Cellulosic Ethanol: Feedstocks, Conversion Technologies, Economics, and Policy Options

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Fast pyrolysis is one of the thermochemical processes that goes directly to a mixture of hydrocarbons instead of ethanol. The projected fuel cost is in the same range as current fossil fuels, but the uncertainty in projected cost is very high. Given that uncertainty, it is useful to examine policy options that might help reduce private sector risk in biofuel investments. The objective was to estimate the distribution of outcomes from investments in a fast pyrolysis conversion technology.

In so doing, we assumed a 2.

How it Works DuPont Process for Converting Biomass to Cellulosic Ethanol

Technical details are shown in Table 1. Table 2 is the share in total cost net present value NPV of the capital, feedstock, and hydrogen components. Uncertainty in future fossil fuel prices was handled in two ways. In the second case, the gasoline price was set to increase over the assumed plant life of 23 years at the rate contained in the U.

Department of Energy, for crude oil with a random component added. However, any thermochemical process like fast pyrolysis is likely to produce a mixture of products including diesel, gasoline, jet fuel, and naphtha. The analysis accounts for all products produced. The diesel production was converted to a gasoline equivalent using the historical relationship between diesel and gasoline prices, and their respective energy contents.

This probability of loss emerges from all four uncertain variables, but future crude oil price is by far the most important factor in driving the investment uncertainty. Next, we consider two policy options. Given that future oil price is an important determinant of riskiness of an investment, a reverse auction may be attractive to potential investors. In a reverse auction, military or civilian purchasers of jet fuel would offer to buy a fixed quantity of the fuel each year for some stipulated period of time, say 15 years.

The qualified bidder with the lowest bid wins the contract thus, reverse auction.

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Another advantage of this option is that it is well known that a major barrier to biofuels investment is the lack of off-take contracts. An off-take contract is a long-term contract between a buyer and a seller with the price and quantity terms delineated in the contract. It is very difficult or impossible to obtain financing without a contract for the fuel being produced.

A reverse auction also solves this problem. In a reverse auction, potential biofuel suppliers bid for the right to supply a pre-specified quantity and type of biofuel for delivery each year for the term of the contract. Thus, with the reverse auction, for both the biofuel supplier and for the purchaser, the price of the biofuel under contract is known with certainty for the contract quantity and duration.

In this scenario we forecasted the result of a forward contract at a fixed price for 45 million gallons per year, which was the lowest amount in the stochastic simulations.

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The facility always produces more than this amount, and we assumed the additional production volume is sold at the market price for that year. Using that forecast, the probability of loss went to zero. It is also important to consider the government cost of implementing such a reverse auction policy. This scenario has about the same expected cost as the reverse auction scenario.

Thus, when the reverse auction and capital subsidy have the same expected cost to the government, the reverse auction is far more effective at reducing risk for potential private sector investors. In essence, the government is absorbing the risk because its subsidy cost could be higher or lower, depending on what happens to crude oil prices in the future. Stepping away from the details of the analysis, a big-picture conclusion also emerges. Thermochemical conversion technologies such as fast pyrolysis may be close to being economic, considering only expected cost; however, the variance due to that cost and future fossil fuel prices is large enough to deter private-sector investment especially in early plants.

Once investment risk is taken into consideration, it is clear that private investments will not be forthcoming without off-take contracts. A reverse auction would result in such an off-take contract. It will be difficult to get these contracts for the first few plants in the private sector because the likely contract price would be higher than the equivalent fossil fuel prices.

Since these plants produce a mixture of products, there would need to be contracts or swapping mechanisms for all the plant outputs. Commercial airlines are only interested in jet or aviation fuel. The U. Navy may be interested in both jet fuel and diesel. The bottom line is that the technologies with the assumptions used in this analysis may be getting very close to being competitive over the likely year production horizon of any commercial plant. However, there are no financing mechanisms available at present to get the industry moving.

The outputs, as described above, are estimates of levelised cost and NPV. The key difference between this model and the flow-sheeting approach used by NREL is that, in the NREL model, the majority of supply-chain parameters are fixed to allow different process designs to be investigated; in our model, the mass balance and equipment structure of each process description are fixed to allow the importance of the other supply-chain parameters to be investigated.

There is, in principal however, no limit to the number of supply-chains that could be described and compared, thus a comprehensive picture may be obtained by using a series of mass balance estimates.

Our model is, in essence, a summation of cost estimates in which numerous assumptions and decisions are embodied. It cannot be tested empirically, but to ensure the overall results are representative, multiple estimates for input parameters were used, taken from a wide range of published and peer-reviewed sources.

