Real-time water data implementation on the rise
Posted 27 September 2016
The water industry now has access to highly sophisticated online water quality monitoring schemes, but is not necessarily making the most of them.
To improve outcomes, a team of industry experts has spent the last three years collaborating on a Water Research Australia (WaterRA) project – Optimisation of Existing Instrumentation to Achieve Better Process Performance
WaterRA and the Water Quality Monitoring and Analysis (WQMA) Network will share the findings in an upcoming AWA webinar on water quality monitoring
“A lot of online monitoring is collected into databases and it is often reviewed historically,” said Deputy Project Leader Rolando Fabris.
“But because of the capabilities with rapid measurement and short measurement intervals it should be possible to use these to make more informed decisions in real time. We just need the framework and software support to allow us to do that.”
The project came about after industry concerns were raised at the WQMA Network’s Ozwater’13 insight workshop.
At that time, industry members’ priorities were to know how to predict failure modes of online instrumentation, how to deal with sensor drift, how frequently to calibrate, how to manage data and simplify instrument operation.
To answer these questions, the project consolidated latest research on online monitoring and process optimisation management in order to develop operational management principles.
“The [AWA] webinar
is going to present some practical tools that the project participants have put together in terms of guidance on how to operate the instruments and maintain them in a way that meets quality control guidelines,” Fabris said.
“A large proportion of [the webinar] is how to deal with your data, ways of using it for error detection and ways of getting real time graphical representations of your data so that you can be making operational decisions from it.”
Later this year, the information will be collated into a framework for data integration and improving process performance with computational modules for smart decision making.