Biomass pyrolysis is the thermal breakdown of biomass using high temperature in the absence of oxygen. Pyrolysis, similar to other thermochemical conversion technologies, results in three products: solid (biochar), liquid (bio-oil), and gas (syngas/producer gas). For pyrolysis, the target product is usually either bio-oil (using fast pyrolysis) or biochar (using slow pyrolysis). Slow pyrolysis has been used for centuries to produce solid, cleaner burning fuels. Only recently (1980s) has fast pyrolysis been recognized as an alternative to produce liquid fuel (Meier and Faix 1999). The main differences between the fast and slow pyrolysis are summarized in Table 4.

Slow pyrolysis was discovered and used many centuries ago, when charcoal and coal-tar were produced using slow pyrolysis of wood and coal. Charcoal was used as a fuel to create a smokeless flame and increase the combustion temperature. The coking process is also used in manufacture of steel. Recent interest in liquid fuels has changed the focus to fast pyrolysis, which results in much higher liquid yield.

Biofuel production (such as ethanol) is a complex process using lignocellulosic feedstock (such as switchgrass, sweet sorghum bagasse, pine wood chips, etc.) compared to sugarcane syrup or corn. The carbohydrates in lignocellulosic feedstock are much more problematic in terms of both solubility and utilizing their different component sugars (mainly glucose, xylose and arabinose) compared to starch in corn or sucrose in sugarcane syrup. The complexity of lignocellulosic feedstock provides different routes for fermentation including direct microbial conversion (DMC), separate hydrolysis and fermentation (SHF) and simultaneous saccharification and co-fermentation (SSCF).

Note:

ag ethanol gA xylose consumed

bThe relevant genotype is given in parantheses. pdc, pyruvate decarboxylase; pfl, pyruvate formate lyase; adhB, alcohol dehydrogenase II;/rd, fumarate reductase, pZB5 carries the genes for xylose isomerase, xylulokinase, transketolase and transalolase cMaximum volumetric productivity

dFigures in parentheses denote number of strains investigated (if more than one)

eThe relevant genotype is given in parenthese. XYL 1, xylose reductase; XYL 2, xylitol dehydrogenase; xyl A, xylose isomerase

Note:

SSF, Simultaneous saccharification and fermentation SSCF, Simultaneous saccharification and co-fermentation SHCF, Separate hydrolysis and co-fermentation

Direct Microbial Conversion (DMC) or Consolidated Bio-Processing (CBP)

Direct Microbial Conversion (DMC) is a consolidated process of production of cellulolytic enzymes (cellulase and xylanase mixture), hydrolysis of lignocellulosic biomass and fermentation into bioproducts such as ethanol in a single vessel. Clostridium phytofermentans would be an ideal microorganism for DMC ethanol production. However, C. phytofermentans has been reported to produce low ethanol yields (less than 0.2% (w/v)) with several by-products such as hydrogen, acetic acid, and formic acid that ultimately lowers ethanol productivity (Warnick et al. 2002).

Second generation lignocellulosic bioenergy crops are often viewed as environmentally sustainable relative to first generation and non-renewable alternatives in the transportation fuel sector (Dale et al. 2011). In addition to the carbon emissions-affiliated metrics discussed above, there are a number of indicators that can provide information on the environmental quality of bioenergy systems (McBride et al. 2011). These include indicators affiliated with soil quality, water quality (and quantity), biodiversity, air quality, and productivity. Here we concentrate on providing a brief overview of effects of agronomic production of switchgrass on (a) soil and water quality, (b) biodiversity, and (c) invasiveness issues. In some contexts more than others, the fact that it would likely be grown in large monocultures at expansive spatial scales is highly relevant.

Arpan Jain,1’3′[17] [18] Terry Walker, A* and Karl Kelly2

Introduction

In the 21st century, renewable sources of energy will be required to fulfill the rapidly growing energy requirements of both developing and developed (Organisation for Economic Co-operation and Development, OECD) nations worldwide. Fossil fuels must be phased out to avoid further increase in global greenhouse gas concentrations that severely threaten biodiversity and a sustainable economic future for the planet (Randers 2012). Biofuels, particularly ethanol and biodiesel for transportation, and biomass-based energy for power provide an attractive alternative to current fuels derived from non-renewable resources. Presently, most of the ethanol produced from biomass in the U. S. is derived from corn via the conversion of corn grain starch into glucose through enzymatic hydrolysis followed by fermentation to ethanol. Unfortunately, the increased demand for corn for use in biofuel production has led to higher prices for all products that utilize

corn, including foods that span from meat and dairy products to processed foods such as high fructose corn syrup (Hill 2009).

