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Results of the IEA Round Robin on Fast Pyrolysis Bio-oil Production Douglas Charles Elliott, Dietrich Meier, Anja Oasmaa, Bert Van De Beld, Anthony V. Bridgwater, and Magnus Marklund Energy Fuels, Just Accepted Manuscript • DOI: 10.1021/acs.energyfuels.6b03502 • Publication Date (Web): 30 Mar 2017 Downloaded from http://pubs.acs.org on April 2, 2017
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Results of the IEA Round Robin on Fast Pyrolysis Bio-oil Production Douglas C. Elliott* Pacific Northwest National Laboratory, Richland, Washington, USA Dietrich Meier, Thünen Institute of Wood Research, Hamburg, Germany Anja Oasmaa, VTT Technical Research Center of Finland, Espoo, Finland Bert van de Beld, BTG Biomass Technology Group BV, Enschede, Netherlands Anthony V. Bridgwater, Aston University, Birmingham, UK Magnus Marklund, SP Energy Technology Center, Piteå, Sweden *author to whom correspondence should be addressed:
[email protected] ABSTRACT An international Round Robin study of the production of fast pyrolysis bio-oil was undertaken. Fifteen institutions in six countries contributed. Three biomass samples were distributed to the laboratories for processing in fast pyrolysis reactors. Samples of the bio-oil produced were transported to a central analytical laboratory for analysis. The Round Robin was focused on validating the pyrolysis community understanding of production of fast pyrolysis bio-oil by providing a common feedstock for bio-oil preparation. The Round Robin included: • distribution of 3 feedstock samples, hybrid poplar, wheat straw, and a blend of lignocellulosic biomasses, from a common source to each participating laboratory; • preparation of fast pyrolysis bio-oil in each laboratory with the 3 feedstocks provided; • return of the 3 bio-oil products (minimum 500 ml) with operational description to a central analytical laboratory for bio-oil property determination. The analyses of interest were: CHN, S, trace element analysis, water, ash, solids, pyrolytic lignin, density, viscosity, carboxylic acid number, and accelerated aging of bio-oil. In addition, an effort was made to compare the bio-oil components to the products of analytical pyrolysis through GC/MS analysis. The results showed that clear differences can occur in fast pyrolysis bio-oil properties by applying different process configurations and reactor designs in small scale. The comparison to analytical pyrolysis method suggested that Py-GC/MS could serve as a rapid qualitative screening method for bio-oil composition when produced in small-scale fluid-bed reactors. Gel permeation chromatography was also applied to determine molecular weight information. Furthermore, hot vapor filtration generally resulted in the most favorable bio-oil product, with respect to water, solids, viscosity, and carboxylic acid number. These results can be helpful in understanding the variation in bio-oil production methods and their effects on bio-oil product composition. KEYWORDS: Fast pyrolysis; bio-oil; biomass;
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INTRODUCTION Bio-oils from fast pyrolysis of lignocellulosic biomass have been defined as represented in CAS #1207435-39-9: “Liquid condensate recovered by thermal treatment of lignocellulosic biomass at short hot vapor residence time (typically less than about 5 seconds) typically at between 450-600 °C, at near atmospheric pressure or below, in the absence of oxygen, and using small (typically less than 5 mm) dry (typically less than 10% water) biomass particles"1. A number of engineered systems have been used to effect high heat transfer into the biomass particle and quick quenching of the vapor product, usually after removal of solid byproduct ”char”, to recover a single phase liquid product. Bio-oil is a complex mixture of, for the most part, oxygenated hydrocarbon fragments derived from the biopolymer structures. It typically contains 15-30% water. Common organic components include acetic acid, methanol, aldehydes and ketones, cyclopentenones, furans, alkyl-phenols, alkyl-methoxy-phenols, anhydrosugars, and oligomeric sugars and waterinsoluble lignin-derived compounds. Nitrogen- and sulfur-containing compounds are also sometimes found depending on the biomass source.”1 A first set of burner fuel specifications has been accepted for fast pyrolysis bio-oil as ASTM D7544.2 The first standard method, ASTM D7579, was obtained for determination of the insoluble solids content. The ASTM method includes the validation results of a 2laboratory test over 10 successive days as represented by the Repeatability measurement. Bio-oils have many important differences from mineral oils.3 The standard analyses have been systematically tested for bio-oils 4-8 and modifications as well as new methods have been suggested when needed. The first IEA (International Energy Agency) Bioenergy Round Robin was carried out by Elliott, McKinley and Overend.9 It was found out that xylene distillation EN 95, which is used for mineral oils, cannot be used for the determination of water content of fast pyrolysis bio-oils because bio-oils contain a significant amount of volatile water-soluble compounds that end up being counted as part of the water fraction by this method. Karl-Fischer titration was recommended as a suitable method for fast pyrolysis bio-oils. Two separate Round Robin tests were initiated in 1997: one within EU PyNe (Pyrolysis Network)10 and the other within IEA PYRA (Pyrolysis Activity).10 From both of them it was concluded that the precision of carbon and hydrogen contents, density, and water content by Karl-Fischer titration was good. High variations were obtained for nitrogen, viscosity, pH, and solids. The conclusion was also that clear instructions for analyses are needed. In 2001 a Round Robin11 was organized within the IEA-EU PyNe (Pyrolysis Network) cooperation. Analyses were carried out by 12 laboratories for four different fast pyrolysis biooils (originating from pine, spruce, hardwood mix, and bark) produced by different largescale pyrolysis processes. Water, solids content, pH, viscosity, stability test, and CHN determinations were included. In general, the accuracy of physical analyses, except the stability test, was good. The Round Robin conducted in 201212-13 was focused on validating the viscosity and stability testing method as an accelerated aging test and subsequent long term storage stability testing. It was concluded that kinematic viscosity is more accurate than dynamic viscosity. This paper is focused on validating the production of consistent quality bio-oil at the collection of R&D laboratories involved in development of this new product. The importance in understanding the variation in bio-oil products increases as new researchers enter the field as has been experienced over the past 5 years.
