Prediction of the terminal settling velocity of natural particles applied in drinking water treatment processes (PPT)

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Delft University of Technology

Kramer, Onno Publication date 2017

Document Version Final published version Citation (APA)

Kramer, O. (2017). Prediction of the terminal settling velocity of natural particles applied in drinking water treatment processes (PPT). 5th IWA Young Water Professionals BeNeLux conference, Ghent, Belgium. Important note

To cite this publication, please use the final published version (if applicable). Please check the document version above.

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Prediction of the terminal settling velocity of natural particles

applied in drinking water treatment processes

Onno Kramer

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Prediction of the terminal settling velocity of natural particles applied in drinking water treatment processes

Waternet

Amsterdam Water Cycle Company

- Drinking Water

- Waste Water

- Water Systems

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Drinking water production processes

- Filtration

- Fluidisation

- Sedimentation

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Drinking water softening

Chemical CaCO

₃

crystallisation (caustic soda)

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1.0 mm

0,3 mm

Seeding material

→

marble pellets

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1.0 mm

0,3 mm

Seeding material

→

marble pellets

500 t/y raw material

→

8000 t/y waste material

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Drinking water softening

(Waternet CO

₂

neutral in 2020)

1.0 mm

0,3 mm

Seeding material

→

marble pellets

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Drinking water softening

(circular economy)

Seeding material

→

marble pellets

→

grinding

→

sieving

Re-using

Valorisation

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Research aim (after process changes)

Investigating the hydraulic behaviour of imperfectly

round spheres in drinking water treatment processes

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A.G. Jones (2002) Crystallization Process Systems

(1/2) Literature study (drag versus Reynolds)

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0,1 1,0 10,0 100,0 1 10 100 1.000 10.000

Dr

ag

co

ef

fic

ie

nt

Reynolds terminal number

Stokes Newton Brown-Lawler

Stokes

(laminar)

Newton

(turbulent)

Empirical models

(intermediate)

(1/2) Literature study (drag versus Reynolds)

- Terminal settling theory

Laminar (Stokes)

Turbulent (Newton)

- Many prediction models

Intermediate regime

- For prefect round spheres

- +/-5% accuracy

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0,1 1,0 10,0 100,0 1 10 100 1.000 10.000

Dr

ag

co

ef

fic

ie

nt

Reynolds terminal number

Stokes Newton Brown-Lawler

Stokes

(laminar)

Newton

(turbulent)

Empirical models

(intermediate)

(1/2) Literature study (drag versus Reynolds)

- Brown-Lawler (2003)

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% 5% 10% 15% Fit o f d rag co rr ela tio ns [% ] Range Analysis [%] Brown-Lawler (2003) Fair-Geyer (1954) Flemmer-Banks (1986) Khan-Richardson (1987) Haider-Levenspiel (1989) Turton-Levenspiel (1986!) Clift et al (1978)

N = 480

20%

(

)

t t D C Re 8710 1 407 . 0 Re 15 . 0 1 Re 24 0.681 + + + = η ρ_f _p _t t v d = Re

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0,1 1,0 10,0 100,0 1 10 100 1.000 10.000

Dr

ag

co

ef

fic

ie

nt

Reynolds terminal number

Stokes Newton

Brown-Lawler Rapid filter sand (242) Crystal sand (15) Garnet (97)

Garnet pellets (626) Calcite pellets (785) Calcite IT (45) Calcite UK (32) Glass pearls (187) Distortion layer (4) IEX balls (6)

(2/2) Experimental data

N = 1046

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(2/2) Experimental data

0,1 1,0 10,0 100,0 1 10 100 1.000 10.000

Dr

ag

co

ef

fic

ie

nt

Reynolds terminal number

Stokes Newton

Brown-Lawler Rapid filter sand (242) Crystal sand (15) Garnet (97)

Garnet pellets (626) Calcite pellets (785) Calcite IT (45) Calcite UK (32) Glass pearls (187) Distortion layer (4) IEX balls (6)

N = 2039

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Explanation of deviation

Deviation caused by variation in:

• Gravitational acceleration

+/- 0.1%

• Specific particle density

+/- 0.4%

• Fluid viscosity and density (temperature) +/- 1.0%

• Particle size (sieve diameter)

+/- 10%

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Explanation of deviation

Deviation caused by variation in:

• Gravitational acceleration

+/- 0.1%

• Specific particle density

+/- 0.4%

• Fluid viscosity and density (temperature) +/- 1.0%

• Particle size (sieve diameter)

+/- 10%

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Take home messages (optional)

Use

all

your data

Be careful with filtering or eliminating less accurate data

Before fitting your data, try to explain derivative deviation

From deviation useful information can be retrieved

Take deviation intro account when predicting or designing processes

Use proven models:

*** Water treatment granular particles

Hydraulic behaviour of round spheres can accurately be calculated

Natural particles behave differently then perfectly round spheres

The measured deviation can decisively be explained

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1

_{Waternet Drinking Water Department}

2

_{HU University of Applied Sciences Utrecht, Institute for}

Onno Kramer

1,2,3

Eric Baars

1

Peter de Moel

3

Wim van Vugt

2

Leon Kors

1

Jan Peter van der Hoek

1, 3

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