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Industrial Crops and Products 56 (2014) 118–127

Contents lists available at ScienceDirect

Industrial Crops and Products

jo u r n al homep age: www.elsev ier .com/ locate / indcrop

yrolysis of ethanol coproducts

hristine Wooda, Kurt A. Rosentraterb,∗, Kasiviswanath Muthukumarappanc

SDSU, Department of Agricultural and Biosystems Engineering, SDSU North Campus Drive, Brookings, SD 57007, United StatesIowa State University, Department of Agricultural and Biosystems Engineering, 3167 NSRIC, Ames, IA 50011, United StatesSouth Dakota State University, Department of Agricultural and Biosystems Engineering, SDSU North Campus Drive, Brookings, SD 57007, United States

r t i c l e i n f o

rticle history:eceived 29 July 2013eceived in revised form 24 February 2014ccepted 28 February 2014vailable online 24 March 2014

a b s t r a c t

The need for new value-added applications for ethanol coproducts grows as the U.S. ethanol industrycontinues to expand. Distillers dried grains with solubles (DDGS), corn gluten meal (CGM), and corngluten feed (CGF) are the primary coproducts of ethanol manufacturing and are mainly utilized as animalfeed. This study examined the use of pyrolysis to extract value from these grains. Characterization of theresulting bio-oil and bio-char included mass density, thermal conductivity, thermal diffusivity, apparent

eywords:istillers dried grains with solubles (DDGS)oproductsyrolysisio-oilio-char

viscosity, kinematic viscosity, and energy content. The bio-oils produced from these ethanol coproductsrequire some changes to be used commercially. The tar present in the crude bio-oils caused them to havedensities greater than one, and caused the oil viscosity to be shear thinning. The pH of these bio-oilsis less acidic and thus more favorable than other bio-oils which could be due to the differences in thefeedstock composition.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

The U.S. ethanol industry has continuously gained momentumver the past decade, increasing its production by nearly eight timesrom 2000 to 2010 (RFA, 2012a). In 2010 the industry produced

record 13.2 billion gallons, replacing around 445 million bar-els of imported oil (RFA, 2011a). It is estimated that 88% of thethanol produced in the United States is produced using dry grindethods, while the remaining 12% is produced from wet milling

rocesses (RFA, 2010). The production of ethanol from corn utilizeshe starch present in the corn, leaving protein, minerals, fat, andber behind in a concentrated form. In the wet milling process, theon-fermentable materials are used to produce corn gluten mealnd corn gluten feed; while in the dry grind process, they are usedo produced distillers dried grains with solubles (DDGS) and dis-illers wet grains (DWG). RFA (2012b) reported that in 2010, around2.5 million metric tons of these grains were produced, which is an

ncrease of nearly 30 million metric tons over what was producedn 2000.

Corn gluten meal is comprised of approximately 90% dry matter,

6% protein, 3.3% fiber, and 2.8% fat; while corn gluten feed is com-osed of approximately 90% dry matter, 20% protein, 11.1% fiber,nd 2.2% fat (ISU, 2008). The swine and poultry industries are the

∗ Corresponding author. Tel.: +1 515 294 4019.E-mail address: karosent@iastate.edu (K.A. Rosentrater).

ttp://dx.doi.org/10.1016/j.indcrop.2014.02.039926-6690/© 2014 Elsevier B.V. All rights reserved.

largest consumers of corn gluten meal and corn gluten feed (ISU,2008), but corn gluten meal has also been studied for its poten-tial uses in horticulture as a natural herbicide (Christians, 1993;McDade, 1999; Webber et al., 2010) and in fish feeds (Lei et al.,2011; Zhong and Qian, 2009).

DDGS is approximately 86.2–93.0% dry matter, 25–35% protein,7.2% fiber, and 3–13% fat (Bhadra et al., 2009; Ganesan et al., 2008;ISU, 2008; Rosentrater and Muthukumarappan, 2006; Shurson andAlhamdi, 2008; and Weigel et al., 1997). Currently, the beef, dairy,swine, and poultry industries are the largest consumers of DDGS(RFA, 2011a; Shurson and Noll, 2005).

As more coproducts are produced, there is a potential that sup-ply may surpass the livestock industry’s demand at some point.Perhaps the demand from the livestock industry may becomerestricted as certain fats within the DDGS limit the amount of DDGSthat certain animals can have in their diets (Tiffany et al., 2008). Inorder to maintain the demand for coproducts, new value added usesand new markets should be pursued (Rosentrater, 2007). The highavailability and low market price makes coproducts an inexpensiveingredient for various compounds. Currently, a very small percent-age of the coproducts market is comprised of deicers, cat litter, ‘lickbarrels’, and worm food (Bothast and Schlicher, 2005). Coproductshave even found their way into the aquaculture industry as feed

ingredients (Kannadhason et al., 2010; Rosentrater et al., 2009a,b;Schaeffer et al., 2009), and could one day find their niche within thehuman food market, as research is also being done to prove the via-bility as human food ingredients (Rosentrater, 2007; Rosentrater

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nd Krishnan, 2006). Studies are also being done to determine ifoproducts can be used to produce biodegradable plastics (Bothastnd Schlicher, 2005; Tatara et al., 2006, 2007).

While most investigations of coproducts over the years haveeen on ways of utilizing them as feed ingredients, recent studiesave begun investigating their potential as sources of energy. Sometudies have even begun analyzing the effectiveness of poweringthanol plants with this bio-based energy (De Kam et al., 2007;orey et al., 2006; Tiffany et al., 2007). Tiffany et al. (2007) mod-

led the feasibility of using the coproducts to provide energy to a90 million L/y and a 380 million L/y dry grind ethanol plant, andound that if all the DDGS produced is used to generate process heatnd energy for the facility, there would be leftover energy whichould be sold to the grid, increasing the rate of return on investmentor the facility. According to Wang et al. (2007), there is approxi-

ately 25 MJ present in every 1 kg DDGS produced, while only 1 MJf electric energy and 10 MJ of thermal energy are required to pro-uce 1 L of ethanol. Wang et al. (2007) also showed that by usinghis biomass as an energy source, ethanol plants could reduce theirreenhouse gas emissions by nearly threefold compared to typicalnergy sources. This energy can be harvested from DDGS directly,y converting it to heat and power, or it can be transformed intoaseous or liquid fuels to be used for energy later (Giuntoli et al.,011). These processes, known as thermochemical conversions,onsist of three main types: combustion, pyrolysis, and gasificationWang et al., 2007).

The pyrolysis of many types of biomass has been widelyxplored by various researchers to determine ideal processingarameters and the composition of the end products (Babu andhaurasia, 2003; Chao et al., 2005; Gheorghe, 2006; Pirikh et al.,003; Van de Velden et al., 2007; Sivasastri, 2013). Pyrolysis cane defined as a thermochemical decomposition process throughhich organic matter is converted to oil, gas, and carbon residue

n the absence of oxygen (Sadaka, 2009). There are two main typesf pyrolysis: fast and slow. Slow pyrolysis is very time consuming,nd has a very low product (tar) yield. Fast pyrolysis proceeds at

much quicker rate, and turns the organic matter directly into aaseous form, which is then condensed into bio-oil and hydrogenSadaka, 2009). Both types are performed in the absence of oxygen.

Only a few studies have begun to explore the effects of pyrolysisn DDGS (Lei et al., 2011; Giuntoli et al., 2011). These studies mostlyxamined how changing the parameters of pyrolysis affected thenal products. In order to fully understand the potential for usingyrolysis to obtain energy from ethanol coproducts, this studysed conventional, also known as slow, pyrolysis to convert cornoproducts, including CGM, high protein DDG, protein fraction ofe-oiled DDGS, fiber fraction of de-oiled DDGS, and traditionalDGS, and then determined various physical and chemical prop-rties of resulting bio-oil and bio-char.

