Guest Column | January 31, 2014

Is The Water Sector Making The Most Of Its Data?

Oliver Grievson

By Oliver Grievson


As technology solutions drive low-cost access to increasing volumes of critical data, Andrew Reeks, Business Manager for the Water Sector at Siemens Industry and Oliver Grievson, Flow Compliance & Regulatory Efficiency Manager at Anglian Water Services, highlight some of the key data collection questions UK water companies need to be thinking about.

This includes not only issues around potential for data overload, but also how access to accurate, timely and inexpensive data could support improvements in day-to-day operational efficiencies and drive future strategic planning.

Overseeing a widespread and complex asset base means UK water industry operators are always seeking methods to ascertain if an element within its infrastructure could fail before it does so.  This will help prevent potentially costly incidents and avoid customer complaints if, for example, service delivery is interrupted.  As a result obtaining and analysing the required data from water company process measurement implementation is becoming ever more critical with strategic decisions needing to be made without delay based on the intelligence provided by live data.  It is heartening to report that the successful measurement of important process variables (PV) has been undertaken by the UK water industry for many years, but as technology innovation opens up more and more possibilities for data-related intelligence to be gathered, what should water companies be assessing in this field for their current and future needs?

Data collection and conversion

The challenge still remains to optimise the current and future delivery of ever increasing volumes of data from across the water company  – including previously untapped measuring sources – and, in turn, convert such data into useful information that can benefit both day-to-day operational objectives, as well as underpin strategic long-term planning.  This presents a new kind of challenge and one the industry should be looking to tackle in partnership with trusted technology vendors.  Data and information strategy influences, among many others, critical areas such as cost control, technology choice, efficiency targets and regulatory responsibilities and it sits at the very centre of the service a water company delivers.  It really is that important.

Widespread impact

But it is a complex area with many pertinent questions; one which has a wide scale impact across a water company’s business. There could, for example, be a requirement for data to be extracted from process points which have in the past been deemed too expensive, difficult or not practical to measure.   By adding even more measurement points, this also creates the possibility of data overload.  Operators need to clearly define what is considered useful; a decision that may well involve the collection of process variables contributing to a single information point.

An example of the data challenge is highlighted by advanced digestion systems.  Information is a potential requirement on sludge flows, therefore there needs to be a measurement made at several points.  The need to retrofit existing plants to ensure this capability has led to technology manufacturers working closely with the water industry to trial different measurement principles to deliver fit for purpose measurement solutions which can deliver the desired information.

The criteria for such measurement solutions mean they are being measured against the total cost of ownership - involving installation, maintenance, and ongoing calibration if required. The subsequent data retrieval has therefore to be delivered to the operator accurately and at a practical cost.

Likewise, the SMART waste network and pumping stations form an integral part of the recyclable (waste) water network. Ensuring the network is SMART means clear visibility if there are any disruptions to the network.  Failure of these networks can cause the water company to lose SIM ratings, or in a worst case scenario cause a spillage resulting in pollution to the environment.

Data-driven efficiencies

In the UK we already have an excellent sewage infrastructure and there is no need to replace or dramatically increase our pumping stations.   However, essential data is required from these existing pumping stations so that operationally critical questions can be accurately answered.  They would include: are the pumps running? Did the pump perform as expected? What is the efficiency of each pump?   Currently there are several examples where technology suppliers including Siemens are working with water companies to trial technologies which give an indication of pump flow rate or an accurate measurement of pump flow, as opposed to a derived and theoretical value.

Such aims present real challenges for the technology.  It needs to operate in extremely arduous conditions, usually on pipes which have several bends.  It must also meet the criteria of minimum installation cost and low maintenance requirements to satisfy the key water companies’ driver of reducing OPEX expenditure.

Generating such essential data has been in train for several years now as the regulator has driven MCERTS requirements for flow to be measured at a treatment works. As a result, the easier applications have been delivered by all the water companies.  However, as we now enter AMP 6, the spotlight is shining on the more challenging applications, some of which will cost several thousands of pounds in infrastructure cost alone before any measurement is taken. Seeking innovative technology solutions to capture data at the lowest cost is now at the forefront of discussions between technology manufacturers and water companies, to ensure the measurement of the process variable is possible, without the need to incur significant expenditure to ensure access.

Data intelligence

Part of any strategic data measurement discussions as the demand for more information increases will involve basic interrogation of the current data collection and measurement status.  For example, is it already being measured? However, the most important question to be asked concentrates on assessing the worth of the data being extracted.  A good example of this would be temperature measurement.  Existing instruments typically have an in-built temperature sensor and the measurement is available to capture via the chosen communication protocol such as Profibus, Hart or Modbus so the data exists at no extra cost.

But the requirement for the temperature data has to be thought through.  Whilst the data is there, is anyone interrogating the reason given to collect data concerning the ambient temperature measurement of a transducer?  Is it actually used? 

But temperature measurement could have a real part to play when it comes to remote assets such as a storm tank or any open tanks exposed to wind chill factor.

But this is only one example - in-built temperature sensors, which exist on most instruments, could quickly and accurately provide temperature data across the treatment process and could provide an indication that there may be a freezing risk. For example, in pipes that flow intermittently, such as pumped flows on recirculation or polymer etc. If undetected, the next time it is called to run, the pump may not be able to perform because the pipe is frozen.  This could cause expensive damage to pumps and the process.  With uncertainty surrounding predictions for future harsh UK winters, accessing such operationally critical data intelligence may be a measurement deemed critical to help safeguard water company infrastructure integrity.

While the examples provided are compelling, the water industry itself needs to determine if the business requirements mean access to such extended data sources will make the sector more efficient, and help to give their customers a better service.

Oliver Grievson & Andrew Reeks are actively collaborating in the areas of Smart Wastewater and Data & Information Use within the UK Water Industry.

Image credit: "Trying & Failing," © 2008 Alan_D, used under a Attribution 2.0 Generic license: