Edgard Gnansounou *, Arnaud Dauriat2

xBioenergy and Energy Planning Research Group (BPE), Ecole Polytechnique Federale de
Lausanne (EPFL), CH-1015 Lausanne, Switzerland
2ENERS Energy Concept, P. O. Box 56, CH-1015 Lausanne, Switzerland
*Corresponding author: Prof. Gnansounou; E-mail: edgard. gnansounou@epfl. ch

1 INTRODUCTION

During its earliest stage, the development of biofuel production in the industrialized countries was mostly driven by agricultural policies. The overproduction and low prices of crops called for diversification. Fuels derived from agricultural feedstocks were considered an ecologically valuable option for price stabilization in addition to fallowing. It was even seen as an alternative to the set-aside strategy. Two more motivations were highlighted. The perspective of oil depletion and concentration of petroleum resources in a limited num­ber of regions which are politically instable increased the concerns about energy insecurity risks. Furthermore, due to global climate change, several industrialized countries committed to reduce their greenhouse gas emissions. The transportation sector was one of the priorities for public incentives.

Indeed, that sector is vulnerable to petroleum products which represent 98% of its final energy consumption worldwide. The high volatility of oil prices and the low competitiveness of sustainable biofuels when oil prices decrease under a certain threshold, make a claim for stable incentives in the early development stage of biofuel markets. However, the fast growth of biofuel production and the rise of the prices of agricultural commodities in 2008 fed some controversies about the sustainability of biofuels. In addition to the risks of competition with food and animal feed, the energy and greenhouse gas (GHG) balances of biofuels were debated. In response to the spread of reluctance to continue supporting publicly the utiliza­tion of biofuels, public authorities in several countries have imposed minimum sustainability targets for biofuels to be eligible for incentives (Escobar et al., 2008; Van Dam et al., 2008).

As an example, on 23 April 2009, the European Union issued a Directive on the promotion of the use of renewable energy with the requirement that each Member State shall ensure by 2020 at least 10% sustainable renewable energy in the final energy consumption of its transport sector. Article 17 of that directive notified the minimum sustainability criterion for biofuels (EC, 2009). For instance, concerning GHG emission reduction with respect to fossil fuels, increasing minimum targets were imposed: 35% in the year of entry into force of the Directive, 50% in 2017, and 60% for biofuels produced from plants that will start from 2017 onward.

The concerns about energy balances are related to both the life-cycle energy efficiency of biofuels and the saving of nonrenewable energy between biofuels and fossil fuels. The latter aspect is relevant with respect to the substitution efficiency of biofuels.

Monitoring the application of minimum targets on GHG emission reduction to biofuels, as well as estimating their substitution efficiency with respect to fossil fuels, is subject to sig­nificant uncertainty and inaccuracy associated with the methodology applied. Assessments of the environmental impact of biofuels (ADEME-DIREM-PWC, 2002; ADEME, 2010; Beer and Grant, 2007; CONCAWE-EUCAR-JRC, 2008; Elsayed et al., 2003; EMPA, 2007a; GM-LBST, 2002; Gnansounou and Dauriat, 2004, 2005; Kim and Dale, 2008; Macedo, 2004; Malca and Freire, 2006; Shapouri et al., 2002; VIEWLS, 2005; Wang, 2005) often signifi­cantly differ in methodological choices and consequently in their results. Table 1 shows an overview of the methodological choices in these studies.

For example, while some studies (Elsayed et al., 2003) are limited to a Well-to-Tank (WtT) approach (thereby excluding the utilization phase), other studies employ a Well-to-Wheel (WtW) approach. When included in the system, the utilization phase is taken into account either in a simplified way (usually by merely considering the difference in the LHV of fuels) or with more details (by considering the actual performance of fuels according to a specific engine technology and/or fuel blend). As far as the functional unit is concerned, the distance traveled (in km) is the unit of choice in most studies (in agreement with the principles of the WtW approach). Allocation by system expansion is the most widely used method, although in some studies a combination of methods is used. For instance, system expansion is combined with allocation by mass in ADEME-DIREM-PWC (2002). Economic allocation is the second most common approach. Fuel blends considered vary from one study to the other (usually between 5% vol. and 15% vol.), depending on the most frequent use of fuel-ethanol in the region of study. All the reviewed studies however also consider ethanol as a fuel component on its own, even though the way this is done does not depend on the actual fuel blend but rather on the difference in the LHV of fuels. Finally, land-use change is included with details in only a few studies (EMPA, 2007b; Elsayed et al., 2003; IFEU, 2004), based on IPCC (2003a) guidelines.

