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Used Cooking Oil
Our knowledge of the performance of the biofuels industry in Canada has improved significantly in the four years since that work was done. In addition, GHGenius has been updated with better data on the biofuel production process and feedstock production systems. An updated report is therefore warranted, given the continued interest in the subject and the updated modelling data. The report will also serve as an updated documentation resource for these pathways in the model.
The biofuel pathways that have been analyzed in this report include five ethanol pathways, corn, wheat, barley and sugar cane based systems and a cellulosic ethanol system based on wheat straw feedstock. Six biodiesel feedstocks have been considered, canola, soybeans, tallow, used cooking oil, palm oil and jatropha. Four feedstocks have been considered for hydrotreated oils, palm, canola, tallow, and soybean oil.
For the sensitivity analysis, the focus has been on the issues that can vary from plant to plant, such as co-product drying, the use of combined heat and power, and the energy source for the thermal energy. In addition, issues that still have some uncertainty, such as changes in soil carbon are evaluated.
Tags: Barley - Biodiesel - Canola - Corn - Ethanol - HRD - Jatropha - Palm Oil - Soybeans - Sugar Cane - Tallow - Used Cooking Oil - Wheat - Wheat Straw
1. International crude oil energy and emissions. Several new data sources have recently been identified. These include the International Oil and Gas Association (a time series from 2002 of the energy and GHG emissions of crude oil production for various regions of the world), data from the Alberta Energy Research Institute studies (some specific useful information for countries such as Mexico, Venezuela, Iraq, and Saudi Arabia), and the World Bank flaring study.
2. Canadian electricity. A time series of electric power production on a regional basis in Canada from 2000 has been be developed from Statistics Canada data. Regional generation efficiencies and proportions of power types have been extracted from the data.
3. Rail energy. Statistics Canada has a time series data for freight movement on Class 1 railways. This data has been compared to similar information from the United States and incorporated in the model.
4. Potash mining. Statistics Canada, CIEEDAC, and NRCan Comprehensive Energy Use Database provide a time series for energy consumption, quantity and type. This has been compared to the NRCan CIPEC report that was used as a data source in the model. The new information has been incorporated into the model.
5. Nitrogen fertilizer. Statistics Canada, CIEEDAC, and NRCan Comprehensive Energy Use Database all have a time series for information on this sector. These data sets do not include process energy consumption but that can be calculated. The data sets have been compared to the NRCan CIPEC report that was the base of data in the model.
6. Corn and Soybeans. Fertilizer and yield time series available from the USDA. Some Statistics Canada yield data on these crops and other Canadian crops is available as well. This time series data has been incorporated into the model.
7. Ethanol and Biodiesel energy requirements. New data from the United States is available for both these alternative fuels. An update and development of a time series for ethanol has been incorporated into the model.
8. Some users have identified a number of enhancements for the functioning of the EV macro in GHGenius. These modifications have been incorporated into GHGenius. They provide more functionality and having them in the public model will allow them to be continually updated as model enhancements are undertaken.
9. Natural gas update. The Canadian Gas Association has provided some recent information on distribution emissions. Unfortunately the report did not provide activity data but that that has been developed from other sources. In addition Statistics Canada has data on the natural gas sector and this will be reviewed to see if it can be worked into the model.
Tags: Biodiesel - Corn - Crude Oil - Electricity - Ethanol - Fertilizer - GHGenius 3.16 - Natural Gas - Soybeans
The two largest pieces of work included:
1. GHGenius has had default values for the production of synthetic crude oil by an integrated mining process. More and more synthetic crude oil is being produced by in situ mining (Steam Assisted Gravity Drainage or Cyclic Steam Stimulation), so pathways and default values for these alternate production systems have been added. There is now full flexibility in the model for combining bitumen extraction methods and integrated or stand alone upgraders.
