Effective placement of sensors for efficient early warning system in water distribution network MASHREKI ISLAM SAMI

Transcription

1 Effective placement of sensors for efficient early warning system in water distribution network MASHREKI ISLAM SAMI

2 AGENDA Introduction and Problem Statement Methodology Experimentation and Analysis Limitations Key Findings Conclusion MASHREKI ISLAM SAMI Chalmers 2

3 Introduction and Problem Statement Water distribution network is important part of infrastrucutural development for any town/city/country. Water safety and quality are fundamental to human development and well-being, WHO UN SDG 6 also prioritizes accessibility and availability of safe driking water MASHREKI ISLAM SAMI Chalmers 3

4 Problem Statement Contamination threats between WTP and consumer Chances of affecting mass population Uncertainties (Intrusion, micorbial growth, leakage) In 2007, an outbreak in Nokia, Finland affected 8453 people with waterborne gastroenteritis for pipeline cross-connection. In 2007, poisoning of water supply caused 71 people poisoned in China. Introduction and Problem Statement MASHREKI ISLAM SAMI Chalmers 4

5 Problem Statement Sensors are expensive Deploying sensors at every node is impractical Optimization requires vast computional resources A large WDN with 10,000 nodes each contaminated and sensor sampling time is 10 min. 72h simulation will give 4.32 million scenarios of contaminations. Storage capacity required is 173 GB and 200 days to end simulation. Introduction and Problem Statement MASHREKI ISLAM SAMI Chalmers 5

6 Objective of Study Event Detection: Using sensor resposne to generate signal for water quality change Sampling and Identify: Collecting water sample and testing to identify type of contamination Biomarks: Tracing source of contamination and characterization (Leakage, Roads, fecal sources etc.) 1. Event Detection 3. Biomarkers and Origin Analysis 2. Sampling and Identify Introduction and Problem Statement MASHREKI ISLAM SAMI Chalmers 6

7 Objective of Study Event Detection: Using sensor resposne to generate signal for water quality change Sampling and Identify: Collecting water sample and testing to identify type of contamination Biomarks: Tracing source of contamination and characterization (Leakage, Roads, fecal sources etc.) 1. Event Detection 3. Biomarkers and Origin Analysis 2. Sampling and Identify Introduction and Problem Statement MASHREKI ISLAM SAMI Chalmers 7

8 Criteria for Event Detection Detection time: How quick sensors can response and generate a signal Detection likelihood: How often and efficiently sensors can detect in corresponding to their placement. Population size: Number of consumers that may be affected Introduction and Problem Statement MASHREKI ISLAM SAMI Chalmers 8

9 Research on Sensor Placement Mathematical Algorithms Graph Theory Linear algebra Numerical analysis Complex methods Genetic algorithm Greedy algorithm Parallel computing Heuristic approach Fast optimization Complex Network Theory Centrality Matrices Stochastic approach Critical region optimization Advancement Introduction and Problem Statement MASHREKI ISLAM SAMI Chalmers 9

10 Methodology Contamination event simulation using EPANET Constructing a pilot scaled WDN in laboratory Analyze real-time data and simulated data Possible analysis for source back-tracking MASHREKI ISLAM SAMI Chalmers 10

11 EPANET Simulation The WDN was modelled in EPANET Chemical injection at each node Chemical concentration at each node Optimization for sensor placement and intrusion point Methodology MASHREKI ISLAM SAMI Chalmers 11

12 Design of water distribution network Methodology MASHREKI ISLAM SAMI Chalmers 12

13 EPANET Simulation Intrusion Methodology MASHREKI ISLAM SAMI Chalmers 13

14 Intrusion N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15 N16 N17 N18 N19 N20 N21 N22 N23 N24 N25 N26 N27 Node ID Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. Conc. N N N N N N N N N N N N N N N N N N N N N Intrusion N N N N N N27out N28out N29out N Total Nodes contaminated MASHREKI ISLAM SAMI Chalmers Methodology 14

15 EPANET Simulation Optimization Graph Theory Simple sorting matrix Strategic Decisions Node Number 1< Cx <2 mg/l 2< Cx <3 mg/l 3< Cx <4 mg/l Cx >4 mg/l Number of Number of Number of Node Node Node times times times Number Number Number Contaminated Contaminated Contaminated Number of times Contaminated Total Number of times Contaminated N1 2 N1 0 N1 5 N N2 2 N2 0 N2 5 N N3 2 N3 0 N3 5 N N4 0 N4 0 N4 0 N4 5 5 N5 0 N5 0 N5 0 N5 4 4 N6 0 N6 0 N6 0 N6 3 3 N7 0 N7 0 N7 0 N7 2 2 N8 0 N8 0 N8 0 N8 1 1 N9 0 N9 0 N9 0 N9 4 4 N10 0 N10 0 N10 0 N N11 0 N11 0 N11 0 N N12 0 N12 0 N12 0 N N13 7 N13 10 N13 0 N N14 3 N14 14 N14 0 N N15 3 N15 13 N15 0 N N16 2 N16 0 N16 5 N N17 2 N17 0 N17 5 N N18 0 N18 0 N18 4 N N19 0 N19 0 N19 0 N N20 5 N20 3 N20 1 N N21 5 N21 3 N21 1 N N22 0 N22 0 N22 0 N N23 0 N23 0 N23 0 N N24 0 N24 0 N24 0 N N25 5 N25 0 N25 4 N N26 5 N26 0 N26 4 N N31 2 N31 1 N31 4 N Methodology MASHREKI ISLAM SAMI Chalmers 15

