Water Journal : Water Journal April 2012
smart systems water APRIL 2012 91 What to Sense or Monitor -- and Where The decision about what parameters to monitor and where to monitor is a complex one that was recently addressed at an intelligent water networks (IWN) workshop in Melbourne. The opinions were sought from a group made up of all the Victorian water utilities, and the key decision triggers and potential advantages of having tools to address these decisions were articulated in a table. This table is presented here with an additional column suggesting the parameters to measure, which are likely to provide the information needed to address the decisions. They fall into a number of categories including information for: (i) deeper knowledge of network operations in real time; (ii) condition of critical assets; (iii) defining the economic costs to the community along with the critical point of optimising investment to provide the greatest productivity yield; (iv) optimising the asset and data risks in terms of meeting the regulator service level key performance indicators; (v) data quality and their limitations in predictive models; (vi) the optimal location of the information collectors (i.e., sensors/detectors); (vii) real time knowledge of the cause of changes in drinking water quality; (viii) the time scale of emerging and potential issues and opportunities within the network; and (ix) better demand management, especially in light of the advanced metering infrastructure being utilised in the electricity sector. Economic Value of Intelligent Water Networks The immediate economic (not financial) value of intelligent water networks lies in addressing the following: • How water quality monitoring would reduce in intensity through remote technologies; • How much additional asset life can be realised over the long run from greater real-time knowledge of asset condition; • How the purchase and installation costs of these technologies, once developed and commercially available, determines their placement/uptake in the Australian urban water network. These are clarified schematically in the diagram at the bottom of this page. Water Quality The cost of monitoring surface water quality in Australia has been estimated at $142--$168 million per year (ANRA, 2002). Although this figure is not specific to urban water, it provides some indication of water quality monitoring costs (one component of the broader cost of ensuring adequate water quality, which also includes treatment costs etc). In any case, the need for water quality is typically more important for urban water than it is for irrigation water. More disaggregated water quality monitoring costs just by one water utility that could be made more efficient and automated by new intelligent technologies include: • Compliance monitoring of drinking water One of Melbourne's water distributer utilities collects water samples from various locations around the network and sends them to a laboratory to be tested for a variety of parameters. The annual cost of this task is approximately $450,000. • Operational monitoring of drinking water (instrument cost) Current unit costs of the remote instruments/ sensors that the water utility uses to stream water quality data are $3,000-- $10,000 (with a mid-point of $6,500) excluding installation, with 16 of them in use throughout the water utility's distribution network. Given an asset life of five to 10 years (with a mid-point of 7.5 years), the annualised cost of these instruments is approximately $14,000. • Operational monitoring of drinking water (instrument cleaning and maintenance) For example, staff from this water utility must travel to sensor sites to clean and calibrate the instruments, which on average takes 30 minutes plus travel. Assuming the task takes one hour in total for one individual, this equates to a total of 416 hours per year across the 16 instruments. In terms of annual cost, this total time multiplied by the average hourly wage in Australia equates to approximately $12,000. Despite the existence of other components to water quality monitoring, by their nature these represent the most likely to be reduced by the new intelligent technologies. Given the values indicated above, most of the potential would appear to be in the first category -- the manual taking of samples from around the network -- which is also consistent with feedback from another major water utility. If the water quality parameters that can be tested by the sensor technologies are comprehensive enough, it is conceivable that much of this labour cost could be eliminated. In addition to the observations of this smaller water distribution utility, Sydney Water Corporation (SWC) reported approximately $5 million is spent per annum on monitoring drinking water quality. This significantly higher figure reflects the structural differences in SWC's distribution and treatment role as compared to the Victorian-based water utility, and also the intensity of water quality monitoring by SWC (post-1998). It is concluded that the potential saving through improved technologies here is some proportion of the sum of these figures ($5.5 million per annum for these two utilities alone), once extrapolated over all Australian urban water service providers. At this point it should also be noted that the values estimated here relate to the cost saving to water quality monitoring. It is possible that, with the assurance of more real-time monitoring, some of the cost of water treatment can also be avoided. The potential saving to water treatment costs has not been quantified at this stage. Apart from the cost considerations, the most important aspect of intelligent systems is the ongoing analysis and monitoring of the system that can alert the operators for any eventuality which otherwise can take days before being noticed. Intelligence Direct Economic Values Flow On Values Water Quality More efficient monitoring and treatment Reduced likelihood of major contamination event Public health benefits Environmental benefits Ability to address water quality perceptional challenges Infrastructure Maintenance Reduced economic level of leakage More efficient asset maintenance Reduced externality costs, i.e., impacts on broader community linked to disruptive events such as a burst water main Improved Information Optimised consumption and demand management Improved capital planning Enhanced consumer satisfaction Reduced water scarcity Increased competitive forces Two examples of some of the quantifiable value of intelligent water networks are in water quality monitoring and asset management.
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