In the particular case of GHG balance, the magnitude of the discrepancy among the results is tremendously high. Farrel et al. (2006), based on a review of corn-based ethanol studies in the USA, attributed the main differences to the way coproducts are accounted for, the value of some input parameters, and some omission/inclusion of ambiguous inputs. Reijnders and Huijbregts

(2003) focused on forest-based biofuels, analyzing the effect of the considered time frame on the emission factors, the choice of previous land use, the allocation of carbon sequestration and emissions during forest growth, and the fate of sequestered carbon after fuel wood harvesting. Bhrjesson (2009) focused on methodological choices and the influence of local conditions in wheat-based ethanol production in Sweden. He addressed the problem of coproducts allocation choices, the choice of the fuel used, and biogenic GHG emissions from cultivated soils.

In fact, quantitative investigations of the differences in the results from one study to the other are not straightforward due to the lack of information concerning the inventory data,

TABLE 1 Comparison of Methodological Choices in Reviewed Studies

System Approach Well-to-Tank

the assumptions made to complement unavailable data and modeling choices about system definition and boundaries, functional units, reference systems, and allocation methods. In the research presented in this chapter, an assessment platform was developed based on an exten­sive review of literature. Combinations of assumptions and modeling choices were defined to investigate the sensitivity of the results to several factors. The focus is mainly put on choices regarding the allocation method, the previous land use, the fuel blends, and the vehicle/fuel performance. This chapter aims to contribute to current discussions on how methodological choices and local conditions influence LCA results, addressing some important points often limitedly treated in the literature. The chapter considers wheat-based bioethanol production in Switzerland as a case study, with the aim of quantifying the variation in GHG emissions and nonrenewable energy use depending on methodological choices. The chapter is an updated version of a previous paper by Gnansounou et al. (2009).

If the air velocity is increased to 5-10 m/s then a CFB system can be achieved, where the sand is carried upward by the flue gases and a more thorough mixing of the bed material and fuel takes place. The sand is then separated from the gas in a hot cyclone or U beam separator at the top of the furnace and fed back into the combustion chamber where the whole process begins again. CFBs deliver very stable combustion conditions, but it involves higher cost. CFB systems exhibit several advantages, such as the adaptation to various fuels with different properties, sizes, shapes, and high moisture (up to 60%), and ash contents up to 50% (http://www. esru. strath. ac. uk/EandE/Web_sites/06-07/Biomass/HTML/combustion_ technology. htm).

The fuels blends considered in the present article include E5, E10, and E85. The LHV, density, and biogenic carbon content of ethanol are taken as 26.8 MJ/kg, 0.790 kg/l, and

0. 520 kg C/kg. The LHV, density, and fossil carbon content of ethanol are taken as

42.5 MJ/kg, 0.750 kg/l, and 0.865 kg C/kg. The characteristics of the fuels blends are calcu­lated according to the respective volume shares of ethanol and gasoline. The effect of consid­ering different fuel blends and/or other hypotheses regarding fuel performance is investigated in the case study section.

As discussed previously, the performance of bioethanol as a vehicle fuel strongly depends on its rate of incorporation into gasoline. Indeed, although bioethanol shows a significantly lower LHV compared with gasoline (which leads to expect an increase in vehicle fuel con­sumption when ethanol is added to gasoline), many vehicle tests in the European context (AEAT, 2002; EMPA, 2002,2007b; IDIADA, 2003) have demonstrated the improved efficiency (expressed in MJth/km) of gasoline-ethanol blends with respect to standard gasoline (Table 2). In 100% of the tests reported in these studies, gasoline-ethanol blends indeed show an improved efficiency (in MJth/km) compared with standard gasoline. On average, energy consumption per km is reduced by 2.7%, 7.5%, and 2.5% with E5, E10, and E85, respectively.

