BEAG utilizes a diverse set of peer-reviewed and documented model platforms, including:
- BeSTA — A biorefinery siting programming model that provides information on minimum cost feedstock as well as greenhouse gas emissions in the feedstock supply chain;
- BioFLAME — A facility siting spatial model;
- DayCENT — A daily time series biogeochemical model that simulates fluxes of carbon and nitrogen between the atmosphere, vegetation, and soil for alternative land uses developed by Colorado State University
- EPIC — An Environmental Policy Integrated Climate model developed to estimate soil productivity as affected by erosion;
- FLARE — A farm level programming model that incorporates risk and uncertainty into the analysis of biofuel systems;
- ForSEAM — A national forest optimization model;
- GREET — A full life-cycle model sponsored by the Argonne National Laboratory;
- IMPLAN — A regional input-output model;
- MOSS — A micro-oriented sediment simulator;
- POLYSYS — A national, inter-regional, agricultural model; and
- SPARROW — A modeling tool that provides changes in water quality indicators related to land use changes.
Collectively, these models are used to quantify supply and demand for biomass; the cost of biomass production; conversion costs; economic feasibility of bio-based energy; regional and national economic and environmental impacts; and market opportunities for biomass, biofuels, and co-products.
The core of BEAG’s modeling efforts combine input-output analyses with partial equilibrium models of the agriculture and forestry sectors. To support these models, BEAG members have extensive experience collecting primary survey information through a variety of channels, including the internet, postal mail, and in-person interviews.
BeSTA
The Bio-energy Site and Technology Assessment (BeSTA) model is a spatially-oriented, mixed-integer mathematical programming model that simulates lignocellulosic biomass (LCB) feedstock supply chain activities at the biorefinery level. Remote-sensing data, including the yields of both traditional and energy crops and soil types at the sub-county level, and road networks are acquired from a geographical information system resource model, BioFLAME. The integration of the mathematical programming and GIS data sources is designed to identify the potentially LCB feedstock supply area and optimal location of the biorefinery and satellite preprocessing facilities by minimizing LCB feedstock’s total plant-gate cost, subject to the size of biorefinery, throughput of the preprocessing facilities, the existing road networks, and the availability of biomass feedstock. In addition to facilities location and feedstock supply area, the monthly harvest, delivery and storage volume of feedstock can be also determined in the model.
