Category Archives: Switchgrass

Char and other Solid Impurities

Bio-oil contains solid contaminants (ash and char), which catalyzes the polymerization and cracking reactions of the bio-oil. The alkali present in the ash such as sodium and potassium also catalyzes cracking reactions. The particulate contents depend on the type of cleaning and filtration system used following the pyrolysis reactor. Cyclone separators are generally used to reduce the particulate contents but removal of finer particulates may require further filtration.

High Viscosity, Increases with Time

Viscosity of biomass is 40-100 cP (shown in Table 5), which increases over time because of polymerization reactions when bio-oil is stored. High viscosity makes it difficult to flow through pipes and valves. The viscosity is especially important when bio-oil is atomized using spray nozzles for direct combustion in burners and engines (Bridgwater 2012).

Low Energy Content

As shown in the Table 5, Energy content of bio-oil (19 MJ/kg) is similar to the energy content of biomass which is about 40% of the energy content of crude oil. The primary reason of its low energy content is its high moisture and oxygen contents.


Properties of bio-oil such as viscosity, composition and phase contents change over time because of polymerization among the many functional groups and phase separation in the bio-oil (Bridgwater 2012). The aging process increases with temperature because of its increased reactivity.

Clemson University/SRNL Process Integration for Switchgrass and Sweet Sorghum to Ethanol

The Clemson University/SRNL Bioethanol collaborative project developed a comprehensive process to convert switchgrass (Panicum virgatum L.) and sweet sorghum (Sorghum bicolor L. including Dale and M81E) to fuel grade ethanol. The project was conducted by Clemson University and the Savannah River National Laboratory (SRNL) originally funded in October of 2008 with continued research through September 2012. In 2010 the research team developed a complementary sweet sorghum process to convert the syrup and the bagasse to ethanol. The commonality of the two processes supports separate or combined feedstock facilities designed to produce ethanol in agricultural regions across the Southeastern US and represents potential processes for similar soils around the world including southern

Figure 6. Process flow diagram for separate hydrolysis and separate fermentation (SHSF) in the production of cellulosic ethanol using switchgrass. Note: Pretreatment and enzyme hydrolysis are considered in the same vessel.

Africa and Australia. The commercial production cost for switchgrass to ethanol is projected at $2.32 per gallon with sweet sorghum at $1.79 per gallon without consideration of potential co-products. In 2011, the team concentrated on the expansion of the application of the switchgrass/sweet sorghum technology to include sugarcane bagasse and coastal loblolly pine. The pretreatment and hydrolysis concepts utilized in switchgrass and sweet sorghum proved directly transferable to bagasse.

The process, with pilot scale confirmation, represents a viable source of green carbon for ethanol production. The switchgrass grows on marginal land, requires little inputs, is drought resistant and can be bailed twice annually using hay bailing equipment common to most farms. Our field research suggests that a single acre can yield approximately 80 gallons of ethanol per ton. Our technology review suggests that the process may be optimized, assuming high levels of xylose conversion to yield 120 gallons per acre. Further, the switchgrass can be stored in the field for several months eliminating most plant related storage requirements.

Figure 7 represents the developed process flow diagram for conversion of switchgrass (Panicum virgatum L.) to ethanol. The process entails milling switchgrass to 1 mm average particle size followed by pretreatment with ammonium hydroxide (SAA) concentrated to 8% by weight and recovered for further reuse. Lignin is separated from the bottoms of the steam stripper section and recovered using sulfuric acid precipitation (pH 2-3) followed by a leaf filter and drying for potential use of high-value products (biomaterials) or for energy use within the biorefinery setting. Enzyme hydrolysis is conducted on the resulting pretreated fibers after a series of water wash steps by using cocktails of cellulases ranging from novel Dyadic and Genecor products. The resulting sugars are then separated with centrifugal decanter and pumped to the fermentation vessels operated in fed-batch mode with the first vessel fermenting glucose fractions using high-ethanol tolerant strains of Saccharomyces and the second vessel fermenting xylose fractions with novel bacteria. Resulting ethanol is then distilled to 92-95% in a series of distillation columns before being dried using conventional swing-bed zeolite absorption.