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MATERIALS AND METHODS The research was carried out within the IEA Bioenergy Agreement Pyrolysis Task 34. This paper describes the analytical methods applied and summarizes results from the tests performed in the participating laboratories. Fifteen institutions from the six participating countries in Task 34 agreed to contribute to this Round Robin and each was supplied with samples of three biomass feedstocks for fast pyrolysis processing. The participants included: USA • Pacific Northwest National Laboratory, USA - Daniel T. Howe and Daniel M. Santosa • National Renewable Energy Laboratory, USA - Kristiina Iisa • Michigan State University, USA - Chris Saffron/Rachael Sak • Iowa State University, USA - Ryan G. Smith • University of Maine, USA - William DeSisto UK • • •
Aston University, UK - Daniel Nowakowski Future Blends, Ltd., UK - Zhiheng Wu University of Leeds, UK - Paul Williams
Germany • Fraunhofer UMSICHT, Germany - Tim Schulzke • Karlsruhe Institute of Technology, Germany - Axel Funke • Thünen Institute of Wood Research, Germany - Dietrich Meier The Netherlands • University of Groningen, Netherlands - Erik Heeres • ECN - Paul deWild Finland • VTT, Technical Research Centre of Finland, Finland - Ville Paasikallio Sweden • SP-Energy Technology Center, Sweden - Ann-Christine Johansson Three biomass feedstocks were provided and distributed by the Idaho National Laboratory (Idaho Falls, Idaho, USA) as funded by the Bioenergy Technologies Office of the U.S. Department of Energy. They were: • Hybrid Poplar wood from Morrow county, Oregon • Wheat straw from Jefferson county, Idaho, and • Blended feedstock (hybrid poplar (70%), wheat straw (15%) and forest thinnings (15%). The forest thinnings consisted of a mixture of hemlock and Douglas fir. All samples were ground to fine particle size, pelletized and then reground in a hammer mill using a 1.3 mm screen to produce a fine meal. The nominal particle size was approximately 1 mm, with the width of largest particles approaching 2 mm. The maximum 3 Environment ACS Paragon Plus
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particle thickness appeared to be approximately 1 mm. Some instructions for the handling of the biomass feedstocks and the bio-oil products were also provided. Analytical methods. Elemental composition: Carbon, hydrogen and nitrogen were determined with model Vario EL from ELEMENTAR, Hanau, Germany. Oxygen was calculated by difference as follows: O% = 100 - [C% + H % + N%]. Water content: The water content was determined by Karl-Fischer titration using the Titroline alpha apparatus from Schott-Geräte GmbH (Mainz, Gemany). The titrant was Hydranal Composite 2 and the solvent "methanol rapid" (a special reagent for accelerated volumetric one-component KF titration) both from Fluka/Sigma Aldrich, Germany. The endpoint of the titration was potentiometrically determined by dead-stop-indication. All determinations were at least made in duplicate. Ash: Ash content was determined according to TAPPI standard (T211 om-02). Approximately 5-6 g of bio-oil were used for ash determination at 775 °C Ash composition: Ash composition was determined by ICP/OES (Inductively Coupled Plasma-Optical Emission Spectrometry). Measurements were performed on a Thermo iCAP 6300 Duo spectrometer. Ca. 350 mg bio-oil were digested in 2.5 mL nitric acid (65%). Solids: Solids were determined according to ASTM D-7579-09. Bio-oil samples was dissolved on a 1:1 mixture of methanol and dichloromethane (DCM) and filtered. Pyrolytic Lignin (PL) content: Pyrolytic lignin content was determined following the method of Scholze and Meier.14 Ca. 1.0 - 1.4 g of bio-oil were added to kitchen mixer filled with 1 L distilled water at room temperature and vigorously stirred. The precipitate (PL) was filtered over a Büchner funnel (filter paper MN 615 (Machery & Nagel, Germany), washed several times with water, dried under vacuum at 40 °C resulting in a powder-like brownish product, and finally weighed. Density: Density was measured at 20 °C using a special pycnometer (10 ml) with separate side capillary. Viscosity: Kinematic viscosity was determined with an automatic capillary viscometer model AVS 350 and the corresponding water baths CT 1650 from Schott (Mainz, Germany). Determination was according to DIN 51562 part 1 and 2. Viscometers were of the Ubbelohde type with capillary diameters of 1.13 and 2.01 mm. Viscosities were measured at 20 and 50 °C. Ageing: 40 mL of bio-oil were placed in a 100 mL glass bottle from Scott equipped with a screw cap. The bottle was closed and put into a preheated oven at 80 °C for 24 hours. Carboxylic acid number (CAN): CAN was determined by manual titration. Ca. 3 g bio-oil was used and titrated with a solution of KOH in ethanol (0.5 mol/L). A combination electrode model BlueLine 12 pH from SI Analytic was used. Gas chromatography/mass spectrometry (GC/MS): Compositions of bio-oils were analyzed using a HP 6890 gas chromatograph from Agilent. Around 60 mg of the sample was dissolved in 1 mL acetone, which contained a known amount of fluoranthene as internal standard (IS) 4 Environment ACS Paragon Plus
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for quantification. Injection split ratio was 1:15 and injection volume was 1 microliter. Separation was carried out on a 60 m 0.25 mm VF-1701ms (Agilent) fused-silica column. The oven was hold constant at 45 °C for 4 min and then heated with 3K/min to 280 °C and held for 20 min. Helium was used as carrier gas with a constant flow of 2 mL/min. Parallel MS/FID detection (mass spectrometry/flame ionization detector) was used for improved qualification and quantification. Ionization energy was 70 eV. Electron impact (EI) mass spectra were obtained on a HP 5972 MS system. Fluoranthene was used as internal standard (IS) for quantification with the FID. Total ion chromatograms (TIC) were analyzed with Mass Finder 4 by comparison of both mass spectra from home-developed MS-library and commercial NIST library. Most of the compounds were quantified by use of standards. For those which were not quantifiable by a suitable standard, and after matching with NIST spectral library, a response factor was assigned based on standards with similar chemical structure. Analytical pyrolysis: Prior to pyrolysis, the biomass samples were milled in a cryo-mill. Approximately 100 µg of fine-milled sample was weighed in a stainless steel pyrolysis cup (Fontier Lab Ltd., Japan) placed into the autosampler of a double-shot Py-2020iD 2020 microfurnace pyrolyzer (Frontier Laboratories Ltd.) mounted on an Agilent 6890 GC system. The GC is equipped with a VF-1701 (Agilent) fused-silica capillary column (60 m 0.25 mm i.d., 0.25 µm film thickness) and an Agilent 5973 mass selective detector (EI at 70 eV, ion source temp 280 °C). Pyrolysis was carried out at 500 °C. For separation with GC, the oven temperature was held at 45 °C (4 min) and raised to 255 at 3K min-1 (70 min) using He as carrier gas (1 mL/min). The compounds were identified using Mass Finder 4 by comparing their mass spectra in NIST and home-developed libraries. Gel permeation chromatography (GPC): Mean molecular weight was determined from biooils and the pyrolytic lignins. GPC was performed on an Agilent 1100 series equipped with PolarGel-L Guard 50 mm × 7.5 mm and 2× Varian PolarGel-L; each 300 mm, I.D. 7.5 mm, using dimethyl sulfoxide with 0.1 wt % LiBr as eluent. Also, 100 µL of solutions containing 2 mg mL−1 of analyte were injected. The system was calibrated using polyethylene glycol (PEG) standards (194 to 21,030 g/mol). A UV detector at 254 nm was used to monitor the sample signals.