Biofuels have received more attention due to their renewable character and the inherent ability to reduce greenhouse gas emissions and geo­political concerns. Significant quantities of lignocellulosic biomass such as sweet sorghum bagasse, corn fiber, corn stover, wheat straw, rice straw, and soybean residues are routinely destroyed as waste primarily through burning, which not only wastes a potential source of biofuel feedstock, but also leads to environmental and health concerns (Claassen et al. 1999). In addition, biomass such as woody biomass can be sustainably produced in many regions of the world, including the United States. At present, lignocellulosic biomass has a worldwide annual production of 10 gigatons (Claassen et al. 1999; Harmsen et al. 2010). Earth’s terrestrial carbon potentially used in photosynthesis amounts to approximately 3,170 gigatons with 2,500 gigatons located in the soils, 560 gigatons in plant matter and 110 gigatons in microbial matter such as marine microalgae species (Jansson et al. 2010). According to Osburn (1993), if 6% of U. S. contiguous land is converted for the cultivation of lignocellulosic biomass and microalgae, all demands of gas and oil may be supplied with no net addition of carbon dioxide to the environment. Woody biomass presents several good possibilities for biofuel production due to its high proportion of convertible polysaccharides.

A gradual shift from biomass sources for biofuel production from crops that are used in food production such as corn requiring excessive inputs to lignocellulosic biomass is currently taking place. By utilizing current technologies, the production costs of ethanol from lignocellulosic biomass are still relatively high due primarily to the cost of cellulose separation, high use of chemicals and energy, hydrolysis and considerable waste production (Sun and Cheng 2002; Harmsen et al. 2010). However, the cost compared to a decade ago has dropped by more than 50% with promising technologies now in the mix.

The greatest challenge will be to better engineer the energy and food systems to reduce cost and to build a resilient society who relishes efficiency and lives within their means. This requires the support of corporate and government leaders, consumers and tax-payers with everyone willing to pay the cost for the switch to renewable energy within the next two decades before uncontrolled climate change effects begin to take hold. The cost for this change has been estimated to be about 5% of the world GDP, a small price to pay for a sustainable planet, but presently the world is unwilling to put even 1% of the GDP towards such an important cause. Once the renewable energy supply chains are in place, the operating cost and abundance of free solar-based (solar, wind, biomass) or geothermal energy without the need for mining of fossil sources clearly holds the long-term advantage for a sustained society. By 2050, conservative estimates show that the world energy supply will increase from about 10% to 40% renewables with about half of that in the form of stored solar or biomass where lignocellulosics play an important role. Renewables will make up a considerable proportion of the energy mix by mid-century, but unfortunately not enough to avoid a 3°C hotter world with up to 50% reduction in biodiversity and sea level rise of 1 to 3 meters by 2100 (Randers 2012). Ironically, the cost to counter the climate effects could easily be on the order of hundreds of trillions of US dollars or close to the world GDP by 2100 just to attempt the rendering of the associated problem that could have easily been avoided with prudent leadership by the current generation rather than placing this burden and cost on the generations to follow.

The genetic variation among ecotypes makes switchgrass challenging to model. The environmental and developmental cues that effect green up, leaf area development, and flowering time need to be well understood for each ecotype. Despite numerous field studies with many of these genetic lines at different locations, isolating the relationship between temperature, drought, and nutrient stress on green up, biomass accumulation, flowering time is difficult. This is because of variation in site-specific factors such as current management, previous management, soil type, and microclimate.

There have been two approaches taken to model biomass production of different ecotypes or groups of ecotypes. The ALMANAC model was used to simulate biomass of four groups of ecotypes (northern lowland, northern upland, southern lowland, and southern upland) at five locations (Kiniry et al. 2008). The model was parameterized with a different maximum leaf area index (LAI) for each ecotype based on how well adapted it was to the location’s climate and environmental conditions. Growing degree days also varied among groups of ecotypes. The maximum potential LAI was assumed to be larger in southern regions and largest for southern lowland ecotypes. Modeled versus measured yields at these locations show that the ALMANAC model did reasonably well at predicting yield all four types at the locations (Table 2). The model had its greatest errors when estimating biomass of the northern lowland type in KS and NE. Yields for the southern upland type were slightly overestimated at each location.