2. A major upgrade of the methodology for calculating land use emissions (direct and indirect).
a. The IPCC 2006 guidance document has some small changes in the sources of N2O that are to be calculated as part of a national inventory. This includes N2O emissions resulting from a loss of soil carbon. This source has been added to the model along with an update of the IPCC default values.
b. An update on the issue of N2O emissions from crops that fix their own nitrogen has been included. There has been an update of the approach included in the model.
c. Environment Canada and Agriculture and Agri-Food Canada have made considerable progress in defining the appropriate regional emission factors for agricultural activities such as fertilizer application, cultivation practices and other land use activities rather than relying on the IPCC Tier 1 values. These emission factors, which are found mostly on sheet W, have now been regionalized.
d. The soil carbon changes calculations in the model have been changed to a more straightforward approach.
e. Within the model we have an above ground carbon offset due to nitrogen fertilization of biomass from fertilizer that is lost offsite. This is not included in the IPCC guidelines. We have modified the model so that this source can be included or excluded from the calculations by the user.
f. A discussion of above and below ground carbon changes has been included. The model has been modified so that the land use assumption for ethanol co-product credits are consistent with the energy and GHG emission credit calculations. A discussion of how to model both the direct and indirect land use changes for the biomass feedstocks in included.
Tags: Biodiesel - Canola - Corn - Crude Oil - Electricity - Ethanol - GHGenius 3.13 - Land Use - Soybeans - Wheat
The main overall purpose of this report is to provide an assessment of existing LCA models that can be applied in determining the environmental footprint of bioethanol and competing fuels (e.g., gasoline) in Canada. The assessment includes analysis to identify the key factors that contribute to differences in the results from different models. This report also provides: an analysis regarding the role of LCA in policy formulation; an overview of other modelling activities oriented to environmental policy development; an overview of what LCA is and how it works; a brief description, assessment and availability (for the purposes and scope of this study) of 37 LCA models, more detailed analysis of 9 models and selection of 2 models for detailed analysis and comparison; sensitivity analysis using one model to identify the factors in the life cycle that most strongly influence LCA results; and recommendations regarding the development and enhancement of LCA modelling for Canada.
Tags: Corn - Ethanol - Wheat
This work investigates some of the issues that impact these emissions and to arrive at some potential recommendations of how the issue could be best modelled in the future.
The work only considered four feedstocks; corn, wheat, canola, and soybeans. These are the primary feedstocks for the first generation biofuels and the ones that are currently facing the greatest growth pressures. The question is where will the feedstocks to produce these biofuels come from? In GHGenius, the default values for most feedstocks assume some combination of increase in yield and substitution for some generic agricultural feedstock and while a case can be made that this has been the historical route it may not apply in the future. This work sought to address a number of questions that impact on this issue.
Avoided transportation emissions resulting from the use of biofuel feedstocks locally rather than exporting these feedstocks have generally been ignored in most discussions regarding land use and bringing more land into production in remote regions. These emissions will vary from country to country depending on transportation modes employed and the destination of customers. The preliminary analysis undertaken here indicates that in the case of Canada these transportation emissions are very large and avoiding these emissions can offset soil carbon losses resulting from brining new land into production elsewhere in the world.
The most significant issue that arose from this work was the impact of the conversion of forests and forestland to biofuel feedstock production. It is this factor that has the potential to eliminate the GHG emissions benefits of most biofuels as they are currently produced. A thorough investigation of this issue is beyond the scope of this work but an overview of the basic facts is presented.
Tags: Biodiesel - Canola - Corn - Ethanol - Land Use - Soybeans - Wheat
Prepared February 2007
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One butanol pathway has been added to GHGenius. A corn to butanol pathway for gasoline blends is the most appropriate pathway to consider for North America applications. This corn to butanol pathway is fully functional including summary information and cost effectiveness calculations. Additional co-products have been added to the model including acetone.
