Evaluation of mathematical models for analyzing flocculent settling data

Je, Chung-Hwan; Kim, Kyung-Sub

doi:10.1002/ep.670210415

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…An earlier study by Je and Kim (2002) observed better performance of mechanistic models over empirical models under certain data ranges. The lack of fit of empirical models was later attributed to limited range as they failed to approximate data around the outer boundaries of the calibrated areas.…”

Section: Error Analysismentioning

confidence: 81%

“…The best fit of the literature based models was obtained from the San's model (Je and Kim, 2002). However, only the results from the modified model derived in this study are presented here.…”

Section: Error Analysismentioning

confidence: 98%

“…In Özer's model, the parameters a 1 , a 2 , a 3 , A 1 , A 2 , and A 3 = mechanistic fit parameters for the iso-percentile lines. The fitting parameters in San's model are a, b, and k. San's and Özer's have been shown to comply with the as assumptions stated in Equations 3 to 7 (Je et al, 2002).…”

Section: Introductionmentioning

confidence: 99%

“…However, the model is valid only when the following boundary conditions and assumptions are satisfied (Je et al, 2002): (a) Settling is only occurring only in the direction of velocity.…”

Section: Introductionmentioning

confidence: 99%

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Computational simulation of flocculent sedimentation based on experimental results

Ramatsoma

Chirwa

2012

Water Science and Technology

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Computerised interpolation algorithms as well as the empirical model for analysing the flocculent settling data were developed. A mechanistic semi-empirical model developed from fundamental physical principles of a falling particle in a viscous fluid was tested against actual flocculation column data. The accuracy of the mechanistic model was evaluated using the sum of the squared errors between the interpolated values (real values) and the model predictions. Its fitting capabilities were compared with Özer's model using nine flocculent data sets of which four were obtained from literature and the rest was actual data from the performed experiments. The developed model consistently simulated the flocculation behaviour of particles in settling columns better than Özer's model in eight of the nine data sets considered. It is recommended that the model's performance be further compared with other models like the Rule based and San's model. The errors due to the use of interpolated values when determining the performance of the empirical models need to be investigated. Furthermore, a three-way rather than two-way interpolation should now be achievable using the interpolation algorithm developed in this study thereby reducing the effects of interpolation bias. The above work opens the way to full automation of design of flocculation sedimentation basins and other gravitational particle separation systems which at present are designed manually and are susceptible to a wide range of human and random errors. Keywords: sedimentation, flocculation, total suspended solids, iso-percentile profiles, empirical modelling, automated design. INTRODUCTIONDuring the design of flocculent sedimentation tanks, data from settling column tests is interpreted by a graphical technique. Samples at different times and depths are analysed for suspended solids concentration and removal rate profiles are manually plotted as a function of time. From the graphs, one can predict or calculate the removal efficiency, overflow rate, and settling velocities. The results from the graphs are then used to determine the desired sedimentation tank parameters. The obvious shortcoming of this procedure is that no two individuals can end up with the exact same design and the process is in itself tedious. In addition to the problem of irreproducibility, the graphical method is sometimes misleading in the region of less variability typically at the beginning of the settling process (San, 1989).

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Section: Error Analysismentioning

confidence: 81%

Section: Error Analysismentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%

“…However, the model is valid only when the following boundary conditions and assumptions are satisfied (Je et al, 2002): (a) Settling is only occurring only in the direction of velocity.…”

Section: Introductionmentioning

confidence: 99%

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Computational simulation of flocculent sedimentation based on experimental results

Ramatsoma

Chirwa

2012

Water Science and Technology

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“…Traditionally, flocculent settling test data are analyzed with a graphical method involving isoremoval lines, but this method is time consuming (Je & Chang, 2004; Je & Kim, 2002; Ong, 1985), can lead to possible errors (Overcamp, 2006; Zanoni & Blomquist, 1975), and fails to take advantage of the entire data set (Rashid & Hayes, 2014). Several researchers (Berthouex & Stevens, 1982; Je & Kim, 2002; Özer, 1994; Rashid & Hayes, 2014; San, 1989) suggested curve fitting equations for isoremoval curves, but none of them reached a broad scientific consensus.…”

Section: Methodsmentioning

confidence: 99%

Struvite recovery efficiency using flocculation in batch and continuous settling systems for ammonia removal of mining wastewater

Pometti

Provost

Rancourt

et al. 2023

Water Environment Research

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An approach to remove ammonia from mining wastewater is to precipitate ammonia into struvite, and flocculation was proved to enhance settling of struvite flocs. But the current literature fails to consider flocculent properties of struvite flocs, and previous studies focused only on small volumes. This study evaluates the effect of ammonia concentration and height on removal efficiency of struvite flocs in a batch system and compares removal efficiency of struvite flocs between a batch and a pilot‐scale continuous settling process to evaluate the potential of using flocculation to recover struvite crystals as a stand‐alone method. Removal efficiency of struvite using flocculation is evaluated depending on depth in a batch system for two different ammonia concentrations (45 and 90 ppm) and in a continuous system for different flowrates. It is shown that a higher concentration promotes flocculation and enhances settling velocities of struvite flocs. The difference between the batch and the continuous processes for the same removal efficiency was significantly higher from what has been reported in the literature: in the continuous process, 89% of struvite flocs have been recovered with a surface overflow rate (SOR) of 1.8 m.h−1, whereas, for the same height, the same efficiency corresponds to SOR = 9 m.h−1 in the batch process. The fragile nature of struvite flocs is potentially responsible for such a difference. Practitioner Points Settling velocities of struvite flocs are highly dependant on concentration and depth. Removal efficiency are considerably higher with a batch settling process for the same surface overflow rate. Flocculation enable 89% of struvite fines to be recovered in a continuous settling process with a SOR of 1.8 m.hs−1.

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Comparison of mathematical methods for the evaluation of wastewater settleability by settling column tests

Carbone

Penna

Piro

2016

Environmental Technology

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Evaluation of mathematical models for analyzing flocculent settling data

Cited by 5 publications

References 6 publications

Computational simulation of flocculent sedimentation based on experimental results

Computational simulation of flocculent sedimentation based on experimental results

Struvite recovery efficiency using flocculation in batch and continuous settling systems for ammonia removal of mining wastewater

Comparison of mathematical methods for the evaluation of wastewater settleability by settling column tests

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