Wastewater treatment models: Current developments & trends

1

Wastewater treatment models: Wastewater treatment models: Wastewater treatment models: Wastewater treatment models:

Current developments & trendsCurrent developments & trendsCurrent developments & trendsCurrent developments & trends

AnvändergruppAnvändergruppAnvändergruppAnvändergruppmodelleringmodelleringmodelleringmodellering ARVARVARVARV

Linköping, SwedenLinköping, SwedenLinköping, SwedenLinköping, Sweden

26 August 201426 August 201426 August 201426 August 2014Peter VANROLLEGHEMCanada Research Chairin Water Quality ModellingTable of ContentsTable of ContentsTable of ContentsTable of Contents

� Use of models in North-America

� New developments and trends

� Micropollutants

� Greenhouse gases

� Suspended solids

� Resource recovery (physicochemical models)

2

2

Use of Use of Use of Use of modelsmodelsmodelsmodels in in in in NorthNorthNorthNorth----AmericaAmericaAmericaAmerica

� History:� 70s: North-America led the development

� 80s-90s: Europe moved into practical application

� 00s: North-America moved further

� 10s: Renewed interest in Europe

� Application:� Design

� Upgrade

� Process optimization – Control development

� Operation support

3

Use of Use of Use of Use of modelsmodelsmodelsmodels in NA for designin NA for designin NA for designin NA for design

� All larger consulting companies use it

� Check guideline-based steady state design

� Direct design (special configurations)

� Capacity evaluation:

� Aeration capacity (blower size)

� Wet weather impact evaluation (special operations)

� Commercial element: good practice

4

3


� Probabilistic design

5


� Probabilistic versus traditional design

6

4

Probabilistic influent generatorProbabilistic influent generatorProbabilistic influent generatorProbabilistic influent generatorRainfall (Markov chain exponential model)DWF (Multivariate AR model)

Influenttime series7

Probabilistic influent generatorProbabilistic influent generatorProbabilistic influent generatorProbabilistic influent generator

� Daily rainfall series

Jan Apr Jul Oct Jan0

5

10

15

20

Time (day)

Ra

infa

ll In

ten

sity (

mm

/da

y)

8

5


� Dry weather flow - average

9

00:00 04:00 08:00 12:00 16:00 20:00 00:00

1500

2000

2500

Time (hour)

Flo

w (

m3

/hr)

Average daily flow pattern

Fourier approximation


� Dry weather flow - variability

10

00:00 04:00 08:00 12:00 16:00 20:00 00:00

1000

2000

3000

Time (hour)

Flo

w (

m3/h

our)

realization1

realization2

realization3

6


� Time series of 1 week of generated dataRainfall (Markov chain exponential model)DWF (Multivariate AR model)

WWFRAIN FLOW

COD_S

COD_tot

TSS NH4

RAIN FLOW

COD_S

COD_tot

TSS NH4

RAIN FLOW

COD_S

COD_tot

TSS NH4

11


� Use in probabilistic design

12

Stochastic input provided by influent generatorOther sources of uncertainty :- model parameters- wastewater composition Probability of compliance with effluent standards

7

Table of ContentsTable of ContentsTable of ContentsTable of Contents



� Micropollutants




13

MicropollutantsMicropollutantsMicropollutantsMicropollutants

� Increased interest since 2000

→→→→ Priority pollutants in EU WFD

� Challenges:� The analysis of low concentrations

� The sheer number of chemicals

� The diversity of impacts

14

Feminization of male fish by endocrine disruptorsCrossed beak in birds caused by chemicals*Joanne Parrott*US EPA

8

MicropollutantsMicropollutantsMicropollutantsMicropollutants

� Increased interest since 2000

→→→→ priority pollutants in EU WFD

� Challenges:� The analysis of low concentrations

� The sheer number of chemicals

� The diversity of impacts

� The diversity of processes affecting them

� Studies at level of WWTP, but also at level of

integrated urban wastewater system (IUWS)

15

16

Unit model

Processes

Sew

er

Sto

rmw

ate

r unit

(wate

r)

Sto

rmw

ate

r unit

(sedim

ents

)

Prim

ary s

ettlin

g

Activ

ate

d s

ludge

tank

Secondary s

ettlin

g

Slu

dge th

ickener

Slu

dge a

naero

bic

dig

este

r

Slu

dge d

ew

ate

ring

Riv

er (w

ate

r)

Riv

er (s

edim

ents

)

