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