Successful implementation takes a variety of forms

CML performance indicator

The first subject is CML, which stands for Customer Minutes Lost. In 2004, we conducted a research project  on indicators to assess the performance of a distribution network. By customer minutes lost we mean the amount of time that the water utility does not meet the supply requirements: either no water is supplied to the customer or the quality of the water that is supplied is inadequate. Within six months, everyone in the water sector was talking about ‘C.M.L.’ or ‘cml’. Within two years, CML had been taken up as a performance indicator for operational management, and even incorporated into the benchmark. My colleagues at the time readily admitted that they had never seen such a rapid implementation. I’ve heard a great deal of doubt expressed about CML as a performance indicator. For example, because the customer experience is not included (is a CML incident at night as bad as one at 7:30 in the morning?), or because the (inadequate) water quality is not recorded, or because it isn’t easy to establish how much money we have left over to limit CML (or at least to keep it from rising). But CML has not yet been replaced. The notion that performance is measurable, and that it can therefore also be targeted (in the long-term), is now ingrained in the minds of everyone in the sector. This will remain so, even if CML is replaced by a better performance indicator.

Soil temperature determines temperature at the tap

The second research outcome I’d like to revisit is that the drinking water temperature  in the distribution network is not constant. Soil temperature at a depth of 1 metre changes over the course of the year and, in some places, can rise above 25 degrees in the summer. This can lead to a sharp rise in drinking water temperature. In 2007, following the measurements made by Ed Smulders (to give credit where credit’s due), who was still with the NV Tilburgsche Waterleiding-Maatschappij (TWM) at the time, we began research on the factors impacting the temperature of drinking water at the tap. This was followed by a whole series of research projects, ranging from quantifying possible measures (deeper installation of mains, installation of green areas, local cooling of drinking water, and more), to defining the consequences of a higher temperature on regrowth in the network, on the risk of water discolouration, on Legionella, and on other things as well. This line of research is still ongoing, but it has been established for years now that the temperature at the tap is a function of soil temperature and not of the temperature at the source. This means that the supply of high-temperature water in the summer is not limited to the water utilities that use surface water for their source. Today, after many years of presentations beginning with this message, more and more people are conscious of this fact. And that’s a good thing, because, with climate change, urbanisation and the energy transition, close attention needs to be paid to the temperature of drinking water. As far as I’m concerned, this is a good example of the implementation of research results. Not so much in the shape of a tool, but of an awareness will can prompt other considerations in the installation and management of the water distribution system.

Modelling water demand with SIMDEUM

The third research outcome is SIMDEUM. In 2003, I started at KWR with the modelling of demand patterns without really knowing where it would lead. In 2006 I developed SIMDEUM. Although SIMDEUM looks suspiciously like a (software) tool, I’m primarily concerned with the implementation of the concept underlying SIMDEUM. I am essentially a researcher and by no means a software developer. So, what is SIMDEUM’s underlying concept? On the one hand, the concept is that it is the customer who determines when and how much water he or she consumes, and thereby when and how much water needs to be produced and transported, and when and how much (waste) water needs to be discharged and treated. Insight into customer behaviour on a small spatial scale (a single household, or even an individual tap) and on a short time scale (one minute, or even one second) is needed, for example, to understand what it is that sensors in the network measure – for instance, because part of the day the water flows to the sensor from one side, and, in another part of the day, from the other side; or what effect new sanitation facilities have on the performance of the distribution and sewage networks; or how a drinking water and sewage network can best be designed, and so on. On the other hand, the SIMDEUM concept is that customer behaviour can be described as a stochastic process, and that this can also produce realistic predictions: through a modelling approach, you can create a practically endless virtual testbed, in which you can calculate the effects of a change in behaviour (like shorter showers) or of different fixtures (like dry toilets), but also the kind of information sensors can give you. Through the smart placement of sensors, you can for instance quickly trace water losses of a specified minimal dimension. Has this concept already been taken up by the sector? In view of the many research projects we have completed since 2006 using SIMDEUM, I have to conclude that this is indeed the case, particularly regarding the concept’s testbed aspect. However, when it comes to seeing the customer as the driving force behind the drinking watercycle and starting point for network design and operational decisions, not everyone has taken this onboard – there are many people, for instance, who still want to design on the basis of tap units. But the ISSO-55 guidelines for the design of drinking water installations in residential and non-residential buildings have, for a number of years now, incorporated design rules for pipes and hot-water preparation systems that are based on SIMDEUM. And the Dunea water utility uses SIMDEUM-based calculation rules in its design of self-cleaning networks. So, this implementation also takes concrete form in the sector.

Conclusions

Can I draw any conclusions from this? It’s difficult, since these are only three, and quite different, examples. I have however noticed that when there is still ‘nothing’, the implementation of ‘something’ can happen quickly. But when there is already ‘something’, then it takes a lot more before ‘something better’ is adopted. Then it becomes a question of the long haul, of personal belief and persistence. And of accepting that what I would consider a successful implementation, namely, the general acceptance of a concept, might be regarded by others as something that they always knew. Or that people only want to speak of implementation once all the research is completely finished. But that naturally never happens, because research is never finished. There is good reason why practically every research report concludes with ‘further research is required’…