The following Table 7 presents a comparison of production costs by feedstock comparing the 2007 projections of NREL stover costs. In this analysis the report identifies the cost based on assumed technical capabilities of the industry. During the switchgrass process development capital estimates and manufacturing models were developed. ASPEN II models generated by SRNL provided the process inputs, outputs, energy and mass balances. The conceptual numbers illustrate that higher ethanol yield was attainable through the processing of all available sugars from the initial cane squeezing and the residual bagasse which is suggested to be 30% or greater than switchgrass. The following numbers represent production targets for pilot phase scale up.

Table 7. Manufacturing cost projection of switchgrass and sweet sorghum verses NREL 2007 stover projections (Clemson University/SRNL manufacturing model compared to Biochemical Production of Ethanol from Corn Stover: 2007 State of Technology Model, Andy Aden, NREL/TP-510-43205, May 2008.)

A Projection of Cellulose to Ethanol By Process Manufacturing Cost Projection and Comparison




2007 Cellulose


S Sorghum

Stover Report

30 mppy

30 mppy




Plant Capital Investment




Capital Depreciation




Personnel and Staffing




Feedstock Materials (Total Delivered)




Process Chemical and Biological Agents




Utilities and Energy Generation




Waste Disposal and Treatment




Co-Products (lignin Only in Electrical Generation)

In Utilities

In Utilities

In Utilities

Total Projected Cost Before Profit and Taxes_________________________ I $1.9082 I $2.3273 I $1.7925

Improvements in Biomass and Biofuel Outputs

Conventional, molecular, and transgenic breeding efforts will be required to increase biomass and ethanol yields necessary to reach government — mandated fuel benchmark (Gressel 2008; Jakob et al. 2009). Conventional breeding, and to a lesser extent, molecular breeding, has provided modern — day switchgrass cultivars via selection of typically high biomass individuals from native populations. In reality, the domestication process in switchgrass

Box 1. Improvement efforts in the bioenergy crop switchgrass on multiple fronts present numerous challenges. This diagram attempts to integrate the primary areas for improvement (ovals) with efforts currently underway or in need of occurring (rectangles) via solid-lined arrows, and how these efforts directly relate to socio-economic and environmental concerns via dashed-lined arrows. Many of the relationships between the efforts and concerns are unknown and are in need of further research efforts.

is in its infancy, and challenges to improve the species will be hampered by its life cycle. In light of this, molecular efforts (Bouton 2007) and transgenic efforts hold promise to attain quick positive results.

Since biofuel output may rightly be considered the most important metric, a logical area to continue research efforts entails overcoming recalcitrance (i. e., to enable cellulose in cell walls to be more readily available for breakdown). There are a number of steps in the biofuel-making process, and improvements targeting recalcitrance can take place at a number of those steps (Fig. 3). With regard specifically to switchgrass, success has been achieved transgenically by downregulation of lignin formation (Fu et al. 2011). It is important to note that continued improvements along these lines will need to abide by and possibly incorporate gene flow prevention, and work in this direction shows promise (see Chuck et al.

2011) . Simultaneously, continued efforts aimed at better understanding of switchgrass gene flow biology is necessary for regulatory effectiveness (Kwit and Stewart 2012; Nageswara-Rao et al. 2013). Other than pollen longevity (Ge et al. 2011), little is known about effective pollination distances that agronomic switchgrass is capable of, and which, if any, wild relatives are

Biomass is harvested and delivered to

the biorefinery

Figure 3. Depiction of the steps involved in lignocellulosic ethanol production. Switchgrass breeding improvements to increase ethanol yields constitutes part of the first of several steps in the process. Image used by permission of Bioenergy Science Center.

Color image of this figure appears in the color plate section at the end of the book.

amenable to hybridization (Kwit and Stewart 2012). It is imperative that we increase our existing knowledge on the drivers of transgene spread to satisfy imminent regulatory and conservation concerns regarding switchgrass and other potential transgenic bioenergy crops. This includes garnering a better understanding on gene flow and the fitness of F1 hybrids and their offspring. While efforts to overcome recalcitrance may not appear to have fitness consequences promoting invasiveness in escaped or introgressed populations, improvements in other areas may (see below).

