Reduce Water Loss, Drive Conservation with ArcGIS |Q & A’s

11-02-2015 08:47 AM

Reduce Water Loss, Drive Conservation with ArcGIS |Q & A’s

Here are the questions and answers to the Reduce Water Loss, Drive Conservation webinar.

  • Q1: Pat, do you have big costumers that are monitored and subtracted to the minimum night flow analysis?
  • A1:  We do have one large wholesale customer that is monitored with SCADA.  We subtract those flows from affected District Meter Area (DMA) meter readings.

  • Q2: How did you determine 0.05 gpm to be the acceptable number for night time leakage or usage?
  • A2: We backed into the 0.05 gpm/connection number to set initial, minimum performance values.  We reviewed initial flow values that came in for DMA’s and began tightening up the worst portions of our system through repairs.  The 0.05 gpm/connection value has turned out to be an achievable goal and will be lowered as we strive to tighten the system further through finding/repairing leaks of lower flow rates. 

  • Q3: What do you mean by spread and level while looking for placing leak loggers?
  • A3:  Level and Spread are two values that are returned by loggers after they’ve been deployed and “listened” for leak noises.  They don’t have anything to do with placement of the loggers.  They indicate how loud (noise level in units of decibels – higher number means louder noise) the noises are that a loggers hear.  Also, spread values indicate how many discrete noises a logger detects.  Fewer noises (lower spread value) help lend certainty to possibility for leak noise being present near loggers.

  • Q4: How many main line valves are in your system?
  • A4: Approximately 7,200

  • Q5: Can you integrate ArcGIS and AMI (i.e., active leak detection) to create real-time NRW assessments and dashboards?
  • A5: I would assume this is possible, but am not aware of anyone that has done it.  We would love to do this, but we do not have AMI data.

  • Q6: How did you determine your KPI of 52mgd?
  • A6:  I assume you are referring to the KPI of 56 gallons per minute on the DMA example shown in the presentation.  There are 1,131 connections in the Mulloy West DMA.  Multiplying our desired KPI of 0.05 gallons per minute per connection by 1,131 gives a desired flow rate of 56 gallons per minute for that DMA.

  • Q7: How do you manage leak logger data? Do all logs go into a single feature class with date and or study group?
  • A7:  All logger data goes into one feature class.  We manage it with a status field.  Depending on the work group, we can sort by class or make features appear/disappear based on real time status changes.

  • Q8: How long did it take to put in place a functional DMA's?   Meaning that the data you were gathering were actually helpful?
  • A8: On the construction/hardware side of the project, it took about six weeks to get the first 30 DMA meters installed and configured.  It took about another week to create the DMA features and then we started receiving useful data.  On the GIS side, it took less than a day to set up the initial DMA map and less than another day to put together the dashboard.

  • Q10: Could you give some insight on what the minimum system size would need to be to make this a worthwhile approach?
  • A10: I would think that any size system could benefit from some form of flow analysis if there are appreciable real losses in non-revenue water that can be covered.  Even if there do not turn out to be appreciable losses at stake at the moment– monitoring flows can provide quicker notifications of when leaks occur – and thereby reduce leak run times for even relatively tight systems. The principle of monitoring Minimum Nighttime Flows (at least for residential customer bases) seems to be a good approach for almost any size system.  How far you go in investment for staffing, hardware, software, etc. would need to take into account several factors:  Cost of water; Regulatory requirements; Source water availability; Public perception; Technological abilities; ROI expectations; etc.

  • Q11:  Can any of these detection tools/systems, identify and locate illegal connections with water theft?  
  • A11: Yes – especially the flow monitoring solution.  Being able to compare actual flows in pipelines with retail meter flow data can help reveal several things that could be the source of discrepancies.  Differences between the two categories can help identify not just leakage, but also: theft; unmetered connections; slow/stopped meters; etc.  

  • Q12: Will the Leak Logger tool automate the location of where the loggers should be placed based on asset and pipe info or does it only show you the coverage area of where you manually input where loggers will be placed?
  • A12: It only shows the coverage area of loggers after they are placed. However, the user can make more than one iteration with the tool to manage efficient/complete placement in the survey area.

  • Q13: You didn't take into account any of the pressure and the pipe length for the minimum night flow?
  • A13: No.  We are interested in excess flow – regardless of system pressure or pipe length.  In the future, we may set less aggressive KPI’s in areas with more pipe/higher pressure, but our initial goal is to see how low we realistically go with the gpm/connection KPI – regardless of pressure or pipe length.

  • Q14: When did you migrate to the Local Government Information Model (LGIM)?  Did you have cost related pushback from management, regarding development of the GIS?
  • A14: Migration to LGIM was about Feb. of 2014.  Management actually led the desire to migrate to LGIM.  They understood that this would need to happen in order to more easily take advantage of existing and future ArcGIS developments.

  • Q15: How would AMI systems metering all consumptions points but those unmetered change this setup?
  • A15: AMI data in this setup would be a great help.  Being able to subtract the known, legitimate consumption from monitored flow values would eliminate some of the guessing we are doing. We are assuming a legitimate consumption (little more than an educated guess that also allows for some amount of leakage) that we calculate by multiplying a factor by the number of connections.   Having actual AMI data would add a lot of certainty to what we’re doing and improve our process considerably.  Question 5 above alludes to Real Time NRW assessments.  We’d love to do that, but AMI would be extremely expensive for us to pull off with current technology.
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