Maryam Astaraie-Imani

1. Reading Group MeetingPhD thesis:Modelling the Performance of an Integrated Urban Wastewater System under Future Conditions29 August 2013 Maryam Astaraie-Imani

2. Outlines 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

3. 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)

4. Aim Improving an Integrated Urban Wastewater System (IUWS) performance under future climate change and urbanisation aiming to maintain the quality of water in water recipients

5. SIMBAlibrary • Matlab/simulink based • User friendly • Capable of integrated modelling of urban wastewater system • Sewer system • Wastewater treatment plant (WWTP) • River

6. Case Study • Semi-real • Norwich wastewater treatment plant

7. Impact analysis of climate change and urbanisation on 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

8. IUWS model input parameters • 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)

10. 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.

11. Regional 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

12. LSA Results RD RI PCW IMP POP Qmaxout Qmaxin Qtrigst Relative variation of AMM concentration to the BC for maximum values of the IUWS model input parameters (%) Relative variation of DO concentration to the BC for minimum values of the IUWS model input parameters (%)

13. GSA Results for AMM concentration B NB

14. Optimisation of the IUWS performance

15. Climate change and urbanisation scenarios

16. Operational control optimisation model • 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)

18. Modified MOGA-ANN performance

19. Optimal Pareto fronts under climate change scenarios

21. Design optimisation model Increasing the storage capacity of whole the catchment • Objectives • Maximise the minimum DO concentration in the river • Minimise the maximum AMM concentration in the river • Optimisation algorithm • Modified MOGA-ANN algorithm

22. Design optimisation model decision variables

23. Minimum storage capacity increase- coefficient

25. Operational control parameters in SCL1 Design parameters in SCL1

26. 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.

27. Risk-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

28. Consequence of Water Quality Failure

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

30. Probability of Water Quality Failure Risk of Water Quality Failure

33. Decision variables of the operational control optimisation model Operational control decision variables in the design optimisation model Design decision variables in the design optimisation model

34. 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.

35. Thanks for the attention !