RESULTS AND DISCUSSION Feedstock analysis, feedback and pyrolysis conditions The three biomass samples included poplar wood, wheat straw, and a blended feed. The feedstock analyses are in Table 1Table 1. Table 1. Analysis (dry basis) of the three biomass feedstocks distributed in the Round Robin Poplar wood Wheat straw Volatiles, % 84.9 72.4 Ash, % 0.9 12.8 Fixed C, % 14.2 14.7 Carbon, wt% 49.9 43.8 Hydrogen, wt% 5.8 5.3 Nitrogen, wt% 0.2 0.6
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Blend 76.8 5.0 15.1 49.0 5.8 0.3
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Although the three feedstocks were originally distributed to 20 laboratories, the fast pyrolysis tests were only successfully completed in the 16 reported here. (One institution had 2 laboratories to perform the tests with two different reactor types). The 16 laboratories were able to provide representative bio-oil samples from 2 or 3 of the feedstocks. The wheat straw has a noticeably high ash content. It is somewhat higher than is typically reported in the literature. With such a feedstock the fast pyrolysis products are affected in that there is typically a reduced bio-oil yield15-16 with higher water and gas production. In this Round Robin the bio-oil samples produced from the wheat straw feedstock consisted of two phases for all laboratories. This type of phase separation, into a more aqueous lighter phase and a less aqueous heavy phase is often seen when processing agricultural residue feedstocks. It is usually attributed to the higher mineral content in the biomass which catalyzes reactions leading to more water and char formation. The higher yield of water leads to the phase separation which can be spontaneously generated by water addition to bio-oil.15 The poplar bio-oils were the most consistently produced product and were received from all participants. Some participants did not provide bio-oil samples from either the wheat straw (labs 11 and 16) or the blended feedstock (lab 10). The phase separated products were not analyzed further except as described in the density measurement section. In the following discussion the four categories "BFB" (bubbling fluid bed), "BFB/EFHVF" (bubbling fluid bed/entrained flow-hot vapor filtration), "screw", and "ablative" will be used according to the subdivisions in Table 2. The bio-oil products were all received and analyzed at the Thünen Institute of Wood Research in Hamburg, Germany. The results of these analyses are presented and discussed below. Water content Water content of bio-oil is a crucial quality criterion and its importance has been discussed in many papers. 17-23 It is a sum of biomass moisture and so-called pyrolysis reaction water. Inevitably, reaction water is formed mainly due to cleavage of glucosidic bonds of cellulose and hemicelluloses. On the other hand, feedstock properties (ash content) and pyrolysis conditions (temperature, hot vapor residence time, char particles) are responsible for secondary cracking reaction leading to water formation. The influence of the feedstock can be seen in Figure 1. There is a general trend of a higher water content obtained from pyrolysis of the blended feedstock which has an ash content of 5 % (see Table 1). The water content in poplar oils covers a very wide range from 9.4 to 51.4. Typically, the water content of a bio-oil made from hardwood is in the range of 25 %. Hence, the extreme values can only be explained through different reactor configurations, abnormal condenser systems, or long residence time of hot vapors. All reported water contents are for single phase bio-oils. It has to be highlighted that samples with above 35 wt% water were not typical fast pyrolysis bio-oils than probably aqueous phases of the products.
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Table 2. Pyrolysis reactor systems and operating conditions as provided by the participants lab. no.
reactor type
operating temperature* [°C] 475 480-500 480-500 480 525 500 500
time at temperature** [s] 35 % which is not typical for fast pyrolysis oils and hence are not considered in the viscosity distribution. However, they fit into the correlation curves of Figure 5 and Figure 6. Clearly, the amount of water embedded in bio-oil has an easily discerned effect in that the higher amount of water decreases the viscosity, at both 20 °C and 50 °C. 200
160 Viscosity 20 °C (cSt)
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120
poplar blend
80
Expon. (poplar) Expon. (blend)
40
0 0
10
20 30 water content [wt%]
40
Figure 5. Water/viscosity @ 20 °C correlation vs. poplar and blend
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Viscosity @50 °C (cSt)
25 20 15 poplar blend Expon. (poplar) Expon. (blend)
10 5 0 0
10
20 30 Water content (%)
40
50
Figure 6. Water/viscosity @ 50 °C correlation vs. poplar and blend The ratio of viscosity (viscosity 20°C/viscosity 50°C) at the two temperatures also changes over the range of dissolved water as shown in Figure 7 and gives a linear negative correlation with a regression coefficient of 0.86. It means that the temperature dependency of the fast pyrolysis bio-oil viscosity is also a function of the water content. 10.0 R² = 0.8628 8.0
6.0 Viscosity ratio
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4.0
2.0
0.0 0
10
20 30 water content [wt%]
40
50
Figure 7. Viscosity ratio vs. water content ratio = viscosity @20°C/viscosity @ 50°C
Carboxylic acid number (CAN) Carboxylic acid number is a crucial parameter in biofuels as it determines the tendency for corrosion. Several attempts are described in the literature to decrease CAN24-27. The CAN distribution is illustrated in Figure 8 and the technology related graph is presented in Figure 9. It is obvious that lab #3 (ablative reactor) produced an oil with the highest CAN and that also
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screw reactors exhibit higher CANs combined with larger water contents. It might be caused by longer residence times enabling more severe cracking towards acids.
