Yuan Yao PhD

Biochar is a carbon-rich byproduct of thermochemical biomass conversions, and is closely linked to the Food-Energy-Water (FEW) nexus through its potential applications in wastewater treatment, agriculture management, and bioenergy, as well as indirect benefit in mitigating climate change. Although biochar has a potential to transform existing FEW nexus into more efficient and sustainable systems, it has not been widely implemented due to the lack of understandings in technical performance, economic feasibility, environmental impacts, and social implications of different combinations of biomass species, conversion technologies, and biochar applications. Such understanding is very hard to be obtained using traditional Life Cycle Assessment (LCA) or Techno-Economic Analysis (TEA) approaches due to intensive needs of process data and methodological limitations in integrating temporal, spatial, and socioeconomic dimensions. This project aims to address the knowledge gaps and methodological challenges by (1) using machine learning approaches to simulate and predict technical performance and life cycle inventory (LCI) of various combinations of biomass species, conversion technologies, process design, operational conditions, and applications of biochar; (2) building an integrated framework that seamlessly incorporate predictive LCA, TEA, Geographic Information System (GIS), and dynamic modeling to evaluate the environmental, economic, and social implications of biochar systems; (3) demonstrating the framework through real-world case studies in different geographic, temporal, and socioeconomic context.

This proposal aims to develop bi-functional oxygen and CO2 sorbents for chemical looping gasification of solids wastes with in-situ syngas conditioning. The novel material and gasification system will eliminate the needs for air separation and syngas conditioning/separation operations. The resulting syngas can readily be used for methane formation. A circulating fluidized bed gasification system and suitable bi-functional sorbents will be developed and demonstrated.

Stormwater runoff threatens public health in several ways, but mitigating these challenges is difficult. Climate change is increasing the frequency and uncertainty around storm events, which makes stormwater damage more difficult to anticipate and manage. Green infrastructure (GI) such as rain gardens, catchment ponds or other strategic landscaping is a good alternative to traditional stormwater management. GI not only provide ecosystem services in stormwater management, but also offers other ecosystem services such as air pollutant removal, urban heat mitigation, wildlife habitat creation. More importantly, it may provide educating value and potentially increase environmental awareness. School systems present a unique opportunity to implement GI. The impervious surface in school grounds represents both a significant contribution to stormwater run-off, as well as an opportunity to install a network of GI. However school systems face the financial challenge for GI implementation and long-term management. To facilitate GI practice in school systems, building cross-sector support for GI is important. There is a need to highlight other benefits for GI (e.g., academic learning, student well-being) to provide justification as well as opportunities for partnership across sectors. This study aims at understanding the range of potential benefits represented in placing GI on school grounds. By uncovering what GI that exists on school grounds and identifying the preferred environment, usage, activities teachers and students assigned to GI, we anticipate making recommendations for how schools initiating GI project may better design for stormwater management as well as outdoor play and education.

The chemical industry is one of the most energy-intensive manufacturing industry and major sources of global greenhouse gas (GHG) emissions. The increasing demand for energy and more severe environmental problems are promoting the development and adoption of emerging technologies in the chemical industry to reduce energy consumption and adverse environmental impacts. Artificial Intelligence (AI) is one of the emerging technology that shows great potential in further reduce the energy consumption and environmental footprints for the chemical industry, especially for those energy intensive commodity chemicals such as ammonia, ethylene, propylene, methanol, etc. However, the lack of credible performance analysis data and baseline information for emerging technologies can deter policymakers and early adopters, whose investments are crucial for accelerating deployment (Martin et al., 2000). Addressing these data and analysis gaps is critical for improving emerging technology adoption such as AI during the coming wave of capital investment. This project aims to address these gaps by developing a metric-based framework to quantify energy and environmental implications of AI applications in the chemical industry, especially for those energy-intensive commodity chemicals.

Increasing consumers’ awareness of the environment and sustainability drives the development of more environmentally friendly products across almost every industrial sector. Recycling and closing the material loop (so-called “circular economy”) is a strong and growing trend. This project aims to identify promising applications for recycled cotton with low environmental footprints, desirable life-cycle costs, and high market and technical potential.