Reduction of diesel NOx emissions is difficult due to the presence of oxygen in the exhaust. In the oxidizing environment of lean exhaust, fuel has proven to be only a marginally effective reducing agent. Urea SCR systems utilize aqueous urea as a means of introducing ammonia as the NOx reduction catalyst. These systems have been shown to be less sulphur sensitive than NOx adsorbers are. The latest information on this issue has been reviewed and the new findings have been incorporated into GHGenius.
Tags: Butanol - Corn - Exhaust Emissions - GHGenius 3.7 - Urea
For the transportation sector there are generally three ways that GHG emissions can be reduced; improve energy efficiency at all stages of the life cycle, use lower carbon intensity fuel sources, or change transportation modes. Combinations of the three approaches are of course also possible.
Renewable fuel producers have some control over the first two categories but they will be looking to maximize the return on investment when they design and build biofuel facilities and not necessarily minimizing GHG emissions. This may lead to the facilities being energy efficient but the types of energy that are used in the facilities may not be optimized.
For this work we are interested in, among other possibilities, the emissions impact that could arise from different farming practices. These practices could result in soil carbon changes and perhaps in changes in above ground biomass. The default values for modelling have therefore been set so that in the base case there is no change in soil carbon, no change in above ground biomass arising from increased crop yields, and no biomass growth resulting from nitrogen run-off lost offsite.
Tags: Biodiesel - Canola - Corn - Ethanol - Lignocellulosic - Palm - Soybeans - Sugar Cane - SuperCetane - Tallow - Wheat - Yellow Grease
The primary work on the ethanol pathways in GHGenius was undertaken in 1999 and while there have been some minor updates since then, the primary data in the model has not been reviewed in the past five years. There are several important issues that have been investigated and added to the model to better reflect the emissions of modern plants. These include:
1. Reviewed and updated energy requirements for grain ethanol plants. There have been significant reductions in the energy requirements of new grain ethanol plants in the past five years. This progress has been reviewed and incorporated into the model.
2. The addition of the capture and liquefaction of carbon dioxide. Ethanol plants produce a very concentrated carbon dioxide stream, which can be captured and liquefied for use in a variety of industrial applications. Alternative sources of carbon dioxide are less concentrated and require more energy to concentrate and purify. Depending on the degree to which carbon dioxide from ethanol plants displace carbon dioxide from other sources there can be an energy and emissions credit applied to the ethanol plant. This alternative processing scheme has been added to the model with the flexibility to activate or not, either fully or partially.
3. In the development of commercial ethanol from lignocellulose plants there are new co-products being developed. Some of these include fertilizers and soil conditioners. The model has been expanded to include fertilizer co-products.
4. There is some information in the literature that suggests that some animals that consume distillers dried grains have lower levels of flatulence. The literature has been surveyed for further information on this issue and this emission credit has been incorporated into the model.
A major part of any life cycle analysis is the collection of the data on the inputs and outputs of the production cycle being analyzed. The quality of the data has a large impact on the quality of the results being calculated. Data quality must balance the available time and resources against the quality of the data required to make a decision regarding overall environmental or human health impact.
In GHGenius the data on many of the production pathways is continually being updated as new information sources emerge or as processes evolve. Recently new information on the emissions of the fertilizer sector in Canada has become available, and new information on nitrous oxide emissions in the agriculture sector are also available. These changes to the model were described in a recent report on the emissions from biodiesel production ((S&T)2, 2005). These changes also impact the ethanol fuel pathways either directly in the case of fertilizer or indirectly in the case of soybean emissions since the distillers dried grains (DDG) displaces soybean meal and the emissions credit for the DDG is a function of the emissions from the soybean lifecycle.
The input variables are another class of data input. These variables can be expected to differ in different regions of the country or in different countries in response to different practices or environmental conditions. These inputs are generally found on the input sheet in GHGenius as one expects them to be changed in different circumstances.