Physical processes

Sedimentation + + + + + +

Resuspension + + +

Volatilization + + + + + + + + +

Sediment-water exchange + +

physicochemical

Adsorption-desorption + + + + + + + + + +

Hydrolysis + + + + + + + +

Photolysis + +

Biological

Aerobic biodegradation + + + + + + + + +

Anoxic biodegradation + + + + + + + + + +

9

17

Process MP properties Other relevant parameters

Physical processes

Sedimentation and

resuspension-

Water depth, bottom shear stress,

critical shear stress, erodibility

constant

VolatilizationHenry’s law constant, molecular

weight

Water depth, wind speed, water

currents and temperature

Sediment-water exchange Molecular weight Sediment porosity

Physicochemical

Adsorption-desorption Partition coefficient (kd or kOC) TSS concentration, organic fraction

HydrolysisFirst-order degradation rate (or half

life)pH, temperature

Photolysis Half-lifeLight intensity, water depth, water

pollution

Biological

Aerobic biodegradation Half-life Oxygen concentration, temperature

Anoxic biodegradation Half-lifeOxygen/nitrate concentration,

temperature

MicropollutantsMicropollutantsMicropollutantsMicropollutants: A case : A case : A case : A case studystudystudystudy

18

� Substance flow analysis (g/d)

10

19Vezzaro et al. (2014) Environ. Modelling & Software 53, 98-111Table of ContentsTable of ContentsTable of ContentsTable of Contents



� Micropollutants




20

11

Wastewater utility GHGWastewater utility GHGWastewater utility GHGWastewater utility GHG

� Greenhouse gases in wastewater systems:

� CO2 (Biodeg., energy, chemicals) 1 CO2eq� CH4 (Anaerobic digestion) 18 CO2eq� N2O (Nitrogen removal) 300 CO2eq

21


22

12

GHG from sewer systemsGHG from sewer systemsGHG from sewer systemsGHG from sewer systems

� CH4 formation

in rising mains

23Guisasola et al. (2009) Water Res. 43: 2874-2884GHG in sewer systemsGHG in sewer systemsGHG in sewer systemsGHG in sewer systems

� CH4 formation in gravity sewers (with O2 transfer)

24

13

What can we do?What can we do?What can we do?What can we do? Add chemicals!Add chemicals!Add chemicals!Add chemicals!

� Chemicals used for sulfide control (Brisbane: 6 M$/yr repair � 1 M$/yr chemical addition)also reduce methane formation

25Zhang et al. (2009) Water Res 43(17), 4123methane production rates

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

with Fe3+ without Fe3+

mg

/L.h

r

Day

-7

Day

-3

Day

-1

Wee

k 1

Wee

k 4

Wee

k 5

Wee

k 10

Wee

k 13

0.0

2.0

4.0

6.0

0 m (Wet well)

828 m (Downstream)

Meth

an

e (m

g C

H4/L

)

Acidified nitrite was added in the sewer intermittently

at 100 mg N/L during Day 0–2 (for 33 hours)

What can we do?What can we do?What can we do?What can we do? Add chemicals!Add chemicals!Add chemicals!Add chemicals!

14


27

GHG GHG GHG GHG emissionsemissionsemissionsemissions fromfromfromfrom WWTPWWTPWWTPWWTP

28282828Scope 1Scope 1Scope 1Scope 1DIRECTScope 2Scope 2Scope 2Scope 2INDIRECT Scope 3Scope 3Scope 3Scope 3INDIRECTBiomass respirationBiomass respirationBiomass respirationBiomass respirationBOD oxidationBOD oxidationBOD oxidationBOD oxidationCredit nitrificationCredit nitrificationCredit nitrificationCredit nitrificationNNNN2222O denitrificationO denitrificationO denitrificationO denitrificationSlude processingSlude processingSlude processingSlude processingSludge disposal (agriculture)Sludge disposal (agriculture)Sludge disposal (agriculture)Sludge disposal (agriculture)

Production ofpurchased materialsPurchased electricity Carbon additionCarbon additionCarbon additionCarbon additionNet Power consumptionNet Power consumptionNet Power consumptionNet Power consumption

15

EvaluationEvaluationEvaluationEvaluation of GHG of GHG of GHG of GHG emissionsemissionsemissionsemissions

� Different approaches to estimate GHG emissions:

� Empirical factors:• e.g. IPCC, 2006; LGO, 2008; NGER, 2008� Simple comprehensive models:• e.g. Cakir and Stenstrom, 2005; Monteith et al., 2005; Bridle et al., 2008; Foley et al., 2009� Dynamic deterministic models:• ASMG1 (Guo & Vanrolleghem, 2014) � N2O• ADM1 (Batstone et al., 2002) � CH4

29

+ complexityBSM2G benchmarking platformGuo & Vanrolleghem (2014) Bioprocess Biosyst. Eng., 37, 151-163.