CAN (mg KOH/g sample)
140
poplar
blend
120 100 80 60 40 20 0 1
2
3
4
5
6
7
8 9 10 11 12 13 14 15 16 Laboratory
Figure 8. Carboxylic Acid Number of bio-oils from poplar and blend
160 BFB
BFB/EF + HVF
screw
ablative
140 120 CAN (mg KOH/g sample)
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100 80 60 40 20 0 10
20
30 Water content [wt%]
40
50
Figure 9. CAN/water correlation vs. technologies
Elemental analysis Figure 10 shows the distribution of carbon, hydrogen and nitrogen in poplar bio-oils. The carbon content ranges from 29.4 to 56.3 wt% on wet basis (w.b.) and from 55.9 to 62.0 wt% on dry basis (d.b.). The hydrogen contents on d.b. ranges from 6.15 to 7.69 wt%. The lower 12 Environment ACS Paragon Plus
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range is dominated by oils from single screw reactors (#6, #8, and #14). Nitrogen values ranged from 0.1 to 0.31 wt% on w.b. (0.2 to 0.28 wt% on d.b.).
hydrogen
nitrogen
60
0.35
50
0.30 0.25
40
0.20 30 0.15 20
0.10
10
Nitrogen [wt%]
Carbon, Hydrogen [wt%]
carbon
0.05
0
0.00 1
2
3
4
5
6
7
8 9 10 11 12 13 14 15 16 Laboratory
Figure 10. Carbon, hydrogen and nitrogen content (on w.b.) of poplar bio-oils Solids content and ash composition The solids content of poplar and blend oils is presented in Figure 11. 2.50
poplar
blend
2.00 Soldis (wt%)
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1.50
1.00
0.50
0.00 1
2
3
4
5
6
7
8 9 10 11 12 13 14 15 16 Laboratory
Figure 11 Solids (w.b.) in bio-oils from poplar and blend The solids loading in the bio-oils exhibits also an enormous range from 0.03 to 1.62 for poplar oil and from 0.02 to 2.24 for oils from the blended feedstock. The solids and ash loading in the bio-oil products is again drastically reduced by the use of the hot vapor filter (#2, #10, #12). The large variation in solids content is reflected in the inorganic elemental content, as shown in Figure 12. Sulfur has a significant presence in all the bio-oils produced from poplar, 13 Environment ACS Paragon Plus
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but the alkali metals and alkaline earths (the next most common components) vary widely. Note that the pyrolysis systems equipped with a hot-vapor filters produced bio-oils with significantly reduced alkali metal and alkaline earth contamination (Figure 13). Also the BFB reactors have the tendency to exhibit a higher amount of solids composed of alkali metals. 800 S
Ca
Mg
K
Na
700 600 500 400 300 200 100 0 1
2
3
4
5
6
7
8 9 10 11 12 13 14 15 16 Laboratory
Figure 12. Ash composition (w.b.) of poplar bio-oil 600
500
400 Alkali (ppm)
Ash elements (ppm)
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300
200
BFB BFB/EF + HVF
100
screw ablative
0 0.0
0.5
1.0 Solids (wt%)
1.5
Figure 13. Correlation of solid and alkali content (w.b.) vs. technology
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Pyrolytic lignin Apart from water and water-soluble components fast pyrolysis bio-oils also contain a waterinsoluble fraction (pyrolytic lignin, PL) that can be precipitated as a fine brown powder. It is derived from lignin as the product of rearrangements of monomeric and oligomeric phenolics. Further it is discussed in the literature that it might partly be produced through thermal ejection of lignin oligomers in the presence of the hot reactor.14, 28-31 The PL distribution is shown in Figure 14. The content varies from 3 to 29 wt% on w.b. (6 to 32 wt% on d.b.). 35
poplar
blend
30 Pyrolytic lignin (%)
25 20 15 10 5 0 1
2
3
4
5
6
7
8 9 10 Laboratory
11
12
13
14
15
16
Figure 14. Content of pyrolytic lignin (wt% on w.b.) in bio-oils from poplar and blend As pyrolytic lignin is the less degraded lignin, a higher degree of pyrolytic conversion results in a lower PL but a higher water content. Figure 15 illustrates the negative correlation between the water content and PL with a regression coefficient of determination of 0.89.
35 poplar
Linear (poplar)
R² = 0.8933
30 25 PyLignin (%)
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20 15 10 5 0 0
10
20
30 40 Water content (%)
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60
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Figure 15. Correlation between water content and pyrolytic lignin content (w.b.) of poplar bio-oils Another relationship is that between the viscosity of the bio-oil and the amount of pyrolytic lignin. As pyrolytic lignin resembles the higher molecular weight portion in bio-oils its content is positively correlated with the viscosity, as depicted in Figure 16 for poplar oil with a regression coefficient of 0.901. 25 R² = 0.9015 20 Viscosity 50 °C (cSt)
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15
10
5
0 5
8
11 14 PyLignin(%)
17
20
Figure 16. Poplar oil viscosity measured at 50 °C as a function of pyrolytic lignin content (wt% w.b.) Molecular weight of Py-lignin (PL) Figure 17 shows no obvious correlation between the PL content and its average weight molecular weight data. It seems that the processes with hot vapor filtration do not crack lignin fragments in the filter, as their values are in the center of all BFB data.
