Maryam Astaraie-Imani
description
Transcript of 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
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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)
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Improving an Integrated Urban Wastewater System (IUWS) performance
under future climate change and urbanisation
aiming to maintain the quality of water in water recipients
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SIMBASIMBA librarylibrary
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Matlab/simulink based User friendly Capable of integrated modelling
of urban wastewater system
Sewer system Wastewater treatment plant (WWTP) River
Case StudyCase Study
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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
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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)
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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]
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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.
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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
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LSA ResultsLSA Results
-100 -80 -60 -40 -20 0 20 40
Qempst
Qtrigst
QRAS
IMP
POP
NH4
RI
Qmaxin
PCW
Qmaxout
RD
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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
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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
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Climate change and urbanisation scenariosClimate change and urbanisation scenarios
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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)
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Operational control optimisation modelOperational control optimisation model
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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
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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
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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
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Objectives
Maximise the minimum DO concentration in the river Minimise the maximum AMM concentration in the river
Optimisation algorithm Modified MOGA-ANN algorithm
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Design optimisation modelDesign optimisation model
Increasing the storage capacity of whole the catchment
Design optimisation model decision variablesDesign optimisation model decision variables
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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
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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.
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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
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Consequence of Water Quality Failure
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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)
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Probability of Water Quality Failure
Risk of Water Quality Failure
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Decision variables of the operational control optimisation model
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Qmaxout (*27500) Qmaxin (*27500) Qtrigst (*2400)2
4
6
8
10
12
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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.
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