Ph.D., Renewable Resources
McGill University · 2013 - 2017
Thesis
Biofuel Supply Chain GeoSpatial and Temporal Optimizer (BioGeSTO)
Published: Strategic spatial and temporal design of renewable diesel and biojet fuel supply chains: Case study of California, USA — Energy, May 2018
About the Program
Doctor of Philosophy (Ph.D.) in Renewable Resources at McGill University, from 2013 to June 2017. My research sat at the intersection of operations research, energy systems, and geospatial modeling. I was co-supervised between the Department of Natural Resources Sciences (Joann Whalen) and the Department of Mechanical Engineering (Jeffrey Bergthorson).
Research
I developed BioGeSTO (Biofuel supply chain GeoSpatial and Temporal Optimizer), an integrated modeling framework for optimizing biofuel supply chains in the United States. The core contribution was a Mixed-Integer Linear Programming (MILP) model that captured both spatial and temporal dimensions of supply chain design, something most prior work in the field treated separately.
The model determined where to build biomass conversion facilities, what capacity and conversion technology to use, when to introduce them, and how to route biomass feedstocks and biofuel products through the network, all while maximizing Net Present Value over a 20-year horizon. It accounted for economies of scale, technology learning curves, oil price uncertainty, and government investment incentives.
Publication
The work was published as "Strategic Spatial and Temporal Design of Renewable Diesel and Biojet Fuel Supply Chains: Case Study of California, USA" in the journal Energy (May 2018). The case study applied BioGeSTO to California from 2020 to 2040, modeling the US military's plan to replace conventional fuels with renewable diesel and biojet fuel.
Key findings: California's biomass resources could meet only 12-19% of its military biofuel targets. Under reference oil prices, only a single HEFA facility was financially viable. Under high oil prices, the supply chain approached its theoretical production limit by 2032. Direct financial incentives had a modest effect, advancing facility introduction by only 1-3 years. Biomass availability was the dominant factor: a 50% increase in feedstock corresponded to a 150% surge in cumulative production.
Technical Details
The MILP formulation included 769,955 continuous variables, 10,962 binary variables, and 146,927 constraints. It modeled piecewise-linearized investment costs with economies of scale, log-linear technology learning, multi-echelon transportation with freight trucks, and demand distribution across military installations based on the Defense Installations Spatial Data Infrastructure dataset. The model was solved using IBM Decision Optimization Cloud.
The supply chain covered four echelons: feedstock production (county-level biomass from the DOE Billion Tons Study plus a custom oilseed crop model for camelina), preprocessing (pelletization, torrefaction, seed pressing), conversion (Fischer-Tropsch and HEFA technologies), and distribution to military markets across California.