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BFB
BFB/EF + HVF
screw
ablative
3000
Dalton (g/mol)
2500 2000 1500 1000 500 0 0.0
5.0
10.0 15.0 20.0 25.0 PyLignin wt% dry basis
30.0
35.0
Figure 17. Correlation between PL content and average weight molecular weight An interesting positive linear correlation is observed between the GPC data from whole biooil vs. that of the PL fraction (Figure 18). This is an interesting result as tedious PL precipitation would be unnecessary to calculate the mean molecular weight of a PL based on GPC data of the whole oil using a graph such as depicted in Figure 18 as a calibration curve. It has to be pointed out that this correlation is valid when GPC is measured using only UV detector which detects only lignin. 3000 2500 Dalton PL (g/mol)
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2000 1500 1000 500 BFB
BFB/EF + HVF
screw
ablative
0 0
500
1000 Dalton bio-oil (g/mol)
1500
2000
Figure 18. Correlation between the average weight molecular weights of bio-oils vs. PL, regression coefficient of 0.88
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GC/MS GC/MS data were generated from all poplar oil samples. Comparison of the nonaromatic vs. mono-aromatic portion showed a negative linear correlation with a regression coefficient of 0.91 (Figure 19). This correlation is valid for all pyrolysis bio-oils tested. 70 BFB
BFB/EF + HVF
screw
ablative
60 50 Aromatics (%)
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40 30 20 10 0 40
45
50
55 60 Non-aromatics (%)
65
70
75
Figure 19. Correlation of nonaromatic fraction vs. aromatic fraction compared with the pyrolysis technologies Analytical Pyrolysis-GC/MS Analytical pyrolysis with GC detection was performed from all feedstocks in order to find correlations between the analytical approach and technical pyrolysis. As can be seen from Figure 20 yields determined with analytical pyrolysis (large marks) give a good indication about the composition of bio-oils. The graph combines the overall results of all pyrolysis biooils tested. Hence, analytical pyrolysis would be a useful tool to predict bio-oil composition. Calculation basis was on area % after normalization to 100 µg sample weight.
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nonaromatics
aromatics
carbohydrates
80 70
yield (wt%)
60 50 40 30 20 10 0 0
1
2
3
4
5 6 7 8 9 10 11 12 13 1: analytical pyrolysis; 2-17: laboratories
14
15
16
17
Figure 20. Yields of main chemical groups as obtained from analytical pyrolysis (large marks) and obtained in bio-oils from poplar Mass balance An attempt was made to establish a mass balance making use of all quantitative results from poplar bio-oils. Figure 21 summarizes the results and illustrates that the undetermined portion varies between 9.5 and 28.5 wt%. The undetermined portion could be assigned mainly to higher molecular weight sugars and other non-volatile components which were not detected with the methods used in this round robin testing. Total GC monomers [%] Py-Lignin [%]
100 Mass fraction (%)
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Energy & Fuels
Water content [%] Undetermined
80 60 40 20 0 1
2
3
4
5
6
7
8 9 10 11 12 13 14 15 16 Laboratory
Figure 21. Mass balance of poplar bio-oils from all participants CONCLUSIONS It is clear that all laboratory reactor systems for bio-oil production do not produce equivalent products. The mechanism of heat transfer used can impact the bio-oil composition through the time-temperature relationship. Also, the measurement of reaction temperature 19 Environment ACS Paragon Plus
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might be inaccurate. In addition, the product collection method can impact the product composition through either filtering out solid byproducts or through vapor separation as a result of collection of the liquid product at different temperatures. The huge data pool and the fact that all samples were analyzed by only one laboratory made it possible to determine correlations: • amounts of the mono-aromatics and non-aromatics in bio-oil negatively correlate as detected by GC/MS, • the mean molecular weight of the bio-oils correlates with that from pyrolytic lignin. • viscosity correlates with pyrolytic lignin content • water content negatively correlates with pyrolytic lignin content • viscosity correlates with water content Four parameters were able to discriminate the four technologies applied (bubbling bed, bubbling bed & entrained flow with hot vapor filtration, screw reactors, and ablative reactors). Water, solids, viscosity, and total acid number were related to the applied technology. Most negative results were obtained from auger (screw) reactors, whereas bubbling fluidized beds with hot vapor filtration showed the most positive