OPERATIONAL DECISION SUPPORT SYSTEM

Transcription

1 OPERATIONAL DECISION SUPPORT SYSTEM APPLIED TO THE LISBON COMBINED SEWER SYSTEM: THE CASE OF AN EXTREME RAINFALL EVENT Nuno Pimentel 1, Pedro Póvoa 1 and José Saldanha Matos 2 1 R&D Department, Águas de Portugal, S.A., Portugal 2 CERIS Civil Engineering Research and Innovation for Sustainability, IST-UL (School of Eng., University of Lisbon), Portugal

2 STRUCTURE OF THE PRESENTATION Introduction and Objectives The AdP Operational Decision Support System Alcântara Wastewater System DSS Demonstration for Extreme Events in Lisbon Conclusions

3 INTRODUCTION AND OBJECTIVES In Portugal, in the past years the water utilities have invested in monitoring equipment and SCADA Systems: More different data from several work fields in the Water sector to analyse; Growth of modelling and predicting tools. Brings new challenges to the water sector: Integrate and analyse all this multidisciplinary information without specialized technical work; Lack of human resources in the management utilities.

4 INTRODUCTION AND OBJECTIVES Climate Change Evidence: Extreme Precipitation Events are becoming more frequent. Also, combined with high tide levels in the Estuaries, originating Coastal Flooding in the drainage system. Source:

5 INTRODUCTION AND OBJECTIVES Sanitation Problems and Impacts: Damage of mechanical equipment and electrical installations; Uncontrolled tons of grit and sand Functional failure of network during storm events (5 hours to stabilize operation); Pumping Stations and WWTP out of service by flooding; Increased risks of CSO. Float flow limiter valve Small orifice flow limiter WWTP, 2009

6 INTRODUCTION AND OBJECTIVES Objective of the presentation: This paper presents the AdP Operational Decision Support System (DSS) that: Integrates technologies and methodologies in different areas (Precipitation forecast, data analysis, modelling integration, etc.) In order to: Improve efficiency in the wastewater system management Environment risk prevention Apply preventive procedures in face of possible occurrence of extreme events

7 THE ADP OPERATIONAL DECISION SUPPORT SYSTEM Wet weather Smart operation (data + model s) Be Inspired Awards Finalist (2012) Honour Award IWA Global Project Innovation Awards (2012) Predictive flood warning Predictive CSO impacts

8 THE ADP OPERATIONAL DECISION SUPPORT SYSTEM

9 THE ADP OPERATIONAL DECISION SUPPORT SYSTEM Visual Alarms Reports for the Flowmeters and Hydraulic models data Meteorological data from IPMA Vs. MM5 Forecast model

10 ALCÂNTARA WASTEWATER SYSTEM Covered Area: 6200 ha 500 km sewer system (mainly combined) and 25 km interceptors Dry Weather Flow: m 3 /day Alcântara WWTP: 3.3 m 3 /s (Dry Weather Flow) 6.6 m 3 /s (Wet Weather Flow) Divided in two zones: High area of Lisbon: Gravity Sewer (Caneiro of Alcântara) Riverside area of Lisbon: 11 Pumping Stations

11 Alcântara SCADA System (treatment process and energy); Flowmeter monitoring the drainage sewage from the high area of Lisbon; 2 rain gauges in the catchment area; SCADA System of all the 11 pumping stations; Quality spectrophotometry probe measuring the WWTP affluent; Buoy in Tagus Estuary measuring tide level and water quality; Hydraulic model for the drainage system and WWTP model for treatment process. ALCÂNTARA WASTEWATER SYSTEM Data gathered in the DSS for Alcântara management system:

12 ALCÂNTARA WASTEWATER SYSTEM Lisbon extreme precipitation events of 13 October of 2014: Duration of 8 hours Maximum precipitation intensity of 40.2 mm/h Total precipitation of 31.8 mm Alcântara downtown AdP Headquarters

13 DSS DEMONSTRATION FOR EXTREME EVENTS IN LISBON Objective: Hydraulic and precipitation model vs. Real-time online data Estimate the possibility of CSO occurrence in the drainage system: Occurrence of CSO: When the total volume of sewage during an precipitation event exceeds the WWTP maximum capacity (for the scenario of 3.3 m 3 /s or 6.6 m 3 /s) Evaluate the adjustment between the hydraulic model and the real-time Four relevant precipitation events: Date Duration [hours] PI Max. [mm/h] Event TP [mm] 12/10/ ,8 19,6 13/10/ ,2 31,8 26/11/ ,4 22,1 27/11/ ,1

14 DSS DEMONSTRATION FOR EXTREME EVENTS IN LISBON Date Max. Flow [m 3 /h] Volume [m 3 ] CSO [3.3 m 3 /s] CSO [6.6 m 3 /s] 12/10/ /10/ /11/ /11/ Date Max. Flow [m 3 /h] Real-time online data Hydraulic and precipitation Forecast model data Volume [m 3 ] CSO [3.3 m 3 /s] CSO [6.6 m 3 /s] 12/10/ /10/ /11/ /11/

15 DSS DEMONSTRATION FOR EXTREME EVENTS IN LISBON Date Max. Flow error [%] Volume error [%] Flow MAPE [%] 12/10/ % 43% % 13/10/ % -57% 52.86% 26/11/ % -23% 38.54% 27/11/ % -10% 45.33% Hydraulic Model: Considerable differences. One of the main reasons is MAPE being sensitive to outliers Considering the Dry Weather period the MAPE Forecast Model Vs. the Real-time data: Mean Absolute Percentage Error (MAPE) (Hydraulic Model) Mean Absolute Scale Error (MASE) (Precipitation Forecast Model) Precipitation Forecast Model: MASE value much lower than 1 which indicates a good adjustment decreases to almost 40% Despite the results visually it has a good adjustment and generally predicted the CSO

16 CONCLUSIONS Conclusions: Despite some differences the forecast models were able to fit to real-time monitoring data during some extreme events; Good prediction in the possible occurrence of CSO; The DSS proved to be a useful tool in the wastewater system management even in case of extreme weather events. Future developments in the DSS: Recalibration of the hydraulic model for more recent rain events data; Risk management tool and alert service, relying on real time assessment and prediction of the dispersion of effluent discharges into receiving water bodies.

17 THANK YOU FORYOUR TIME! Nuno Pimentel 1, Pedro Póvoa 1 and José Saldanha Matos 2 n.pimentel@adp.pt p.povoa@adp.pt