Design of highly distributed biofuel production systems

• Main Author: Luo, Dexin
• Published: Georgia Institute of Technology 2013
• Subjects: Global sensitivity analysis; Mobile processing; Production capacity; Optimization; Lifecycle assessment; Algae; Biofuel; Biomass energy; Ethanol fuel industry; Biomass energy industries; Cost effectiveness
• Online Access: http://hdl.handle.net/1853/45878

This thesis develops quantitative methods for evaluation and design of large-scale biofuel production systems with a particular focus on bioreactor-based fuel systems. In Chapter 2, a lifecycle assessment (LCA) method is integrated with chemical process modeling to select from different process designs the one that maximizes the energy efficiency and minimizes the environmental impact of a production system. An algae-based ethanol production technology, which is in the process of commercialization, is used as a case study. Motivated by this case study, Chapter 3 studies the selection of process designs and production capacity of highly distributed bioreactor-based fuel system from an economic perspective. Nonlinear optimization models based on net present value maximization are developed that aim at selecting the optimal capacities of production equipment for both integrated and distributed-centralized process designs on symmetric production layouts. Global sensitivity analysis based on Monte Carlo estimates is performed to show the impact of different parameters on the optimal capacity decision and the corresponding net present value. Conditional Value at Risk optimization is used to compare the optimal capacity for a risk-neutral planner versus a risk-averse decision maker. Chapter 4 studies mobile distributed processing in biofuel industry as vehicle routing problem and production equipment location with an underlying pipeline network as facility location problem with a focus on general production costs. Formulations and algorithms are developed to explore how fixed cost and concavity in the production cost increases the theoretical complexity of these problems.