Information Technology

Large Meter Testing: Where are utilities today? AWWA CA-NV Meter Committee Workshop; August 20, 2019

By: Kristine Gali, Technical Program Manager, and Heidi Smith, Global Product Manager

The American Water Works Association nonprofit was founded in 1881 with a focus on providing education and training opportunities for drinking water professionals. Since its inception, the association has grown to roughly 50,000 members, multiple sections across the US, and multiple committee’s within each section. The California-Nevada Section of AWWA held its Summer Meeting and Workshop on August 20, 2019 with a focus on large meter testing, the importance of regular testing, methods of conducting field and bench tests, and a panel on how to develop a testing program.

 With emerging water regulations across the US, utilities are becoming increasingly focused on annual water audits. Even more importantly, utilities are focusing on how to gather sufficient data to most accurately complete these water audits. The water that enters the water distribution system should equal the amount of water either consumed, lost, or exported. On the other hand, without accurate metering, it is impossible to confirm how much water is associated with each of these variables of the equation. Having a regular testing program for large meters helps utilities monitor not only production meters, but some of their highest revenue generating meters as well and ultimately hone in on accurate water audit inputs.

Reasons and consequences for large meter failures

Michael Simpson from M.E. Simpson Co. shared 7 reasons for large meter failures and their consequences:

-       age,

-       mechanical wear and tear,

-       corrosion,

-       mineral buildup,

-       fouling due to debris,

-       misuse or operation outside of the meters range, and

-       inadequate plumbing before and/or after the meter

The consequences of these potential failures include inaccurate billing of customers, lost revenue, over and under feeding of chemicals (in plant meters only), inaccurate annual reports and usage estimates, and an overall loss of control.

Meters will naturally age and experience wear and tear. Depending on water quality, other factors such as corrosion, mineral buildup, and fouling may ultimately affect meters as well. Finally, the installation configuration and operation of the meter itself can compromise the accuracy of meter reads. Due to the variety of issue types, the life expectancy of large meters can be difficult to predict without closely monitoring the meters as well as the quality of water running through them over their lifetime.

Simpson recommends that utilities test and calibrate their large meters annually. A survey of the largest US utilities also found that testing of large meters occurred on an annual basis (AWWA, M6, pg 58). Testing should be conducted using a certified test meter or a pitot rod. It should be noted that testing of the 4 to 20 mA signal between a meter head and the SCADA system is not considered a valid test. 

What is a Pitot Test?

The pitot test is a common field testing method which measures differential flow pressure to determine flow velocity and ultimately flow volume (i.e. Q=VA) within a pipe. The pitot tube is inserted into the live pipe to determine the flow profile across the entire inner diameter of the pipe. Since the flow equation depends on the cross sectional area of flow, it is important to carefully measure the inner diameter of the pipe such as with a Polcon Pipe Caliper. It should also be noted that various pipe obstructions such as valves and elbows can alter the flow profile, therefore it is important to take pitot measurements on a straight line of pipe and at multiple depths within the pipe to ensure the most accurate flow volume can be determined. Furthermore, since flows may vary throughout the day, a 24-hour test is also recommended. 

Ultrasonic strap-on test meter

Strap-on ultrasonic test meters can also be used to field testing large meters in-place. The meters are minimally intrusive and similar to a pitot tube, do not disrupt flow. Measurements are calculated based on the transit-time difference method. It should be noted that the test meter should be certified and calibrated prior to use and that the manufacturer specifications for installing the test meter are followed as most require a specified amount of straight pipe both up-stream and down-stream of the test location. Furthermore, it should be noted that “once the testing begins, the testing order is from the low flows to the higher flows. Experience has shown that when most meters begin to wear or lose accuracy, it occurs at the lower flows rather than the higher” (AWWA, M6 pg 85). An advantage of ultrasonic strap-on meters is their ability to measure low flows therefore covering most, if not all flow ranges within the large meter. 

Contracted-out services

Utilities may opt to contract out services to companies such as Mars Company or to organizations such as Utah Water Research Laboratory (UWRL) for their testing and calibration of large meters. Mars Company has been offering water meter testing and technology services since 1986. At UWRL, meters and even volumes of water, can be shipped for testing in a laboratory which simulates the field piping installation. 

