(193f) Optimal Depot Size and Location Selection in Biofuel Supply Chain Under Temporal and Spatial Variabilities over a 10-Year Period

Numerous studies have been conducted to quantify biomass availability, supply uncertainty, and to design biomass supply chains with minimized total logistics costs. These studies are mostly conducted relied on biomass yield and qualities at a single point in time and ignored variability that may arise simply due to variation in environmental factors. However, the most economical size and location for biomass depot should be defined based on temporal and spatial variability of feedstock supply and quality. Using static biomass quality attributes in biofuel supply chain design can lead to system designs with non-robust critical material attributes (CMAs) for preprocessing operation and non-consistent critical quality attributes for biofuel conversion process. To address this issue, an innovative computational model was developed to determine the optimal biofuel supply system configuration over a 10-year period (2010- 2019) considering the variabilities that temporally affect both biomass quantity and quality. In this analysis, the variability of biomass yield, quality parameters were linked to weather factors, especially precipitation and drought events occurred during growing season. Modeling results identified the optimal depot location and size in the selected supply region, under variability of herbaceous biomass sources and quality parameters encountered in the biomass sources over a 10-year period. The resulted total delivered feedstock cost of $83.78/dry ton was lower compared to the cost of $91.75/dry ton in the case where optimal depot location and size were determined from 1-year biomass sources and quality variability. In conclusion, this novel computation tool can be used to design the least cost supply chain configuration that delivers feedstock blend that meets or exceeds the CMAs for biofuel production, more importantly, with the ability to handle temporal and spatial variability of feedstock supply and quality.