30

16


31Corominas et al. (2012) Biotechnol. Bioeng., 109, 2854-2863

Breakdown of GHG emissions ((((kg CO2e·m-3)))) No controlNo controlNo controlNo control Yes controlYes controlYes controlYes control %%%%Bio-treatment GHG emissions 0.4510.4510.4510.451 0.3760.3760.3760.376 ----17171717Biomass respiration 0.179 0.178 -1BOD oxidation 0.212 0.212 0Credit nitrification -0.168 -0.167 -1N2O emissions 0.228 0.152 -33Sludge processing GHG emissions 0.2310.2310.2310.231 0.2310.2310.2310.231 0000Net power GHG emissions 0.0000.0000.0000.000 ----0.0380.0380.0380.038 ----Power 0.311 0.272 -13Credit power GHG emissions -0.311 -0.310 0Embedded GHG emissions from chemical use 0.0990.0990.0990.099 0.0990.0990.0990.099 0000Sludge disposal and reuse GHG emissions 0.1930.1930.1930.193 0.1930.1930.1930.193 0000� Comparison of no controlno controlno controlno control and yes control yes control yes control yes control

(DO control in aerobic reactors, DO = 2mg·L-1)Benchmarking control Benchmarking control Benchmarking control Benchmarking control strategiesstrategiesstrategiesstrategies

32

17

Benchmarking control Benchmarking control Benchmarking control Benchmarking control strategiesstrategiesstrategiesstrategies

33

EQI (kg pollution day-1)

7000 8000 9000 10000 11000 12000 13000 14000 15000

OC

I

10000

12000

14000

16000

18000

20000

22000

24000

A1

A2A5

A4A12

A13

A3

A9

A16

A11

A17

A10 A7

A8

A6A15

A14

1.0

1.5

2.0

2.5

3.0

3.5

800010000

1200014000

160009000

12000

15000

18000

21000

24000

Avo

ided

- in

du

ce

d im

pa

ct

EQI

OC

I

A10

A11 A16

A9A17

A7A8

A6

A15

A14

A5A4

A12

A13A3

A2A1New dimension: Greenhouse gases$ Water quality $

� Overall result of our studies so far:� Compromise between:

� Effluent quality� Treatment costs� GHG emissions

Flores-Alsina et al. (2014) Sci. Total Environ., 466-467, 616-624.Wastewater utility GHGWastewater utility GHGWastewater utility GHGWastewater utility GHG

34

18

GHG emissions from WW utilityGHG emissions from WW utilityGHG emissions from WW utilityGHG emissions from WW utility

35

-5000

0

5000

10000

15000

20000

CO

2E

qu

iva

len

ts (

kg

/d)

open_loop_Qcarbon_2 open_loop_Qcarbon_4 Scenario1DO_2 Scenario1DO_1.3 Scenario2DO_1.3/1.7 Scenario2DO_1.5/1.5

Biotreatment

N2O equivalent

Power

(generated)

Chemical use

Total plant emission

Sewer

Total system emission

Aeration

Power

(consumed) 8 %25 %Guo et al. (2012) Towards …Wat. Sci. Tech., 66(11), 2483-2495.Table of ContentsTable of ContentsTable of ContentsTable of Contents



� Micropollutants




36

19

Suspended solids Suspended solids Suspended solids Suspended solids –––– the sourcethe sourcethe sourcethe source

� Households + run-off from catchment

+ resuspension from sewer sediments

37Tik et al. (2014)Proc. Internat. Conf. Urban Drainage (ICUD2014)

Suspended solids Suspended solids Suspended solids Suspended solids –––– the sourcethe sourcethe sourcethe source

� Ambition: Integrated water quality simulation

38

WESTTik et al. (2014)Proc. Internat. Conf. Urban Drainage (ICUD2014)

20

The future WWTP The future WWTP The future WWTP The future WWTP �� WRRFWRRFWRRFWRRF

39

CEPTCEPTCEPTCEPTBiogasBiogasBiogasBiogas, N, P, N, P, N, P, N, P

Bachis et al. (2014) Proceedings WWTmod2014

�Adaptation of model of Bachis et al. (2012) incorporating 5 particle classes � 10 layers�Calibrated with field data

-

+

+

+

+

-

-

-

-

+

-

+

-

-

+

-

+

-

1

2

4

5

10

Qin

Qover

Qunder

VsH2OPrimary Primary Primary Primary clarifier modelclarifier modelclarifier modelclarifier model

Bachis et al. (2012) IWA Particle Separation Conference

� ’PSVD’ model

� Particle Settling Velocity

Distribution

� No size, density, size !