. Materials and methods

.1. Sample collection and experimental design

CGM, DDGS, de-oiled DDGS, and high protein DDG (HP) werebtained from commercial fuel ethanol plants in South Dakota. Thee-oiled DDGS was then separated into a high protein fraction (PF)nd a high fiber fractions (FF) using sieving and aspiration. Thisesulted in a total of five different samples with various protein andber concentrations. All were stored in plastic storage bags at roomemperature until needed for pyrolysis. Two pyrolysis reactions

ere performed per coproduct sample, for a total of ten reactions.fter processing, the resulting bio-oil was stored in plastic screw-

op bottles in a refrigerator, and the bio-char was stored in plastictorage bags until analysis at room temperature. Three replications

Products 56 (2014) 118–127 119

were performed for each physical property measured on the bio-oil(unless noted otherwise). Thermal conductivity, thermal diffusiv-ity, energy content, mass density, and apparent viscosity weredetermined. Rheological measurements were also taken at threedifferent temperatures (10, 25, and 40 ◦C).

2.2. Raw materials

The proximate composition for the raw materials was deter-mined by an external laboratory (Servi-Tech Laboratories, Hastings,NE). The particle size distribution and color of the raw materials wasdetermined using the same methods used for the bio-char.

2.3. Pyrolysis

The apparatus in Fig. 1 (located in the SDSU bioprocessing lab)was used to perform slow pyrolysis reactions. Each reaction beganwith 500 g of sample in a sealed steel chamber of approximately6589 cm3 (20 cm long with 10 cm internal diameter). The cham-ber was equipped with a purging inlet tube and an exhaust outletleading to the distillation apparatus. The collection apparatus wascomprised of four Allihn condenser columns with water jackets,and two glass bulbs (Chemglass Life Science, Vineland, NJ) to col-lect and sample the oil. To assist with the condensation of oil, watercooled to 6 ◦C was cycled through the water jackets using an F3-V Refrigerated Cryostats (HAAKE, Paramus, NJ). The outlet afterthe fourth condenser was connected to hosing which released theproduced syngas into a bucket of water to remove any additionalcondensable compounds before releasing the syngas into the air.The steel chamber was placed within an Isotemp Programmablemuffle furnace (650–750, Fisher Scientific, Pittsburg, PA), whichallowed the heating rate and temperature to be defined. Beforeheating, the chamber and distillation system were purged withnitrogen gas for ten minutes in order to evacuate oxygen from thevessel.

For each pyrolysis reaction, the sealed steel chamber withcoproduct sample was heated to 600 ◦C at 40 ◦C/min. The pyrol-ysis reaction proceeded in three individual steps: (1) moisture andsome volatiles were removed from the feedstock; (2) more com-plex volatiles and some gasses are removed leaving bio-char; (3)bio-char was decomposed further and chemical rearrangementreleases more volatiles and gasses producing a less reactive bio-char (Demirbas, 2004). The reaction was allowed to progress untilsyngas production was no longer visible. At that point, the furnacewas powered off and allowed to cool for two hours before oil andchar were collected. When collected the mass of the bio-char andbio-oil were taken.

2.4. Bio-oil

The yield of the bio-oil was determined through mass balance.The mass of the bio-oil collected was compared to the mass ofthe original feedstock sample in order to determine the mass yield(100 × (mass bio-oil/mass feedstock) = yield bio-oil).

2.4.1. Physical properties2.4.1.1. Density. Mass density for the bio-oil was determinedusing a specific gravity cup (Model H-38000-12, Cole-ParmerInstrument Co., Barrington, IL). Material was poured into thecup (mass = 83.55 g; volume = 83.2 cm3) excess material was thenremoved, and the filled cup was weighed on a balance. Density was

then calculated as the ratio of sample mass to sample volume.

2.4.1.2. Energy content and thermal properties. The lower heat-ing values of the bio-oil samples were measured using a bomb

120 C. Wood et al. / Industrial Crops and Products 56 (2014) 118–127

Fig. 1. Pyrolysis apparatus.

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alorimeter (1260 Isoperibolic, Parr Instrument, Moline, IL). Twoeplications were completed for each oil sample.

Thermal conductivity and diffusivity were determined for theio-oil samples with a thermal properties meter (KD2, Decagonevices, Inc., Pullman, WA) that utilized the line heat source probe

echnique (Baghe-Kahandan et al., 1981). These readings wereaken at three different temperatures (10, 25, and 40 ◦C).

.4.1.3. Viscosity. The apparent viscosity of the bio-oils was deter-ined at three different temperatures in order to account for

emperature variations that occur as the oils are moved and pro-essed. Apparent viscosity is a fluid’s resistance to flow when forces applied. It is expected that as either the shear rate or tempera-ure are increased, the apparent viscosity of a fluid would decrease,nd the relationship can be defined as a nonlinear power functionepresented by: � = k�n (� = apparent viscosity (Pa s); k = empiricalegression constant (Pa s s); � = shear rate (1/s); and n = empiricalxponential constant (dimensionless)).

Apparent viscosity was measured for the bio-oils using aheometer (HAAKE Rheostress1, Thermo Electron Corporation,ew Castle, DE), with a stainless steel cone and plate attachment

Model LO4079 C60/1 Ti). In order to maintain constant tempera-ure during testing, water from a set-temperature water bath wasumped through the base plate. The shear rate for each sample was

nitiated at 10 (1/s), and was increased up to 100 (1/s) by incre-ents of approximately 1 (1/s). Rheological measurements were

lso taken at three different temperatures (10, 25, and 40 ◦C). Theinematic viscosity was also calculated from the apparent viscos-ty and density of the bio-oil: � = �/�; (� = kinematic viscosity (cSt);

= apparent viscosity (Pa s); � = density (kg/m3)).

.4.2. Chemical properties

.4.2.1. Potential hydrogen. The bio-oil’s potential hydrogen (pH)as measured at room temperature (24 ± 1 ◦C) using a digital pHeter (Fisher Scientific, Accumet model AB15).

2.4.2.2. GC–MS. Bio-oil composition analysis was done using gaschromatography–mass spectrometry (GC–MS) (Agilent Technolo-gies, Santa Clara, CA) with a DB5 column (Supelco, Sigma–AldrichCo. LLC, St. Louis, MO), using a mobile phase of helium at a flow rateof 1.2 mL/min, and a sample volume of 2 �L. Samples were held for2 min at 45 ◦C and then heated to 290 ◦C at 5 ◦C/min. Samples werethen held for 5 min. A split ratio of 50:1 and a thermal axis tem-perature of 300 ◦C were used. Before running the samples throughthe GC–MS, the bio-oil samples were diluted to ratio of 1:10 withethanol, and then filtered through a 2 �m filter. The samples werethen allowed to set for one day and then again diluted to a ratioof 1:5 and filtered to ensure the samples were homogenous andparticulate free before injecting into the GC–MS. Once the sampleswere run, the product peaks were assigned specific compounds anda quality value by an automatic National Institute of Standards andTechnology (NIST) mass spectral library search.

2.5. Bio char

Similarly to the bio-oil, the yield of the bio-char was determinedthrough mass balance. The mass of the bio-char collected was com-pared to the weight of the initial feedstock to determine the percentyield.

2.5.1. Physical properties2.5.1.1. Density. Mass density for the bio-char was determinedusing a specific gravity cup (Model H-38000-12, Cole-ParmerInstrument Co., Barrington, IL). Material was poured into thecup (mass = 83.55 g; volume = 83.2 cm3) excess material was thenremoved, and the filled cup was weighed on a balance. Density wasthen calculated as the ratio of sample mass to sample volume.