bio-oil characteristics, mainly in terms of solids and ash content. The difference here might be explained by longer residence times in auger reactors. Another interesting observation was that none of the laboratories was able to produce a single phase bio-oil from wheat straw, they were all phase separated into an aqueous and a heavy tarry phase. This was hypothesized based on literature to be due to the high ash content of the straw decreasing oil yield and enhancing reactions causing water formation. The phase separation of the pyrolysis liquids from the blended feedstock was less pronounced. All biooils from hybrid poplar arrived as a single phase liquid and could be analyzed completely. Overall, the use of Round Robins is useful both for providing insights on differences between performance of laboratory and bench-scale pyrolysis units and helps the participants by allowing comparison of results with other laboratories. It must be pointed out that fast pyrolysis bio-oils are completely different from mineral oils or biodiesels. Special care has to be used in the proper product collection, handling and sampling of these bio-oils in order to ensure the homogeneity of the bio-oil. ACKNOWLEDGEMENTS The feedstocks preparation team at INL (Garold Gresham, Marnie Cortez, Tyler Westover, Neal Yancey, Kevin Kenney and Richard Hess) is acknowledged for their effort. All of the participating laboratories are acknowledged for their operation of their processing systems and preparation of bio-oil samples. The analytical team at Thünen Institute (Dietrich Meier, Anna-Lena Schleuß, Alina Forner, Silke Radtke, Ariane Hartmann, Patrick Eidam, Christian Geyer, and Ingrid Fortmann) is also gratefully acknowledged for their effort. Finally, all the funding agencies for the laboratories are also acknowledged, including the U.S. Department of Energy, Bioenergy Technologies Office under contract DE-AC06RLO 1830, for the lead author. 20 Environment ACS Paragon Plus
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Energy & Fuels
REFERENCES (1) IEA http://www.pyne.co.uk/?_id=18 (accessed Dec. 13, 2016). (2) Oasmaa, A.; Elliott, D. C.; Müller, S., Quality control in fast pyrolysis bio-oil production and use. Environmental Progress & Sustainable Energy 2009, 28 (3), 404-409. (3) Oasmaa, A.; van de Beld, B.; Saari, P.; Elliott, D. C.; Solantausta, Y., Norms, standards, and legislation for fast pyrolysis bio-oils from lignocellulosic biomass. Energy Fuels 2015, 29 (4), 2471-2484. (4) Elliott, D. C. Analysis and upgrading of biomass liquefaction products; Pacific Northwest Laboratory: Richland, Washington, USA, 1983. (5) Oasmaa, A.; Leppämäki, E.; Koponen, P.; J. and Tapola, E. L., Physical characterisation of biomass-based pyrolysis liquids: Application of standard fuel oil analyses. VTT: Espoo, Finland, 1997; Vol. 306, p 87. (6) Oasmaa, A.; Peacocke, C., A guide to physical property characterisation of biomass-derived fast pyrolysis liquids. Espoo, 2001; p 65. (7) Oasmaa, A. Fuel oil quality properties of wood-based pyrolysis liquids. Dissertation, University of Jyväskylä, 2003. (8) Oasmaa, A.; Peacocke, C., Properties and fuel use of biomass derived fast pyrolysis liquids - A guide. VTT: Espoo, 2010; p 79 + app. 46 p. (9) McKinley, J. W.; Overend, R. P.; Elliott, D. C. In The ultimate analysis of biomass liquefaction products: the results of the IEA round robin #1, Proceedings of Specialists Workshop on Biomass Pyrolysis Oil Properties and Combustion, Estes Park, CO, USA, Sept. 26-28; NREL, Ed. NREL paper CP-430-7215: Estes Park, CO, USA, 1994; pp 34-53. (10) Meier, D., New methods for chemical and physical characterization and round robin testing. In Fast Pyrolysis of Biomass: A Handbook, CPL Ltd., Newbury, UK, Czernik, S.; Diebold, J.; Meier, D.; Oasmaa, A.; Peacocke, C.; Piskorz, J.; Radlein, D.; Bridgwater, A. V., Eds. 1999; pp 92-101. (11) Oasmaa, A.; Meier, D., Norms and standards for fast pyrolysis liquids. 1. Round robin test. J. Anal. Appl. Pyrolysis 2005, 73, 323-334. (12) Elliott, D. C.; Oasmaa, A.; Preto, F.; Meier, D.; Bridgwater, A. V., Results of the IEA round robin on viscosity and stability of fast pyrolysis bio-oils. Energy Fuels 2012, 26, 3769-3776. (13) Elliott, D. C.; Oasmaa, A.; Meier, D.; Preto, F.; Bridgwater, A. V., Results of the IEA round robin on viscosity and aging of fast pyrolysis bio-oils: Long-term tests and repeatability. Energy Fuels 2012, 26 (12), 7362-7366. (14) Scholze, B.; Meier, D., Characterization of the water-insoluble fraction from fast pyrolysis liquids (pyrolytic lignin). Part I. Py-GC/MS, FTIR, and functional groups. J. Anal. Appl. Pyrolysis 2001, 60, 41-54. (15) Oasmaa, A.; Sundqvist, T.; Kuoppala, E.; García-Perez, M.; Solantausta, Y.; Lindfors, C.; Paasikallio, V., Controlling