The DAYCENT model has also been used to simulate different switchgrass ecotypes; in this case two lowland ecotypes, Alamo and Kanlow, and four upland ecotypes, Blackwell, Cave-in-Rock, Sunburst, and Trailblazer (Lee et al. 2011). Simulations were validated for four locations in Central Valley of CA. It was assumed that each cultivar had the same

Field trial data from Casler et al. (2004). Table reproduced from Kiniry et al. (2008).

maximum LAI. Instead, the root to shoot ratio, baseline temperature, optimum temperature, and maximum temperature were adjusted for each cultivar. The upland types (Blackwell, Cave-in-Rock, Sunburst, Trailblazer) were assumed to allocate more primary production to root biomass than the lowland (Alamo and Kanlow) types. The lowland ecotypes were also assumed to have a higher optimum and maximum temperature. The DAYCENT model also did a reasonable job estimating the observed yields with the R2 of the one-to-one relationship between observed and measured ranging from 0.66 to 0.90.

Modeling Water Use Efficiency

Water use efficiency (WUE) can be vitally important for predicting which areas are suitable for switchgrass biofuel production. To avoid competition with farmland already used for food, fiber, and feed production, the areas most likely for switchgrass production are on less productive soils where soil, water and nutrients often limit production (Perlack et al. 2005). In dryland production systems, limited rainfall and/or limited capacity of soils to store moisture becomes an important issue for switchgrass production. Plant water use becomes a major concern in irrigated regions, where competition between agriculture and other water demands arise. Direct measurements of WUE are important but require labor-intensive procedures involving soil water measurement with neutron access tubes, gravimetric measurements of soil moisture from soil cores, or use of weighing lysimeters. Likewise, measurements of WUE require plant harvesting to determine dry weight of plants. To adequately define WUE for a range of soils, plant species, and climatic conditions will require considerable resources and time.

Gasification is conversion of solid fuels, such as biomass, into primarily gaseous fuels called syngas or producer gas (Fig. 1). The conversion occurs through partial oxidation of biomass at high temperatures breaking down biomass polymers into small gaseous compounds such as CO, H2, CO2 and CH4. For gasification to occur, two things are required: the presence of oxidizing agent (gasifying agent) and high temperature (>600-700°C). Oxygen, air and steam have been used as gasifying agents. When oxygen or air is used, partial oxidation of biomass provides heat for the endothermic reaction eliminating the need for an external heat source. Steam requires an external heat source to raise the temperature to about 900°C.

Heating

Chemical

reactions

Catalysis

Y

Gasification

Figure 1. Operations in biomass conversion through gasification.

ILs are low symmetric weak intra-molecular organic cationic salts in cyclic, aromatic and long alkyl chains with good distribution of charges. These remain in liquid phase below 100°C. ILs can solubilize different lignocellulosic biomass, including softwood, hardwood and all grasses at low temperature. In addition, using ILs can reduce the cost of saccharification enzymes. Factors including high costs and the difficulty in recycling ILs are major barriers in the commercialization of this technology. The current market price of imidazolium IL is around $500/kg (Drapcho et al. 2008).

Commercial forage harvest systems include those that produce (1) small bales; (2) large cylindrical solid bales; (3) large rectangular solid bales; (4) loosely chopped material; (5) pressed modules based on cotton module systems; and (6) chopped relatively wet material for ensilage

systems (Cundiff 1996; Cundiff and Marsh 1996; Worley and Cundiff 1996; Sokhansanj and Turhollow 2002; Gallagher et al. 2003; Kumara and Sokhansanj 2007). Given these conventional forage harvest technologies, for large volumes of dry matter, and to collect for field storage and transport substantial distances, large rectangular solid bales is the least-cost system for harvesting biomass from perennial grasses in the Southern Plains (Thorsell et al. 2004).

Table 5 includes an enterprise budget prepared to produce an estimate of the cost to produce biomass from an established stand of switchgrass (Epplin 1996). Established stands are expected to require no tillage or herbicide. Field operations are limited to fertilizer application, mowing with a self propelled windrower, and baling. Harvesting costs were based on an average yield of 8.97 dry Mg/ha/yr. The budget reflects a cost of $82.64/ ha for establishment with conventional tillage (from Table 3), $116.63/ha for fertilizer and fertilizer application, and $111.20/ha for land rent. The budgeted rate of fertilizer includes 90 kg/ha/yr of N and 22 kg/ha/yr of P2O5. Based on reported custom rates, the windrowing activity is budgeted to cost $33.56/ha and the baling activity $17.25/bale (Doye and Sahs 2012). Windrowing (mowing and preparing a windrow) is modeled as a per hectare cost while baling and hauling is modeled as a function of yield. The budget assumes that biomass is baled at 15 percent moisture into rectangular solid bales (1.22m x 1.22m x 2.44m, 635 kg), loaded, and transported from the

field by a tractor trailer truck. The key cost parameters are biomass yield, land rental rate, harvesting costs, and the cost to transport the biomass to a conversion facility.

