CAREER: A Computational Framework for Multiscale Optimization of Sustainability for Process Supply Chains

  • You, Fengqi (PD/PI)

Project: Research project

Project Details


Overview: The need to reduce dependence on imported oil and lower greenhouse gas emissions has renewed the urgency for developing sustainable solutions. Drop-in fuels are liquid hydrocarbon fuels, such as gasoline, diesel and jet fuels, which can be sustainably produced from cellulosic and algal biomass. These drop-in fuels deliver more energy per gallon than bio-ethanol. Their production uses non-food crops or inedible waste products and does not divert food away from the animal or human food chain. More importantly, drop-in fuels are infrastructure-compatible that can be considered as petroleum substitutes, processed at existing oil refineries, transported through existing pipelines, dispensed at existing fueling stations, and used to fuel today’s cars, trucks and jets. Infrastructure-compatibility allows these fuels to overcome the market barriers resulting from the current vehicle technology and fuel distribution infrastructure. It is anticipated that development of drop-in fuels would be the most rapid option for reaching the Nation’s 2022 biofuels production target, while meeting the sustainability requirements. To accelerate the transition toward the large-scale and sustainable production of advanced biofuels, the supply chains of drop-in fuels should be designed and optimized across multiple temporal and spatial scales, for better economic and environmental performance. Intellectual Merit: The objective of this research is to resolve the fundamental issues associated with multi-scale optimization of sustainable supply chains. This work will augment supply chain optimization tools to address sustainability and uncertainty issues. The research activities are expected to lead to the following major scientific contributions: (1) a systematic study and a novel multi-scale modeling and optimization framework for drop-in fuel supply chains; (2) a novel and transformative multi-objective life cycle optimization framework for environmental sustainability of drop-in fuel supply chains based on functional unit; and (3) a new and transformative approach for quantifying the role of uncertainties at multiple temporal scales for drop-in fuel manufacturing supply chains. Transformative Nature: The proposed work is novel and transformative, because resolution of the major research challenges, including (a) a multi-scale optimization framework for energy supply chains, (b) a functional-unit-based life cycle optimization method for sustainability, and (c) a hybrid approach for handling uncertainties at multiple scales, will contribute significantly to research and practices in academia, industry, and policy. Broader Impact: The proposed research has potential to accelerate the discovery of transformative drop-in fuels supply chains that will lead to large-scale, cost-effective and sustainable production and use of advanced biofuels. Additionally, the novel and transformative theoretical, algorithmic, and computational results will be made available to broader academic community and used by other researchers to tackle previously intractable problems. The proposed research will also be effectively integrated into education and outreach activities in the following areas: (1) enhancing undergraduate students’ participation in research; (2) contribution to the enhancement of an existing workshop program on energy systems modeling for students in the Chicago area; and (3) working with a local, predominantly minority high school for an interactive STEM Saturdays outreach program. We intend to capitalize on the numerous existing educational an
Effective start/end date3/1/166/30/16


  • National Science Foundation (CBET-1554424-001)


Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.