These input variables for the different ethanol pathways have been reviewed and where better data now exists for some variables, this information has been added to GHGenius. It is important to keep in mind that the general approach to the data in GHGenius is to model the most likely scenarios, not the best-case scenario where practices or existing equipment would have to change to produce the modelled results.
Several new co-products have been added to GHGenius that are applicable to the ethanol pathways. The ability to model the capture of carbon dioxide and have that product displace gas from alternative production sources in now available. Cellulosic ethanol production processes produce large amounts of lignin, which can be processed by different means. It is possible to burn the material and produce electricity or it may be possible to sell the material for other applications. One application may be as a source of fertilizer and this capability has been added to the model. The handling of other co-products such as acetic acid has been improved in the model.
The co-product credits for DDG have been reviewed. The displacement factors for DDG have been reduced as more feed trials suggest lower displacement ratios that are used in other models and have been used in previous versions of GHGenius. The value of DDG as animal feed is a very complicated subject as there are many components in a typical ration and it is almost impossible to design a reasonably sized experiment to isolate the impact of one of the components. There is a significant amount of information on the impact of diets on methane production from cattle. It is clear that introducing dietary supplements, particularly ones with by-pass protein will reduce the methane production rate in dairy and beef cattle. Estimates of the impact of DDG on methane emission rates have been made and incorporated into the model.
Tags: Corn - Ethanol - GHGenius 2.6 - Lignocellulosic - Wheat
Prepared November 2004
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They have also established a $100 million Ethanol Expansion Program to assist with the construction of new ethanol plants in Canada. The funding under the Ethanol Expansion Program is part of a larger bio-fuels strategy that also includes the extension of the National Biomass Ethanol Program, research and development under the biotechnology component of the Technology and Innovation Strategy and an investment in bio-diesel.
There has been little economic and financial analysis of ethanol within a Canadian context. The few published and unpublished studies carried-out so far for the public sectors have dealt mostly with potential socio-economic impacts and have attracted little interest from the investment community due to their lack of focus on profitability, both short and long term. Policy and decision makers, financial institutions, and other economic players need the more detailed, formal analysis framework in order to make investment decisions regarding the development of these fuels.
The primary objective of this study is to assess the current and future economics of ethanol plants in Canada and to develop estimates of demand, supply, and prices (costs and selling) of this fuel. The results are then used to develop a template-like analytical tool for various models of ownership structure, to help assess the financial performance of various types of fuel ethanol (regional and feedstock specific) plants across Canada.
The work was carried out in Phases and stages. This report covers Phase 1, for fuel ethanol. A similar report has been prepared for biodiesel.
The specific objectives of Phase 1, Stage 1 were to:
· Review literature on economic and financial performance of ethanol plants.
· Identify successful plants and reasons for success.
· Quantify feedstock resources and production costs.
· Develop a comprehensive financial model.
· Develop a supply curve.
The objectives of Phase 1, Stage 2 were to:
· Identification of market barriers.
· Evaluate policy tools including.
o Government capital investment
o Favourable tax treatment
o Infrastructure investment
o R&D funding
o Renewable content mandates
o Emission taxes
· Examine the potential for regionalization of tools.
· Quantification of levels of support required.
· Investigate other approaches to market development.
Phase 1, Stage 3 of the work focuses on the international aspects of a developing ethanol industry and considers the threats and opportunities that international trade in biofuels presents. The specific tasks of this stage include:
· Identification of the level of international trade.
· Production cost comparison with the potential exporters of fuel ethanol.
· Analysis of the import alternatives that ethanol users in Canada would face.
· Evaluate the impacts that ethanol imports might face and identify measures that might mitigate the impacts.
· Evaluate the impact of trade agreements on enabling or disabling Canadian industry competitiveness.
The next phase of the work will include some GHG analyses.
Tags: Corn - Economic - Ethanol - Lignocellulosic - Wheat
Prepared for Agriculture and Agri-Food Canada in August 1999
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Tags: Corn - Ethanol