Cost to transport is assumed to be a function of yield and distance. The average transportation distance from the field to the biorefinery is assumed to be 48 km. Wang (2009) estimated the cost of transportation specifically for moving biomass from a field to a conversion plant. She assumed that a standard flatbed trailer truck could carry an average load of 14.5 dry Mg. For a diesel fuel price of $0.79/L, Wang’s equation can be summarized as the cost $/dry Mg = 0.8796 + 0.1983 * km (one way distance).

Costs per hectare and costs per dry Mg are computed for yields of 4.48, 8.97, and 13.45 Mg dry matter/ha. The estimated breakeven costs are $65/Mg for a 13.45 dry Mg/ha yield and $121/Mg for a 4.48 Mg/ha yield. Table 6 contains a summary of findings to changes in the estimated breakeven price of biomass when the costs of several important cost items are doubled. For a yield of 8.97 Mg/ha, doubling the cost of land would increase the breakeven price by 17 percent from $79 to $92/Mg. Doubling the transportation cost would increase the breakeven price by 13 percent, and doubling the fertilizer cost would increase the cost by 16 percent. Doubling the cost to bale the material increases the cost by 34 percent if the

“Fertilizer is assumed to be applied in February or March.

bThe price of urea ($0.44/kg) is presented in the budget. This cost translates into a price of $0.96/kg of actual nitrogen.

cIf soil test values of phosphorus are sufficient, no P2O5 is recommended. The budgeted DAP application includes 9 kg N/ha and 22 kg P2O5/ha. The budget reflects the cost of 81 kg N from urea and 9 kg N from DAP to achieve the level of 90 kg of actual N/ha. dHarvest is budgeted to occur in October or November.

eAverage transportation distance is assumed to be 48 km. Estimated transportation cost is based on a diesel fuel price of $0.79/L and the equation ($/dry Mg) = 0.8796 + 0.1983 * km (one way) (Wang 2009).

yield is 8.97 Mg/ha and by 42 percent if the yield is 13.45 Mg/ha. By this measure, the estimated cost to deliver feedstock is sensitive to baling cost. The cost to deliver a flow of feedstock may depend critically on managing baling and other harvest cost.

There are several types of pyrolysis reactors used for producing bio-oil with the most common being fluidized-bed (bubbling and circulating), ablative, vacuum, entrained-bed, and auger reactors. For pyrolysis to be "fast," two key design parameters need to be considered: the reactor must allow high heat transfer to the biomass and residence time must be short. A slow heat transfer and longer residence time lead to large production of gaseous and solid fuels.

SHF may be performed two different ways: separate hydrolysis and separate fermentation (SHSF) and separate hydrolysis and co-fermentation (SHCF). In SHSF, production of cellulolytic enzymes, hydrolysis of pretreated biomass and fermentation are performed in separate vessels. SHSF would allow performing each step at its corresponding optimum conditions. Generally, the production of cellulolytic enzymes using T. reesei is performed at 25-30°C and at pH 4.5-5.5. Hydrolysis of pretreated lignocellulosic biomass is performed in the temperature range of 50-55°C at pH 4.5-5.5. Ethanol fermentation is performed in the temperature range of 30-35°C at pH 5-6. Saccharomyces cerevisiae and Pichia stipitis may be respectively used to ferment both glucose and xylose to ethanol. However, P stipitis requires micro aeration (1 mmol of air per liter per hour) for xylose metabolism (Fig. 5). Iogen, Inc. (based in Canada) uses SHSF for the ethanol production. The optimal process integration would result in higher ethanol productivity.

SHCF is very similar to SHSF in that the hydrolysis of pretreated biomass and fermentation are performed in separate vessels. Unlike SHSF, in SHCF fermentation of different sugars (such as glucose and xylose) is performed in the same vessel. Simultaneous utilization of sugars for ethanol fermentation may be performed using either a single microorganism culture or co-culture of microorganisms. Most of the microorganisms use glucose as a carbon source to produce ethanol and are resistive to xylose uptake. The simultaneous utilization of xylose along with glucose for ethanol production could be approached several different ways.

