January 24, 2008 | General

Global Warming Intensity Of Ethanol

BioCycle January 2008, Vol. 49, No. 1, p. 50
Examples using the BEACCON Model – Biofuels Emissions And Cost Connection – illustrate how process improvements can reduce the global warming intensity of operations.
Steffen Mueller and Richard Plevin

TWO RECENT developments may provide incentives for ethanol producers to carefully evaluate the global warming intensity (GWI) of their product: the emergence of low carbon fuel standards and the possibility for ethanol producers to obtain carbon credits by switching to low GWI production methods.
In January 2007, California Governor Arnold Schwarzenegger signed an Executive Order establishing a groundbreaking Low Carbon Fuel Standard (LCFS) for transportation fuels sold in the state. By 2020, the standard will reduce the carbon intensity of California’s passenger vehicle fuels by at least 10 percent. The LCFS requires fuel providers to ensure that the mix of fuel sold into the California market meets, on average, a declining GWI measured in CO2-equivalent gram per unit of fuel energy sold (CO2e/MJ).
Compliance with the LCFS requires an examination of the carbon intensity of fuels, which is also based on a life cycle analysis. A life cycle analysis for corn ethanol determines the GWI contributions for each production stage including agriculture (on-farm energy use, irrigation energy, fertilizer energy), distribution to the ethanol plant, conversion at the ethanol plant (GWI introduced from purchased electricity and thermal energy) and distribution to the terminal.
Since transportation fuels are generally a blend of petroleum and additives such as ethanol, the overall GWI for the transportation fuel can be greatly affected by the GWI of each of the components. As a result, ethanol will no longer be a commodity, but a differentiated product based on price and GWI; ethanol producers will demand higher prices for low-GWI ethanol.
Carbon markets like the Chicago Climate Exchange (CCX) provide another possibility for ethanol producers to realize extra profits from low-GWI production methods. This summer, Corn Plus, one of the largest ethanol plants in the state of Minnesota, announced that it will reduce its greenhouse gas emissions to 30 percent below its plant emission baseline by switching from natural gas fired boilers to biomass gasification systems. This fuel switching arrangement may enable Corn Plus to obtain carbon credits from CCX.
Ethanol producers have several options to reduce the GWI of their product. These include: switching to a low GWI energy system to process fuels (substituting coal with natural gas, or substituting natural gas with biomass similar to the Corn Plus approach detailed above); changing the configuration of their energy system by employing efficient waste heat recovery technologies such as combined heat and power systems (CHP); installing different ethanol processing technologies; sourcing the corn feedstock from low GWI agricultural practices; and others.
To evaluate benefits from installing low-GWI technologies at a facility, an existing ethanol plant must first determine the GWI of its current system to establish a baseline for future decisions and then look at the trade-off between the current and the low-GWI production methods. Several models allow a determination of the ethanol GWI on a life cycle basis (Argonne National Laboratory’s GREET Model, University of Nebraska’s Bess Model). However, none of these models include a determination of the financial tradeoffs from using ethanol production practices with lower GWI impacts.
The newly released BEACCON (Biofuels Emissions and Cost Connection) model does just this. To illustrate, BEACCON allows the user to assess both the GWI implications from, for example, installing a coal-fired system instead of a natural gas fired one and the cost differential between the two energy systems, thereby enabling financial tradeoff decisions.
Version 1.0 of the model focuses on dry mill corn ethanol plants with a capacity of 100 million gallons per year. The model separately calculates: Production costs of ethanol, taking into account plant costs for capital, corn, energy feedstock, labor, chemicals and other cost components; Energy system costs, taking into account energy system capital costs, thermal fuel and electricity feedstocks; GWI of ethanol by major contributing component, with each component assessed on a life cycle basis: agricultural phase, thermal energy feedstocks and electricity feedstocks; Emissions factors from various sources including Argonne National Lab’s GREET model and US Environmental Protection Agency AP-42 publications.
