dc.contributor.author | Bertone, E | |
dc.contributor.author | O'Halloran, K | |
dc.contributor.author | De Oliveira, GF | |
dc.date.accessioned | 2018-01-10T02:33:07Z | |
dc.date.available | 2018-01-10T02:33:07Z | |
dc.date.issued | 2016 | |
dc.identifier.isbn | 9788890357459 | |
dc.identifier.uri | http://hdl.handle.net/10072/124203 | |
dc.description.abstract | This paper describes a comprehensive raw water intake selection decision support tool
which has been developed for a dual source drinking water treatment plant (WTP) in South-East
Queensland, Australia. The WTP receives water from small Little Nerang (LND) dam by gravity and
from the upper intake of Hinze dam (HUI) via electrical pumps. The core part of the optimisation model
can predict treatment chemical dosages and costs given the quality of the raw water source, through a
number of data-driven, chemical and mathematical models. This prediction tool was run over an
historical data set and it was found that an optimal intake selection would imply an increased use of
the LND source. However, WTP operators typically minimise the LND usage due to its limited storage
capacity and thus high depletion risk in case of intensive withdrawal rates and unfavourable weather
conditions (i.e. no rain). As a consequence, a probabilistic data-driven model was also developed,
which predicts, 6 weeks ahead, the most likely volume of LND. The model is based on historical data
correlations and takes as inputs the seasonal streamflow forecast from the Bureau of Meteorology, as
well as prescribed raw water intake volumes. A Monte-Carlo approach is used to account for
uncertainty. Hence, whenever LND is selected as the optimal source, WTP decision-makers can
select the optimal intake volume in order to minimise treatment costs, but also the risks of wasteful
dam releases or depletion of LND. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.publisher | International Environmental Modelling & Software Society (iEMSs) | |
dc.publisher.place | United States | |
dc.publisher.uri | http://www.iemss.org/sites/iemss2016/ | |
dc.relation.ispartofconferencename | iEMSs 2016 | |
dc.relation.ispartofconferencetitle | Environmental Modelling and Software for Supporting a Sustainable Future, Proceedings - 8th International Congress on Environmental Modelling and Software, iEMSs 2016 | |
dc.relation.ispartofdatefrom | 2016-07-10 | |
dc.relation.ispartofdateto | 2016-07-14 | |
dc.relation.ispartoflocation | Toulouse, France | |
dc.relation.ispartofpagefrom | 721 | |
dc.relation.ispartofpageto | 728 | |
dc.relation.ispartofvolume | 3 | |
dc.subject.fieldofresearch | Water resources engineering | |
dc.subject.fieldofresearchcode | 400513 | |
dc.title | Data-driven dam water level forecasting and intake optimisation models for proactive water treatment management | |
dc.type | Conference output | |
dc.type.description | E1 - Conferences | |
dc.type.code | E - Conference Publications | |
dc.description.version | Version of Record (VoR) | |
gro.faculty | Griffith Sciences, Griffith School of Engineering | |
gro.rights.copyright | Copyright remains with the author(s) 2016. The attached file is reproduced here in accordance with the copyright policy of the publisher. For information about this conference please refer to the conference’s website or contact the author(s). | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Bertone, Edoardo | |