Maryam Astaraie-Imani

Reading Group Meeting

PhD thesis:

Modelling the Performance of an Integrated Urban Wastewater System under Future

Conditions

29 August 2013

Maryam Astaraie-Imani

BACKGROUND Aim INTEGRATED URBAN WASTEWATER SYSTEM (IUWS) IMPACT ANALYSIS

Sensitivity Analysis

OPTIMISATION OF THE IUWS PERFORMANCE Climate Change and Urbanisation Scenarios Operational Control Optimisation Model Design Optimisation Model Risk-based Optimisation Model

Summary of findings

2

BACKGROUND

BEng/BSc in Civil Engineering (1996-2001)

MEng/MSc in Water & Hydraulic Engineering (2004-2006)

Thesis Title: Risk-based Floodplain Management

PhD in Water Engineering (2008-2012)

Thesis Title: Modelling the performance of an Integrate Urban Wastewater System under future conditions

Associate Research Fellow in Safe & SuRe project (2013-2015)

3

Improving an Integrated Urban Wastewater System (IUWS) performance

under future climate change and urbanisation

aiming to maintain the quality of water in water recipients

4

SIMBASIMBA librarylibrary

5

Matlab/simulink based User friendly Capable of integrated modelling

of urban wastewater system

Sewer system Wastewater treatment plant (WWTP) River

Case StudyCase Study

6

SC7(Tank)

CSO discharge

Pump 1

Primary Clarifier

ReactorSecondary Clarifier

Storm Tank

Pump 2Waste Sludge

Ret

urn

Flo

w

Eff

luen

t

Return Sludge

Discharge

RiverReach 7 Reach 10

SC1

SC4(Tank)

SC3

SC5

SC6(Tank)

SC2(Tank)

Inflow

Sewer System Wastewater Treatment Plant

CSO

flow

Dispose

Semi-real

Norwich wastewater treatment plant

7

Impact analysis of climate change and urbanisation Impact analysis of climate change and urbanisation on the IUWS performanceon the IUWS performance

IUWS model input parameters Climate change parameters Urbanisation parameters Operational control parameters

IUWS model output parameters Dissolved Oxygen concentration (DO) Ammonium concentration (AMM)

Local sensitivity analysis One-at-a-time method (Tornado Graph)

Global sensitivity analysis Regional sensitivity analysis (RSA) Method

Climate change parameters

Rainfall depth increase (RD) Rainfall intensity increase (RI)

Urbanisation parameters

Per capita water consumption (PCW) Population increase (POP) Imperviousness increase (IMP) Ammonium concentration in DWF (NH4+)

Operational control parameters

Maximum outflow rate from the sewer system (i.e. last storage tank) (Qmaxout) Maximum inflow to the wastewater treatment plant (Qmaxin) Threshold at which the storm tank is triggered to be emptied (Qtrigst) Emptying flow rate of storm tank (Qempst) Return activated sludge is taken from the secondary clarifiers (QRAS)

8

IUWS model input parametersIUWS model input parameters

Parameter Unit Nominal value Value/Range

RD % 0 [10, 20, 30]

RI % 0 [10, 20, 30]

POP % 0 [4.5, 15]

IMP % 0 [5, 15]

PCW litre/person/day 180 [80, 260]

NH4+ mg/l 27.7 [20, 30]

Qmaxout m3/d 5× DWF* [3×DWF*, 8×DWF*]

Qmaxin m3/d 3× DWF* [2×DWF*, 5×DWF*]

Qtrigst m3/d 24192 [16416, 31104]

Qempst m3/d 12096 [6912, 24192]

QRAS m3/d 14688 [6912, 24192]

9

Sensitivity Analysis Sensitivity Analysis

One at a time method

Select one IUWS model input and change its value from default to upper or lower value in the considered range. Keep the other input parameter values at their nominal values.

Run the IUWS model and evaluate the relevant IUWS model outputs. Calculate the relative difference (percent change) for the analysed IUWS

model outputs relative to the BC. Rank the obtained relative differences in a descending order and identify

the most sensitive IUWS model input parameters.

10

Regional Sensitivity AnalysisRegional Sensitivity Analysis Identify the most important parameters from LSA Generate samples by using Latin Hypercube Sampling (LHS) Run the IUWS model Determine the behavioural (B) & non-behavioural (NB) groups of samples Provide the CDF of B & NB samples Kolmogorov-Smirnov (KS) test

11

LSA ResultsLSA Results

-100 -80 -60 -40 -20 0 20 40

Qempst

Qtrigst

QRAS

IMP

POP

NH4

RI

Qmaxin

PCW

Qmaxout

RD

12

Relative variation of DO concentration to the BC for minimum values of the IUWS model input parameters (%)

-10 0 10 20 30 40 50 60 70

Qmaxout

Qempst

Qtrigst

RI

QRAS

IMP

NH4

POP

RD

PCW

Qmaxin

Relative variation of AMM concentration to the BC for maximum values of the IUWS model input parameters (%)

RDRI

PCWIMPPOP

Qmaxout

Qmaxin

Qtrigst

GSA Results for AMM concentrationGSA Results for AMM concentration

13

3 4 5 6 7 80

0.2

0.4

0.6

0.8

1

Qmaxout(*27500,m3/d)3 4 5 6 7 8

0

0.2

0.4

0.6

0.8

1

Qmaxout(*27500,m3/d)

