Access Type

Open Access Dissertation

Date of Award

January 2018

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Civil and Environmental Engineering

First Advisor

Yongli Zhang

Abstract

Integration of algae cultivation with wastewater treatment has received increasing interest as a cost-effective strategy for biofuel production. However, there has been no full assessment of algal biofuel production with wastewater on macro-scale by taking into account wastewater resources, land availability, CO2 emission resources, and geographic variation. This research addressed and evaluated the use of wastewater for algae cultivation, in terms of modeling and laboratory experiments. The first goal of this research was to develop a spatially explicit lifecycle model, by integrating life cycle assessment (LCA), and Geographic Information Systems (GIS) analysis, for the evaluation of the environmental and economic performance of algal biofuel production with wastewater across the whole U.S. (total 12,455 WWTPs are evaluated). Results indicate that growing algae in wastewater for biofuel production would be both environmentally and economically sustainable. The potential production of algal crude oil is 0.98 billion gallons/yr.

As the second goal, we performed laboratory experiments for a better understanding of the potential of using the heterotrophic and mixotrophic methods (mixotrophic uses both light and carbon sources for energy), in addition to biofilm attached growth in algal wastewater. Because one of the main issues in algae cultivation is the harvesting process which is energy intensive, the attached growth can help reduce the cost. In this research, we evaluated the results of growing attached algae in different material and surface roughness. This novel algae cultivation strategy, mixotrophic microalgae biofilm, can help improve the productivity and cost-efficiency of algal biofuel production. In contrast to previous methods, this improved approach can achieve high productivity at low cost by harnessing the benefits of mixotrophic growth’s high efficiency, i.e., capable of subsisting on inorganic and organic carbons thus unaffected by limited light, and microalgae biofilm’s low harvesting cost. Our results, as one of the first studies of this type, proved that microalgae biofilms under mixotrophic condition exhibited significantly higher productivity and quality of biofuel feedstock: 2-3 times higher of biomass yield, 2-10 times higher of lipid accumulation, and 40 – 60 % lower of ash content when compared to microalgae biofilms under autotrophic condition.

Finally, for the third goal, we have evaluated the potential of using life cycle optimization (LCO) and Machine Learning in the sustainability analysis. Availability of CO2 emission from coal and natural gas power plants has been evaluated to be used in HRAP in an LCO model. We considered a hypothetical model of providing CO2 gases to the nearby HRAP system. Environmental effects have been optimized based on the following minimizing function 1) energy needed for pumping wastewater and CO2 function 2) water needed to produce pipes, and finally, function 3) eutrophication potential. The primary results indicate that an HRAP close to the WWTP is the optimized condition in a distributed system on the CO2 pipe. The second challenge that we have evaluated in our research is the application of machine learning. We collected some of those independent parameters that affect the normalized CO2 generation. The results indicate that for different accuracy scenarios the model can predict very well. For example, considering the 75% accuracy (the absolute error between prediction and actual data is less than 25%), our machine learning model can accurately predict in 1,822 out of 2491 WWTPs. Besides that, our work resulted in some additional applications and award in the area of Internet of Things, community engagement, and data mining.

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