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Modeling, statistical analyses, and life cycle assessment of anaerobic bioreactors for the treatment of organic wastes and resource recovery
Callahan, Jennie L.
Callahan, Jennie L.
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2023
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Abstract
Increased application of anaerobic bioreactors can accelerate the transformation of wastewater treatment to energy-efficient resource recovery. These technologies are viable alternatives to traditional aerobic wastewater treatment practices due to their ability to generate methane-rich biogas from the microbial decomposition of organic matter within waste without the need for costly aeration. The biogas can be captured and used in heating or electricity production, potentially eliminating the need for consumption of external fossil fuel-based electricity or natural gas. Barriers to implementation of anaerobic treatment methods may include costs associated with upgrading existing facilities, lack of knowledge of how the treatment processes work or not knowing how adoption may benefit a particular facility. To overcome these barriers and bring anaerobic bioreactors into mainstream use, decision support tools are needed. Computer models and simulations, including life-cycle analysis for environmental impacts, can generate predictions regarding treatment abilities, methane production, carbon dioxide emissions, and costs. These predictions can be used by decision makers to help determine if implementation of anaerobic bioreactors is the best decision for them.
To address these research needs, this dissertation creates decision support tools through multiple modeling methods of anaerobic bioreactors, including computer simulation, statistical analyses, waste characterization, and life cycle assessment. The bioreactors examined during the research included three pilot-scale anaerobic baffled reactors (ABRs) treating wastewater in Colorado and full-scale anaerobic co-digestion at a water resource recovery facility in New York. Outcomes of the study of the ABRs include successful modeling of constituent removal and methane generation within 9% through identification and modification of key default parameters within a commonly used wastewater treatment computer simulation program. Additionally, statistical analysis of the wastewater characteristics and performance of the three ABRs identified distinct differences between the systems, but also average constituent removal efficiencies, effluent concentrations, and methane generation. These results can be used to assist with the design and operation of future pilot- or full-scale ABRs operating in colder climates. Finally, three waste streams (wastewater sludge, food, and fats, oils, and grease wastes) were characterized and used to develop a stoichiometric model for methane generation, energy production, and environmental impacts associated with anaerobic co-digestion, with adjustable parameters for use in future research.
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