Stress Resistance

Along with large-scale, intensive production of switchgrass, agronomic trait improvement, such as disease and insect resistance, will become more and more important (Gressel 2008). Native switchgrass has extensive genetic diversity with fair resistance to the majority of potential pathogens (Bouton 2007). However, without knowledge of the genetic basis of disease resistance in switchgrass and the structure of pathogen populations, current and future switchgrass breeding programs that target high biomass yield and improved feedstock quality are likely to reduce the genetic diversity of disease resistance (Tanksley and McCouch 1997). Airborne foliar fungal pathogens like rust have a great potential to cause nationwide epidemics on switchgrass, resulting in significant biomass yield losses (Gustafson et al. 2003). Foliar diseases, in addition to reducing yields, can reduce the availability of saccharifiable cellulose due to increased lignification of host cell walls (Moerschbacher 1989; Parrish and Fike 2009; Shen et al. 2009). Among all potential switchgrass diseases that could negatively impact the commercial production of switchgrass, rust caused by the fungus Puccinia emaculata Schwein is the most destructive and widespread disease problem (Zeiders 1984; Gravert and Munkvold 2002; Gustafson et al. 2003; Krupinsky et al. 2004; Parrish and Fike 2005; Carris et al. 2008; Zale et al. 2008; Crouch et al. 2009; Hirsch et al. 2010; Tomaso-Peterson and Balbalian 2010).

Additionally, switchgrass seedheads can be heavily infected by smut and bunt caused by Tilletia maclagani (Berk.) G. P. Clinton and T. pulcherrima Syd. & P. Syd., respectively. While the impact of bunt infection on switchgrass production is not clear beyond plant inspection issues (Carris et al. 2008), smut has been shown to severely reduce seed and biomass yields in Iowa (Gravert et al. 2000; Thomsen et al. 2008) and has heavily infected Nebraska switchgrass accessions in Oklahoma (S. Marek, personal communications). In general, these seedborne diseases should be remediated by treating seeds with fungicides (Taylor and Harman 1990). Switchgrass is also affected by numerous fungal leaf spot diseases (Roane and Roane 1997; Gravert and Munkvold 2002; Farr and Rossman 2010), including anthracnose caused by Colletotrichumgraminicola (Ces.) G. W. Wilson and C. navitas J. A. Crouch, B. B. Clarke & B. I. Hillman (Crouch et al. 2009; Li et al. 2009), Helminthosporium leaf spot, caused by Bipolaris sorokiana (Sacc.) Shoemaker and B. oryzae (Breda de Haan) Shoemaker (Zeiders 1984, Artigiano and Bedendo 1995; Krupinsky et al. 2004; Tomaso-Peterson and Balbalian 2010), and to a minor extent tar spot, caused by Phyllachora graminis (Pers.) Fuckel, as well as undocumented diseases caused by Pyrenophora sp. and Phaeosphaeria sp. (Farr and Rossman 2010; and S. Marek, unpublished observations), could potentially impact biomass yields. In addition to these fungal diseases, at least two viral diseases, Panicum mosaic and barley yellow dwarf, affect switchgrass, with the former disease sometimes causing the death of tillers and plants (Sill and Pickett 1957; Garrett et al. 2004). Host resistance is the most effective, economical, and environmentally friendly way to control plant disease. Screening germplasm to identify resistance resources to various switchgrass diseases and developing durable and broad spectrum disease resistance will be one of the key breeding objectives in the future. Other than traditional breeding selection, genetic engineering may also have great contributions for disease control in switchgrass (Punja 2001; Stuiver and Custers 2001; Venter 2007; Collinge et al. 2008).

In addition to biotic stress, abiotic stress tolerance, such as tolerance to salinity and drought, will also be very useful. Towards that direction, Ceres has introduced a salinity-tolerance gene into switchgrass, which allows the plants to grow in sea water. The company implied that the unprecedented salt tolerance level could help in growing switchgrass (and other crops) on the 15 million acres of salt-affected soils in the U. S., as well as growing switchgrass in over a billion acres of abandoned cropland all over the world.

