Izvorni znanstveni članak
https://doi.org/10.30765/er.2232
Prediction models for manganese, iron and ammonium in raw water for a drinking water treatment plant Butoniga (croatia)
Goran Volf
orcid.org/0000-0002-7058-9012
; Department for Hydrotehnics, Faculty of Civil Engineering, University of Rijeka, Radmile Matejčić 3, 51000 Rijeka, Croatia
Morana Krbavčić
; Department for Hydrotehnics, Faculty of Civil Engineering, University of Rijeka, Radmile Matejčić 3, 51000 Rijeka, Croatia
Ivana Sušanj Čule
; Department for Hydrotehnics, Faculty of Civil Engineering, University of Rijeka, Radmile Matejčić 3, 51000 Rijeka, Croatia
Sonja Zorko
; Istarski Vodovod d.o.o., 52420 Buzet, Croatia
Sažetak
Drinking water treatment plant Butoniga is one of the main water supply facilities for potable water in Istria (Croatia). Water for treatment process is captured from the Butoniga reservoir which is a small and relatively shallow reservoir. As such, the reservoir is very sensitive to eutrophication and degradation processes caused by climate change and human activities in the watershed. In summer months during tourist season, when at highest water demand and lowest water level at the reservoir, the water temperature is the most critical parameter during treatment process. To capture colder water, raw water for treatment is taken from the lowest water intake, i.e. from the deepest layer in the Butoniga reservoir. This layer has another problem, namely increased concentrations of manganese, iron and ammonium under lower pH values. This study provides prediction models for manganese, iron and ammonium for seven days in advance, which are some of the most critical parameters during summer months and have significant influence on treatment process of raw water. For modelling purposes, machine learning software Weka was used to build models in form of model trees. Obtained prediction models for manganese, iron and ammonium have high accuracy compared to the measured data with a good prediction of the peak values. Therefore, obtained models can help in optimization of the treatment processes at the treatment plant, which are depending on the quality of raw water in Butoniga reservoir.
Ključne riječi
drinking water treatment plant; prediction models; manganese; iron; ammonium; butoniga reservoir; machine learning; model trees; performance and optimization; treatment processes
Hrčak ID:
311078
URI
Datum izdavanja:
11.12.2023.
Posjeta: 256 *