All these data, however, refer to fuel blends rather than ethanol specifically. In order to evaluate the WtW net GHG emissions of ethanol, it is necessary to define the fuel efficiency of the ethanol component in fuel blends. This is done in this chapter by assuming that the fuel efficiency (in km/MJth) of the gasoline component in fuel blends is equal to that of standard gasoline on its own, and that the difference is entirely explained by the presence of bioethanol in the fuel blend. If we assume an average fuel consumption of 2.564 MJth/km for gasoline (as reported in ecoinvent), the specific fuel consumption of ethanol (in MJth/km) is calculated according to the data in Table 2. The results are reported in Table 3.

Super critical water gasification (SCWG) technology is suitable for wet biomasses and organic wastes. This technology takes advantage of the large amount of water in biomasses by using the water as a reaction medium, eliminating the costly feedstock-drying step. Supercritical water has a low dielectric constant close to that of organic compounds. The organic reactions under supercritical water, therefore, become more homogeneous, resulting in a higher reaction rate. The free radical condition of supercritical water also enhances the gas formation, leading to the high gas yield. As compared to conventional dry gasification,

SCWG produces a lower amount of tarry material and char as byproduct, due to the higher solubility and reactivity of the organic compounds in supercritical water. Nevertheless, because tar and char are difficult to gasify, they act as a drier to achieve complete gasification. The formation of tar and char also causes a reduction in the energy efficiency of the process by means of reactor plugging, heat exchanger fouling, and catalyst deactivation (Chuntanapum and Matsumura, 2010).

Biomass is harvested as part of a constantly replenished crop. This maintains a closed carbon cycle with no net increase in atmospheric CO2 levels. There are five basic categories of material, that is, virgin wood, forestry materials, materials from arboricultural activities or from wood processing; energy crops: high-yield crops grown specifically for energy applications; agricultural residues: residues from agriculture harvesting or processing; food waste, from food and drink manufacture, preparation and processing, and postconsumer waste; industrial waste and coproducts from manufacturing and industrial processes.

Feedstocks that are used directly in a manner that is given to us by nature fall under the category of natural feedstocks. The first-generation biofuels use the edible biomass for producing biofuels. Some of them are sunflower seeds, jojoba oil, soya bean oil, safflower seeds for biodiesel production, and corn and sugar cane for producing ethanol. In contrast, the second-generation biofuels are produced from non edible feedstocks like lignocellulosic feedstocks which include agro residue (stalk, husk), forest residue (branch, twigs, bark, leaves), and several others.

In addition to growing currently available feedstocks on available land to produce biofuels, the realization of dedicated energy crops with enhanced characteristics would represent a significant step forward. The genetic sequences of a few key biomass feedstocks are already known, such as Poplar (Tuskan et al., 2006), and there are more in the sequencing pipeline. This genetic information gives scientists the knowledge required to develop strategies for engineering plants with far superior characteristics, such as diminished recalcitrance to conversion (Himmel et al., 2007).

Another area where genetic engineering could produce dramatically positive results is the development of perennial feedstocks that can reach high-energy densities over a short time with minimal fertilization and water consumption. By combining the known targeted climates and soil types present in the available conservation reserve program (CRP) and mar­ginal lands with tailored feedstocks, it may be possible to develop grasses and short-rotation woody crops that maximize carbon and nitrogen fixation within these ecosystems. In addition to modifying the intrinsic polysaccharide/lignin composition and central metabolism of the feedstock itself, several research groups are attempting to express enzymes that are capable of breaking down cellulose into glucose directly within plants.

Life-cycle analysis (LCA) or assessment is an internationally renowned methodology for evaluating the global environmental performance of a product along its partial or whole life cycle, considering the impacts generated from "cradle to grave." At its early age, the meth­odology was mainly dedicated to industrial products. Although the ISO 14040-series (ISO, 2006a, b) provides the standard for LCA, it was applied in a variety of ways and thus often leads to diverging results, especially in the case of biofuels. LCA of biofuels is often limited to energy and/or GHG balance. Several LCA studies (ADEME, 2010; ADEME-DIREM-PWC, 2002; Beer and Grant, 2007; CONCAWE-EUCAR-JRC, 2008; Elsayed et al., 2003; EMPA, 2007a; GM-LBST, 2002; Gnansounou and Dauriat, 2004; Macedo, 2004; Malca and Freire, 2006; Shapouri et al., 2002; VIEWLS, 2005; Wang, 2005) have been completed with various frameworks, scopes, accuracy, transparency and consistency levels, making it difficult to compare the results on a rational basis, even when addressing the same biofuel pathway (Panichelli et al., 2008).

