introduces a qualitative method for model component sensitivity analysis. Traditionally, model sensitivity is evaluated for model parameters. In this paper, the concept is translated into an evaluation of model structure sensitivity. Similarly to the one-factor-at-a-time (OAT) methods for parameter sensitivity, this method varies the model structure components one at a time and evaluates the change in sensitivity towards the output variables. As such, the effect of model component variations can be evaluated towards different objective functions or output variables. The methodology is presented for a simple lumped hydrological model environment, introducing different possible model building variations. By comparing the effect of changes in model structure for different model objectives, model selection can be better evaluated. Based on the presented component sensitivity analysis of a case study, some suggestions with regard to model selection are formulated for the system under study: (1) a non-linear storage component is recommended, since it ensures more sensitive (identifiable) parameters for this component and less parameter interaction; (2) interflow is mainly important for the low flow criteria; (3) excess infiltration process is most influencing when focussing on the lower flows; (4) a more simple routing component is advisable; and (5) baseflow parameters have in general low sensitivity values, except for the low flow criteria. (C) 2014 Elsevier B.V. All rights reserved. |
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Wos |
000347589600057 |
Publication Date |
2014-10-08 |
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ISSN |
0022-1694 |
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UA library record; WoS full record; WoS citing articles |
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no |
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Call Number |
UA @ admin @ c:irua:123809 |
Serial |
7395 |
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Author |
Mortier, S.T.F.C.; Van Hoey, S.; Cierkens, K.; Gernaey, K.V.; Seuntjens, P.; De Baets, B.; De Beer, T.; Nopens, I. |
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Title |
A GLUE uncertainty analysis of a drying model of pharmaceutical granules |
Type |
A1 Journal article |
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Year |
2013 |
Publication |
European journal of pharmaceutics and biopharmaceutics |
Abbreviated Journal |
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Volume |
85 |
Issue |
3:b |
Pages |
984-995 |
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Keywords |
A1 Journal article; Pharmacology. Therapy; Sustainable Energy, Air and Water Technology (DuEL) |
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Abstract |
A shift from batch processing towards continuous processing is of interest in the pharmaceutical industry. However, this transition requires detailed knowledge and process understanding of all consecutive unit operations in a continuous manufacturing line to design adequate control strategies. This can be facilitated by developing mechanistic models of the multi-phase systems in the process. Since modelling efforts only started recently in this field, uncertainties about the model predictions are generally neglected. However, model predictions have an inherent uncertainty (i.e. prediction uncertainty) originating from uncertainty in input data, model parameters, model structure, boundary conditions and software. In this paper, the model prediction uncertainty is evaluated for a model describing the continuous drying of single pharmaceutical wet granules in a six-segmented fluidized bed drying unit, which is part of the full continuous from-powder-to-tablet manufacturing line (Consigma (TM), GEA Pharma Systems). A validated model describing the drying behaviour of a single pharmaceutical granule in two consecutive phases is used. First of all, the effect of the assumptions at the particle level on the prediction uncertainty is assessed. Secondly, the paper focuses on the influence of the most sensitive parameters in the model. Finally, a combined analysis (particle level plus most sensitive parameters) is performed and discussed. To propagate the uncertainty originating from the parameter uncertainty to the model output, the Generalized Likelihood Uncertainty Estimation (GLUE) method is used. This method enables a modeller to incorporate the information obtained from the experimental data in the assessment of the uncertain model predictions and to find a balance between model performance and data precision. A detailed evaluation of the obtained uncertainty analysis results is made with respect to the model structure, interactions between parameters and uncertainty boundaries. (C) 2013 Elsevier B.V. All rights reserved. |
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Wos |
000330200800019 |
Publication Date |
2013-03-29 |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
0939-6411 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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no |
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Call Number |
UA @ admin @ c:irua:114876 |
Serial |
8005 |
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