True density of the bio-char was determined using a multi-volume pycnometer (Model No. 1305, Micromeritics, Norcross, GA,USA). Calibration of the equipment was performed with a metalball provided by the manufacturer. True density was determined

C. Wood et al. / Industrial Crops and Products 56 (2014) 118–127 121

Table 1Raw material properties.a

Sample Moisture (%) DM (%) Crude protein (% d.b.) NDF (% d.b.) Crude fat (% d.b.)

DDGS 6.7 93.3 27.0 28.0 11.1CGM 7.0 93.0 68.6 6.0 1.9Fiber fraction 5.2 94.8 25.1 47.6 1.9Protein fraction 5.6 94.4 33.7 37.1 3.5High protein 5.6 94.4 40.7 31.7 3.9

Sample Particle size (mm) Color

10 50 90 HUNTER L Hunter a Hunter b

DDGS 0.35a 1.51a 3.45a 40.05a 13.06a 21.02a

(0.05) (0.58) (1.15) (0.01) (0.00) (0.00)CGM 0.26a 0.59b 1.49b 41.91b 11.79b 22.01b

(0.01) (0.01) (0.28) (0.00) (0.00) (0.00)Fiber fraction – – – 49.05c 8.99c 20.54c

(0.01) (0.01) (0.01)Protein fraction 0.79c 1.10c 1.45b 47.99d 9.24d 19.65d

(0.01) (0.01) (0.31) (0.01) (0.01) (0.00)High protein 0.32a 0.90b 1.80b 45.88e 8.55e 20.20e

(0.01) (0.06) (0.08) (0.00) (0.00) (0.01)

a DM is dry matter; NDF is neutral detergent fiber; d.b. is dry basis; Values followed by the same letter (a, b, and c) are not significantly different ( ̨ = 0.05, LSD) from other bio-oil samples for that property. HUNTER L is Hunter brightness/darkness parameter; Hunter a is Hunter redness/greenness parameter; Hunter b is Hunter yellowness/bluenessp eal;

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arameter; DDGS is distillers dried grains with solubles; and CGM is corn gluten mnd then aspirated; protein fraction is the heavier fraction produce when de-oiled

arentheses are standard deviation (SD).

y the volume displacement method using helium gas (Chang,988). Then, using this measured true density and the measuredass density, the porosity of the bio-char was calculated using:———— (�t − �m)/�t; (� is porosity (%); �t is true density (g/cm3); andm is mass density (g/cm3)).

.5.1.2. Energy content and thermal properties. The lower heat-ng values of the bio-char samples were measured using a bombalorimeter (1260 Isoperibolic, Parr Instrument, Moline, IL). Twoeplications were completed for each char sample.

The glass transition temperature (Tg) of the bio-char samplesas measured using a differential scanning calorimeter (DSC) (Q

eries, TM Model Q200, TA Instruments, New Castle, DE). The DSCas equipped with an auto-sampler, using sealed aluminum her-etic tins for the samples. Liquid nitrogen was used to cool the

amples to −15 ◦C prior to testing. The samples were then heatedt a rate of 5 ◦C/min until reaching 150 ◦C. Helium, at a flow rate of0 mL/min, was used as a carrier gas. An empty sealed aluminumell was used as a reference. The heat flow vs. temperature curve forach sample was generated by the DSC. A visible vertical shift wasound in each of these curves. The upper transition points on theertical shifts were marked manually, and then the DSC softwareetermined the Tg onset, midpoint, and endpoint. The Tg midpointas then recorded as the glass transition temperature. Two repli-

ations were completed for each sample.

.5.1.3. Particle size. The particle size distribution of the bio-charnd raw material was measured using a digital image analysis tool,amisizer (Retsch Technology, Haan, Germany). The distributionas defined based on three sizes within the entire population: d10,50, d90. The d50 value is the median particle size within the pop-lation and 50% of the population is greater than this size and 50%

s smaller than this size. Similarly, 10% of the population is smallerhan the D10 size; and 90% of the population is smaller than D90ize.

.5.1.4. Color. Color of the bio-char was determined using a spec-rophotocolorimeter (Model CM 2500d, Minolta, Japan) usinghe Hunter Lab color space. HUNTER L value quantified the

fiber fraction is the lighter fraction produced when the de-oiled DDGS was sieved was sieved and then aspirated; high protein refers to high protein DDG. Values in

brightness/darkness of the bio-char, Hunter a value depicted theredness/greenness, and Hunter b denoted the yellowness/blueness.

2.5.2. Chemical properties2.5.2.1. Proximate analysis. Proximate analysis of the raw productsas well as the bio-char included the determination of moisturecontent percent dry matter, crude protein, neutral detergent fiber(NDF), crude fat, carbon, and nitrogen. These analyses were con-ducted externally by Servi-Tech Laboratories, Hastings, NE. The ashcontent of the bio-char was also determined following StandardMethod 08-03.

2.6. Data analysis

Data analysis was completed for each test using Excel v. 2010(Microsoft, Redmond, WA) software to determine mean valuesand standard deviations, and two-way analysis of variance wasconducted using general linear models using SAS (2004) V.8 (SASInstitute, Cary, NC), using a type I error rate (˛) of 0.05, to determinemain and interaction effects, and least significant differences (LSD)between sample means, if differences were significant. Rheologicaldata was modeled with the PROC NONLIN regression procedure inSAS.

3. Results and discussion

3.1. Raw materials

The proximate compositions for the raw materials are shown inTable 1. Raw materials moisture content impacts the reaction timeand the composition of the bio-oil produced, so it is important thatthese products had similar initial moisture contents. Moisture con-tent of the raw materials ranged from 5.2% (FF) to 7.0% (CGM), andaveraged 6%. The main difference between the initial raw productswas their fiber and protein contents. The protein content rangedfrom 25% (FF) to 69% (CGM), and the fiber content ranged from 6%

(CGM) to 48% (FF).

Particle size can have a significant effect on the products pro-duced through pyrolysis; the smaller the particles, the more surfacearea exposed, causing the heating rate to increase, which results in

122 C. Wood et al. / Industrial Crops and Products 56 (2014) 118–127

Table 2Properties of bio-oil.a

Sample Yield (%) Density (g/cm3) Lower heating value (MJ/kg) pH

DDGS 46.57ab 1.06b 22.56ab 6.55d

(2.32) (0.01) (2.10) (0.22)CGM 50.94a 1.09ab 24.00bc 9.85a

(0.67) (0.05) (1.55) (0.16)Fiber fraction 43.00bc 1.10a 28.10a 6.37d

(4.46) (0.00) (1.64) (0.14)Protein fraction 39.92bc 1.11a 16.59bc 8.56c

(0.61) (0.01) (4.80) (0.15)High protein 39.37c 1.10a 12.35c 8.92b

(3.00) (0.01) (0.38) (0.05)

a Values in parentheses are standard deviation (SD). Values for bio-oils followed by the same letter (a, b, and c) are not significantly different ( ̨ = 0.05, LSD) from otherb GM iD e wheD

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io-oil samples for that property. DDGS is distillers dried grains with solubles; and CDGS was sieved and then aspirated; protein fraction is the heavier fraction producDG.

ess char produced (Demirbas, 2004). The particle sizes of the rawaterials can be found in Table 1. The DDGS had the largest particle

ize, with 90% of its particles falling under 3.452 mm, while the FFad the smallest particle size, with 90% of its particles falling below.42 mm.

The color of the raw material can be seen in Table 1. The color ofhe coproduct samples visibly varied from one another. The CGMas an orange color, while the DDGS was gold, and the remainingere combinations of yellow and tan. This variation is visible in theUNTER L, Hunter a, and Hunter b values presented.

