A stochastic model for sewer base flows using Monte Carlo simulation
| dc.contributor.advisor | Jacobs, H. E. | en |
| dc.contributor.author | Flores, Garth | en |
| dc.contributor.other | Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering. | en_ZA |
| dc.date.accessioned | 2015-05-20T09:13:52Z | |
| dc.date.available | 2015-05-20T09:13:52Z | |
| dc.date.issued | 2015-03 | en |
| dc.description | Thesis (MSc)--Stellenbosch University, 2015. | en_ZA |
| dc.description.abstract | ENGLISH ABSTRACT: This thesis deals with understanding and quantifying the components that make up sewage base flows (SBF). SBF is a steady flow that is ubiquitous in sewers, and is clearly seen when measuring the flow rate in the sewer between 03:00 and 04:00. The components of SBF are: ● return flow from residential night use, ● return flow from leaking plumbing, ● groundwater infiltration, ● stormwater inflow. By understanding each component of SBF, this research can answer the burning question as to how much of the SBF was due to plumbing leaks on residential properties. While previous work on SBF had been done, the work focused on groundwater ingress and stormwater inflows, and thus not much had been said about plumbing leaks. Furthermore, previous work focused on SBF as an isolated sewer related topic, whereas this research integrated SBF as both a sewer related topic and water conservation and demand management (WCDM) topic. Due to the high variability in each of the SBF components, a method of quantifying each component was developed using residential end-use modelling and Monte Carlo simulations. The author constructed the Leakage, Infiltration and Inflow Technique Model (LIFT Model). This stochastic model was built in MS Excel using the @Risk software add-on. The LIFT Model uses probability distributions to model the inflow variability. The results of the stochastic model were analysed and the findings discussed. This research can be used by water utilities as a tool to better understand the SBF in networks. Armed with this knowledge, water utilities could make informed decisions about how to best reduce the high SBF encountered in networks. | en_ZA |
| dc.description.abstract | AFRIKAANSE OPSOMMING: Hierdie verhandeling bespreek die begrip en berekening van die komponente van riool nagvloei. Die nagvloei was duidelik wanneer die vloei in die rioolstelsel tussen 03:00 en 04:00 gemeet is. Die verskillende komponente van die nagvloei is: ● huishoudelike gebruik, ● lekkende krane en toilette, ● grondwaterinfiltrasie, en ● stormwaterinvloei. ’n Begrip van die komponente van nagvloei kan die brandende vraag van hoeveel nagvloei die gevolg van lekkende krane en toilette is, na aanleiding van die navorsing beantwoord. Vorige werk het op beter begrip van die grondwaterinfiltrasie en stormwaterinvloei gefokus en lekke het nie veel aandag geniet nie. Vorige werk het net op nagvloei as geïsoleerde rioolonderwerp gefokus, terwyl hierdie navorsing nagvloei as ’n onderwerp wat met riool verband hou, sowel as ’n waterverbruik- en behoeftebestuursonderwerp, ondersoek. As gevolg van die groot verskil tussen elk van die komponente van die nagvloei, is ’n metode ontwikkel wat elke komponent kwantifiseer deur gebruik te maak van eindgebruik-modelle en Monte Carlo-simulasies. Die outeur het die Leakage Infiltration and Inflow Technique Model (LIFT-Model) gebou. Hierdie stogastiese model is in MS Excel, met behulp van die @Risk sagtewarebyvoeging gebou. Die LIFT-Model gebruik waarskynlikheidverspreidings om invloeivariasie te modelleer. Die resultate van die stogastiese model is ontleed en die bevindinge bespreek. Hierdie navorsing mag moontlik deur watervoorsieningsmaatskapye as instrument gebruik word om nagvloei in rioolstelsels beter te verstaan. Hierdie nuwe kennis kan watervoorsieningsmaatskapye in staat stel om ingeligte besluite te neem rakende die beste metodes om te volg om nagvloei te verminder. | af_ZA |
| dc.format.extent | 113 pages : illustrations | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/10019.1/96692 | |
| dc.language.iso | en_ZA | en_ZA |
| dc.publisher | Stellenbosch : Stellenbosch University | en_ZA |
| dc.rights.holder | Stellenbosch University | en_ZA |
| dc.subject | Sewage base flows (SBF) | en_ZA |
| dc.subject | Monte Carlo simulation | en_ZA |
| dc.subject | Sewers | en_ZA |
| dc.subject | UCTD | en_ZA |
| dc.title | A stochastic model for sewer base flows using Monte Carlo simulation | en_ZA |
| dc.type | Thesis | en_ZA |