BEACCON includes: Energy balances and cost requirements of 12 different ethanol plant energy systems; Advanced plant configurations including combined heat and power systems, gasification, cofiring and nuclear colocation systems; Prevailing energy prices in nine different states (home to over 80 percent of current installed ethanol plant capacity); Life cycle global warming intensity of all corresponding electricity grid regions; Coal, natural gas and biomass fuel feedstock prices including different prices for coal commodities from all major U.S. coal basins; Costs and emission reductions associated with several conversion process improvements that are in commercial use today – fractionation, corn oil extraction, corn kernel fiber conversion and raw starch hydrolysis.
The analytical capabilities of the BEACCON model are best illustrated in several examples. Consider an ethanol plant operator in Illinois who runs a standard plant utilizing a natural gas boiler system to meet the process energy needs. In light of rising fuel costs, as well as potential future low-GWI incentive structures, the operator is considering a retrofit to the existing plant. Using BEACCON, the operator models the implications of retrofitting the plant with either a CHP system or a gasifier converting biomass to produce gas as a substitute for natural gas.
Figure 1 provides results of a BEACCON analysis. The three bars show the GWI for each energy system with the scale displayed on the left y-axis; the two circles show the payback in years for each retrofit scenario with the scale displayed on the right y-axis. As can be seen, the natural gas fired CHP system has an overall lower GWI than the boiler system, due to a decrease in GWI contribution from purchased electricity. The biomass-fueled gasifier retrofit system has the lowest GWI due to the low GWI contribution of biomass. Using the default BEACCON cost assumptions, the operator also gains a firsthand understanding of the financial implications of a retrofit project. Since a CHP system will use more thermal fuel but less purchased electricity, CHP paybacks depend significantly on prevailing energy prices in the region. In the case of the Illinois plant, CHP retrofit applications may likely pay back within 3.5 years; a gasifier retrofit may provide a payback of 1.8 years. Both retrofit applications, however, will prepare the ethanol plant for low-carbon incentive structures due to its lower GWI.
Expanding on the above analysis, BEACCON also can be used to provide valuable indications of how these retrofit applications would fare under various low-carbon incentive structures. Figure 2 shows the results assuming an effective cost of carbon emissions of $30/metric ton of CO2e. As can be seen, the paybacks for the retrofit applications drop significantly, with CHP now achieving a 2.1 year payback (1.7 for the gasifier system) since cleaner production methods pay less for their CO2e emissions.
In the last example, an ethanol plant operator in Wisconsin is considering retrofitting the plant with CHP or a gasifier, but additionally wants to employ the advanced processing technology of corn oil extraction. Corn oil extraction increases total fuel volume by about 7 percent by removing corn oil with centrifuges after the ethanol distillation process and converting the oil into biodiesel. The process alters the plant’s ethanol GWI due to lower thermal energy requirements from reduced dryer loads, increased electricity requirements to run the centrifuges and coproduction of biodiesel (a coproduct credit is assigned to the ethanol based on an estimate of the petroleum diesel displaced by the biodiesel).
Furthermore, the location of the ethanol plant in Wisconsin also results in slightly lower GWI values than a similar plant in Illinois, since the GWI of the incumbent electricity grid is lower in Wisconsin (939 g/kWh) than in Illinois (1,063 g/kWh). Again, BEACCON also can assess the financial implications from a combined investment in an energy system and a process improvement retrofit project. The analysis (Figure 3) indicates a 2.7 year payback for retrofitting a standard boiler plant in Wisconsin with a CHP system and going to corn oil extraction, and a 1.8 year payback from installing a biomass gasifier combined with corn oil extraction.
As mentioned above, these are selected examples of the type of analyses that can be performed with the model. In fact, BEACCON also can model several other ethanol plant energy systems including coal-fired ones as well as other types of process improvements across several other key ethanol producing states. By making the model widely available, it is the hope of the authors that ethanol plant operators, fuel producers, developers, policy makers, environmental groups and others take advantage of this tool.
Steffen Mueller is a Research Assistant Professor at the University of Illinois at Chicago Energy Resources Center and a Senior Partner with Life Cycle Associates. Richard Plevin is a Senior Partner with Life Cycle Associates and a doctoral student in the Energy and Resources Group at UC Berkeley. BEACCON is implemented in Microsoft Excel® and may be downloaded free of charge at

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