Author ORCID Identifier

https://orcid.org/0000-0002-7197-0608

Semester

Fall

Date of Graduation

2023

Document Type

Dissertation

Degree Type

PhD

College

Statler College of Engineering and Mineral Resources

Department

Civil and Environmental Engineering

Committee Chair

Kevin Orner

Committee Member

Lian-Shin Lin

Committee Member

Leslie Hopkinson

Committee Member

Emily Garner

Committee Member

Stetson Rowles

Abstract

Conventional organic waste management practices such as landfilling contribute to global warming and degrade environmental and human health. Alternatively, organic waste can be treated to recover energy, nutrients, and carbon through resource recovery technologies. Data science could benefit such technologies by improving their efficiency through data-driven process modeling along with reducing environmental and economic burdens via life cycle assessment (LCA) and techno-economic analysis (TEA), respectively.

A critical review of 616 peer-reviewed articles published during 2002–2022 was conducted on the use of data science methods in resource recovery from organic waste. Although applications of machine learning (ML) methods have drastically increased over time for modeling resource recovery technologies, the reviewed studies exhibited significant knowledge gaps at various model development stages. In terms of sustainability analysis, an increasing number of studies included LCA with TEA to quantify both environmental and economic impacts, although focusing less on sensitivity and uncertainty. Based on such lessons learned from the reviewed literature, an actionable guide was developed for practitioners to effectively leverage data science methods in resource recovery. The guide revealed two prevailing knowledge gaps in terms of providing adequate contexts for implementing sustainable organic waste management: lack of social impact assessment (SIA) and integration of ML methods with LCA and TEA. Addressing these gaps can help promote sustainable resource recovery using both commercially available and emerging technologies in rural regions with limited technical and financial resources.

To promote commercially available technologies in rural regions, a community-centric approach was followed with the hypothesis that LCA and TEA methods informed by stakeholder engagement and integrated with environmental and economic justice-based social factors can inform context-sensitive sustainable organic waste management. The hypothesis was tested in Hardy County (one of the largest agricultural regions in rural West Virginia) by comparing the sustainability of current organic waste management practices (landfilling, land application, and transportation) with three community-integrated anaerobic digestion scenarios. The quantified overall sustainability score (based on community-preferred weights of environmental, economic, and social factors) coincided with the community’s organic waste management perception, where the current practices received the second-highest score. The results were, therefore, useful in integrating community-informed knowledge with established sustainability analyses to promote relevant West Virginia policies to promote sustainable organic waste management in rural regions through anaerobic digestion.

To promote emerging technologies in rural regions, an industry-centric approach was followed with the hypothesis that integration of ML with LCA, TEA, and SIA can inform context-sensitive implementation of organic waste management. The hypothesis was tested for treating transported-out poultry litter in Hardy County by comparing two scenarios: pyrolysis and hydrothermal carbonization. A comparison of ML predictions and overall sustainability score (based on life cycle-based weights of environmental, economic, and social factors) suggested that pyrolysis would provide better trade-off between process efficiency and sustainability than hydrothermal carbonization when operating at 500 °C with a residence time of one hour and heating rate of 20 °C/min. The results were helpful in providing operational contexts for a farm-level pyrolysis facility in Hardy County (installed a decade ago) to scale-up its operation to County-level instead of switching to an alternative technology.

Overall, the findings of this dissertation can inform practitioners about the effective utilization of various data science methods and facilitate synergistic opportunities between agricultural communities and industries for sustainable organic waste management in rural regions.

Embargo Reason

Publication Pending

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