.2. Bio oils

The bio-oil was condensed out of the syngas and collected fornalysis. The mass of the bio-oil was determined and then com-ared to the mass of the initial feedstock to determine product yieldy mass balance. The yield of bio-oil ranged from 39% (HP) to 50%CGM) (Table 2). The reported yield values for the bio-oil are inaccu-ate to a certain degree due to some technical issues while collectinghe entire sample. First it was assumed that the condensation of theio-oil was not 100% efficient and syngas left the system carryingome condensable liquids, and second a small amount of bio-oilould not be transferred out of the condenser columns due to taruild up. The bio-oil yields fell within the same range as what wasetermined by Lei et al. (2011) where DDGS bio-oil yields rangedrom 26% to 50%.

.2.1. Physical properties

.2.1.1. Density. It was determined that the DDGS bio-oil had theowest mass density (1.06 g/cm3) and the PF had the highest

ass density (1.11 g/cm3) (Table 2). These densities were higherhan expected, since oils generally are less dense than water1.00 g/cm3), but this is most likely due to the tar and partic-lates present in the crude bio-oils. The values are also muchreater than the mass density values of commonly used fuels: diesel0.863 g/cm3), ethanol (0.785 g/cm3), and gasoline (0.791 g/cm3)Waterland et al., 2003). The mass density of other pyrolysis bio-ils were slightly higher and ranged from 1.16 to 1.28 g/cm3 (Bat al., 2004; Junming et al., 2008; Scholze, 2002)

.2.1.2. Energy content and thermal properties. The energy contentf the bio-oils was determined by measuring the lower heatingalue (LHV). This is measured by the amount of heat released when

material is combusted and combusted products are returned to50 ◦C. The LHV (Table 2) for the bio-oils ranged from 12.4 (HP)

o 28.1 MJ/kg (FF) (5300–12,000 Btu/lb). These values were muchower than the heating values of commonly used fuels: diesel2.0 MJ/kg (18,000 Btu/lb), ethanol 27.0 MJ/kg (12,000 Btu/lb), andasoline 43.9 MJ/kg (19,000 Btu/lb) (Waterland et al., 2003). The

s corn gluten meal; fiber fraction is the lighter fraction produced when the de-oiledn de-oiled DDGS was sieved and then aspirated; high protein refers to high protein

heating values were comparable to the 20–28 MJ/kg that Lei et al.(2011) found for DDGS bio-oil. The heating values of other pyroly-sis bio-oils have been shown to be in the range of 16–28 MJ/kg forvarious feedstocks (wood, rice husk, grasses, nut shells, seeds, etc.)(Ba et al., 2004; Junming et al., 2008; Neves et al., 2011; Scholze,2002; Bridgwater and Peacocke, 2000; Huang et al., 2008).

The thermal conductivity and diffusivity of the bio-oil sam-ples were measured at three different temperatures (10, 25, and40 ◦C). These values can be found in Table 3. The thermal con-ductivities for the bio-oil ranged from 0.21 to 0.34 W/m ◦C (10 ◦C),0.21 to 0.42 W/m ◦C (25 ◦C), and 0.36 to 0.85 W/m ◦C (40 ◦C). Thethermal conductivity of the bio-oils increased as the temperatureof the oil increased. Overall the conductivity of the DDGS bio-oilsample increased 181% from 0.27 W/m ◦C (10 ◦C) to 0.76 W/m ◦C(40 ◦C). The FF bio-oil increased by 179%, the CGM increased by135%, the PF bio-oil increased by 43%, and the HP bio-oil sampleincreased by 14%. This behavior indicates that as the temperatureof the bio-oils increase, their ability to transfer heat by conductionincreases, as does the rate of energy loss. These conductivity valuesare greater than what has been found for other common crude oils0.12–0.13 W/m ◦C (0–50 ◦C) (Elam et al., 1989).

Thermal diffusivity of the bio-oils ranged from 0.09 to0.11 mm2/s (10 ◦C), 0.10 to 0.13 mm2/s (25 ◦C), and 0.11 to0.65 mm2/s (40 ◦C). Similar to the conductivity, as the tempera-ture of the oils increased, the thermal diffusivity increased. Overall,the diffusivity of the DDGS bio-oil increased 531% from 0.10 mm2/s(10 ◦C) to 0.65 mm2/s (40 ◦C). The FF bio-oil increased by 113%, theCGM bio-oil increased by 108%, the PF bio-oil increased by 98%, andthe HP bio-oil sample increased by 14%. This behavior indicates thatas the temperature of the bio-oils increased, their ability to conductheat relative to their ability to store heat increased. The diffusivitiesof these oils are much lower than commonly used fuels: diesel fuel4.6 mm2/s, ethanol 10.0 mm2/s, and gasoline 6.4 mm2/s (Waterlandet al., 2003).

3.2.1.3. Viscosity. Viscosity is the measure of how a fluid behaveswhen force is applied. Knowing how the fluid reacts to beingpumped, stirred, or to temperature changes is vital in transport-ing and processing it. For this reason, the apparent viscosity of thebio-oils produced was measured as the shear rate was increasedand the temperature was varied. In addition to the apparent vis-cosities, the kinematic viscosity of the bio-oils was also plotted(Fig. 2). The data collected from the three replications were com-bined into a single regression line for each bio-oil at each of thethree temperatures in order to depict the overall behavior as speed

changed. The plots of the apparent viscosity behavior can be seenin Fig. 3, and the average k and n regression values can be found inTable 3. The apparent viscosity of the bio-oils produced ranged from0.00093 to 0.00824 Pa s (40 ◦C) as shear rate was increased. These

C. Wood et al. / Industrial Crops and Products 56 (2014) 118–127 123

Table 3Temperature dependent properties of bio-oil.a

Sample Temp. Thermal Rheological

Conductivity (W/m ◦C) Diffusivity (mm2/s) k (Pa s s) n (dimensionless)

DDGS 10 0.27bcy 0.10aby 0.77ax −0.86by

(0.07) (0.01) (1.17) (0.42)CGM 10 0.21cy 0.09by 0.02bx −0.34ax

(0.02) (0.01) (0.04) (0.30)Fiber fraction 10 0.30aby 0.11ax 0.03b −0.48a

(0.07) (0.01) (0.02) (0.22)Protein fraction 10 0.34ay 0.10aby 0.02bx −0.34axy

(0.08) (0.01) (0.01) (0.07)High protein 10 0.31aby 0.10by 0.20abx −0.58aby

(0.02) (0.00) (0.19) (0.12)

DDGS 25 0.33bcy 0.13ay 0.01bx −0.37ax

(0.06) (0.03) (0.00) (0.07)CGM 25 0.21dy 0.10by 0.01bx −0.32ax

(0.03) (0.01) (0.00) (0.06)Fiber fraction 25 0.29cy 0.11abx 0.09ax −0.79cy

(0.06) (0.02) (0.06) (0.27)Protein fraction 25 0.37aby 0.12abxy 0.03bx −0.44aby

(0.05) (0.02) (0.02) (0.07)High protein 25 0.42ax 0.11abxy 0.04by −0.61bcy

(0.06) (0.01) (0.02) (0.21)

DDGS 40 0.76abx 0.65ax 0.01bx −0.38bx

(0.13) (0.29) (0.00) (0.14)CGM 40 0.50bcx 0.20bx 0.02bx −0.50abx

(0.20) (0.12) (0.02) (0.18)Fiber fraction 40 0.85ax 0.24bx 0.23ay −0.73ay

(0.44) (0.24) (0.31) (0.43)Protein fraction 40 0.49bcx 0.21bx 0.01by −0.30bcx

(0.11) (0.14) (0.00) (0.14)High Protein 40 0.36cxy 0.11bx 0.00by −0.20cx

(0.08) (0.01) (0.00) (0.10)

a Values in parentheses are standard deviation (SD). Values for bio-oils followed by the same letter (a, b, and c) are not significantly different ( ̨ = 0.05, LSD) from otherbio-oil samples for that property. Values for a given bio-oil sample followed by the same letter (x, y, and z) are not significantly different ( ̨ = 0.05, LSD) between temperatures.DDGS is distillers dried grains with solubles; CGM is corn gluten meal; and Temp is temperature; fiber fraction is the lighter fraction produced when the de-oiled DDGS wassieved and then aspirated; protein fraction is the heavier fraction produce when de-oiled DDGS was sieved and then aspirated; high protein refers to high protein DDG.

