An interesting challenge facing many water authorities is the infiltration of storm water into the sewer system, which substantially increases treatment costs (energy and chemical) and the risk of spills.
There are standard formulas for analyzing rainfall dependent infiltration and inflow (RDII) that can both measure the impact and evaluate effectiveness of remedial activity, but these are complex and time consuming to do manually.
The challenge the Intelligent Water Networks (IWN) faced was to implement an effective RDII algorithm to make this data available to managers in real time.
Capturing data and implementing user-configurable analyses in the PI System is a straightforward task. In this case the fairly static catchment area data was integrated together with rainfall and treatment plant data and a standard algorithm for RDII analysis was implemented using the PI System Analyses tools. In very simple terms the ‘normal’ treatment plant flow was compared with the flow following rainfall events to measure the volume of stormwater infiltrating each system.
The Big Data and Analytics trial demonstrated the ability of OT & IT data sources to collect related data into a single model, the calculation of RDII and ground water infiltration using agreed algorithms, the backfilling of analyses using data from the past two years and presentation of data in real time dashboards.
The obvious benefits are the substantial time saving in generating the data and having access to real time, and historical RDII data so that comparisons can be made before and after remedial action. IWN expect this to provide efficiency in automation of the process and assist with strategic decision making in terms of prioritising remedial activity or changing the approach to remediation – for example if evidence suggests limited benefit in current practices.
Having evidence of the actual costs of stormwater infiltration on water treatment, and the value of remediation, may influence budget decisions around funding remediation in worst affected areas.
Long term IWN would expect that remedial activity can be directed to the areas most affected ultimately reducing water treatment costs and lowering risk of a spill.
Data & Analytics Program Lead David Bergmann said: “comparison of internal data with external data such as weather information was always a tedious and manual exercise, now we have the opportunity to integrate with internal operational data for new insights, problem solving and efficiency gains.”
For further information contact the D&A Program Lead - firstname.lastname@example.org.