(714f) Screening Method for Selection of Biomass Feedstock and Location for Biorefineries

A way to reduce the effects of global warming and become independent from oil is a transition from an oil economy to a bio economy where fuels and chemicals are produced from sustainable biomass sources. Utilizing biomass increases the complexity of the synthesis and design problem and new methods have to be developed to obtain sustainable processes. In the synthesis problem one of the major challenges is choosing a biomass and location. Finding all the possible alternatives is usually infeasible due to the time investment related to data collection and it is hard to ensure its completeness. For that reason, a heuristic method has been developed to limit the search space and the amount of data to be collected. The method aims at selecting a set of biomass feedstocks and locations with the following characteristics: low at-field cost, high substrate content and low field-to-plant transportation cost. The transportation is quantified through the field yield and biomass compression density. The method work-flow is organised to go from high data-availability to low data-availability as the number of potential candidates are reduced. This reduces the time spent collecting data. The method is developed with agricultural residues in mind, but can easily be adapted to other forms of biomass e.g. forest residues or energy crops.

The 8 steps in the method are: 1) selection of the biomass species of interest, 2) retrieval of composition, compressibility, harvest index and alternative uses data, 3) collection of harvest yield data of residues from different locations, 4) ranking of all candidates from highest glucose yield to lowest glucose yield based on field location, 5) calculation of the total harvested area of top candidates and discard locations where the harvested area is too low, 6) collection of biomass prices for the top candidates, 7) generation of graphics: a) biomass yield/km² vs. glucose yield/km², b) glucose price vs. glucose yield/km², 8) evaluation of candidates. In steps 7 and 8, the top candidates are evaluated and chosen. From plot a) candidates closest to the y=x-line and furthest away from the origin are of interest and from plot b) candidates in the upper-left part of the plot are of interest. At the end of the method a limited number of alternatives are left, which can be investigated further e.g. through superstructure optimization.

The method has been applied to a case study where biomass is used to produce succinic acid. From the described graphics, wheat straw in France is evaluated to have the best potential and it tested through the superstructure optimization methodology developed by Bertran et al. (2016). The superstructure of alternatives also includes wheat straw from Brazil for comparison.

Reference list:

Bertran, M.-O., Frauzem, R., Zhang, L., & Gani, R. (2016). A Generic Methodology for Superstructure Optimization of Different Processing Networks. Computer-Aided Chemical Engineering, 38, 685–690. doi:10.1016/B978-0-444-63428-3.50119-3