Systems Informatics and Analysis - Engineering and Science of Biomass Feedstock Production and Provision - page 209

The model is freely downloadable ( http://www.bee.aua.gr ) and can be used to build
user-specifi c scenarios. The model has been used to study the production of Arundo
donax L . (giant reed) and Miscanthus × giganteus in Greece [ 78 ] and switchgrass in
Italy [ 79 ]. Monti et al. [ 79 ] determined the break-even yield for different scenarios
using the Bee model and data generated from an experimental plot in Bologna, Italy.
Instead of calculating the cost of production, this study fi xed the farm-gate price of
€ 55 Mg −1 and calculated the minimum yield necessary to achieve breakeven. The

results showed that the actual observed yield was lower than the break-even yield
for all the scenarios, suggesting that switchgrass cultivation was not a profi table
venture in Italy. The main reason for this was the high cost of irrigation, harvesting/
baling, and land rent, which accounted for 80 % of annual equivalent cost
( 511-1,257 ha −1 ).

De Mol et al. [ 80 ] developed a simulation as well as an optimization model to
study the biomass supply chain logistics. However, instead of a process-based
approach, as used by most other studies, they used a network-based approach.
Various source locations and destinations were modeled as nodes while the trans-
portation options were modeled as links. The same network structure and database
were used to develop both models, and a user interface was also developed. The
simulation model Biologics (BIOmass LOGistics Computer Simulation) using
PROSIM was employed to calculate the costs and fl ows for different structures. It is
a pull model where demand at the energy plant initiates movement of biomass units.
In addition to cost and energy consumption, the simulation model also gives the
number of transport units required.
Turhollow and Sokhansanj [ 81 ] developed a spreadsheet-based model to study
corn-stover supply. Nilssen [ 82 ] developed a dynamic simulation model named
SHAM (Straw Handling Model) in the Arena environment, which looked at the
impact of climate and geography on the cost of straw collection and transport in
Sweden.
One of the advantages of using a simulation approach is the greater fl exibility to
develop scenarios and run simulations. The object-oriented approach also makes the
addition of new information, such as new equipment, easy. It is possible to conduct
optimization by comparing simulation results for different scenarios through inde-
pendent runs, known as simulation-based optimization. However, this approach is
not feasible when the number of solutions is many. Rigorous optimization models,
therefore, have become more prevalent in recent times. Development of such mod-
els is more complex, and their solution is also computationally more challenging as
compared to simulation models. Some of these models are reviewed next.

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8.3.3.2 Optimization Models

Work by Jenkins and Arthur [ 83 ] is perhaps the fi rst application of optimization for
biomass production systems. They used dynamic programming on a network model
to determine the optimal transportation network using a formulation similar to the