Fig. 2. Relationships between apparent viscosity and shear rate as a function of temperature for the bio-oil samples. DDGS is distillers dried grains with solubles; and CGMis corn gluten meal; FF is the lighter fraction produced when the de-oiled DDGS was sieved and then aspirated; PF is the heavier fraction produce when de-oiled DDGS wassieved and then aspirated; HP refers to high protein DDG.

124 C. Wood et al. / Industrial Crops and Products 56 (2014) 118–127

F ried gw produD

vb2

aNaib

33dhy23pdscddia

3mcpcmwpf

ig. 3. Relationships between kinematic viscosity and shear rate. DDGS is distillers dhen the de-oiled DDGS was sieved and then aspirated; PF is the heavier fractionDG.

iscosities are much lower than those reported for other crudeio-oils, 0.025–0.10 Pa s (40 ◦C) (Sadaka, 2009; Sadaka and Boateng,009).

While most refined oils are considered Newtonian fluids, it ispparent that these bio-oils, at least in their crude states, are non-ewtonian. They are in fact, shear thinning or pseudoplastic fluids,s their viscosities decreased as shear rate increased. This behaviors suited for use as a lubricant, but may not be ideal for utilizing theio-oil as a fuel.

.2.2. Chemical properties

.2.2.1. Potential hydrogen. The pH of the bio-oils in this study wasetermined to range from 6.5 to 9.9 (Table 2). These values are muchigher than the pH values for most bio-oils produced through pyrol-sis (wood bio-oil, 2.4–3.5 (Ba et al., 2004; Sadaka, 2009; Scholze,002); rice husk bio-oils, 2.8 (Junming et al., 2008); big blue stem,.4; prairie cordgrass, 3.5; corn stover 3.6 (Sivasastri, 2013); andine bark and needle bio-oils, 3.6–4.6 (Hassan et al., 2009)). Thisifference could be due to the differences in the feedstock compo-ition; the DDGS has a much higher protein content and lower fiberontent than the other materials. Since the pH of the bio-oils pro-uced in this study was more neutral, the bio-oils are much moreesirable than other bio-oils since they would have to be neutral-

zed so they are not corrosive to materials such as carbon steel andluminum.

.2.2.2. GC–MS. The bio-oils were analyzed by GC–MS to deter-ine resulting compositions. While there were hundreds of

ompounds found within the bio-oils, only the most significanteaks were recorded. Table 4 lists the molecular formulas asso-iated with the larger peaks and the potential compounds found to

atch using the NIST library. As expected, the compounds foundithin the bio-oils were mostly hydrocarbons. Amongst the com-ounds found were ethyl benzene and xylene, which are generallyound along with benzene and toluene in petroleum hydrocarbons

rains with solubles; and CGM is corn gluten meal; FF is the lighter fraction producedce when de-oiled DDGS was sieved and then aspirated; HP refers to high protein

(e.g. gasoline). These four volatile organic compounds (VOCs) arereferred to as BTEX and are commonly found in petroleum and var-ious organic chemical products. These VOC’s give off a strong odorand are easily ignited (EEA, 2010). These findings were comparableto those of other DDGS based bio-oils (Lei et al., 2011) and otherbio-oils (Mullen and Boateng, 2008).

3.3. Bio-char

The bio-char was collected from the reactor once it was cooled.Mass was determined and then compared to the mass of the initialfeedstock to determine product yield by mass balance. The yield ofbio-char (Table 5) ranged from 25% (CGM) to 30% (PF), which wasslightly higher than the yield reported for corn cobs and corn stover(18.9% and 17.0%, respectively) (Mullen et al., 2010).

3.3.1. Physical properties3.3.1.1. Density. The mass densities (Table 6) of the bio-charswere found to vary from 0.23 g/cm3 (FF) to 0.44 g/cm3 (HP),which is lower than literature values for bio-char mass den-sity (0.97–4.67 g/cm3 (Karaosmanoglu et al., 2000; Ozcimen andKaraosmanoglu, 2004). The true densities of the bio-char sampleswere determined to be approximately ten times greater than themass density of the bio-chars. The true density of the FF bio-charwas determined to be 2.20 g/cm3, while that of the HP bio-char wasdetermined to be 4.50 g/cm3. The true densities and the mass den-sities were then used to determine the porosities of the bio-charsamples. Porosity (Table 6) was found to range from 89.39% (CGM)to 90.23% (HP).

3.3.1.2. Energy content and thermal properties. Energy content

and thermal property results for the bio-chars can be foundin Table 6. The lower heating values for the bio-chars rangedfrom 27.0 MJ/kg (PF) to 30.0 MJ/kg (12,000–13,000 Btu/lb) (HP),which was comparable to the lower heating values found in other

C. Wood et al. / Industrial Crops and Products 56 (2014) 118–127 125

Table 4GC–MS compounds present in bio-oil.a

Retention time (min) Molecular formula Potential compounds DDGS CGM FF HP PF

2.201/2.264 C8H10 Xylene; ethyl benzene X X X X2.402 C7H9 2,6 dimethyl-pyridine X X2.464 C8H8 Styrene X X X2.842 C7H9 Dimethyl-pyridine X X X3.105 C15H17 N-Benzyl-2-phenethylamine X3.105 C9H12 7-Ethyl-1,3,5-cycloheptatriene; Propyl-benzene X X3.403 C9H12 1,2,3-Trimethyl-benzene; 1-ethyl-n-methyl-benzene X X X4.204 C9H8 Indene; 1-ethynyl-n-methyl-benzene X X4.347 C10H14 Butyl-benzene X X X4.35 C9H10 Benzenepropanal X4.805 C11H22 N-Undecene; (Z)-2-dodecene X5.589 C10H10 1-Butynyl-benzene; 1-methyl-1H-indene X X X5.692 C11H16 Pentyl-benzene X X X6.956 C7H8 n-Methyl-Phenol X7.609 C13H28 Tridecane X X X X7.757 C11H10 N-Methyl-naphthalene; benzocycloheptatriene X X12.312 C12H14 1-Cyclopentenylphenylmethane X14.469 C15H29 Pentadecanenitrile X X

C16H31 Hexadecanenitrile X X X15.202 C16H32 n-Hexadecanoic acid X X X16.129 C12H24 4-Dodecen-1-ol X16.844 C18H32 (Z,Z)-9,12-Octadecadienoic acid; (Z)-9,17-Octadecadienal X X X16.913 C20H36 Ethyl ester 9,12-octadecadienoic acid X17.01 C18H34 Oleic acid; (Z)-n-Octadecenal X17.141 C14H29 Tetradecanamide X X

a DDGS is distillers dried grains with solubles; CGM is corn gluten meal; FF is fiber fraction of de-oiled DDGS; HP is high protein DDG; and PF is the protein fraction ofde-oiled DDGS. X indicates the potential presence of the indicated compound.

