ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
Displaying 61 - 63 of 63
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- Creators: Fox, Peter
Description
Large-scale cultivation of photosynthetic microorganisms for the production of biodiesel and other valuable commodities must be made more efficient. Recycling the water and nutrients acquired from biomass harvesting promotes a more sustainable and economically viable enterprise. This study reports on growing the cyanobacterium Synechocystis sp. PCC 6803 using permeate obtained from concentrating the biomass by cross-flow membrane filtration. I used a kinetic model based on the available light intensity (LI) to predict biomass productivity and evaluate overall performance.
During the initial phase of the study, I integrated a membrane filter with a bench-top photobioreactor (PBR) and created a continuously operating system. Recycling permeate reduced the amount of fresh medium delivered to the PBR by 45%. Biomass production rates as high as 400 mg-DW/L/d (9.2 g-DW/m2/d) were sustained under constant lighting over a 12-day period.
In the next phase, I operated the system as a sequencing batch reactor (SBR), which improved control over nutrient delivery and increased the concentration factor of filtered biomass (from 1.8 to 6.8). I developed unique system parameters to compute the amount of recycled permeate in the reactor and the actual hydraulic retention time during SBR operation. The amount of medium delivered to the system was reduced by up to 80%, and growth rates were consistent at variable amounts of repeatedly recycled permeate. The light-based model accurately predicted growth when biofilm was not present. Coupled with mass ratios for PCC 6803, these predictions facilitated efficient delivery of nitrogen and phosphorus. Daily biomass production rates and specific growth rates equal to 360 mg-DW/L/d (8.3 g/m2/d) and 1.0 d-1, respectively, were consistently achieved at a relatively low incident LI (180 µE/m2/s). Higher productivities (up to 550 mg-DW/L/d) occurred under increased LI (725 µE/m2/s), although the onset of biofilm impeded modeled performance.
Permeate did not cause any gradual growth inhibition. Repeated results showed cultures rapidly entered a stressed state, which was followed by widespread cell lysis. This phenomenon occurred independently of permeate recycling and was not caused by nutrient starvation. It may best be explained by negative allelopathic effects or viral infection as a result of mixed culture conditions.
During the initial phase of the study, I integrated a membrane filter with a bench-top photobioreactor (PBR) and created a continuously operating system. Recycling permeate reduced the amount of fresh medium delivered to the PBR by 45%. Biomass production rates as high as 400 mg-DW/L/d (9.2 g-DW/m2/d) were sustained under constant lighting over a 12-day period.
In the next phase, I operated the system as a sequencing batch reactor (SBR), which improved control over nutrient delivery and increased the concentration factor of filtered biomass (from 1.8 to 6.8). I developed unique system parameters to compute the amount of recycled permeate in the reactor and the actual hydraulic retention time during SBR operation. The amount of medium delivered to the system was reduced by up to 80%, and growth rates were consistent at variable amounts of repeatedly recycled permeate. The light-based model accurately predicted growth when biofilm was not present. Coupled with mass ratios for PCC 6803, these predictions facilitated efficient delivery of nitrogen and phosphorus. Daily biomass production rates and specific growth rates equal to 360 mg-DW/L/d (8.3 g/m2/d) and 1.0 d-1, respectively, were consistently achieved at a relatively low incident LI (180 µE/m2/s). Higher productivities (up to 550 mg-DW/L/d) occurred under increased LI (725 µE/m2/s), although the onset of biofilm impeded modeled performance.
Permeate did not cause any gradual growth inhibition. Repeated results showed cultures rapidly entered a stressed state, which was followed by widespread cell lysis. This phenomenon occurred independently of permeate recycling and was not caused by nutrient starvation. It may best be explained by negative allelopathic effects or viral infection as a result of mixed culture conditions.
