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Optimizing a Chemically Enhanced Primary Treatment Process for Operational Decision Support

dc.contributor.authorMcDonald, Jane
dc.contributor.copyright-releaseYesen_US
dc.contributor.degreeMaster of Applied Scienceen_US
dc.contributor.departmentDepartment of Environmental Engineeringen_US
dc.contributor.ethics-approvalNot Applicableen_US
dc.contributor.external-examinerN/Aen_US
dc.contributor.manuscriptsNot Applicableen_US
dc.contributor.thesis-readerMargaret Walshen_US
dc.contributor.thesis-readerKhaled Benisen_US
dc.contributor.thesis-supervisorAmina Stoddarten_US
dc.date.accessioned2024-08-08T17:33:59Z
dc.date.available2024-08-08T17:33:59Z
dc.date.defence2024-08-06
dc.date.issued2024-08-06
dc.description.abstractThe Wastewater System Effluent Regulations set national effluent water quality standards for wastewater treatment facilities in 2015. This stated effluent discharge limits of less than 25 mg/L for both total suspended solids (TSS) and carbonaceous biochemical oxygen demand (cBOD5). This led to a collaboration between Dalhousie University and Halifax Water to optimize treatment at the Dartmouth Wastewater Treatment Facility (DWWTF). The goal of this study was to provide data-driven decision support for operations at the DWWTF to optimize TSS removal. This is the first step prior to the implementation of a secondary biological treatment process to minimize the subsequent solids load and allow for the target of cBOD5 removal. The study resulted in the creation of two decision trees that may be refined and used for operational decision support regarding optimal chemical dosing during average daily flow (< 3750 m3/h) and average wet weather flows (> 3750 m3/h).en_US
dc.identifier.urihttp://hdl.handle.net/10222/84390
dc.language.isoenen_US
dc.subjectChemically Enhanced Primary Treatmenten_US
dc.subjectWastewateren_US
dc.subjectTotal Suspended Solidsen_US
dc.subjectWastewater System Effluent Regulationsen_US
dc.subjectOptimizationen_US
dc.titleOptimizing a Chemically Enhanced Primary Treatment Process for Operational Decision Supporten_US
dc.typeThesisen_US

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