improving sewer flow monitoring using spatial optimization in gis
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Chris Skehan. Improving Sewer Flow Monitoring using Spatial Optimization in GIS. Today’s Outline. What is RDII // Studying RDII The Monitor Discussion of the Problem Design and Methods Results. Rain Derived Inflow and Infiltration. Also known as RDII, I/I, or I&I - PowerPoint PPT PresentationTRANSCRIPT
Chris Skehan
What is RDII // Studying RDII The Monitor Discussion of the Problem Design and Methods Results
Today’s Outline
Rain Derived Inflow and Infiltration
Also known as RDII, I/I, or I&I
RDII is the main cause of SSO’s, street and basement flooding, and can damage to nearby receiving waters
Capacity Issues
Aging infrastructure, point source, indirect
National and local interest to measure, reduce, and eliminate RDII
What does RDII Look Like?
Flow Monitoring Technology
Flow monitoring technology is able to log sewer flow data in a given collection system over a unit time.
These measurements can then be compared to each other meter, to a storm event, to a land area, etc.
Flow Monitor Placement
Mitchell and Stevens (2005) provide a guideline for determining the number of Flow Monitors
Limit your upstream influence to 10,000 - 15,000 LF at locations convergence point (8,500)
Limits greater than 15,000 LF will often dilute results
So where do we put these meters now that we have a range???
Well….…Flow Monitoring: sewage flow rates are monitored at various locations within the municipal sewage collection system. The flow data is analyzed, along with rainfall data, to determine if there is excessive I&I within the study area.
…by inserting special measuring devices into the sewer lines, crews can monitor the water flowing through them. If the flow increases during rainstorms, it's a sure sign of infiltration.
…Flow meters are being installed in five strategic locations around the city providing a true measurement of the amount of wastewater being carried by the City collection system. The information gathered from these devices will help to identify, monitor and manage the City’s I & I issues.
•Meet all objective of study•Gathers all necessary data needed to reduce and eliminate majority of I/I•Minimizes Cost – Maximizes Value•Respect Target Areas (Worst First!)
The Problem Continued
Currently there is no standard methodology for determining the optimal placement of a flow monitor
As the size of the system increases, determining optimal placement becomes difficult
Range of limits can cause problems Example – 3,000,000 LF – 200 to 300 flow monitors
Very Little Flexible & Time Consuming
The Problem Continued
Conundrum exists between the number of monitors and their placement in the system
~19,000 LF~10,000 LF**
~9,000 LF
**However – 1 additional meter was consumed
Non-Optimized Methods
Ad-Hoc – as large systems and studies will have many different results among engineers determining the flow monitors placement
Local Optimization Approach – Local, Zoomed, Fixed Extent to solve the location
Sequential Technique – Single monitor placement until all flow monitors are exhausted
Optimized Methods
Deterministic – Approach that will yield the same results every time, regardless of user
Global Optimization – Account for the entire system when determining results (problem areas can be weighted)
Simultaneous Technique – Solve for the placement of every meter used in the study simultaneously
Design Utilize Operations Research (OR) – a proven
methodology of optimization
Branches of OR - Graph Theory and Network Flows
The Facility Location Problem (and Model)P-Median Problem
Locating P "facilities" relative to a set of "customers" such that the sum of the shortest demand weighted distance between "customers" and "facilities" is minimized.
Professor Phil Kaminsky – University of California at Berkeley
2M
2.6
2.1
0.8
1.0
0.90.8
2.3
2.2
1.0
2.7
5
4
7
1
23
2
5
10
14
4.7
Imagine the problem for 3 million LF and 500,000 manholes
One Way Network: where Math can help solve such a complex problem
Data And Methods Used 3 GIS shapefile layers to complete this study
Lawrence sewer lines & manholes Non-Optimized locations determined by Engineer in a 2009
study
Shapefiles were not topologically integrated, and had to be converted to standard geographical file format – Geometric Network
Topology is essential for creating a network layer and utilizing a facility location model
Data and Methods Continued A Facility Location Model was used to
solve this problem
3 Keys to a Facility Location Problem
1. Defining the Network between facilities and clients
2. Calculating the Connections Costs
3. Calculating the Facility Costs
Data and Methods Continued Network was defined by using the Directed
Graph preserved in the Sewer Lines Layer
Connections Costs were calculated by building a distance matrix using length between each facility and client constraints was set (53 Facilities & 15,000 LF)
Facility Costs were assumed to be equal among all potential clients (all nodes equal)
Data and Methods Continued Network Partitioning was utilized on both set of
locations
Provided a way to compare the to location sets
Provided a good visual representation of any upstream influences to a flow monitor
May be a promising time saving feature for creating flow monitoring basins
Results
Figure 4 – The Non-Optimized Locations hand selected by the engineer (Green Circles) vs. the Optimized Locations selected by the facility location model (Blue Squares).
Figure 5 – The Non-Optimized Locations and the calculated network partition for each location. Each color theme represents an individual basin that would influence the flow monitor located at the downstream point of convergence.
Figure 6 – The Optimized Locations and the calculated Network Partition for each location. Each color theme represents an individual basin that would influence the flow monitor located at the downstream point of convergence.
Number of Locations
Utilized Line
SegmentsRelative
EfficiencyTotal LF
Monitored% of System Monitored
Average LF for Each Flow Monitor
Non Optimized Locations 53 2067 66% 519,218 ~43% 10,383
Optimized Locations 53 3136 100% 755,817 ~63% 14,153
Table 1 - The statistical data which was extracted from the network partitioning for the non optimized locations versus the optimized locations. Note the increase in the Relative Efficiency and the percentage of system that would be monitored if the optimized locations would have been utilized during the study. In both examples, the average basin size is still within the limits of Stevens (2005) criteria.
Key Results Non-optimized locations would monitor approximately 43% of the
entire collection system versus 63% with the optimized locations
Relative Efficiency was 66% for the non-optimized locations vs. 100% for the optimized locations
Average Upstream Influence (basin size) increased from 10K vs. 14K (constraint can be adjusted as needed)
Still within the Limits of Stevens (2005)
Results were extracted in less than 60 minutes for the entire system (locations)
An estimated $100,000 in monitoring would have been needed to measure the additional sections of sewer system
Conclusion A facility location model can be utilized to
improve optimization for flow monitor placement in RDII studies
Flexible Approach in terms of constraints, time, and effort
Promising tool for time reduction in RDII studies
Considerations Connection errors in data - termed
“network islands” in this study
Accounting for lift stations – or any other directional flow connections of a collection system
Consider multiple results versus a single optimal result Increase # of Meters to Use or use the as-many-as-
needed function in the facility location model
Figure 8 – This figure shows the disconnected network areas represented in red, as the connected network is shown in black.
Questions