Large Meter Testing Practices

Portable large meter tester

Portable large meter tester

Overall, both field testing methods, pitot and ultrasonic, have their own advantages and disadvantages that should be weighed by the utility. Utilities should ensure that proper training is conducted for all personnel responsible for large meter testing. Contracted-out services offer another alternative for utilities. As stated in the AWWA M6 manual, “no phase of water-utility operation has been handled in so many different ways as the testing of water meters...The confusion and wide variance in testing procedures result from the fact that the testing of water meters in ordinary shop practice is primarily concerned with meters that are not new but that have been removed from service and repaired. Each individual has had to begin with the information available and develop testing procedures” (AWWA, M6 pg 59). Each utility much test analyze their own system and large meter individually. A panel of utility personnel from Las Vegas, San Jose Water Company, Walnut Valley, Golden State Water, and MWD revealed the following:

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  • Utilities are using a mix of in-house and contracted-out large meter testing services

  • Testing programs vary from 180 days to 4 years. Some vary the testing frequency based on the amount of revenue that the meter generates

  • For utilities with large quantities of large meters, a statistical calculator such as Roasoft, is used to determine how many of its new meters to test. 

  • Utilities are currently managing test data on spreadsheets but are looking for software to better manage their data in a central location while also allowing for integration with their customer care portals, MDM systems, and other analytics software platforms

Overall, more and more utilities are proactively testing and replacing their large meters and are uncovering significant savings. Methods for developing these programs still vary today, but new testing services, research, and analytics are continuing to be developed to help utilities uncover significant savings through more proactive approaches to meter maintenance


Apparent Water Loss, Optimized Vision, and Entrepreneurship: Q&A with Valor Founder and CEO Dr. Christine Boyle

By Elizabeth Harvell of UNC Environmental Finance Center

Earlier this year, Valor Water Analytics (Valor) was acquired by Xylem Inc., a $13B water technology company that services utility and commercial clients across 150 countries. While this is big news in its own right within the water industry, it’s especially exciting for the Environmental Finance Center: Valor Founder and CEO Dr. Christine Boyle previously worked as a research assistant at the EFC while pursuing her doctorate in water resource planning from the University of North Carolina at Chapel Hill.

Read the full interview on the UNC EFC Blog.

Valor Water Analytics Acquired by Water Giant Xylem

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We are excited to announce that Valor Water Analytics (Valor) was recently acquired by industry leader Xylem Inc (NYSE: XYL). Xylem is a $13B water technology company that services utility and commercial clients across 150 countries.

Dr. Christine Boyle founded Valor in 2013 with a mission to bring big data solutions to water utilities in order to improve their financial and water resource sustainability. To accomplish this, Valor created a suite of world-class software products. Valor’s products are now deployed in ten states across the USA, including notable utilities such as American Water and Suez. Its “Hidden Revenue Locator” product is widely recognized as a best-in-class technology for automated loss detection. The company remains committed to integrating its technology with all meters across the US and beyond. Valor will now execute on this ambitious vision under the Xylem umbrella.

The alignment of Valor and Xylem in product and vision made this acquisition the right strategy for Valor’s next stage of growth. Under Xylem, Team Valor continues and will spearhead Xylem’s Silicon Valley branch and lead Xylem’s advanced data science initiatives. Valor’s product lines will join Xylem’s existing suite of advanced analytics products. This exit demonstrates the value of building an innovative water technology that brings measurable value to the water sector.

Valor had previously raised $2.8M from investors such as the Urban Innovation Fund, Y Combinator, 500 Startups, Apsara, Hydro Venture Partners, Shore Ventures, Syzygy, and Matadero Ventures. These investors supported this exit and are excited for the next chapter of Valor.

Valor is looking forward to solving the world’s water issues as part of Xylem’s world-class team of dedicated water professionals.

Why Eliminating Ambiguity in Your Data Matters

By David Wegman, CTO @ Valor

The next time you strike up a conversation with your friendly neighborhood computer, take note of how long it takes before you get frustrated.  Despite the advances in artificial intelligence over the past decades -- and despite the incredible capacity humans have for adaptation -- human-computer interaction is unnatural (from the human perspective, anyway).  Every touch point where people provide input to computers, or receive output from computers, is an opportunity for misunderstanding.

