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Real-Time Monitoring and Forecasting of Odor after Dewatering in Wastewater Treatment

dc.contributor.advisorBaecher, Gregory Ben_US
dc.contributor.authorJanpengpen, Ampunen_US
dc.description.abstractDistrict of Columbia Water and Sewer Authority (DCWASA) operates an advanced wastewater treatment plant that treats approximately 370 mgd of wastewater and generates over 400,000 wet tons of biosolids each year. The reduction of biosolids odors at the plant before distribution to the field sites is therefore, a high priority. Each unit process of a wastewater treatment plant has the potential to generate odor-causing compounds. Total Reduced Sulfur is one of the main components of the odor-generated by wastewater treatment facilities and is the dewatered solids emissions monitored by this study. Many studies have indicated that odors are generated from the upstream processes and from storage, but this research presents information about the odors arising after dewatering. It demonstrates a statistical model that forecasts which factors are significant contributions to odors and which can be used to forecast biosolids odor. The model indicates that secondary west odd blanket depth is the most significant factor for odor level after dewatering and at the depth more than 1.8 ft will be caused more odors. In addition, this research provides basic information to compare odor after lime addition which is the next phase of research, and this model can assist DCWASA to schedule routes for distribution of biosolids to field sites.en_US
dc.format.extent1454339 bytes
dc.titleReal-Time Monitoring and Forecasting of Odor after Dewatering in Wastewater Treatmenten_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentCivil Engineeringen_US
dc.subject.pqcontrolledEngineering, Civilen_US
dc.subject.pquncontrolledreal-time monitoringen_US
dc.subject.pquncontrolledtotal reduced sulfuren_US
dc.subject.pquncontrolledmultiple regression modelen_US

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