Modeling of biomass delivery systems

Linear programming models have been used to analyze system interactions in biomass delivery systems. Dunnett et al. [53] proposed a program to optimize scheduling of a biomass supply system for direct combustion. This model simulated storage on farms and delivery to one location with a variable demand for heat. They suggest that costs of biomass handling can be improved 5 to 25% with the model recommendations. Bruglieri and Liberti proposed a "branch and bound" nonlinear model to determine biorefinery locations as well as the optimum transport method [54]. Their model focused on multiple feedstocks but did not use actual equipment performance data.

A model comprised for multiple purposes can bring attributes of benchmarking, simulation, and linear programming together to solve for the best solution. Leduc et al. a system of wood gasification plants optimized [55]. Their model focused on establishing a biorefinery plant in a location that is suitable for distribution of the product being manufactured (in this case, methanol). Other similar models have focused on silage handling operations [56].

A number of models have proven that a single chain of handling procedures can be optimized, but fail to adequately address the "disconnect" caused by storage, specifically satellite storage. Unfortunately, few models consider different harvest systems (or feedstock) supplying a single biorefinery. For example, one equipment system delivers chopped material directly from the field to the plant (probably from fields close to the plant), and a second set of equipment bales material and places it in storage which will be delivered during months when direct delivery of field chopped material is not possible.

As described earlier, a satellite storage location (SSL) is a pre-designated location that is used as a storage location for the biomass collected by the producers within a defined geographic region. SSLs are a logical transition point between "agricultural" and "industrial" operations and thus are critical elements in a logistics system design.