Pregled bibliografske jedinice broj: 1266711
Linear and Non-Linear Modelling of Bromate Formation during Ozonation of Surface Water in Drinking Water Production
Linear and Non-Linear Modelling of Bromate Formation during Ozonation of Surface Water in Drinking Water Production // Water, 15 (2023), 8; 1516, 16 doi:10.3390/w15081516 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1266711 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Linear and Non-Linear Modelling of Bromate Formation during Ozonation of Surface Water in Drinking Water Production
Autori
Gregov, Marija ; Jurinjak Tušek, Ana ; Valinger, Davor ; Benković, Maja ; Jurina, Tamara ; Surać, Lucija ; Kurajica, Livia ; Matošić, Marin ; Gajdoš Kljusurić, Jasenka ; Ujević Bošnjak, Magdalena ; Ćurko, Josip
Izvornik
Water (2073-4441) 15
(2023), 8;
1516, 16
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
ozonation ; modelling ; drinking water ; artificial neural network ; bromate
Sažetak
Bromate formation is a complex process that depends on the properties of water and the ozone used. Due to fluctuations in quality, surface waters require major adjustments to the treatment process. In this work, we investigated how the time of year, ozone dose and duration, and ammonium affect bromides, bromates, absorbance at 254 nm (UV254), near-infrared (NIR) spectra, and fluorescent components (humic-like and tyrosine-like) during surface water ozonation. Linear and non-linear models were used to determine and predict the relationships between input and output variables. Season, ozonation dose and time were correlated with the output variables, while ammonium affected only bromates. All coefficients of determination (R2) for the multiple linear regression models were >0.64, while R2 for the piecewise linear regression models was >0.89. The season had no effect on bromate formation in either model, while ammonium only affected bromides and bromates. Three input variables influenced UV254 in both models. The artificial neural network (ANN) model with the season, ozonation dose and time, ammonium, and NIR spectra was an effective way to describe water ozonation results. The multilayer perception neural network 14-14-5 had the lowest errors and was the best ANN model with R2 values for training, testing, and validation of 0.9916, 0.9826, and 0.9732, respectively.
Izvorni jezik
Engleski
Znanstvena područja
Prehrambena tehnologija, Interdisciplinarne biotehničke znanosti
POVEZANOST RADA
Projekti:
MZOE-KK.05.1.1.02.0003 - Ublažavanje negativnih utjecaja klimatskih promjena na obradu voda površinskih akumulacija pri dobivanju vode za ljudsku potrošnju flokulacijom i ozoniranjem (Ujević Bošnjak, Magdalena; Matošić, Marin, MZOE - Shema za jačanje primijenjenih istraživanja za mjere prilagodbe klimatskim promjenama) ( CroRIS)
Ustanove:
Hrvatski zavod za javno zdravstvo,
Prehrambeno-biotehnološki fakultet, Zagreb
Profili:
Josip Ćurko
(autor)
Tamara Jurina
(autor)
Davor Valinger
(autor)
Ana Jurinjak Tušek
(autor)
Livia Kurajica
(autor)
Maja Benković
(autor)
Magdalena Ujević Bošnjak
(autor)
Marin Matošić
(autor)
Marija Gregov
(autor)
Jasenka Gajdoš Kljusurić
(autor)
Citiraj ovu publikaciju:
Časopis indeksira:
- Current Contents Connect (CCC)
- Web of Science Core Collection (WoSCC)
- Science Citation Index Expanded (SCI-EXP)
- SCI-EXP, SSCI i/ili A&HCI
- Scopus