the phase stability of biomass fast pyrolysis bio-oils. Energy Fuels 2015, 29 (7), 4373-4381. (16) Oasmaa, A.; Solantausta, Y.; Arpiainen, V.; Kuoppala, E.; Sipila, K., Fast pyrolysis bio-oils from wood and agricultural residues. Energy Fuels 2010, 24, 1380-1388. (17) Venderbosch, R. H., A Critical View on Catalytic Pyrolysis of Biomass. ChemSusChem 2015, 8 (8), 1306-1316. (18) Paasikallio, V.; Lindfors, C.; Kuoppala, E.; Solantausta, Y.; Oasmaa, A.; Lehto, J.; Lehtonen, J., Product quality and catalyst deactivation in a four day catalytic fast pyrolysis production run. Green Chem. 2014, 16 (7), 3549-3559. (19) Lehto, J.; Oasmaa, A.; Solantausta, Y.; Kyto, M.; Chiaramonti, D. Fuel oil quality and combustion of fast pyrolysis bio-oils; 87; VTT Technical Research Centre of Finland: Espoo, Finland, 2013; p 79. 21 Environment ACS Paragon Plus
Energy & Fuels
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(20) Oasmaa, A.; Källi, A.; Lindfors, C.; Elliott, D. C.; Springer, D.; Peacocke, C.; Chiaramonti, D., Guidelines for transportation, handling, and use of fast pyrolysis bio-oil. 1. Flammability and toxicity. Energy Fuels 2012, 26 (6), 3864-3873. (21) Oasmaa, A.; Kuoppala, E.; Selin, J.-F.; Gust, S.; Solantausta, Y., Fast Pyrolysis of Forestry Residue and Pine. 4. Improvement of the Product Quality by Solvent Addition. Energy Fuels 2004, 18 (5), 1578-1583. (22) Czernik, S.; Bridgwater, A. V., Overview of applications of biomass fast pyrolysis oil. Energy Fuels 2004, 18 (2), 590-598. (23) Johansson, A. C.; Wiinikka, H.; Sandstrom, L.; Marklund, M.; Ohrman, O. G. W.; Narvesjo, J., Characterization of pyrolysis products produced from different Nordic biomass types in a cyclone pilot plant. Fuel Process. Technol. 2016, 146, 9-19. (24) Sundqvist, T.; Oasmaa, A.; Koskinen, A., Upgrading Fast Pyrolysis Bio-Oil Quality by Esterification and Azeotropic Water Removal. Energy Fuels 2015, 29 (4), 25272534. (25) French, R. J.; Stunkel, J.; Black, S.; Myers, M.; Yung, M. M.; Iisa, K., Evaluate Impact of Catalyst Type on Oil Yield and Hydrogen Consumption from Mild Hydrotreating. Energy Fuels 2014, 28 (5), 3086-3095. (26) Capunitan, J. A.; Capareda, S. C., Characterization and separation of corn stover bio-oil by fractional distillation. Fuel 2013, 112, 60-73. (27) Ferrell, J. R., III; Olarte, M. V.; Christensen, E. D.; Padmaperuma, A. B.; Connatser, R. M.; Stankovikj, F.; Meier, D.; Paasikallio, V., Standardization of chemical analytical techniques for pyrolysis bio-oil: history, challenges, and current status of methods. Biofpr 2016, 10, 496-507. (28) Scholze, B.; Hanser, C.; Meier, D., Characterization of the water-insoluble fraction from fast pyrolysis liquids (pyrolytic lignin). Part II. GPC, carbonyl groups, and 13CNMR. J. Anal. Appl. Pyrolysis 2001, 58-59, 387-400. (29) Bayerbach, R.; Nguyen, V. D.; Schurr, U.; Meier, D., Characterization of the water-insoluble fraction from fast pyrolysis liquids (pyrolytic lignin) Part III. Molar mass characteristics by SEC, MALDI-TOF-MS, LDI-TOF-MS, and Py-FIMS. J. Anal. Appl. Pyrolysis 2006, 77, 95-101. (30) Bayerbach, R.; Meier, D., Characterization of the water-insoluble fraction from fast pyrolysis liquids (pyrolytic lignin) Part IV. Structure elucidation of oligomeric molecules J. Anal. Appl. Pyrolysis 2008, 85, 98-107. (31) Fratini, E.; Bonini, M.; Oasmaa, A.; Solantausta, Y.; Teixeira, J.; Baglioni, P., SANS analysis of the microstructural evolution during the aging of pyrolysis oils from biomass. Langmuir 2006, 22 (1), 306-312.
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Energy & Fuels
Supporting Information—three tables of analytical results derived from the bio-oil samples provided in the Round Robin Supplemental Table 1. Round Robin Data from the 16 Participating Laboratories for Hybrid Poplar Bio-oil (w.b.) Density [g/cm³] Viscosity @ 20°C [cSt] Viscosity @ 50°C [cSt] ratio Aging: Viscosity @ 20°C [cSt] Aging: Viscosity @ 50°C [cSt] Water content [%] TAN [mg KOH/g] Ash content in oil @ 775°C [%] C [%] H [%] N [%] Py-Lignin [%] Solids [%] Na [ppm] K [ppm] Mg [ppm] Ca [ppm] S [ppm] GPC bio-oil [g/mol] GPC py-lignin [g/mol]
1 1.181
2 1.190
3 1.159
4 1.212
5 1.145
6 1.132
7 1.163
8 1.1
9 1.195
10 1.149
11 1.202
12 1.176
13 1.199
14 1.077
15 1.147
16 1.121
61.52
143.0
25.4
too high
55.7
5.7
40.4
n/a
93.2
25.4
81.7
55.7
140.1
n/a
24.5
n/a
10.9
19.2
6.1
n/a
n/a
2.4
9.0
n/a
14.1
5.9
14.1
9.9
19.5
n/a
7.0
n/a
5.6 83.9
7.4 Precipitation
4.2 9.6
n/a
n/a
2.3 n/a
4.5 n/a
n/a
6.6 n/a
4.3 9.8
5.8 240.2
5.7 n/a
7.2 189.02
n/a
3.5 n/a
n/a
16.6
Precipitation
Precipitation
n/a
n/a
n/a
n/a
n/a
n/a
3.3
28.7
n/a
24.413
n/a
n/a
n/a
28.6
18.1
32.1
9.4
27.3
40.9
29.5
47.4
21.0
31.4
22.4
24.3
22.85
45.27
29.43
51.43
85.6
65.5
133.6
55.1
71.8
89.1
85.5
99.4
89.9
83.3
95.2
75.5
