Steady-state analysis of activated sludge processes with a settler model including sludge compression

Diehl, Stefan; Zambrano, Jesús; Carlsson, Bengt

doi:10.1016/j.watres.2015.09.052

Cited by 18 publications

(17 citation statements)

References 33 publications

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“…Also, there were some recent studies that considered power‐law functions instead of Veslind and Takács's exponential equations to analyze hindered settling (Bürger, Careaga, & Diehl, ; Diehl, Zambrano, & Carlsson, ; Torfs et al, ; Valle Medina & Laurent, ). In Medina and Laurent's study, they used Bürger–Diehl's transport equation (Bürger, Diehl, Farås, Nopens, & Torfs, ) to model the sludge settling behavior in a batch settling column and in a full‐scale SST.…”

Section: Applications Of Cfd Modeling Research and Engineering Practicementioning

confidence: 99%

Development and applications in computational fluid dynamics modeling for secondary settling tanks over the last three decades: A review

Gao

Stenstrom

2019

Water Environment Research

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Secondary settling tanks (SSTs) are a crucial process that determines the performance of the activated sludge process. However, their performance is often far from satisfactory. In the last 30 years, computational fluid dynamics (CFD) has become a robust and cost-efficient tool for designing new SSTs, modifying the geometries of existing SSTs and improved control techniques in wastewater treatment plants. The first part of this review paper discusses the different approaches to model the motion of particles in SSTs. The applications of different multiphase approaches and the widely applied single-phase approach in different SST studies are reviewed. The second part reviews current CFD research and engineering practice, focusing on the formation and the effect of density currents, effects of different design variables, parameter uncertainties in modeling structures, and atmospheric conditions. Finally, challenges and future improvements of sub-models (sludge settling, rheology, turbulence, and flocculation) in the SST model framework are identified. • Practitioner points• The first journal review for the CFD applications in SSTs over the last decade. • The controversy over the relationship between SOR and SST performance can be largely explained by the prediction of the CFD model. • Density decoupling in the turbulence model is possible for well-baffled SSTs. • The relative importance of three modeling parameters is summarized. • Recommendations for future data collection are provided. • Key wordscomputational fluid dynamics; density-driven flow; secondary settling tank; wastewater treatment

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Section: Applications Of Cfd Modeling Research and Engineering Practicementioning

confidence: 99%

Development and applications in computational fluid dynamics modeling for secondary settling tanks over the last three decades: A review

Gao

Stenstrom

2019

Water Environment Research

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“…The development of more detailed models for dynamic simulations is an active area of research. An example of such a model is the one‐dimensional hindered‐compression‐dispersion model due to Bürger and Diehl, which has been used in a number of recent publications . A significant problem associated with the use of more detailed settling unit models is obtaining the necessary experimental data to calibrate them.…”

Section: Equationsmentioning

confidence: 99%

An analysis of organic carbon removal in a two‐reactor cascade with recycle and a two‐reactor step‐feed cascade with recycle

Nelson

Bradshaw‐Hajek

2019

Asia-Pacific J Chem Eng

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We analyse the steady-state operation of two process configurations that employ a reactor cascade with recycle. The first process configuration is a two-reactor cascade in which the feed stream enters into the first reactor. The second process configuration is a two-reactor step-feed cascade in which the feed stream enters the second reactor. In each process configuration, part of the effluent stream is recycled back into the first reactor. The reactors in the cascade need not be of equal volume. The reaction is assumed to be a biological process governed by Monod growth kinetics with a decay coefficient for the microorganisms. The stability of the washout solution is determined analytically for both process configurations. It follows from the stability analysis that that there is a range of residence times over which the effluent concentration leaving a step-reactor cascade is lower than that leaving a standard reactor cascade. Steady-state diagrams are presented showing the effluent concentration as a function of the residence time. We find that if the desired effluent concentration is not too low, then the process configuration that minimises the residence time is the step-feed reactor cascade. However, for much cleaner waste streams, the process configuration that minimises the residence time is the standard reactor cascade.KEYWORDS activated sludge, multistage continuous culture, staging, step-feed reactor, wastewater, water treatment Asia-Pac J Chem Eng. 2020;15:e2392.wileyonlinelibrary.com/journal/apj

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“…Some examples of methods applied to monitor a SST include image analysis (Grijspeerdt 1997) and modelbased approaches (Traoré et al 2006;Yoo et al 2002). Even though, the prediction of the sludge concentration profile is still far from satisfactory (Li and Stenstrom 2014), which makes it problematic to perform a good monitoring of the SST, mathematical models have also been used for predicting the sludge profile; see, for example, 1D model proposed by Diehl et al (2016) and 3D model proposed by Xanthos et al (2010).…”

Section: Introductionmentioning

confidence: 99%

Machine learning techniques for monitoring the sludge profile in a secondary settler tank

2019

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The aim of this paper is to evaluate and compare the performance of two machine learning methods, Gaussian process regression (GPR) and Gaussian mixture models (GMMs), as two possible methods for monitoring the sludge profile in a secondary settler tank (SST). In GPR, the prediction of the response variable is given as a Gaussian probability density function, whereas in the GMM the probability density function is built as a weighted sum of Gaussian distributions. In both approaches, a residual is calculated and a fault detection criterion is implemented via a recursive decision rule. As case study, GMM and GPR were tested using real data from a sensor measuring the suspended solids concentration as a function of the SST level in a wastewater treatment plant in Bromma, Sweden. Results suggest that GMM gives a faster response but is also more sensitive than GPR to changes during normal conditions.

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Steady-state analysis of activated sludge processes with a settler model including sludge compression

Cited by 18 publications

References 33 publications

Development and applications in computational fluid dynamics modeling for secondary settling tanks over the last three decades: A review

Development and applications in computational fluid dynamics modeling for secondary settling tanks over the last three decades: A review

An analysis of organic carbon removal in a two‐reactor cascade with recycle and a two‐reactor step‐feed cascade with recycle

Machine learning techniques for monitoring the sludge profile in a secondary settler tank

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