 

Even as our systems are getting smarter all the time, there are some simple steps we can take to eliminate ambiguity.  Data architects serve an important role, helping to ensure that information is not lost or garbled in translation.  These techniques are essentially an investment.  Every minute spent on avoiding problems up front can save much more time down the road when things aren't working properly.

 

Date formats

 

Which came first, 3/7/2017 or 5/4/2017?  The answer depends on where you are in the world when asking the question.  In the United States, dates are commonly represented as month/day/year, so these dates would usually be interpreted as March 7 and May 4, respectively.  In many other countries, dates are represented as day/month/year, so they would be interpreted as July 3 and April 5, respectively.

 

This becomes problematic when a data file, which includes date information, is read by a computer system.  Each time the system encounters a field known to be a date, it must decide how to interpret the information.  Fortunately, most modern systems allow us to choose the format of the date for inputting and outputting dates.  However, if the date format is not chosen carefully, it can result in one of the most pernicious types of errors in computer systems: one which does not raise a flag immediately and lays dormant for some time.  A date which is incorrectly interpreted can result in a myriad of problems, as was widely publicized at the end of the last century.

 

Given enough data points, it may eventually become clear whether dates have been written starting with the month or day (e.g. if one of the values is 3/15/2017, the format cannot be day/month/year because 15 cannot refer to the month, so the format is probably month/day/year).  This approach is suboptimal because it requires an additional step which is not guaranteed to work properly in all cases.  A better approach is to avoid the problem altogether by taking care when choosing a date format.

 

To eliminate ambiguity when working with dates, when possible, use the format YYYY/MM/DD.  This represents a four-digit year, followed by a two-digit month, followed by a two-digit day.  March 7, 2017 would be represented as 2017/03/07.  This format is widely understood and eliminates the ambiguity that can occur when the year appears at the end.

 

Field delimiters

 

A common method for storing tabular data is in CSV (comma-separated values) format.  In a CSV file, each line contains one row of a table.  Within each line, a delimiter character appears in between each value, demarcating the columns.  The delimiter character is usually a comma or a tab.

 

A problem can arise when one of the values that needs to be stored contains the delimiter character.  For example, a person's name may contain a comma (e.g. "Martin Luther King, Jr.").  In this situation, a line containing this value will contain an extra delimiter character.  Software which treats each occurrence of the delimiter as a new column may be confused by the fact that the number of columns is inconsistent from one line to another.

 

One strategy is to choose a delimiter character which does not appear in any of the values.  This technique can help minimize problems, however it is not guaranteed to completely avoid them as new data files are created in the future.  A better approach is to wrap values that may contain delimiter characters in double quotes, and to ensure that literal double quote characters are specifically labeled (or "escaped," in programmer speak) using a backslash character.  This ensures that the data file will be parseable regardless of the data that needs to be stored.

 

 

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Units of measure

 

Sally's water meter recorded 350 gallons of water used.  John's recorded 200 cubic feet of water used.  Who used more water?  This is a question with a simple answer (John did).  But what if the units were not specified?  If all we know is that Sally used 350 and John used 200, we might decide that Sally used more, but only if we first assume that their meters record water using the same units.  Even if that assumption is correct, if we don't know the units, we won't be able to bill properly for the water or compare the quantity to an amount stored in other systems.

 

Quantitative values (i.e., measurements) should always have units specified.  When preparing a data file, you can provide information about units as a separate field.  For example:

 

Alternatively, units can be provided in documentation which accompanies the data.  One advantage of including units information inline in the data is that anyone with the data will automatically have units information, even if the documentation is not accessible.  Another benefit is that the units can vary from one record to another, as in the earlier example of two different people whose water meters recorded in different units.  However, in some cases it may not be practical to provide units inline, and good documentation can help fill this gap.

 

Keep clear and carry on

 

Data parsing errors are not unusual.  However, with a small investment they can be minimized.  By avoiding common data pitfalls and making the right choices at the outset, you will eliminate unnecessary troubleshooting and set yourself up for success.