Choice of optimum feedstock portfolio for a cellulosic ethanol plant – A dynamic linear programming solution

When the lignocellulosic biofuels industry reaches maturity and many types of biomass sources become economically viable, management of multiple feedstock supplies – that vary in their yields, density (tons per unit area), harvest window, storage and seasonal costs, storage losses, transport distance to the production plant – will become increasingly important for the success of individual enterprises. The manager’s feedstock procurement problem is modeled as a multi-period sequence problem to account for dynamic management over time. The case is illustrated with a hypothetical 53 million annual US gallon cellulosic ethanol plant located in south west Kansas that requires approximately 700,000 metric dry tons of biomass. The problem is framed over 40 quarters (10 years), where the production manager minimizes cumulative costs by choosing the land acreage that has to be contracted with for corn stover collection, or dedicated energy production and the amount of biomass stored for off-season. The sensitivity of feedstock costs to changes in yield patterns, harvesting and transport costs, seasonal costs and the extent of area available for feedstock procurement are studied. The outputs of the model include expected feedstock cost and optimal mix of feedstocks used by the cellulosic ethanol plant every year. The problem is coded and solved using GAMS software. The analysis demonstrates how the feedstock choice affects the resulting raw material cost for cellulosic ethanol production, and how the optimal combination varies with two types of feedstocks (annual and perennial).Cellulosic ethanol, feedstock, switchgrass, miscanthus, corn stover, optimization, biofuels, biomass, energy, renewable, Agribusiness,

Testing for Speculative Behavior in US Corn Ethanol Investments

Crude oil price speculation during 2000s could have increased installed capacity in corn ethanol plants beyond what was warranted by the market factors. We use Muth’s commodity pricing model and Flood and Garber’s tests to test for speculative investment in US corn ethanol industry. The ethanol price expectations are derived using a system of supply-demand-inventory describing US ethanol markets under rational expectations (perfect foresight). These price expectations can help differentiate the installed capacity into two: capacity supported by the market fundamentals and the probable capacity that is installed based on speculation. Econometric estimation procedures and functional form approximations are discussed.ethanol, speculation, commodity prices, investment, Agricultural Finance, Financial Economics, Q14, Q41, D8, L71,

Spatially and Temporally Optimal Biomass Procurement Contracting for Biorefineries

This paper evaluates the optimal composition of annual and perennial biomass feedstocks for a biorefinery. A generic optimization model is built to minimize costs – harvest, transport, storage, seasonal, and environmental costs – subject to various constraints on land availability, feedstock availability, processing capacity, contract terms, and storage losses. The model results are demonstrated through a case study for a midwestern U.S. location, focusing on bioethanol as the likely product. The results suggest that high-yielding energy crops feature prominently (70 to 80%) in the feedstock mix in spite of the higher establishment costs. The cost of biomass ranges from 0.16 to 0.20 $l-1 (US$ 0.60 to $0.75 per gallon) of biofuel. The harvest shed shows that high-yielding energy crops are preferably grown in fields closer to the biorefinery. Low-yielding agricultural residues primarily serve as a buffer crop to meet the shortfall in biomass requirement. For the case study parameters, the model results estimated a price premium for energy crops (2 to 4$ t-1 within a 16 km (10-mile) radius) and agricultural residues (5 to 17 $ t-1 in a 16 to 20 km (10 to 20 mile) radius

Choice of optimum feedstock portfolio for a cellulosic ethanol plant – A dynamic linear programming solution

When the lignocellulosic biofuels industry reaches maturity and many types of biomass sources become economically viable, management of multiple feedstock supplies – that vary in their yields, density (tons per unit area), harvest window, storage and seasonal costs, storage losses, transport distance to the production plant – will become increasingly important for the success of individual enterprises. The manager’s feedstock procurement problem is modeled as a multi-period sequence problem to account for dynamic management over time. The case is illustrated with a hypothetical 53 million annual US gallon cellulosic ethanol plant located in south west Kansas that requires approximately 700,000 metric dry tons of biomass. The problem is framed over 40 quarters (10 years), where the production manager minimizes cumulative costs by choosing the land acreage that has to be contracted with for corn stover collection, or dedicated energy production and the amount of biomass stored for off-season. The sensitivity of feedstock costs to changes in yield patterns, harvesting and transport costs, seasonal costs and the extent of area available for feedstock procurement are studied. The outputs of the model include expected feedstock cost and optimal mix of feedstocks used by the cellulosic ethanol plant every year. The problem is coded and solved using GAMS software. The analysis demonstrates how the feedstock choice affects the resulting raw material cost for cellulosic ethanol production, and how the optimal combination varies with two types of feedstocks (annual and perennial)

GHG Trading Framework for the U.S. Biofuels Sector

Substitution of petroleum fuels with biofuels such as ethanol and biodiesel has been shown to reduce greenhouse gas (GHG) emissions. These GHG reductions can be traded in the emerging carbon markets, and methodologies for quantifying and trading are still being developed. The main challenges in developing such GHG trading framework are analyzed. An outline of such a framework is presented that depends on the life cycle assessment of GHG reductions, along with a combination of project specific and regional standard performance measures. The advantages of assigning GHG property and trading rights to biofuel producers are discussed. At carbon prices of $10 per metric ton, estimated additional revenues to biofuel producers range from$ 17 to 64 million dollars per billion gallons of corn ethanol and cellulosic ethanol respectively

Testing for Speculative Behavior in US Corn Ethanol Investments

Crude oil price speculation during 2000s could have increased installed capacity in corn ethanol plants beyond what was warranted by the market factors. We use Muth’s commodity pricing model and Flood and Garber’s tests to test for speculative investment in US corn ethanol industry. The ethanol price expectations are derived using a system of supply-demand-inventory describing US ethanol markets under rational expectations (perfect foresight). These price expectations can help differentiate the installed capacity into two: capacity supported by the market fundamentals and the probable capacity that is installed based on speculation. Econometric estimation procedures and functional form approximations are discussed

BIOMASS SUPPLY FOR BIOFUEL PRODUCTION: ESTIMATES FOR THE UNITED STATES AND CANADA

The potential supply of biomass feedstocks in the US and Canada is estimated using a static supply function approach. Estimated total biomass available at a price of $100 per metric ton is 568 million metric tons in the US and 123 million tons in Canada, which together can displace 23-45 billion gallons of gasoline. Sufficient biomass, mainly agricultural and mill residues, will be available at prices of around$50/ton to meet the advanced biofuel mandates of the US Energy Independence and Security Act of 2007. The estimates of agricultural residue supply are very sensitive to the assumed fraction of residues that can be sustainably removed from the field, and the potential of municipal solid waste as a feedstock depends on which components can be economically converted into liquid biofuels