(128b) Three-Stage Design of Microalgae-Based Biofuel Supply Chain Using GIS

Kang, S. - Presenter, Korea Advanced Institute of Science and Technology
Heo, S., Korea Advanced Institute of Science and Technology (KAIST)
Realff, M., Georgia Institute of Technology
Lee, J. H., Korea Advanced Institute of Science and Technology (KAIST)
Microalgae have long been considered as promising feedstocks for biofuel production owing to their advantages such as fast growth in nonarable lands. In spite of their intrinsic merits, the large-scale production of microalgae-based biofuel is yet to be realized. The major hurdle for the commercialization is the relatively high production cost compared to petroleum-based fuels. Therefore, to expedite the full-scale commercialization, cost reduction at various levels is imperative.

With this in mind, this research proposes a three-stage optimization model for the design of microalgae-based biofuel supply chain. The biorefinery design stage decides the scale, technologies, and species to be used in the biorefineries for the supply chain. The mass and energy balance inside the biorefinery are calculated and used to estimate the capital costs and operating costs in relation to the species and processing pathways implemented. Given the information from the first stage, candidate locations for biorefineries are selected using the geographical information system (GIS) based on three criteria, which are the weather, resources, and land. This stage greatly narrows down the candidate lands available for the biorefineries, so that the computational burden for the next stage is reduced. In the ensuing mathematical optimization stage, a mixed integer linear programming (MILP) optimization model is designed to implement multi-period strategic and tactical decisions of the supply chain under the objective of total cost minimization. The model applies the multiple processing pathways, species, and capacity options chosen in the first stage into each biorefinery location and incorporates seasonal variation factors such as microalgae productivities and water evaporations into the analysis. The proposed framework is demonstrated through a case study cast in the State of Texas for the duration of seven years. Despite the optimization, the result shows that the biofuel from microalgae does not meet the projected price with the current technology. Therefore, sensitivity analysis and scenario analyses are carried out to suggest future directions for improvement.