Adhikari et al. (2009) has reported that the thermotolerant yeast Kluyveromyces sp. IIPE453 MTCC 5314 may consume a wide range of mono — and disaccharide sugars including glucose, xylose, mannose, arabinose simultaneously at temperature range of 40-65°C and pH range of 3.5-5.5 with ethanol productivity 13.8 gl-1h-1 on sugarcane bagasse in continuous

Figure 5. Overview of the xylose metabolic pathway found in yeasts such as Pitchia stipitis including the engineered xylose isomerase (XI) reaction (Pitkanen et al. 2005; Chu and Lee

2007).

Abbreviations: HXT, hexose transporters; Sym, symporter; XR, xylose redutase; XDH, xylitol dehydrogenase; XI, xylose isomerase; XK, xylulokinase; X5P, xylulose-5-phosphate; TKL, transketolase; TAL, transaldolase; S7P, sedoheptulose-7-phosphate; GA3P, glyceraldehyde — 3-phosphate; RPE, L-ribulose-5-phosphate 4-epimerase; Ru5P, L-ribulose 5-phosphate; RKI, ribose-5-phosphate isomerase; R5P, ribose-5-phosphate; E4P, erythrose 4-phosphate; F6P, fructose 6-phosphate; GND, 6-phosphogluconate dehydrogenase; 6PG, 6-phospho-D-gluconate; 6PGL, 6-phospho-D-glucono-1,5-lactone; ZWF, glucose-6-phosphate-1-dehydrogenase; G6P, glucose-6-phosphate; HXK, hexokinase; PFK, 6-phosphofructokinase; F6P, fructose — 6-phosphate; F16BP, fructose 1,6-bisphosphate; PGI, glucose-6-phosphate isomerase; FBA, fructose-bisphosphate aldolase; DHAP, dihydroxyacetone phosphate; GPD, glycerol-3- phosphate dehydrogenase; GO3P, glycerol-3-phosphate; GPP, glycerol-3-phosphatase; TPI, triose-phosphate isomerase; TDH, glyceraldehyde-3-phosphate dehydrogenase; BPG, 1,3-bisphosphoglycerate; PEP, phosphoenol pyruvate; PPPh, phosphoenolpyruvate phosphatase; PDC, pyruvate decarboxylase; ADH, alcohol dehydrogenase; ALD, acetaldehyde dehydrogenase; ACS, acetyl-CoA-synthetase; PYC, pyruvate carboxylase; PDB, pyruvate dehydrogenase beta subunit; PCK, phosphoenol pyruvate carboxykinase; AcCoA, acetyl coenzyme A; CIT, citrate synthase; CITR, citrate; ACO, aconitate hydratase; ICTR, isocitrate; AKG, alpha-ketoglutarate; IDP, isocitrate dehydrogenase kinase; KGD, alpha-ketoglutarate decarboxylase; SucCoA, succinyl CoA; LSC, succinyl-CoA ligase; SUC, succinate; SDH, succinate dehydrogenase; FUM, fumarate; FUMH, fumarate hydralase; MAL, malate; MDH, malate dehydrogenase; OAA, oxaloacetate; ICL, isocitrate lyase; GLO, glyoxylate; and, MLS, malate synthase.

fermentation. In addition, Kluyveromyces sp. IIPE453 MTCC 5314 may be recycled up to 20 days in the continuous process at 50°C (Adhikari et al. 2009).

Xylose isomerase (XI) enzyme converts xylose to xylulose that could be easier to use as a carbon source by microorganisms such as yeast S. cerevisiae for ethanol production. Xylose isomerase can be used either separately or along with cellulolytic enzymes. The optimum activity of xylose isomerase is at a pH of 7-8 and at a temperature range of 60-80°C. However, a urease coated xylose isomerase could work under acidic conditions as urease coats a separate inner basic environment from the exterior acidic environment (Rao et al. 2007). Rao (2007) has mentioned that using the urease coated xylose isomerase along with 0.05 M tetrahydroxyborate could convert 86% of xylose into xylulose under acidic condition at temperature 34°C. However, the uptake of xylulose by yeast decreases with increasing ethanol concentration at less than 4% (w/v) in the media (Chiang et al. 1981; Chandrakant and Bisaria 1998).

The use of genetically engineered microorganisms such as Zymomonas mobilis and Escherichia coli may simultaneously consume both glucose and xylose in co-fermentation. The genetically engineered bacteria improve the ethanol yield from 0.39 g g-1 to 0.44-0.52 g g-1 with high productivity up to 0.18-0.96 g l1 h1 (Olsson and Hahn-Hagerdal 1996). The main issues with the genetically engineered strains are stability, reproducibility and regulatory issues with use of biomass byproduct for animal feed.