Concluding Remarks

Switchgrass is an important biomass/biofuel crop which would contribute substantially to our renewable energy in the future. Although molecular and genetic engineering studies just started several years ago, exciting results on quality improvement of switchgrass as a biofuel feedstock have been obtained. In addition, value-added engineering has emerged, which could be the first step towards improving the economics of biofuel production from lignocellulosic materials. With substantially improved transformation technology, many genes, which have been shown useful in model plant species, or emerge from molecular and genomic studies, could be introduced into switchgrass for its improvement via biotechnology.

As in other outcrossing transgenic plants, transgene escape, mainly through pollen grains, will be a concern. Kausch et al. (2009) has a detailed discussion to address the issue and potential solutions. Interested readers are referred to that review article.

Biofuel Production Processes: Pretreatment

Cellulosic ethanol production process involves a series of steps including preprocessing of feedstock (transportation, grinding, sieving), pretreatment (chemical or biological to remove lignin), enzyme hydrolysis (to produce fermentable sugars), fermentation (for production of ethanol, biomass) and downstream processing (separation of biomass from product, distillation, purification). The cost of ethanol production using lignocellulosic feedstock is relatively high with lower yield based on the current technologies. The main challenges are with pretreatment, enzyme hydrolysis cost, fermentation and downstream processing of lignocellulosic feedstock.

Modeling Climate Change

In addition to considering environmental sustainability, long-term sustainable biofuel production from switchgrass requires that high levels of biomass production be maintained over time. Field trials have shown that switchgrass yields are sensitive to spatial variation in temperature and precipitation (Casler and Boe 2003; Casler et al. 2004). Future climate change may similarly alter the capacity of biofuel production. Therefore, modeling biomass production under future climatic conditions and elevated atmospheric carbon dioxide concentrations is necessary to ensure maintenance of high yields over time without supplemental nutrients and irrigation.

Efforts to estimate future biomass of switchgrass using mechanistic models have been limited. Brown et al. (2000) used EPIC to predict the future yield of swtichgrass in four states (MO, IN, NE, and KS) using future weather data from the NCAR-RegCM2 model. Their results predicted that switchgrass yields would increase in this region by more than 8 Mg ha1. Behrman et al. (2013) used ALMANAC to estimate future yields across the entire central and eastern U. S. for current climate conditions under two climate change scenarios for the ten-year interval from 2080 to 2090 using the CCCMA-CGCM2 model. The A2 scenario was chosen to represent a pessimistic future scenario that predicts a large increase in atmospheric carbon dioxide and large increases in temperature. The B2 scenario was chosen as a "middle of the road" scenario that corresponds to a moderate increase in carbon dioxide levels and a smaller increase in temperature. Similar to Brown et al. (2000) the ALMANAC model also predicts increased yield for MO, IN, NE, KS. Furthermore, regions in Eastern Texas are predicted to have large decreases in yield by 2080s, whereas ND and SD are predicted to have large increases in yield. Modeling of future climate change scenarios predicts warmer minimum temperatures that will shift the USDA Hardiness Zones northward and may make conditions suitable for lowland switchgrass types to thrive further north in upland regions.

Mechanistic models can be used to locate areas that produce relatively high yields over a time. Areas with high long-term potential were determined using yields reported from Behrman et al. (2013) for three climate scenarios. Areas that continually produce yields greater than 10 Mg ha1 for all three climates are labeled as high long-term potential and areas with low long­term potential have high yields for only one of the three climate scenarios (Fig. 1). Assessing the potential of an area to sustain high productivity has the potential benefit of minimizing the amount of land conversion needed to meet production demands. Land-use change increases greenhouse gas emissions and is the primary factor responsible for the loss of biodiversity (Searchinger et al. 2008; Fletcher et al. 2011).


Particulates of the syngas include ash and char. Ash is composed of minerals such as metal oxides, whereas char is composed of carbon. The most commonly used technology for particulate removal is cyclone separator. Cyclone separator removes particulate by applying centrifugal force on the particle and letting it move downward for collection. Several designs such as 1D-2D (1 dimension width and 2 dimension height), 1D-3D (1 dimension width and 2 dimension height) and 2D-2D (1 dimension width and 2 dimension height) have been described by Parnell and others for removing these particulates (Parnell 1982, 1990). Particulate with smaller size (fine particulates) can be removed by electrostatic precipitator (ESP) and other technologies if downstream syngas applications require syngas to have lower particulate content.