80 100 120 140 160 180 200 220 240 2600

0.5

1

PCW (lit/person/day)80 100 120 140 160 180 200 220 240 2600

0.5

1

PCW (lit/person/day)

BNB

Optimisation of the IUWS performanceOptimisation of the IUWS performance

14

Climate change and urbanisation scenariosClimate change and urbanisation scenarios

15

Objectives

Maximise the minimum DO concentration in the river Minimise the maximum AMM concentration in the river

Decision variables

Qmaxout , (m3/d)

Qmaxin , (m3/d)

Qtrigst, (m3/d)

Optimisation algorithm Modified MOGA-ANN algorithm (CCWI, 2011)

16

Operational control optimisation modelOperational control optimisation model

17

2.8 3 3.2 3.4 3.6 3.8 4 4.2 4.43

3.5

4

4.5

5

DO Concentration (mg/l)

AM

M C

once

ntra

tion

(mg/

l)

Ng=3000, N

d=50

NSGA-II

50 200 50063

64

65

66

67

68

69

Size of new data set

Rat

io o

f com

puta

tiona

l tim

e

r

educ

tion

(%)

Training set size 1000Training set size 2000Training set size 3000

18

Mod

ifie

d M

OG

A-A

NN

per

form

ance

Mod

ifie

d M

OG

A-A

NN

per

form

ance

Optimal Pareto fronts under climate Optimal Pareto fronts under climate change scenarioschange scenarios

19

20

Objectives

Maximise the minimum DO concentration in the river Minimise the maximum AMM concentration in the river

Optimisation algorithm Modified MOGA-ANN algorithm

21

Design optimisation modelDesign optimisation model

Increasing the storage capacity of whole the catchment

Design optimisation model decision variablesDesign optimisation model decision variables

22

Minimum storage capacity increase- coefficientMinimum storage capacity increase- coefficient

ScenarioMinimum increase-coefficient (%), (c)

Increased storage capacity (m3)

Cost (Million $), (C)

SCB 100 % 13,200 494,340

SCL1 675 % 89,100 1,219,800

SCL2 500 % 66,000 1,058,400

23

24

25

Qmaxout Qmaxin Qtrigst QST2 QST4 QST62

4

6

8

10

12

Ope

ratio

nal c

ontro

l par

amet

ers

Operational control parameters in SCLOperational control parameters in SCL11

ST7 ST2 ST4 ST60

20

40

60

80

100

Stor

age

Tank

's co

ntrib

utio

n-co

effic

ient

(%)

Design parameters in SCLDesign parameters in SCL11

Summary of the results from the design and operational control optimisation models

Operational control optimisation has the potential to improve the quality of water under the considered climate change scenarios.

Operational control optimisation under the combined climate change with urbanisation scenarios can improve the water quality indicators to some extent.

RD has more potential than RI in worsening the quality of water under future climate change.

The values of the urbanisation parameters (specifically PCW) are very decisive as water quality indicators.

Combination of urbanisation with climate change (in some extent) have the potential to intensify water quality deterioration.

Improving the system performance only by optimising the operational control is not adequate enough, to meet both economic and water quality criteria, under the examined climate change and urbanisation scenarios.

Considering the combined impacts of climate change and urbanisation for the system performance improvement, increases costs over just climate change impacts.

26

Risk-based improvement of the IUWSRisk-based improvement of the IUWS

Risk-based IUWS optimisation model objectives Minimising the risk of DO concentration failure Minimising the risk of un-ionised Ammonia concentration failure

Risk= Consequence × Probability of water quality failure

Risk-based IUWS optimisation model decision variables Operational control decision variables (similar as above) Design decision variables (similar as above)

Modified MOGA-ANN algorithm Uncertainty in urbanisation parameters

27

28

Consequence of Water Quality Failure

29

1 2 3 4 5 6

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

DO concentration (mg/l)

Con

sequ

ence

Empirical CDF of freshwater long term data for DO concentration (mg/l)

30

Probability of Water Quality Failure

Risk of Water Quality Failure

31

32

Decision variables of the operational control optimisation model

33

Qmaxout (*27500) Qmaxin (*27500) Qtrigst (*2400)2

4

6

8

10

12

14

Dec

isio

n va

riabl

e va

lue

Qmaxout (*27500) Qmaxin (*27500) Qtrigst (*2400) QST2 (*DWF) QST4 (*DWF) QST6 (*DWF)2

4

6

8

10

12

14

Dec

isio

n va

riabl

e va

lue

Operational control decision variables in the design optimisation model

ST7 ST2 ST4 ST60

10

20

30

40

50

60

70

80

90

Storage Tank

Dec

isio

n va

riabl

e va

lue

Design decision variables in the design optimisation model

Summary of the risk-based optimisation model results

Uncertainty of the urbanisation parameters under RD brings about greater risk to the IUWS than RI.

The risk of failures under the considered climate change and urbanisation parameters results in greater stress for DO than un-ionised Ammonia.

The duration and frequency of water quality failures are determining factors of the tolerable risk level for the health of aquatic life.

Improving the considered operational control of the IUWS in isolation did not show enough potential to reduce the risk of water quality failures to meet the tolerable risk levels.

Improving the design of the IUWS (in addition to the operational control) was required in this study to mitigate the risk of water quality failures.

Decisions about the tolerable level of risk are vital to determine the required strategy (ies) for the system improvement(s) in the future. Therefore, having comprehensive knowledge about the ecosystem under study is important for the planners to reduce the future unavoidable risks in their decisions.

34

35