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The Energy Independence and Security Act (EISA) 2007 mandate the use of 21 billion gallons of advanced biofuels including 16 billion gallons of cellulosic biofuels by the year 2022. While much previous advanced biofuel related research has focused on cellulosic ethanol, advanced drop-in-biofuels such as biobutanol and renewable diesel are gaining significant attention because of their attractive combustion properties, compatibility with existing vehicle fleet, fuel distribution, and retailing infrastructure. While corn ethanol production has increased fast enough to keep up with the mandates, production of cellulosic and advanced biofuels has been well below the targets despite significant government support. A number of pilot and demonstration scale advanced biofuel facilities have been set up, but commercial scale facilities are yet to become operational. Scaling up this new biofuel sector poses significant economic and logistical challenges for regional planners and biofuel entrepreneurs in terms of feedstock supply assurance, supply chain development, bioefinery establishment, and setting up transport, storage and distribution infrastructure. Economies of scale in processing mean that, future cellulosic biorefineries are expected to be large-scale facilities using multiple sources of feedstocks. Assuring a reliable supply of feedstock in adequate quantity and appropriate quality at reasonable cost and low environmental impacts is a key factor driving emergence of a sustainable bioenergy sector. Assuming that a biorefinery is set up in a region that has more than adequate biomass potential, biorefinery managers then face the problem of contracting with producers for the actual supply quantities of feedstock over the expected operational life time of the biorefinery. These supply contracts specify the quantities of different feedstocks (e.g. agricultural crops, perennial grasses, woody biomass), the timing of the deliveries, and the geographical location of production. In other words, through these supply contracts, the biorefinery managers essentially have an opportunity to design the biomass harvest-shed both temporally and spatially. Considerations in determining the optimal mix of these supply contracts include: (i) lowering procurement costs (harvest, baling, transport, storage, and seasonal costs), (ii) maximizing fuel yields and minimizing conversion costs, (iii) reducing in greenhouse gas (GHG) emissions to qualify as a cellulosic biofuel under the federal renewable fuels standard or similar regulations, and possibly for tradable GHG credits, and (iv) meeting contracting constraints to assure supply, for example while annual crop producers may be willing to supply under annual contracts, perennial grass producers may demand longer term contracts with varying quantities matching the temporal yield patterns. In addition to the above criteria used by biorefinery managers, regional planners may impose additional constraints related to protection of ecosystem services, habitat protection, water resources, traffic patterns, and congestion. In this article, we develop a multi-period optimization model aimed determining the optimal mix of woody biomass, annual crops and perennial grasses for a biorefinery, taking into account the necessary contract terms, feedstock costs, transport costs, GHG emissions and other environmental impacts, production capacity constraints etc. The decision variables of the optimization model are the acreages of various feedstocks (woody biomass, 3 annual crops and perennial grasses) that are contracted for harvesting during each month of a 25 year planning horizon. While the model is structured to be applicable to a generic biorefinery regardless of location, we parameterize the model using information for a hypothetical biorefinery located in the Midwest, producing biobutanol. Two versions of the model are developed, one optimizing the private costs faced by the biorefinery manager, and a second version taking the perspective of a regional planner with additional optimization criteria and social constraints. Mathematical programming software GAMS and solver program MINOS are used to code and solve the formulated optimization programs. A growing body of literature has previously addressed issues surrounding the supply of biomass feedstock for biofuel production (e.g. Epplin et al., 2007; Mapemba et al., 2007; Mapemba et al., 2008; Sokhansanj et al., 2009; Khanna et al., 2010; Kang et al., 2010). While drawing on previous research, the models developed in this article have several novel features. (i) Existing studies treat the available biomass quantities in the region as exogenously given and then try to minimize procurement costs. In comparison, this model treats biomass acreage to be harvested as an endogenous decision variable subject to overall biomass availability constraints. (ii) Unlike most existing studies, in this model transport costs are endogenously determined as a function of harvesting decisions. (iii) The temporal yield patterns of energy crops are modeled explicitly unlike many other studies which use steady state average yields. (iv) GHG emissions are also endogenously determined based on feedstock sourcing decisions. (v) The legal, institutional, and ecosystem sustainability constraints that are necessary from a regional planning perspective are also incorporated. (vi) While almost all previous studies model cellulosic ethanol biorefineries, this model is specifically aimed at biobutanol biorefineries. As a result, these models provide better insights into the realities of biomass procurement, especially for the emerging drop-in advanced biofuel production.

The Michigan Agriculture Environmental Assurance Program (MAEAP) is a voluntary environmental program (VEP) initiated in 1999 by a coalition of state government agencies and agricultural, environmental and conservation groups in Michigan. We survey Michigan livestock producers and conduct discussion group sessions with state environmental regulators to assess the incentives, motivations, and barriers for participating in MAEAP. Economic theory identifies two major motivations for firms’ participation in voluntary environmental programs: ‘regulatory preemption’ and ‘signaling.’ Under a ‘regulatory preemption’ scenario’, firms engage in voluntary pollution reduction through VEPs to reduce the risk of future regulations. Under a ‘regulatory preemption’ scenario’, theory posits that firm participation will be greatest among those most likely to be affected by anticipated future stringent regulations. Survey findings suggested that producers anticipate future expansion of stringent environmental regulation and expected that MAEAP certified farms would be perceived and treated as ‘environmentally’ more responsible. Alternatively, DEQ officials appeared to view MAEAP using the ‘regulatory pre-emption’ lens. Regulators’ and producers’ views were influenced by past political maneuvering of the MAEAP program.

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., Published in BioResources, Volume 4, Number 3, 2009, Pages 1070-1087.

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.

Fuel ethanol demand is projected to increase because of proposed ban on methyl tertiary butyl ether (MTBE) in gasoline, renewable fuels standard, and the revised eight-hour ozone standards. In this paper, several scenarios of increased fuel ethanol demand and its effects on crop and feed prices, farm income and state finances under current tax-subsidy structure, are analyzed using a multi-sector econometric model AGMOD.

The thermal characteristics of the electroslag remelting process have been investigated on a laboratory scale unit. The heat generation and distribution in the slag bed is discussed. The voltage gradients in the slag bed are predicted using a resistance network analogue and tested against experimental results. A self-consistent model for electrode temperature gradients in the electroslag remelting process has been tested against experimental results. An unsteady-state method has been used to determine the electrical resistance and the overall heat transfer coefficient of the interface region, liquid slag/slag skin/copper wall in the electroslag furnace. An accurate heat balance of the process is carried out on laboratory scale ingots. Attention has been devoted to the influence of various melt parameters i.e. polarity, power input, geometry, atmosphere etc. on the melt rate. The power requirements and the melt rate for industrial ingots are predicted and compared with the data collected from the literature. The liquid metal pool volumes in ESR ingots are predicted from the operational data. The pool profiles are computed using a finite difference technique and compared with the experimentally obtained profiles.