� Developed for storm tanks

40

21

PSVD model PSVD model PSVD model PSVD model –––– ViCAsViCAsViCAsViCAs mmmmethodologyethodologyethodologyethodologyViCAs (Settling Velocity in Sanitation), Chebbo&Gromaire, 2009

41

PSVD model PSVD model PSVD model PSVD model –––– ViCAsViCAsViCAsViCAs mmmmethodologyethodologyethodologyethodologyViCAs (Settling Velocity in Sanitation), Chebbo&Gromaire, 2009

42

22

PSVD model PSVD model PSVD model PSVD model –––– ViCAsViCAsViCAsViCAs mmmmethodologyethodologyethodologyethodology

43

Not all particles settle over the same column height ==> Mass not directly associated to a class of velocity

PSVD model PSVD model PSVD model PSVD model –––– ViCAsViCAsViCAsViCAs mmmmethodologyethodologyethodologyethodology

44

Not all particles settle over the same column height ==> Mass not directly associated to a class of velocity46% TSSVs < 1 m/h54% TSSVs > 1 m/h

23

PSVD model PSVD model PSVD model PSVD model –––– ViCAsViCAsViCAsViCAs methodologymethodologymethodologymethodology

� ViCAs allows modelling

chemically enhanced primary treatment (CEPT)

45

Time (d)

3 4 5 6 7 8 9

Flo

w ra

te (m

3/d)

5000

10000

15000

20000

25000

30000

35000

40000

Concentr

ation (

g/m

3)

100

200

300

400

500

600

700

800

Time (d)

3 4 5 6 7 8 9

Co

nce

ntr

atio

n (

g/m

3)

50

100

150

200

250

300

Simulated TSS

Observed TSS

Primary clarifier: CalibrationPrimary clarifier: CalibrationPrimary clarifier: CalibrationPrimary clarifier: Calibration

Lessard & Beck (1988)Journal of Environmental EngineeringTS

S

Norwich WWTP, Norwich WWTP, Norwich WWTP, Norwich WWTP, EnglandEnglandEnglandEnglandFlow rate (1,000 m3/d) 10 - 35Overflow rate range (m/h) 0.6 – 2Shape Circular

TS

S

46

24

Time (d)

1.0 1.2 1.4 1.6 1.8 2.0

Concentr

ation (

g/m

3)

0

20

40

60

80

100

120

Eastern Quebec WWTP

Time (d)

1.0 1.2 1.4 1.6 1.8 2.0

Flo

w ra

te (m

3/h)

1.0e+5

1.5e+5

2.0e+5

2.5e+5

3.0e+5

3.5e+5

4.0e+5

4.5e+5

Co

nce

ntr

atio

n (

g/m

3)

100

120

140

160

180

200

220

240

260

Primary clarifier: CalibrationPrimary clarifier: CalibrationPrimary clarifier: CalibrationPrimary clarifier: CalibrationEasternEasternEasternEastern QuebecQuebecQuebecQuebec WWTP, CanadaWWTP, CanadaWWTP, CanadaWWTP, CanadaFlow rate range (1,000 m3/d) 120 - 470Overflow rate range (m/h) 1 – 3.7Shape RectangularT

SS

TS

S

April 9th 2010

47

2.0

Time (d)

1.0 1.2 1.4 1.6 1.8 2.0

0

20

40

60

80

100

120

Eastern Quebec WWTP

Time (d)

1.0 1.2 1.4 1.6 1.8 2.0

1e+5

2e+5

2e+5

3e+5

3e+5

4e+5

4e+5

5e+5

5e+5

Con

ce

ntr

atio

n (

g/m

3)

0

100

200

300

400

Primary clarifier: Primary clarifier: Primary clarifier: Primary clarifier: ValidationValidationValidationValidation

TS

S C

once

ntr

atio

n (

g/m

3)

TS

S

EasternEasternEasternEastern QuebecQuebecQuebecQuebec WWTP, Canada WWTP, Canada WWTP, Canada WWTP, Canada Flow rate range (1,000 m3/d) 120 - 470Overflow rate range (m/h) 1 – 3.7Shape RectangularApril 17th 2010