Table 5Yield and composition of bio-char.a

Sample Yield (%) Moisture (%) DM (%) Carbon (% d.b.) C:N ratio (d.b.) Ash (%)

DDGS 29.30ab 3.20b 96.80d 61.48a 9.60a 57.05a

(0.09) (0.71) (0.71) (0.33) (0.33) (9.60)CGM 25.17c 4.05ab 95.95a 49.11b 4.05c 53.56a

(0.51) (0.21) (0.21) (0.10) (0.10) (3.25)Fiber fraction 29.82ab 2.15c 97.85e 56.74ab 10.00a 36.67c

(0.27) (0.49) (0.49) (0.15) (0.15) (2.79)Protein fraction 29.97a 4.40a 95.60b 60.73a 7.27b 45.67b

(0.14) (0.14) (0.14) (1.02) (1.02) (5.88)High protein 27.68b 3.10bc 96.90c 57.26ab 8.10b 52.12ab

(1.85) (0.14) (0.14) (0.10) (0.10) (2.70)

by thb is nitr

sB2Ls(

Tfv

3islsmmieT

b

diameter was CGM at 0.59 mm, but the particle size measurementsfor the CGM bio-char cannot be accurately reported unfortunately.This was because the char from the CGM became one large moltenform rather than remaining in granular form. The bio-char with

a Values in parentheses are standard deviation (SD). Values for bio-oils followedio-oil samples for that property. DM is dry matter; C is carbon; d.b. is dry basis; N

tudies (19.3–30 MJ/kg (Boateng, 2007; Boateng et al., 2007;ulmau et al., 2010; Gercel and Cayir, 2006; Karaosmanoglu et al.,000; Mullen et al., 2010; Ozcimen and Karaosmanoglu, 2004). TheHV of the bio-char is directly related to the final chemical compo-ition of the bio-char, specifically the ash and volatile compositionBulmau et al., 2010).

The glass transition (Tg) values for the bio-chars can be found inable 6. Fig. 4 provides an example heat flow vs. temperature curveor a bio-char sample and indicates where the Tg values occur. Thealues of the midpoint Tg ranged from 46 ◦C to 51 ◦C.

.3.1.3. Particle size. Particle size of bio-char can play a vital role ints transport and storage as well as find its uses. A smaller particlesize has the potential to decrease the bulk density of the material, soess space would be required for storage. In addition to this, particleize can play a vital role in bio-char’s interactions as a soil amend-ent. A small particle size with large porosity may contribute theost to enhancing soil quality, while a large particle size would

ncrease the stability of the C within the soil environment (Mullen

t al., 2010). The particle sizes for the bio-chars can be found inable 6.

From the particle size data presented in Tables 1 and 6, it cane seen that pyrolysis did in fact change the particle size of the

e same letter (a, b, and c) are not significantly different ( ̨ = 0.05, LSD) from otherogen; DDGS is distillers dried grains with solubles; and CGM is corn gluten meal.

material. The DDGS raw material had the largest median diameter(d50), 1.51 mm, but its resulting bio-char had the smallest mediandiameter (0.19 mm). The raw material with the smallest median

Fig. 4. Example heat flow vs. temperature curve for the bio-char.

126 C. Wood et al. / Industrial Crops and Products 56 (2014) 118–127

Table 6Physical properties of bio-char.a

Sample Mass density (g/cm3) True density (g/cm3) Porosity (%) Lower heatingvalue (MJ/kg)

Glass transition (◦C)

DDGS 0.43b 4.36b 90.23a 27.32d 49.43ab

(0.01) (0.04) (0.13) (0.14) (2.90)CGM 0.38c 3.55d 89.39b 29.16b 50.42a

(0.01) (0.04) (0.17) (0.16) (3.32)Fiber fraction 0.23e 2.20e 89.58c 28.09c 46.04b

(0.00) (0.04) (0.28) (0.39) (3.32)Protein fraction 0.36d 3.60c 89.99c 26.97d 50.81a

(0.00) (0.01) (0.15) (0.09) (0.50)High protein 0.44a 4.50a 90.22a 29.96a 50.02ab

(0.01) (0.06) (0.23) (0.82) (3.25)

Sample Particle size distribution Color

10 50 90 HUNTER L Hunter a Hunter b

DDGS 0.08a 0.19a 0.40a 13.63a −0.16a −0.32a

(0.01) (0.01) (0.01) (0.00) (0.01) (0.01)CGM 0.13b 0.71b 1.74b 19.98b 0.04b 0.48b

(0.01) (0.07) (0.24) (0.00) (0.01) (0.01)Fiber fraction 0.45c 0.76b 1.17c 15.94c −0.18c −0.46c

(0.02) (0.01) (0.04) (0.01) (0.01) (0.01)Protein fraction 0.60d 0.91c 1.52d 16.13d −0.12d −0.23d

(0.00) (0.00) (0.00) (0.01) (0.01) (0.01)High protein 0.10a 0.38d 1.04e 17.60e −0.04e 0.10e

(0.02) (0.06) (0.16) (0.00) (0.01) (0.01)

a Values in parentheses are standard deviation (SD). Values for bio-char followed by the same letter (a, b, and c) are not significantly different ( ̨ = 0.05, LSD) from other bio-c Huntep al; fibt S was

tmcci

3oastctrtrc

33spccfo2emtttf(d

har samples for that property. *HUNTER L is Hunter brightness/darkness parameter;arameter; DDGS is distillers dried grains with solubles; and CGM is corn gluten mehen aspirated; protein fraction is the heavier fraction produce when de-oiled DDG

he largest median diameter, 0.91 mm, was the PF which had a rawedian diameter of 1.10 mm. Based on the data presented, it can be

oncluded that the pyrolysis reactions did reduce the median parti-le diameter of the material, but there was no correlation betweennitial particle size and final particle size.

.3.1.4. Color. When bio-char is added to soils, it changes the colorf the soil by darkening it. By darkening the soil, bio-char canffect soil’s ability to absorb more soil, energy in turn increasingoil temperatures (Sohi et al., 2010). Increasing the soil tempera-ure potentially extends the growing season, accelerates nutrientycling, and accelerates snow melt (Sohi et al., 2010). The color forhe bio-char can be seen in Table 6, while the color of the raw mate-ial can be found in Table 1. Based on the data presented in theseables, no conclusions can be made on a relationship between theaw material color and the bio-char color. All bio-char samples wereonsidered black.

.3.2. Chemical properties

.3.2.1. Proximate analysis. Chemical composition and physicaltructure of bio-char is variable as it is directly related to theyrolysis parameters and feedstock (Spokas, 2010). The proximateomposition of the bio-char produced from the pyrolysis reactionan be found in Table 5. The moisture content of the bio-char rangedrom 2.15% (FF) to 4.4% (PF), which was within the range found forther bio-chars 0.9–6.9% (Rocca et al., 1999; Karaosmanoglu et al.,000; Ozcimen and Karaosmanoglu, 2004; Boateng, 2007; Boatengt al., 2007; Mullen et al., 2010). The raw FF did have the lowestoisture content; however, the CGM had the highest moisture con-

ent of the raw materials. This indicates that the moisture content inhe bio-char may not be directly related to the moisture content in

he initial feed product. The carbon content of the bio-chars rangedrom 49% (CGM) to 61% (DDGS). The ash contents ranged from 37%FF) to 57% (DDGS) which was within a similar as bio-char pro-uced by other studies (0.9–54.6% (Boateng, 2007; Boateng et al.,

r a is Hunter redness/greenness parameter; Hunter b is Hunter yellowness/bluenesser fraction is the lighter fraction produced when the de-oiled DDGS was sieved and

sieved and then aspirated; high protein refers to high protein DDG.

2007; Karaosmanoglu et al., 2000; Mullen et al., 2010; Ozcimen andKaraosmanoglu, 2004; Rocca et al., 1999).