82
110.9
69.4
79.4
0.112
0.000
0.024
0.1730
0.000
0.2855
0.000
0.000
0.0422
0.0
0.000
0.0087
0
0.3608
0.000
0.000
40.40 7.81 0.14 17.52 0.51 33.0 217.6 27.0 124.3 67.3 1641
49.01 7.55 0.20 19.01 0.08 14.7 0.9 0.5 3.8 63.3 919
37.87 8.25 0.12 12.23 0.36 5.1 66.3 9.2 35.7 95.1 901
56.24 7.16 0.31 28.68 1.62 239.5 176.1 29.3 123.1 115.5 918
43.65 8.40 0.20 20.59 0.514+ 55.2 107.4 25.2 79.2 125.1 1032
32.5 8.5 0.1 9.40 0.5 47.1 37.3 46.7 173.1 123.3 1631
40.0 8.1 0.1 15.93 0.818 12.0 112.1 21.9 90.4 106.5 1136
29.4 9.14 n/a 2.99 0.347+ 6.9 5.4 1.3 17.1 95.3 935
44.8 7.7 0.1 15.99 0.416+ 11.0 21.2 4.7 106.3 90.2 965
39.2 8.3 0.1 11.75 0.0 2.2 16.6 7.3 28.5 87.1 984
43.3 7.7 0.2 16.11 0.500 6.7 105.2 6.5 28.5 86.6 1591
43.6 8.0 0.2 16.57 0.076 2.8 17.0 0.3 1.1 78.6 971
44.0 7.4 0.2 18.3 0.114 4.2 29.7 6.0 19.5 137.1 1440
31.7 9.0 0.0 3.9 0.325+ 5 0.8 0.7 3.2 143.5 649
40.8 8.7 0.2 13.73 0.025+ 5.1 8.5 36.5 2.8 203.2 797
n/a n/a n/a 3.62 0.247 22.8 175.2 34.2 98.2 87.1 954
2422
1442
1389
1479
1760
2133
1597
1278
1480
1626
2507
1746
2162
1237
1131
1682
23 ACS Paragon Plus Environment
Energy & Fuels
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Supplemental Table 2. Round Robin Data from the 16 Participating Laboratories for Wheat Straw Bio-oil (w,b.) Density [g/cm³] Viscosity @ 20°C [cSt] Viscosity @ 50°C [cSt] Aging: Viscosity @ 20°C [cSt] Aging: Viscosity @ 50°C [cSt] Water content [%] TAN [mg KOH/g] Ash content in oil @ 775°C [%] C [%] H [%] N [%] Py-Lignin [%] Solids [%] Na [ppm] K [ppm] Mg [ppm] Ca [ppm] S [ppm] GPC bio-oil [g/mol]
1 1.134
2 1.087
3 1.122
4 n/a
5 n/a
6 n/a
7 n/a
8 n/a
9 1.097
10 1.026
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
71.6
74.4
n/a
n/a
0.3
11 no sample
12 1.043
13
14
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
22
n/a
71.4
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
n/a
2.3
n/a
29 9.10 0.49 5.220
n/a n/a n/a n/a
n/a n/a n/a n/a
n/a n/a n/a n/a
n/a n/a n/a n/a
51.76 9.11 0.64 n/a
n/a 8.75 0.72 n/a
17 10.28 0.23 n/a
n/a n/a n/a n/a
n/a n/a n/a 10.61
n/a n/a n/a n/a
n/a n/a n/a n/a
n/a n/a n/a n/a
0.24 40.5 1289.0 43.4 103.9 403.0 2115
3.4 1.1 0.2 1.7 361.0 1261
9.9 471.3 29.7 110.7 521.8 1069
n/a n/a n/a n/a n/a n/a 1572
n/a n/a n/a n/a n/a n/a 1232
21.9 794.7 97 374.2 875.7 1770
3.035 22.8 1208.5 106.9 337.6 737.5 2049
0.008 8.8 0.8 0.6 7.8 270.5 2092
15 203.3 7 132.6 320.3 1335
10.6 725.3 82.6 445.3 922.1 1600
2 2.3 0.1 1 678.2 1869
5 55.1 7.3 24.1 1132.5 2437
4.9 46.7 3.5 11.9 692.7 1777
24 ACS Paragon Plus Environment
15 Two fractions
11.58/65.44
16 no sample
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Energy & Fuels
Supplemental Table 3. Round Robin Data from the 16 Participating Laboratories for Blended Feedstock Bio-oil (w.b.) Density [g/cm³] Viscosity @ 20°C [cSt] Viscosity @ 50°C [cSt] ratio Aging: Viscosity @ 20°C [cSt] Aging: Viscosity @ 50°C [cSt] Water content [%] TAN [mg KOH/g] Ash content in oil @ 775°C [%] C [%] H [%] N [%] Py-Lignin [%] Solids [%] Na [ppm] K [ppm] Density [g/cm³] Mg [ppm] Ca [ppm] S [ppm] GPC bio-oil [g/mol]
1 1.171
2 1.159
3 1.151
4 1.117
5 1.116
6 1.157
7 1.132
8 1.053
9 1.171
10 no sample
11 1.187
12 1.130
13 1.169
14 1.075
15 2-fractions
16 1.135
32.7
74.3
8.4
n/a
n/a
n/a
n/a
n/a
84.7
57.1
n/a
59.4
n/a
2-fractions
n/a
8.3
11.2
3.7
n/a
n/a
n/a
n/a
n/a
13.4
10.6
n/a
13.3
n/a
2-fractions
n/a
3.9 35.2
6.6 Precipitation
2.3 Precipitation
n/a
n/a
n/a
n/a
n/a
6.3
5.4 n/a
n/a
2-fractions
n/a
5.9
Precipitation
Precipitation
n/a
n/a
n/a
n/a
n/a
n/a
n/a
2-fractions
n/a
29.7
24.5
34.7
17.3
27.2
36.1
26.4+
60.4
21.51
26.56
34.3
28.46
46.3+
17.48/55.54
28.05
87.5
67.8
125.1
67.2
60.5
n/a
63.7
89.4
85.8
86.6
60.6
74.6
n/a
2-fractions
58.6
0.110
0.000
0.000
0.025
0.0406
0.5567
0.123
0
0.000
0.0406
0
0.000
0.000
2-fractions
0.1597
39.10 8.10 0.18 14.70
45.73 8.01 0.32 18.10
37.77 8.31 0.17 11.18
n/a n/a n/a n/a
n/a n/a n/a n/a
n/a 8.31 0.22 24.43+
n/a 8.47 0.28 n/a
22.7 9.75 n/a 2.13+
45.5 7.8 0.2 17.81
41.4 7.9 0.2 15.26
40.48 8.52 0.25 14.05
41.8 7.8 0.2 18.32
40.1 8.8 0.2 n/a
2-fractions 2-fractions 2-fractions 2-fractions
47.7 8.3 0.3 31.47+
0.41 24.8 501.6 1.171
0.36 12.1 1.0 1.159
0.42 6.2 39.8 1.151
0.67 n/a n/a 1.117
0.47 n/a n/a 1.116
1.570+ 21.6 381.9 1.157
2.244 12 280.5 1.132
0.0200 6.4 0.7 1.053
0.204 12.1 81.7 1.171
0.578+ 6.7 192.5 1.187
0.065+ 4 13.4 1.130
0.028 5.3 39.6 1.169
0.225+ 4.6 8.3 1.075
2-fractions 2-fractions 2-fractions 2-fractions
1.086 13 147.8 1.135
21.7 77.9 91.9 1369
0.4 2.5 101.1 974
11.1 43.5 76.2 893
n/a n/a n/a 878
n/a n/a n/a 915
78.5 327 338.4 1542
53.7 168.9 332.5 1495
0.6 9.6 135.5 634
7.2 67.2 125 984
6.9 30.4 110.3 1491
0.5 1.6 129.7 1193
5.3 13.1 275.2 1194
2 7.2 194.6 995
2-fractions 2-fractions 2-fractions 2-fractions
35.6 103 213 1216
25 ACS Paragon Plus Environment
4.5
