Design of a biomass-to-biorefinery logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks
Bioenergy has become an important alternative source of energy to alleviate the reliance on petroleum energy. Bioenergy offers significant potential to mitigate climate change by reducing life-cycle greenhouse gas emissions relative to fossil fuels. The Energy Independence and Security Act mandate the use of 21 billion gallons of advanced biofuels including 16 billion gallons of cellulosic biofuels by the year 2022. It is clear that Biomass can make a substantial contribution to supplying future energy demand in a sustainable way. However, the supply of sustainable energy is one of the main challenges that mankind will face over the coming decades. For instance, many logistical challenges will be faced in order to provide an efficient and reliable supply of quality feedstock to biorefineries. 700 million tons of biomass will be required to be sustainably delivered to biorefineries annually to meet the projected use of biofuels by the year of 2022. This thesis is motivated by the urgent need of advancing knowledge and understanding of the highly complex biofuel supply chain. While corn ethanol production has increased fast enough to keep up with the energy mandates, production of biofuels from different types of feedstocks has also been incremented. A number of pilot and demonstration scale advanced biofuel facilities have been set up, but commercial scale facilities are yet to become operational. Scaling up this new biofuel sector poses significant economic and logistical challenges for regional planners and biofuel entrepreneurs in terms of feedstock supply assurance, supply chain development, biorefinery establishment, and setting up transport, storage and distribution infrastructure. The literature also shows that the larger cost in the production of biomass to ethanol originates from the logistics operation therefore it is essential that an optimal logistics system is designed in order to keep low the costs of producing ethanol and make possible the shift from fossil fuels to biofuels. In many ways biomass is a unique renewable resource. It can be stored and transported relatively easily in contrast to renewable options such as wind and solar, which create intermittent electrical power that requires immediate consumption and a connection to the grid. This thesis presents two different models for the design optimization of a biomass-to-biorefinery logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks. This work compares the performance and solutions obtained by two types of metaheuristic approaches; genetic algorithm and ant colony optimization. Compared to rigorous mathematical optimization methods or iterative algorithms, metaheuristics do not guarantee that a global optimal solution can be found on some class of problems. Problems with similar characteristics to the one presented in this thesis have been previously solved using linear programming, integer programming and mixed integer programming methods. However, depending on the type of problem, these mathematical or complete methods might need exponential computation time in the worst-case. This often leads to computation times too high for practical purposes. Therefore, this thesis develops two types of metaheuristic approaches for the design optimization of a biomass-to-biorefinery logistics system considering multiple types of feedstocks and shows that metaheuristics are highly suitable to solve hard combinatorial optimization problems such as the one addressed in this research work.
Trueba, Isidoro, "Design of a biomass-to-biorefinery logistics system through bio-inspired metaheuristic optimization considering multiple types of feedstocks" (2013). ETD Collection for University of Texas, El Paso. AAI1540005.