Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Biotechnology and Bi...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Biotechnology and Bioengineering
Article . 2013 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
versions View all 3 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Modeling and optimization of granulation process of activated sludge in sequencing batch reactors

Authors: Bing-Jie Ni; Han-Qing Yu; Kui-Zu Su; Kui-Zu Su;

Modeling and optimization of granulation process of activated sludge in sequencing batch reactors

Abstract

AbstractAerobic granulation is a promising process for wastewater treatment, but this granulation process is very complicated and is affected by many factors. Thus, a mathematical model to quantitatively describe such a granulation process is highly desired. In this work, by taking into account all of key steps including biomass growth, increase in particle size and density, detachment, breakage and sedimentation, an one‐dimensional mathematic model was developed to simulate the granulation process of activated sludge in a sequencing batch reactor (SBR). Discretization methodology was applied by dividing operational time, sedimentation process, size fractions and slices into discretized calculation elements. Model verification and prediction for aerobic granulation process were conducted under four different conditions. Four parameters indicative of granulation progression, including mean radius, biomass discharge ratio, total number, and bioparticle size distribution, were predicted well with the model. An optimum controlling strategy, automatically adjusted of settling time, was also proposed based on this model. Moreover, aerobic granules with a density higher than 120 g VSS/L and radius in a range of 0.4–1.0 mm were predicted to have both high settling velocity and substrate utilization rate, and the corresponding optimum operating conditions were be determined. Experimental results demonstrate that the developed model is appropriate for simulating the formation of aerobic granules in SBRs. These results are useful for designing and optimizing the cultivation and operation of aerobic granule process. Biotechnol. Bioeng. 2013; 110: 1312–1322. © 2012 Wiley Periodicals, Inc.

Country
Australia
Related Organizations
Keywords

Optimization, 660, Sewage, aerobic granulation, Models, Theoretical, Waste Disposal, Fluid, Aerobiosis, Diffusion, Bioreactors, Operating conditions, Calibration, Biomass, mathematical model, Biotechnology

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    33
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
33
Top 10%
Top 10%
Top 10%