Sand-Cycle receives positive feedback in Norwegian pilot research

Sand-cycle receives positive feedback of researchers that have used Sand-Cycle in their pilot testing with continuous sand filtration at WwTW Gardermoen in Norway. They are enthusiastic about the use of this tool and reported their positive feedback.

International Water Association (IWA) publication

The research, executed on behalf of the Regional Research Council of the city of Oslo, focused upon nitrogen en phosphorus removal in a continuous sand filter after MBBR treatment. Very low effluent levels were consistently established; results have been published by IWA in the 80th Volume, Issue 2, of Water Science & Technology (see this link: https://doi.org/10.2166/wst.2019.266 to read the full article).\

Robust and easy to use tool

The research team indicated Sand-Cycle to be a very convenient tool for monitoring good operating filter conditions. The results are reliable and, in addition, Sand-Cycle contributed to the quality and intensity of the research. Quick interventions were possible, once Sand-Cycle detected process anomalies, reducing valuable research off-time.

These positive experiences with Sand-Cycle confirm what other users already have experienced. Sand-Cycle frequently receives positive feedback from other international references.

Sand-Cycle dashboard

The Sand-Cycle dashboard below clearly illustrates the recorded distinction between a good and bad performing filter. The scatter between the average and the standard deviation values indicates the homogenity level of the system. Greater spread (thicker band) implies that few parts of the filters flow significantly faster or slower. This case of thick band also depicts relatively lower performance level of the filter.

Sand-Cycle receives positive feedback

The figure above is a Sand-Cycle dashboard of the pilot at RWZI Gardermoen on March 1st 2018. The graph shows the average sand circulation speed (blue line) during 48 hours. In addition, it shows the related standard deviation (upper and lower gray lines). In this way, this graph gives real-time insight whether the filter is running good or bad.

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