ContributorsThompson, Matthew (Author) / Rittmann, Bruce E. (Thesis advisor) / Fox, Peter (Committee member) / Krajmalnik-Brown, Rosa (Committee member) / Arizona State University (Publisher)
Created2015
Description
This dissertation presents a new methodology for the sustainable and optimal allocation of water for a river basin management area that maximizes sustainable net economic benefit over the long-term planning horizon. The model distinguishes between short and long-term planning horizons and goals using a short-term modeling component (STM) and a long term modeling component (LTM) respectively. An STM optimizes a monthly allocation schedule on an annual basis in terms of maximum net economic benefit. A cost of depletion based upon Hotelling’s exhaustible resource theory is included in the STM net benefit calculation to address the non-use value of groundwater. An LTM consists of an STM for every year of the long-term planning horizon. Net economic benefits for both use and non-use values are generated by the series of STMs. In addition output from the STMs is measured in terms of sustainability which is quantified using a sustainability index (SI) with two groups of performance criteria. The first group measures risk to supply and is based on demand-supply deficits. The second group measures deviations from a target flow regime and uses a modified Hydrologic Alteration (HA) factor in the Range of Variability Approach (RVA). The STM is a linear programming (LP) model formulated in the General Algebraic Modeling System (GAMS) and the LTM is a nonlinear programming problem (NLP) solved using a genetic algorithm. The model is applied to the Prescott Active Management Area in north-central Arizona. Results suggest that the maximum sustainable net benefit is realized with a residential population and consumption rate increase in some areas, and a reduction in others.
ContributorsOxley, Robert Louis (Author) / Mays, Larry (Thesis advisor) / Fox, Peter (Committee member) / Johnson, Paul (Committee member) / Murray, Alan (Committee member) / Arizona State University (Publisher)
Created2015
Description
Water quality assessment is essential for maintaining healthy ecosystems and protecting human health. Data interrogation and exploratory data analysis techniques are used to analyze the spatial and temporal variability of water quality parameters, identifying correlations, and to better understand the factors that impacts microbial and chemical quality of water. The seasonal dynamics of microbiome in surface waters were investigated to identify the factors driving these dynamics. Initial investigation analyzed two decades of regional water quality data from 20 various locations in Central Arizona, USA. Leveraging advanced data science techniques, the study uncovered correlations between crucial parameters, including dissolved organic carbon (DOC), ultraviolet absorbance (UVA), and specific ultraviolet absorbance (SUVA). These findings provide foundational insights into the dynamic of overall water quality. A comprehensive 12-month surface water sample collection and study was conducted to investigate potential bias in bacterial detection using EPA approved Membrane Filtration (MF) technique. The results underscore that while MF excels in recovering bacteria of public health significance, it exhibits biases, particularly against small and spore-forming bacteria and Archaea, such as Bacilli, Mollicutes, Methylacidiphilae, and Parvarchaea. This emphasizes the importance of complementing standard microbiology approaches to mitigate technological biases and enhance the accuracy of microbial water quality testing, especially for emerging pathogens. Furthermore, a complementary study of microbial dynamics within a model drinking water distribution systems (DWDSs) using treated water from the same source water as the above study. The influence of pipe material and water temperature on the microbiome and trace element composition was investigated. The research unveiled a preferential link between pipe material and trace elements, with water temperature significantly impacting the microbiome to a higher degree than the chemical composition of water. Notably, Legionellaceae and Mycobacteriaceae were found to be prevalent in warmer waters, highlighting the substantial influence of water temperature on the microbiome, surpassing that of pipe material. These studies provide comprehensive insights into the spatial and temporal variability of water quality parameters. Analyzing microbial data in depth is crucial in detecting bacterial species within a monitoring program for adjusting operational conditions to reduce the presence of microbial pathogens and enhance the quality of drinking water.
ContributorsAloraini, Saleh (Author) / Abbaszadegan, Morteza (Thesis advisor) / Fox, Peter (Committee member) / Perreault, Francois (Committee member) / Alum, Absar (Committee member) / Arizona State University (Publisher)
Created2023