Understanding the Impacts of Biomass Blending on the Uncertainty of Hydrolyzed Sugar Yield from a Stochastic Perspective

Feedstock price and availability are key challenges for biorefinery development. Biomass blending has been suggested as a route to overcome these limitations. However, the impacts of feedstock blending on the uncertainty in hydrolyzed sugar yields remain unclear. This study quantifies the uncertainties in the sugar yields from hydrolysis of the blends of corn stover, switchgrass, and grass clippings by considering both feedstock compositional variation and model uncertainty. The results indicate that feedstock blending reduces the uncertainties in sugar yields and delivers feedstock of more uniform quality. A 60/35/5 blend of corn stover, switchgrass, and grass clippings on average achieves a glucose yield of 32.6 g/100 g of biomass, which is comparable to those of corn stover (33.3 g/100 g) and switchgrass (32.9 g/100 g), but drastically higher than that of grass clippings (21.7 g/100 g). This same blend also achieves the lowest variance in glucose yield (2.9 g/100 g) compared to corn stover (3.1 g/100 g), switchgrass (3.3 g/100 g), and grass clippings (5.6 g/100 g). A further investigation on the breakdown of the variability of the hydrolyzed sugar yields reveals that the reduction in the variability of sugar yields for blended feedstocks is achieved by reduced feedstock compositional variation. Based on these results, the optimization of blending ratios is performed with respect to three objectives: (1) to maximize the probability of meeting the sugar yields target, (2) to maximize the expected sugar yields, and (3) to maximize sugar yields per unit feedstock expense, while satisfying constraints of feedstock availability and price. The maximized probability of meeting the sugar yield target, expected sugar yield, and glucose yield per unit feedstock expense are 91.33%, 32.66 g of sugar/100 g of biomass, and 40.83 g/$, respectively. The optimization method developed in this study is readily applied to other combinations of feedstocks, biofuel production processes, and constraints.