The primary objective of this work is to identify the optimal bioethanol production plant capacity and configuration based on currently available technology for all the processing sections involved. To effect this study, a systematic method is utilized which involves the development of a superstructure for the overall technology selection, process simulation and model regression of each processing step as well as equipment costing and overall economic evaluation. The developed optimization model is also designed to incorporate various biomass feedstocks as well as realistic maximum equipment sizing thereby ensuring pragmatism of the work. For this study, the criterion for optimization is minimum ethanol price.
The secondary and more interesting aim of this work was to develop a systematic method for evaluating the economics of biomass storage due to seasonal availabilities. In essence, a mathematical model was developed to link seasonal availabilities with plant capacity with subsequent integration into the original model developed. Similarly, the criterion for optimization is minimum ethanol price.
The results of this work reveal that the optimal bioethanol production plant capacity is ~2800 MT biomass/day utilizing Ammonia Fiber Explosion pretreatment technology and corn stover as the preferred biomass feedstock. This configuration provides a minimum ethanol price of $1.96/gal. Results also show that this optimal pretreatment choice has a relatively high sensitivity to chemical cost thereby increasing the risk of implementation. Secondary to this optimal selection was lime pretreatment using switchgrass which showed a fairly stable sensitivity to market chemical cost.
For the storage economics evaluation, results indicated that biomass storage is not economical beyond a plant capacity of ~98 MMgal/yr with an average biomass shortage period of 3 months. The study also showed that for storage to be economical at all plant capacities, the storage scheme employed should be general open air land use with a corresponding biomass loss rate as defined in the study of 0.5 percent per month.