Flo

w rate (m

3/d)

48

25

DWDWDWDW

2 PSVD

WWWWWWWW

2 PSVD

Integrated system evaluationIntegrated system evaluationIntegrated system evaluationIntegrated system evaluationInputsInputsInputsInputs

49

12 24

Flo

w ra

te (m

3/h)

0

2e+1

4e+1

6e+1

8e+1

1e+2

1e+2

1e+2

Co

nce

ntr

atio

n (

g/m

3)

0

20

40

60

80

100

12 24

Flo

w ra

te (m

3/h)

0

2e+1

4e+1

6e+1

8e+1

1e+2

1e+2

1e+2

Co

nce

ntr

atio

n (

g/m

3)

0

20

40

60

80

100

Emptying impact period on primary clarifier

Time (h)

Integrated system evaluation Integrated system evaluation Integrated system evaluation Integrated system evaluation Primary clarifier effluent compositionPrimary clarifier effluent compositionPrimary clarifier effluent compositionPrimary clarifier effluent composition

Q WWF

Q DWF

TSS 1

TSS 2

TSS 3

TSS 4

TSS 5

DWDWDWDW

WWWWWWWWDWDWDWDW

24 ton

WWWWWWWW

46 ton

TSS 2

TSS 4

TSS 5

140

120

100

80

60

40

20

0

140

120

100

80

60

40

20

0

Flo

w rate (1

03.

m3/h

)F

low

rate (10

3.m

3/h)

50

26

Integrated system evaluation Integrated system evaluation Integrated system evaluation Integrated system evaluation Primary clarifier effluent (Dry Weather)Primary clarifier effluent (Dry Weather)Primary clarifier effluent (Dry Weather)Primary clarifier effluent (Dry Weather)

0102030405060Class1 Class2 Class3 Class4 Class5

INLETOUTLETTSSmass (ton)

51

Integrated system evaluation Integrated system evaluation Integrated system evaluation Integrated system evaluation Primary clarifier effluent (Wet Weather)Primary clarifier effluent (Wet Weather)Primary clarifier effluent (Wet Weather)Primary clarifier effluent (Wet Weather)

0102030405060Class1 Class2 Class3 Class4 Class5

INLETOUTLETTSSmass (ton)

52

27




� Micropollutants




53

““““WurfsWurfsWurfsWurfs””””

� Water resource recovery facility (WRRF)

54Verstraete & Vlaeminck (2011)Int. J. Sust. Dev. World Ecol., 18, 253-264.

28

Resource recovery processesResource recovery processesResource recovery processesResource recovery processes

� Stripping (NH3, fatty acids)

� Precipitation (struvite)

� Filtering (paper fibers)

� Extraction (PHA)

� Ion exchange (NH4+)� Reverse osmosis (H2O)

� Phase separation (butanol)

� Pyrolysis, gasification, incineration (energy)

� Chemically enhanced primary treatment (COD)

55

All

physico-

chemical

unit

processes

Modelling Modelling Modelling Modelling

physicochemical processesphysicochemical processesphysicochemical processesphysicochemical processes

� We’ve done it

simply:

� Aeration: Kla (Csat-C)

� pH: f(pKa, TAN, Alk, …)

� Precipitation: MeOH/MeP

� Membrane: J = TMP/μ.(Rm+Rf+Rc)56

29



� We have to do it

differently:

Temperature:

57Fernandez T., Grau P., Beltran S., Ayesa E. (2014)Water Res., 60, 141-155.Modelling Modelling Modelling Modelling


� We have to do it differently:

Gas exchange:

58Fernandez T., Grau P., Beltran S., Ayesa E. (2014)Water Res., 60, 141-155.

30




Precipitation:

59




Precipitation:

It gets a

little crowded

in wastewater

60

Mg2+ HCO3-Na+ Ca2+Mg2+SO42- Cl- K+Na+ CO32-HPO42-CaCO3 (aq)CaHCO3+MgHCO3+, CaHPO4 (aq)

CaSO4 (aq)CaOH+

MgSO4 (aq)NaHPO4-NaCO3- NaHCO3 (aq)MgH2PO4+ CaAc+

NaAc (aq)NaSO4-

31




Precipitation:

61




Precipitation:

Ion pairs

increase

solubility

62

32




Precipitation:

Ionic

strength

increases

solubility

63


physicochemical physicochemical physicochemical physicochemical processes in WRRFsprocesses in WRRFsprocesses in WRRFsprocesses in WRRFs

6464Céline Vaneeckhaute (2014) PhD thesis, Université Laval, in preparation

33



65

Model Model Model Model

stiffnessstiffnessstiffnessstiffness ! ! ! !