The main assumptions found in the literature when estimating the reduction of GHG emissions of biofuels compared to fossil fuels are described in detail in a technical report by the Laboratory of Energy Systems (LASEN) of EPFL (Gnansounou et al., 2008a). Before introducing the general framework of the analyses made in this chapter, a short review of the most significant methodological issues of LCA is proposed, with a focus on the cases of biofuels.

The fuel particles are transported into an externally heated silicon carbide (SiC) tube pneumatically through an insulated and water-cooled injector. Prior to the injection, the feed­ing stream, composed of air and fuel particles, has to pass through an agitation chamber for "disaggregation and filtering of pulses in the feeding." The feeding fuel is ignited by a natural gas/air burner at the reactor entrance (Jimenez and Ballester, 2006). There are three main stages that occur during biomass combustion: drying, pyrolysis and reduction, and com­bustion of volatile gases and solid char (IEA, International Energy Agency, Task 32: biomass combustion and co-firing: an overview. http://www. ieabioenergy. com/MediaItem. aspx? id=16).The combustion of volatile gases contributes to more than 70% of the overall heat generation. It takes place above the fuel bed and is generally evident by the presence of yellow flames.

Combined Heat and Power (CHP): Production of electricity and heat from one energy source at the same time is called CHP. In almost all cases, the production of electricity from biomass resources is most economical when the resulting waste heat is also captured and used as valuable thermal energy—known as CHP or cogeneration (http://www. epa. gov/chp/ documents/biomass_fs. pdf). Biomass is most economical as a fuel source when the CHP system is located at or close to the biomass feed stock. In some cases, the availability of bio­mass in a location may prompt the search for an appropriate thermal host for a CHP applica­tion. In other circumstances, a site may be driven by a need for energy savings to search for biomass fuel within a reasonable radius of the facility (http://www. epa. gov/chp/basic/ renewable. html).

Using biomass instead of fossil fuels to meet energy needs with CHP provides many poten­tial environmental and economic benefits, which can include (i) reduced greenhouse gas and other emissions, (ii) reduced energy costs, (iii) improved local economic development,

(iv) reduced waste, (v) expanded domestic fuel supply, (vi) reduced transmission and distribution losses. CHP offers distributed generation of electrical and/or mechanical power; waste heat recovery for heating, cooling, or process applications; and seamless system integration for a variety of technologies, thermal applications, and fuel types into existing building infrastructure. CHP systems typically achieve total system efficiencies of 60-80% for producing electricity and thermal energy (http://www. epa. gov/chp/documents/ biomass_fs. pdf).

The WtW net GHG emissions of ethanol are calculated as the product of the WtT net GHG emissions and the specific fuel consumption of ethanol in the fuel blend (as reported in Table 3). The WtW net GHG emissions of ethanol (expressed in kg CO2 eq./km) are then com­pared to those of unleaded gasoline.

1.1 Net Energy Use and Energy Substitution Efficiency

The net energy use of a fuel most often refers to the consumption of nonrenewable primary energy along the life cycle of a biofuel or fossil fuel. Although the energy balance is often lim­ited to the comparative energy efficiency of fuels production, the actual performance of fuel blends must be taken into account in order to obtain a global picture of the potential sub­stitution of nonrenewable energy associated with biofuels. In order to measure the efficiency of nonrenewable primary energy substitution over the life cycle of bioethanol, the concept of energy substitution efficiency is defined later, including both production and utilization of the biofuel.

According to the data in Table 3, the most efficient use of fuel-bioethanol is in the form of E10 (1.174 MJth/km compared to 1.413 MJth/km for bioethanol as E5 and 2.485 MJth/km for bioethanol as E85). The energy substitution efficiency is here defined as the ratio of the savings of nonrenewable primary energy of a given bioethanol system (incl. production and use) with respect to conventional gasoline to the savings of nonrenewable primary energy of an ideal bioethanol system (i. e., bioethanol with a zero nonrenewable primary energy consumption and utilization as E10).