PH and Temperature Control

The composition of cellulolytic enzymes produced by T. reesei Rut C-30 is influenced by changing pH (Juhasz et al. 2004). An increase in p-glucosidase activity has been reported at pH 6 in comparison with a lower pH range of 4-5 (Tangnu et al. 1981; Nagieb et al. 1985). The purpose of supplied chemicals for the pH control is not limited to providing a stable environment for enzyme production but also to fulfill the requirement for the enzyme synthesis. A citric acid and aqueous ammonium hydroxide solution is used for pH control. In addition, citric acid helps by inducing cellulase production while aqueous ammonium hydroxide provides a nitrogen source for enzyme synthesis (Watson and Nelligan 1983; Kadam and Keutzer 1995).

Temperature affects the growth rate of T. reesei and xylanase production. Xylanase production was studied within the temperature range of 17°C to 37°C, and increased as temperatures approached 37°C. However, cellulase production decreases with increasing temperatures within this range. Therefore, a temperature shift could be a useful tool in the selectivity of cellulolytic enzyme production (Xiong 2004).

Cost Estimates from Model

A summary of estimated costs for supplying a biorefinery with a flow of switchgrass biomass feedstock is provided in Table 11. For the average net yield of 10 Mg/ha, the estimated cost to deliver a flow of switchgrass is $60/Mg (Table 11). This cost is less than the conventional budget estimate reported in Table 5 of $79/Mg based on a yield of 8.97 Mg/ha. If the budgeted yield estimate in Table 5 is set equal to 10 Mg/ha, the budget cost estimate would decline to $75/Mg. The cost estimates between the two methods for establishment, land rental, and fertilizer are very similar. However, the cost estimates differ for harvest and transportation.

Based on the conventional budget, the harvest cost estimates, assuming a yield of 10 Mg/ha would be $32/Mg. This cost is twice as much as the $16/ Mg estimate obtained from the programming model. This cost difference reflects the potential economies that could result from a coordinated harvest system and a nine-month harvest window. The estimated transportation costs are $10.40/Mg for the budget and $16.06/Mg for the model. These costs differ because the average field to biorefinery distance is assumed to be 48 km in the budget, but is estimated to be 76.55 km by the model. The

Table 11. Comparison of estimated costs with 2011 prices and with price of diesel fuel used for harvest and transportation doubled and with land rental prices doubled.




Fuel Price Doubled

Land Rent Doubled

Land rent





Establishment and maintenance cost





Fertilizer cost





Harvest cost





Field storage cost





Transportation cost





Total cost of delivered feedstock





Average net yield





Source: Griffith (2012).

average transportation distance is sensitive to the size of the biorefinery and biomass requirements and to the imposed restriction to limit switchgrass to no more than 10 percent of a county’s cropland and improved pasture land.

If the price of fuel used to harvest and transport biomass doubled from the base level of $0.79/L to $1.58/L, the cost to deliver switchgrass increases to $73/ Mg (Table 11). The land lease rates are doubled and the model solved to determine sensitivity to land rental rates. The estimated cost to deliver biomass increases by $12/Mg from $60/Mg (base scenario) to $72/Mg when the land rental rate is doubled.

Many Functional Groups

Bio-oil is a mixture of many compounds with different functions groups. Acids, alcohols, aldehydes, esters, ketones, sugars, phenols, gicaols, syringols, furans, lignin derived phenols and extractible terpene with multiple functional groups comprise most of the bio-oil (Mohan et al. 2006; Lee et al. 2008; Guo et al. 2011).

Bio-oil Upgrading

Many of the undesirable properties of bio-oil are due to its high oxygen content. Hence primary purpose of bio-oil upgrading is to reduce oxygen contents so that upgraded bio-oil can be converted into power, fuels and chemicals. The primary techniques used to upgrade bio-oil are: (i) hydrodeoxygenation, (ii) catalytic vapor upgrading, (iii) aqueous reforming, and (iv) steam reforming.