65



� Two options to solve dynamic models:

66

1: ODEAll reactions: Ordinary differential equations (ODE) 2: DAESlow reactions: differentialequations (ODE) Fast reactions: algebraic equations calculated at each iteration stepTailored code to solve water chemistry External software tool (PHREEQC)

34

Setting up a reduced PCM modelSetting up a reduced PCM modelSetting up a reduced PCM modelSetting up a reduced PCM model

67

I. Physicochemical

component

selection II. Speciation

calculation

III+IV. Selection of

species/reactions

⟹ reduced model

SO4 NH4 H PO4Na K Ca Mg CO3Fe Cl Al N2 HS

Ac Pr Bu Va

PHREEQC/ PHREEQC/

MINTEQC

73 species 12 acid-base reactions43 ion-pairing reactions22 precipitation reactions7 gas-liquid reactions Corrections:- Ion activity- Temperature ± 253 species

Modelling WRRFs Modelling WRRFs Modelling WRRFs Modelling WRRFs –––– ADM1.5ADM1.5ADM1.5ADM1.5

� To properly model P in WRRFs,

we need to extend ADM1 (only COD & N):

� Consider Fe and its interaction with Sulphide (FeS!)

� Phosphate fate, including PAO’s

� P-release from organics

68

35

ADM1 Extension 1: ADM1 Extension 1: ADM1 Extension 1: ADM1 Extension 1: SulfurgenesisSulfurgenesisSulfurgenesisSulfurgenesis

� Model of Knobel & Lewis (2002)

69Knobel & Lewis (2002) Water Research, 36(1), 257-265.

ADM1 Extension 1: ADM1 Extension 1: ADM1 Extension 1: ADM1 Extension 1: SulfurgenesisSulfurgenesisSulfurgenesisSulfurgenesis

� Model of Knobel & Lewis (2002)

� 4 Types of bacteria

� Electron acceptor: SO42-� Electron donor & carbon source for growth:• Pro, Bu, Ac• Donor = H2 and carbon source = CO2� Inhibition factor H2S included

70Knobel & Lewis (2002) Water Research, 36(1), 257-265.

36

ADM1 Extension 2: HydrolysisADM1 Extension 2: HydrolysisADM1 Extension 2: HydrolysisADM1 Extension 2: Hydrolysis

� Incorporation in disintegration/hydrolysis:

release of P, K and S, based on the

composition of bacterial cells

71

ADM1 Extension 3: EBPRADM1 Extension 3: EBPRADM1 Extension 3: EBPRADM1 Extension 3: EBPR

� PAO’s (heterotrophs with anaerobic metabolism)

� Poly-P accumulation in cells (1-2%P → 5-7%P)

� Reactions in AD (Ikumi, 2011):

� Release of polyphosphate (PP) (+ release of K, Ca and Mg) with uptake of acetate by PAOs while they are still alive� Maintenance by hydrolysis of PP� Decay of PAOs� Hydrolysis of poly-hydroxy-alkanoate (PHA) when PAOs die

72

(Last supper ;-)

Ikumi (2011) PhD thesis, University of Cape Town.

37

Current state of PCM of WRRFsCurrent state of PCM of WRRFsCurrent state of PCM of WRRFsCurrent state of PCM of WRRFs

73

PhreeqCFast reactions:- Selectivemodel code- Reduceddata base ModelicaPhysico-chemicalslow transfer

ModelicaBiochemicalslow transfer (ADM 1)Tornado Simulation results√ √

√

√ √

√

√

√ Alternative simulation results (PhreeqC)CheckOn-goingExperimentaldata√

ModelModelModelModel----based optimizationbased optimizationbased optimizationbased optimization

of resource of resource of resource of resource recovery trains in WRRFsrecovery trains in WRRFsrecovery trains in WRRFsrecovery trains in WRRFs

7474Céline Vaneeckhaute (2014) PhD thesis, Université Laval, in preparation

38




� Micropollutants




75

AcknowledgementsAcknowledgementsAcknowledgementsAcknowledgements

76

Canada Research Chairin Water Quality ModellingOtto

Mønsted

Wastewater treatment models: Current developments & trends

Documents

Transcript of Wastewater treatment models: Current developments & trends