4. Conclusions

Record quantities of ethanol coproducts may potentially providenew sources of energy which can potentially be harvested ther-mochemically. Pyrolysis was used to produce bio-oils with similarproperties to bio-oils produced from other biomaterials. The pHof the DDGS based bio-oil was determined to be less acidic. Thetars present in the bio-oils affected the mass as well as the appar-ent viscosities of the bio-oils. Pyrolysis could potentially be usedto harvest energy from DDGS and other ethanol coproducts, butmore research is needed to optimize pyrolysis parameters to obtaindesirable bio-oil and bio-char characteristics and to refine them.

Acknowledgements

The authors would like to thank Chinnadurai Karunanithy (CK),and Arulprakash Sivasastri for technical assistance with laboratorymeasurements

References

Ba, T., Chaala, A., Garcia-Perez, M., Rodrique, D., Roy, C., 2004. Colloidal propertiesof bio-oils obtained by vacuum pyrolysis of softwood bark. Characterization ofwater-soluble and water-insoluble fractions. Energy Fuels 18, 704–712.

Babu, B.V., Chaurasia, A.S., 2003. Optimization of pyrolysis of biomass usingdifferential evolution approach. In: Proc. of the Second Intl. Conf. on Computa-tional Intelligence, Robotics, and Autonomous Syst. Computational Intelligence,Robotics, and Autonomous Systems, Singapore, pp. 787–792.

Baghe-Kahandan, M., Choi, S., Okos, M., 1981. Improved line heat source thermalconductivity probe. J. Food Sci. 46, 1430–1432.

Bhadra, R., Muthukumarappan, K., Rosentrater, K.A., 2009. Flowability properties of

commercial distillers dried grains with solubles (DDGS). Cereal Chem. 86 (2),170–180.

Boateng, A.A., 2007. Characterization and thermal conversion of charcoal derivedfrom fluidized-bed fast pyrolysis oil production of switchgrass. Ind. Eng. Chem.Res. 46 (26), 8857–8862.

s and

B

B

B

B

C

C

C

D

D

E

E

G

G

G

G

H

H

I

J

K

K

L

M

M

M

M

N

O

P

R

R

C. Wood et al. / Industrial Crop

oateng, A., Daren, A., Daugaard, E., Goldberg, N., Kevin, M., Hicks, B., 2007. Bench-scale fluidized-bed pyrolysis of switchgrass for bio-oil production. Ind. Eng.Chem. Res. 46, 1891–2189.

othast, R., Schlicher, M., 2005. Biotechnological processes for conversion of corninto ethanol. Appl. Microbiol. Biotechnol. 67, 19–25.

ridgwater, A.V., Peacocke, G.V.C., 2000. Fast pyrolysis processes for biomass. Renew.Sust. Energy Rev. 4 (1), 1–73.

ulmau, C., Marculescu, C., Badea, A., Apostol, T., 2010. Pyrolysis parameters influ-encing the bio-char generation from wooden biomass. U.P.B. Sci. Bull. Ser. C 72(1), 30–38.

hang, C.S., 1988. Measuring density and porosity of grain kernels using a gas pyc-nometer. Cereal Chem. 65 (1), 13–15.

hao, C., Qing, L., Qiang, Y., Kruttschnitt, T., Pruckner, E., 2005. Comparative exper-iments on recycling of oil sludge, oil shale, and biomass waste in a continuousrotating pyrolysis reactor. In: Proc. 5th Asia-Pacific Conf. Combustion, Adelaide,Australia.

hristians, N.E., 1993. The use of corn gluten meal as a natural preemer-gence weed control in turf. In: Carrow, R.N., Christians, N.E., Shearman, R.C.(Eds.), Intl. Turfgrass Soc. Res. J., vol. 7. Intertec Publishing Corp., Over-land Park, KS, pp. 284–290 (Chapter 35) Available at: http://www.hort.iastate.edu/gluten/pdf/cornglut3.pdf (accessed 12.02.13).

e Kam, M.J., Morey, R.V., Tiffany, D.G., 2007. Integrating biomass to produce heatand power at ethanol plants. In: ASABE Paper No. 076232. ASABE, St. Joseph, MI.

emirbas, A., 2004. Effects of temperature and particle size on bio-char yield frompyrolysis of agricultural residues. J. Anal. Appl. Pyrolysis 72 (2004), 243–248.

EA, 2010. Benzene, Toluene, Ethylbenzene, Xylene (as BTEX). European Envi-ronment Agency, Copenhagen, Denmark, Available at: http://glossary.eea.europa.eu/terminology/concept html?term=benzene,%20toluene,%20ethylbenzene,%20xylenes%20%28as%20btex%29 (accessed 28.03.13).

lam, S., Tokura, I., Saito, K., 1989. Thermal conductivity of crude oils. Exp. Therm.Fluid Sci. 2 (1), 1–6.

anesan, V., Rosentrater, K.A., Muthukumarappan, K., 2008. Effect of flow agentaddition on the physical properties of DDGS with varying moisture content andsoluble levels. Trans. ASABE 51 (2), 591–601.

ercel, F., Cayir, G., 2006. Energy applications of biomass: pyrolysis of apricot stone.Energy Source. A 28, 611–618.

heorghe, C., 2006. Investigations of Potentialities of Biomass Pyrolysis at HTAGSystem. University of Bucharest, Power Engineering, Bucharest, Romania.

iuntoli, J., Gout, J., Verkooijen, A., De Jong, W., 2011. Characterization of fast pyrol-ysis of dry distillers grains (DDGS) and palm kernel cake using a heated foilreactor: nitrogen chemistry and basic reactor modeling. Ind. Eng. Chem. Res. 50(8), 4286–4300.

assan, E.M., Yu, F., Ingram, L.L., Steele, P.H., 2009. The potential use of whole-treebiomass for bio-oil fuels. Energy Source. A 31, 1829–1839.

uang, Y., Kuan, W., Lo, S., Lin, C., 2008. Total recovery of resources and energyfrom rice straw using microwave-induced pyrolysis. Bioresour. Technol. 99,8252–8258.

SU (Iowa State University), 2008. Ethanol Coproducts for Cattle: The Process andProducts. Iowa State University, University Extension, Ames, IA, Available at:http://www.extension.iastate.edu/publications/ibc18.pdf (accessed 02.01.12).

unming, X., Jianchun, J., Yunjuan, S., Yanju, L., 2008. Bio-oil upgrading by means ofethyl ester production in reactive distillation to remove water and to improvestorage and fuel characteristics. Biomass Bioenergy 32, 1056–1061.

araosmanoglu, F., R-Ergudenler, A.I., Sever, A., 2000. Biochar from the straw-stalkof rapeseed plant. Energy Fuels 14, 336–339.

annadhason, S., Rosentrater, K.A., Muthukumarappan, K., 2010. Twin screw extru-sion of DDGS based aquaculture feeds. J. World Aquacult. Soc. 41, 1–15.

ei, H., Ren, S., Wang, L., Bu, Q., Julson, J., Holladay, J., Ruan, R., 2011. Microwavepyrolysis of distillers dried grain with solubles (DDGS) for biofuel production.Bioresour. Technol. 102, 6208–6213.

cDade, M.C., (MS thesis) 1999. Corn gluten meal and corn gluten hyrolysate forweed control. Iowa State University, Department of Horticulture, Ames, IA.

orey, R., Tiffany, D., Hatfield, D., 2006. Biomass for electricity and process heat atethanol plants. Appl. Eng. Agric. 22 (5), 723–728.

ullen, C., Boateng, A., 2008. Chemical composition of bio-oils produced by fastpyrolysis of two energy crops. Energy Fuels 22 (3), 2104–2109.

ullen, C., Boateng, A., Goldberg, N., Lima, I., Laird, D., Hicks, K., 2010. Bio-oil and bio-char production from corn cobs and stover by fast pyrolysis. Biomass Bioenergy34 (2010), 67–74.

eves, D., Thunman, H., Matos, A., Tarelho, L., Gomez-Barea, A., 2011. Characteriza-tion and prediction of biomass pyrolysis products. Prog. Energy Combust. Sci.37, 611–630.

zcimen, D., Karaosmanoglu, F., 2004. Production and characterization of bio-oiland biochar from rapeseed cake. Renew. Energy 29, 779–787.

irikh, J., Channiwala, S., Ghosal, G., 2003. Product distribution from woody biomassin a bench-scale pyrolyzer. In: Proc. Intl. Conf. Mech. Eng., Dhaka, Bangladesh.

