Modeling cake buildup under TMP-step filtration in a membrane bioreactor: Cake compressibility is significant

Bugge, Thomas Vistisen; Jørgensen, Mads Koustrup; Christensen, Morten Lykkegaard; Keiding, Kristian

doi:10.1016/j.watres.2012.06.015

Cited by 53 publications

(46 citation statements)

References 19 publications

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“…The value of P a is sludge specific as sludge cake compressibility can vary with floc size, sludge type, sludge conditioning, operating temperature and pH [13,24,28,29]. Therefore, P a was estimated via parameter estimation during model calibration using a non-linear least squares parameter estimation method (Levenberg-Marquardt algorithm) with TMP as fitted output and residual sum of squares (J)…”

Section: Model Parametersmentioning

confidence: 99%

“…The default model approach is the use of lumped (non-distributed) parameter models which sufficiently characterise simple fouling behaviour [12][13][14]. These models however, are inapplicable in membranes with nonuniform shear and cake distributions and provide limited insight into transitionary behaviour from satisfactory to unsatisfactory performance.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic multidimensional modelling of submerged membrane bioreactor fouling

Boyle-Gotla

Jensen

Yap

et al. 2014

Journal of Membrane Science

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Section: Model Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Dynamic multidimensional modelling of submerged membrane bioreactor fouling

Boyle-Gotla

Jensen

Yap

et al. 2014

Journal of Membrane Science

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“…4), as per Bugge et al [13] and Jørgensen et al [14]. It allowed accounting for the cake compression due to both the deformation of soft sludge flocs and the structural rearrangement of particles [15].…”

Section: Cake Layer Compression and Sub-critical Foulingmentioning

confidence: 99%

“…Similar behaviour was observed on operating days 143 -181 (see Figure 1a), when TMP was 0.1 -0.2 bars. The considerable increase in αC due to the increase in TMP highlights the need to model not only cake build-up but also cake compression in filtration processes of this type [13,14]. Since it is possible to simulate RI and RC from the predicted ωC and ωI values, the weighted average daily distribution for RT can be modelled in the long-term (see Figure 3b).…”

Section: Irreversible Fouling Is Represented In the Model As A Directmentioning

confidence: 99%

Mathematical modelling of filtration in submerged anaerobic MBRs (SAnMBRs): Long-term validation

Robles

Ruano

Ribes

et al. 2013

Journal of Membrane Science

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ElsevierRobles Martínez, Á.; Ruano García, MV.; Ribes Bertomeu, J.; Seco Torrecillas, A.; ngerobma@upv.es AbstractThe aim of this study was the long-term validation of a model capable of reproducing the filtration process occurring in a submerged anaerobic membrane bioreactor (SAnMBR) system. The proposed model was validated using data obtained from a SAnMBR demonstration plant fitted with industrial-scale hollowfibre membranes. The validation was carried out using both lightly and heavily fouled membranes operating at different bulk concentrations, gas sparging intensities and transmembrane fluxes. Across a broad spectrum of operating conditions, the model correctly forecast the respective experimental data in the long term. The simulation results revealed the importance of controlling irreversible fouling in order to ensure sustainable long-term membrane performance. KeywordsIndustrial-scale hollow fibre modules; long-term validation; resistance in series; filtration model; submerged anaerobic membrane bioreactor Highlights 2A filtration model for SAnMBRs has been validated using long-term data.The model was validated for lightly and heavily fouled full-scale HF membranes.Different MLTS levels, gas sparging intensities and permeate fluxes were applied.The model reproduced the experimental data accurately.

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“…To account for cake layer compression, αC was defined as time-and TMPdependent as per Bugge et al (2012) and Jørgensen et al (2012). In addition, to account for sub-critical fouling, an additional dependence of αC on time was considered in the model (Robles et al, 2013a), as shown in Eq.…”

mentioning

confidence: 99%

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR)

Robles

Ruano

Ribes

et al. 2014

Bioresource Technology

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ElsevierRobles Martínez, Á.; Ruano García, MV.; Ribes Bertomeu, J.; Seco Torrecillas, A.; Ferrer, J. (2014). Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR) angel.robles@uv.es AbstractThe results of a sensitivity analysis of a filtration model for submerged anaerobic HighlightsA global sensitivity analysis of a filtration model for AnMBRs was conducted.It was conducted using a revised version of the Morris screening method.Sensitivity results significantly simplified the input subset to be calibrated.Only one input factor could be removed from the model.

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Modeling cake buildup under TMP-step filtration in a membrane bioreactor: Cake compressibility is significant

Cited by 53 publications

References 19 publications

Dynamic multidimensional modelling of submerged membrane bioreactor fouling

Dynamic multidimensional modelling of submerged membrane bioreactor fouling

Mathematical modelling of filtration in submerged anaerobic MBRs (SAnMBRs): Long-term validation

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR)

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