1. Introduction

Assumptions were also discussed with experts from within the NILE project and were moderated accordingly. The sensitivity analysis embodied within the model also helps to identify those areas where greatest precision is necessary. This section describes the principal components of generic LE supply-chains. Representative values for the most important parameters affecting cost performance are identified and normalised.

Estimates of biomass costs were identified in reports from national governments, the EU, trade associations and the academic literature.

For established biomass resources, such as pulplogs, market indices were also available. One of the consequences of using such a diverse range of sources was that estimates were highly variable in terms of units, currency, extent of processing, location and biomass form such as chips, logs, etc. To enable estimates to be compared on a similar basis, each estimate was normalised using the following assumptions. Total feedstock costs were then calculated as the sum of cost at roadside, transport cost and size reduction cost.

Given the diversity of potential feedstock supply-chains, it should be recognised that these assumptions represent a simplification. Nevertheless, the level of resolution corresponds with the quality and availability of data and is consistent with the analytical methods underpinning a number of UK government reports including the recent UK biomass strategy [ 14 ].

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The commercial performance of cellulosic ethanol supply-chains in Europe

Using the normalising assumptions above, a survey of feedstock prices in Europe was undertaken for straw, waste paper, energy crops, imported biomass, mill residues, forest residues, pulplogs and sawlogs. Cost estimates varied widely for all the types of feedstocks considered. Given that the variation in prices between resource types was comparable to the variation within each type, we decided not to differentiate between resources, but to use mid-point, high and low cost values as inputs to the model. Mid-point values corresponding to the geometric mean were chosen with high and low estimates corresponding to the 15th and 85th percentiles respectively that is, approximately one standard deviation from the mean , see Table 3.

Yields and material flows are interdependent; consequently, detailed investigation of a specific conversion process would typically use a flow-sheeting model to solve the mass and energy balances and identify the impacts of process alterations. Developing new process models for all the conversion processes of interest, however, was beyond the scope of this study, and would entail a level of resolution in excess of the minimum required for the high-level comparison of supply-chain systems.

Instead, data from pre-existing process models was used, and where necessary, adapted. For each process configuration the mass balance was fixed and assumed to be invariant with changes in plant capacity. Capital costs were adjusted to reflect plant capacity using a scale relationship, see 'incorporating scale effects' below. The unit cost of inputs, the cost of finance and other parameters could then be varied. The cost of finance was considered to be an independent variable, principally determined by the market cost of capital and investors' appetite for risk. Process data for enzymatic and dilute acid processes utilising softwood was obtained from the University of Lund [ 26 ].

A publication of AAEA

The data was derived from Aspen Plus models of a 25 odthour -1 around 55 million litres year -1 ethanol stand-alone facility, validated by laboratory scale experiment. Neither process included pentose fermentation, this is considered further below. Detailed mass balance data, together with cost assumptions for each process are shown in Table 4. The 'reference processes' were adapted to include pentose fermentation using the following assumptions:.


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Modifying the reference process models in this way is clearly a significant simplification. Nevertheless, the approach permits an initial assessment of the relative importance of different process configurations within the context of the whole supply-chain. If comprehensive flow-sheet models were developed for each chain, a number of additional changes might be observed.

These include: a reduction in the yield of solid fuel, or an increase in the demand for energy supplied externally to the process as the yield of ethanol increases. As part of the validation process, these assumptions, and the resulting mass balances, were discussed with the team that developed the original models and were moderated accordingly.

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Returns-to-scale are anticipated. For this approach, two pieces of information are necessary:. For feedstocks, returns-to-scale are a function of the marginal cost of supply plus the additional transport cost of collecting material from a larger area. This iteration of the model incorporated a range of costs, but assumed that transport costs were constant.

The experience curve concept derives from the empirical observation that the unit costs of technology often decrease at a more or less fixed rate — the progress ratio PR — with every doubling of cumulative production. This idea was incorporated into mainstream economic literature by Arrow [ 28 ] in a review of 'the economic implications of learning by doing' and has been widely applied to the manufacturing sector [ 29 ].

This is because most elements of the conversion process, fermentation tanks, distillation columns, etc. When considering the impact of both experience curves and scale effects there is also a risk of double counting as one of the factors that may contribute to the learning rate is increasing returns-to-scale. For these reasons, learning effects were only applied to two elements of the conversion process: the capital cost of pre-treatment and the unit price of enzymes.

This estimate might be considered pessimistic, but the price of pre-treatment reactors is, to a large extent, determined by the commodity price of corrosion-resistant stainless steel, which has been increasing. The justification for choosing a higher rate was that the current enzyme market is very small; consequently, significant scope for cost reduction may exist.

The cost of financing a project reflects the perceived investment risk: consequently, the finance cost for a proven technology using an established plant design will be significantly less than for a first plant.

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