FA (Renewable Fuels Association), 2010. RFA Issues Weekly Ethanol Production

Data. Biofuels Journal. Renewable Fuels Association, Washington, DC, Availableat: http://www.biofuelsjournal.com/info/bf articles.html?ID=95665 (accessed12.02.13).

FA (Renewable Fuels Association), 2011a. Building Bridges to a More Sustain-able Future: 2011 Industry Outlook. Renewable Fuels Association, Washington,

Products 56 (2014) 118–127 127

DC, Available at: http://www.ethanolrfa.org/page/-/objects/pdf/outlook/RFAOutlook 2011.pdf (accessed 12.02.13).

RFA (Renewable Fuels Association), 2012b. Industry Resources: Co-products. Renewable Fuels Association, Washington, DC, Available at:http://ethanolrfa.org/pages/industry-resources-coproducts (accessed24.10.12).

RFA (Renewable Fuels Association), 2012a. Statistics. Renewable Fuels Associa-tion, Washington, DC, Available at: http://www.ethanolrfa.org/pages/statistics(accessed 12.02.13).

Rocca, P.A.D., Cerrella, E.G., Bonelli, P.R., Cukierman, A.L., 1999. Pyrolysis of hard-woods residues: on kinetics and chars characterization. Biomass Bioenergy 16,79–88.

Rosentrater, K.A., 2007. Corn ethanol coproducts – some current constraints andpotential opportunities. Int. Sugar J. 109 (1307), 2–11.

Rosentrater, K.A., Muthukumarappan, K., 2006. Corn ethanol coproducts: genera-tion, properties, and future prospects. Int. Sugar J. 108 (1295), 648–657.

Rosentrater, K.A., Muthukumarappan, K., Kannadhason, S., 2009a. Effect of ingredi-ents and extrusion parameters on aquafeeds containing DDGS and potato starch.J. Aquacult. Feed Sci. Nutr. 1 (1), 22–38.

Rosentrater, K.A., Muthukumarappan, K., Kannadhason, S., 2009b. Effect of ingredi-ents and extrusion parameters on properties of aquafeeds containing DDGS andcorn starch. J. Aquacult. Feed Sci. Nutr. 1 (2), 44–60.

Rosentrater, K., Krishnan, P., 2006. Incorporating distillers grains in food products.Cereal Foods World 51 (2), 52–60.

Sadaka, S., 2009. Pyrolysis. In: Pyrolysis and Bio-Oil. Cooperative Extension Services,Fayetteville, AR (Chapter 2).

Sadaka, S., Boateng, A.A., 2009. Pyrolysis and Bio-Oil Fact Sheet, Avail-able at: www.uaex.edu/Other Areas/publications/PDF/FSA-1052.pdf (accessed12.02.13).

Schaeffer, T.W., Brown, M.L., Rosentrater, K.A., 2009. Performance characteristicsof Nile Tilapia (Oreochromis niloticus) fed diets containing graded levels offuel based distillers’ grains with solubles. J. Aquacult. Feed Sci. Nutr. 1 (4),78–83.

Scholze, B., (PhD diss.) 2002. Long-term stability, catalytic upgrading, and applica-tion of pyrolysis oils – improving the properties of a potential substitute for fossilfuels. Zur Erlangung des Doktorgrades im Fachbereich Chemie der Universitat,Hamburg, Germany.

Shurson, J., Alhamdi, A.S., 2008. Quality and new technologies to create corn co-products from ethanol production. In: Babcock, B.A., Hayes, D.J., Lawrence, J.D.(Eds.), Using Distillers Grains in the U.S. and International Livestock and PoultryIndustries. Iowa State University, Ames, IA, pp. 231–256.

Shurson, J., Noll, S., 2005. Feed and alternative uses for DDGS. In: Proc., Energy fromAgriculture: New Technologies, Innovative Programs & Successes Conference,Farm Foundation, NRCS, and USDA Office of Energy Policy and New Uses, St.Louis, MO, December 14–15, 2005, pp. 1–11.

Sivasastri, A., (MS thesis) 2013. Conventional pyrolysis of corn stover, prairie cordgrass, and big blue stem using batch type pyrolysis unit. South Dakota StateUniversity, Department of Agricultural and Biosystems Engineering, Brookings,SD.

Sohi, S.P., Krull, E., Lopez-Capel, E., Bol, R., 2010. A review of biochar and its use andfunction in soil. In: Sparks, D.L. (Ed.), Advances in Agronomy. Academic Press,Burlington, MA, pp. 47–82 (Chapter 2).

Spokas, K., 2010. Review of the stability of biochar in soils: predictability of O:Cmolecular ratios. Carbon Manag. 2, 289–303.

Tatara, R., Rosentrater, K.A., Suraparaju, S., 2006. Design properties for molded, corn-based DDGS-filled phenolic resin. Ind. Crops Prod. 29, 9–15.

Tatara, R., Rosentrater, K.A., Suraparaju, S., 2007. Compression molding of phenolicresin and corn-based DDGS blends. J. Polym. Environ. 15, 89–95.

Tiffany, D., Morey, R.V., De Kam, M., 2007. Economics of biomass gasifica-tion/combustion at fuel ethanol plants. Appl. Eng. Agric. 25 (3), 391–400.

Tiffany, D., Morey, R.V., De Kam, M., 2008. Use of distillers by-products and cornstover as fuels for ethanol plants. In: Proc. Transition to a Bioeconomy: Integra-tion of Agricultural and Energy Systems. Farm Foundation, Atlanta, GA.

Van de Velden, M., Xianfeng, F., Ingram, A., Baeyens, J., 2007. Fast pyrolysis of biomassin a circulating fluidized bed. In: Proc. 12th Intl. Conf. on Fluidization – NewHorizons in Fluidization Eng., Vancouver, Canada.

Wang, L., Kumar, A., Weller, C., Jones, D., Hanna, M., 2007. Coproduction of chemi-cal and energy products from distillers grains using supercritical fluid extractionand thermochemical conversion technologies. In: ASABE Paper No. 076064, Min-neapolis, Minnesota, June 17–20, 2007.

Waterland, L., Venkatesh, S., Unnasch, S., 2003. Safety and Performance Assessmentof Ethanol/Diesel Blends (E-diesel). National Renewable Energy Labora-tory, Golden, CO, Available at: http://www.nrel.gov/docs/fy03osti/34817.pdf(accessed 28.02.13).

Webber, C.L., Shrefler, J.W., Taylor, M.J., 2010. Influence of corn gluten meal onsquash plant survival yields. HortTechnology 20 (4), 696–699.

Weigel, J.C., Loy, D., Kilmer, L., 1997. Feed Co-Products of the Dry Corn Milling

Process. Renewable Fuels Association/National Corn Growers Association,Washington, DC/St. Louis, MO.

Zhong, G., Qian, X., Hua, X., 2009. Effects of feeding with corn gluten meal on trypsinactivity and mRNA expression in Fugu obscurus. Fish Physiol. Biochem. 37 (3),453–460.