Hydrothermal gasification is the conversion of solid biomass into gaseous and/or liquid products in the presence of steam. Different hydrothermal biomass gasification processes are under development. In contrast to biomass gasification processes without water, biomass with the natural water content ("green biomass") can be converted completely and energeti­cally efficiently to gases. Depending on the reaction conditions, methane or hydrogen is the burnable gas produced. Some processes use catalysts. In recent years, significant progress was achieved in the development of various hydrothermal biomass gasification processes. However, some challenges still exist and technical solutions are needed before large-scale production facilities can be built (Kruse, 2009).

Biomass is an organic material which stores sunlight in the form of chemical energy. It is available on a renewable basis. Here, we specifically mention the lignocellulosic biomass from plants and residues from various agricultural activities. Biomass is an organic mate­rial that is composed of polymers that have extensive chains of carbon atoms linked to macromolecules. The polymer back bone consists of chemical bonds linking carbon with carbon, or carbon with oxygen, or sometimes other elements such as nitrogen or sulfur. Instead of describing polymers in terms of the atomic structure of the chain, most can be viewed as assemblies of some larger molecular unit. In the case of cellulose, that unit is the glucan moiety, essentially a molecule of glucose with one molecule of water missing (C6H10O5)n. For hemicellulose, the unit is often a 5-carbon sugar, called xylose. However, hemicellulose polymers are not linear chains as in the cellulose polymer. Some are branched and other monomer units have side chains, with acetyl groups being very common. The lignin polymers are composed of phenyl propane subunits linked at various points on the monomer through C—C and C—O bonds. In addition, there are often side chain moieties such as methoxy groups. Wood-based biomass is available in large quantities and is cheap. It consists of three major components, that is, lignin, cellulose, and hemicellulose.

(i) Cellulose: It contains linear polysaccharides in the cell walls of wood fibers, consisting of D-glucose molecules bound together by p-1,4-glycoside linkages. Biomass comprises 40-50% cellulose.

(ii) Hemicellulose: It is an amorphous and heterogeneous group of branched polysaccharides (copolymer of any of the monomers of glucose, galactose, mannose, xylose, arabinose, and glucuronic acid). Hemicellulose surrounds the cellulose fibers and is a linkage between cellulose and lignin (15-30%). Hemicelluloses are heterogeneous polymers

of pentoses (e. g., xylose, and arabinose), hexoses (e. g., mannose, glucose and galactose), and sugar acids. Unlike cellulose, hemicelluloses are not chemically homogeneous. Hemicelluloses are relatively easily hydrolyzed by acids to their monomer components consisting of glucose, mannose, galactose, xylose, arabinose, and small amounts of rhamnose, glucuronic acid, methylglucuronic acid, and galacturonic acid. Hardwood hemicelluloses contain mostly xylans, whereas softwood hemicelluloses contain mostly glucomannans. Xylans are the most abundant hemicelluloses. Xylans of many plant materials are heteropolysaccharides with homopolymeric backbone chains of 1,4-linked р-D-xylopyranose units. Xylans from different sources, such as grasses, cereals, softwood, and hardwood, differ in composition. Besides xylose, xylans may contain arabinose, glucuronic acid, and acetic, ferulic and p-coumaric acids. The degree of polymerization of hardwood xylans (150-200) is higher than that of softwoods.

(iii) Lignin: It is a highly complex three-dimensional polymer of different phenylpropane units bound together by ether (C—O) and carbon-carbon (C—C) bonds. Lignin is concentrated between the outer layers of the fibers, leading to structural rigidity and holding the fibers of polysaccharides together (15-30%). Generally, softwoods contain more lignin than hardwoods. Lignins are divided into two classes, namely, guaiacyl lignins and guaiacyl-syringyl lignins. Although the principal structural elements in lignin have been largely clarified, many aspects of their chemistry remain unclear.

In addition, small amounts of extraneous organic compounds, that is, extractives, proteins, and inorganic constituents are found in lignocellulosic materials (about 4%; Stocker, 2008). Biomass residues like wheat straw, corn stover, or sugar cane bagasse contain much ash and N, S, Cl, and these quantities also depend on the geographical source.