Woody biomass is one of the most abundant organic sources on earth, and it has a large potential to produce bioenergy and bio-based products to replace fossil-based counterparts. Understanding the environmental impacts of bioenergy and biochemicals derived from woody biomass is critical for stakeholders (e.g., policymakers, landowners, energy and chemical companies) to make decisions related to policy and technology development. Life Cycle Assessment (LCA) is a standardized method to evaluate the environmental impacts of a product’s life-cycle, and has been widely used to understand the environmental benefits/trade-offs of different biomass applications. The use of woody biomass to product bioenergy and biochemicals creates an opportunity economic and regional advantages, but adds significant complexity to the LCA. In particular, the growth rate of the woody biomass, the decay of residues not used for a product or fuel, and the large variations in the quality and performance of the woody biomass in a conversion process all add complexity. Both regrown and decay can take place of decades makes the sequencing of the analysis critical. Biomass supply chains also have many uncertainties and system variations (e.g., biomass transportation, site preparation, etc.) that will have significant impacts on the LCA results. The goal of the project is to understand the LCA attributes around different woody biomass production systems and quantify the impacts of uncertainty on the LCA results.

The overall goal of this work is to provide DOE with high-quality information that allows for a detailed, comprehensive analysis of the benefits and liabilities of using woody feedstocks for the production of biofuels on a regionally specific basis. Specifically, this work will provide 1. data that allows for comprehensive LCA evaluation of the implications of using forest and manufacturing residues from current, regionally specific commercial systems for the production of biofuels. These systems include softwoods in the Pacific Northwest (PNW) and Southeast (SE) US, and Northeastern (NE) US. 2. data on the potential for using regionally-specific dedicated SRWC for production biofuels, e.g., Poplar in the PNW, Eucalyptus in the SE and Willow in the NE. 3. an LCA of the impacts of using woody biomass as a feedstock for different biochemical and thermochemical biofuels production processes 4. an analysis of the impacts of natural variations in wood composition and pretreatments production scenarios on the LCA of wood based biofuels 5. an analysis of the GHG implications of using woody feedstocks for the production of both biofuels and the current commercial suite of short-lived and durable wood products. Taken as a whole this research will provide a definitive assessment of the technical, and environmental, impacts of broad use of woody biomass for the production of transportation fuels. This assessment is necessary if forest biomass is to be widely used for biofuels that require, in advance, an understanding of the consequences of such a course of action. In addition, Life Cycle Inventory data and Assessments (LCI/LCA) on greenhouse gas emissions will be necessary to understand qualification of biofuels made from forest based biomass under the Energy Independence and Security Act (EISA) of 2007. The impacts of different policies and other alternatives management strategies will be characterized as sensitivity scenarios to better inform the adoption of appropriate policies, marketing, and investment strategies to reach energy independence goals with reduced greenhouse gas (GHG) emissions while effectively managing cellulosic resources.

Energy and water are two critical sources for food production, developing sustainable agriculture system requires a wise management and balance among food, energy, and water systems. Intensive efforts have been made by the research community, government agencies, and the industry to generate data to meet the needs of various stakeholders, but such data is highly scattered and have not been integrated yet for the potential application of big data. We believe many solutions to addressing complex sustainability issues in U.S. food supply chains, especially those related to water and energy use, could be generated through the integration of different datasets and by providing easy-accessing, knowledge-sharing data management platforms or frameworks for decision makers in policy, academic, and industrial communities. In this project, we propose to host a 1.5 days workshop to gather experts from government agencies, academia, and the industry to discuss, brainstorm, and identify critical issues and future directions of big data investment for addressing FEW challenges. The topic is highly aligned with multiple program area priorities, such as bioenergy, natural resources, and environment, agriculture economics and rural communities, critical agricultural research and extension, and agriculture systems and technology. Insights generated in the workshop will be significantly helpful for USDA to identify priorities, future barriers, and funding needs to benefit U.S. food and agriculture, energy, and water sectors.

Torrefaction has gained commercial interest in the past few years due to its potential to become a renewable, drop-in replacement for coal. The goal of this Task is to conduct a cradle to grave life cycle assessment (LCA) study of woody biomass supply, production of torrefied pellets, and their combustion in an existing coal-fired power plant with the corresponding displacement of coal. Specific Goals: There are both financial and environmental arguments for and against the use of torrefied biomass as a replacement for coal. The environmental arguments center on the greenhouse gas emissions that contribute to global warming, although other emissions such as mercury, and sulfur oxides and nitrogen oxides also need to be included. The sources of the biomass and the ‘business as usual’ case are both critical aspects of any LCA and they are particularly challenging in this work since there are a very wide variety of alternatives. This work explicitly recognizes the fact that there are multiple alternatives and does not attempt to identify a single preferred scenario. Rather this work seeks to detail the torrefaction and combustion processes, and include a wide variety of biomass supply alternatives, breaking them down into discrete blocks that allows for useful comparisons. This work will be done in such a way that stakeholders can use the information for evaluate scenarios of interest to them.