16 EPANET Simulation Intrusion Points Upstream Sensors Location Downstream Methodology MASHREKI ISLAM SAMI Chalmers 16

17 Scaled WDN Model 50 mm diameter PVC pipes supported on wooden frame and attached with clips. Flexible PVC pipes used for inflow and outflow. Methodology MASHREKI ISLAM SAMI Chalmers 17

18 Materials and Intruments Electrode L7781 HD Electrode L8281 HD ph electrodes Methodology MASHREKI ISLAM SAMI Chalmers 18

19 Chemical Injections Three different solutions were used for the experiment of sensor resposne. Electrode L7781 HD Acetic Acid 24% - Very Good Sodium Chloride - Slow & inconsistent Commerical Chlorine soln. - Not Good Electrode L8281 HD Methodology MASHREKI ISLAM SAMI Chalmers 19

20 Experimentation and Analysis - 27 scenarios Electrode L7781 HD - Inflow: 22 l/m Electrode L8281 HD - Repeat 2-3 times MASHREKI ISLAM SAMI Chalmers Experimentation & Analysis 20

21 Sensor Activities Raw data graphs merged into one Electrode L7781 HD Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 21

22 Real-time Intrusion at Node N4 Outflow at node N27 & N28 Electrode L7781 HD Epanet Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 22

23 Real-time Intrusion at Node N4 Outflow at node N27 & N29 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 23

24 Real-time Intrusion at Node N4 Outflow at node N28 & N29 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 24

25 Analysis of Intrusion at N4 Intrusion chemical travel in laminar flow. Electrode L7781 HD Dispersion/diffusion rate is low. Advective flow pattern observed. Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 25

26 Real-time Intrusion at Node N23 Outflow at node N27 & N28 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 26

27 Real-time Intrusion at Node N23 Outflow at node N27 & N29 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 27

28 Real-time Intrusion at Node N23 Outflow at node N28 & N29 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 28

29 Analysis of Intrusion at N23 Intrusion chemical travel in laminar flow. Electrode L7781 HD Dispersion/diffusion rate is low. Advective flow pattern observed. Electrode L8281 HD Quickly out of the system at N29 Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 29

30 Real-time Intrusion at Node N31 Outflow at node N27 & N28 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 30

31 Real-time Intrusion at Node N31 Outflow at node N27 & N29 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 31

32 Real-time Intrusion at Node N31 Outflow at node N28 & N29 Electrode L7781 HD Epanet Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 32

33 Analysis of Intrusion at N31 Intrusion chemical travel in laminar flow. Electrode L7781 HD Dispersion/diffusion rate is high. Advective flow behaviour is less. Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 33

34 Source Back-tracking Stochastic approach Contour mapping Critical region optimization Electrode L7781 HD Electrode L8281 HD Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 34

35 Source Back-tracking Electrode L8281 HD Electrode L8281 HD Electrode L7781 Electrode HD L8281 HD Intrusion N4 Intrusion N23 Intrusion N31 Sensor N Sensor N Sensor N Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 35

36 Source Back-tracking N1 N4 Intrusion Node N4 Intrusion Node N23 Intrusion Node N31 Sensor N Sensor N Sensors N N13 N26 Experimentation & Analysis MASHREKI ISLAM SAMI Chalmers 36

37 Limitations ph sensor data insufficient for for source back-tracking. More dynamic and hydraulic data are necessary for source back-tracking. EPANET time outputs changes with user defined parameters. Flow charateristics and pipe properties were not analyzed. MASHREKI ISLAM SAMI Chalmers 37

38 Key Findings Simple sorting matrix, Graph theory, Complex network theory etc. are easy to use and efficient optimizing techniques. Chemical tends to flow in laminar in the pipe system EPANET is convenient to simulate WDN for hydraulic analysis but time-variant chemical flow is not available. Simple & cheap ph sensors are efficient in detecting sudden changes in ph of water. MASHREKI ISLAM SAMI Chalmers 38

39 Conclusions The study highlights the possibilities of sensor localization in WDN using simpler methods Intrusion at downstream nodes for the designed WDN can be detected by sensors upstream enabling early warning signal Source tracking was not possible due to limitations and uncertainties associated with EPANET and simple sensors Computer program specifically developed for source tracking can contribute greatly to future water distribution network. MASHREKI ISLAM SAMI Chalmers 39

40 Further Works Probabilistic Analysis Mike Urban by DHI WaterGEM by Bentley WaterCad by Bentley Machine Learning MASHREKI ISLAM SAMI Chalmers 40

41 chalmers.se/sv/centrum/dricks/publikationer/sidor/default.aspx

42 Thank You