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The correlation of feedstock and bio-oil compounds distribution Jian Li, Yingquan Chen, Haiping Yang, Danchen Zhu, Xu Chen, Xianhua Wang, and Hanping Chen Energy Fuels, Just Accepted Manuscript • Publication Date (Web): 24 May 2017 Downloaded from http://pubs.acs.org on May 29, 2017

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Title The correlation of feedstock and bio-oil compounds distribution Author names and affiliations Jian Lia, Yingquan Chena, Haiping Yanga*，Danchen Zhua, Xu Chena, Xianhua Wanga, Hanping Chena,b a

State Key Laboratory of Coal Combustion, School of Power and Energy Engineering,

Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, PR China b

Department of New Energy Science and Engineering, School of Power and Energy

Engineering ,University of Science and Technology, Wuhan, Hubei 430074, PR China *Corresponding Author: Name: Haiping Yang Tel.: +86 027 87542417x8211 (H. Yang) Email addresses: [email protected] (H. Yang), Postal address: State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, PR China (H. Yang). Name: Yingquan Chen Tel.: 086+027-87542417-8211 Email addresses: [email protected], Postal address: State Key Laboratory of Coal Combustion, Huazhong University of Science and Technology, 1037 Luoyu Road, Wuhan, Hubei 430074, PR China.

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Abstract: The correlation of feedstock with bio-oil compounds distribution was explored with straw, shell, woody and algae biomass. The pyrolysis of 20 typical feedstock samples were performed using Pyrolysis gas chromatography mass spectrometry (Py-GC/MS) with final temperature of 550℃, heating rate of 10000℃/s and residence time of 10s. Biomass samples share a common characteristic such as high oxygen, low nitrogen and sulfur, which is different from coal. The results revealed that there is a strong positive correlation between ketones and cellulose, furans and holocelluose, phenols and lignin, but negative correlation between short-chain acids and ash content, hydrocarbons and cellulose. Woody biomass produced higher phenols, straw biomass produced high ketones, shell biomass produced high furans while algae produced high fatty acids. However some special points showed as enteromorpha algae produced high furans and tobacco stems produced high N-contained compounds. Keywords: biomass, pyrolysis, bio-oil, feedstock, Py-GC/MS 1. Introduction Biomass is the only carbon neutral renewal energy that can be transformed to liquid fuel 1-3. Many processes that convert biomass to liquid fuels begin with pyrolysis, followed by catalytic upgrading of the resulting crude liquid4-6. Pyrolysis is an environmentally friendly technology which could transformed biomass to bio-oil efficiently2, 7. Over two hundred kinds of compounds were detected in bio-oil8 and some of these


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compounds could be used as substitutes for conventional fuel and high-value chemicals. However the bio-oil composition was influenced by temperature, residence time, sample size, reactor type9-13. At the same time, biomass feedstock play a key role in bio-oil production process 14-16. So it is interesting to explore what should we produce from variant biomass feedstock. We have traced back the previous researches, trying to find out the correlation of feedstock with the property of bio-oil and the results are listed in Table 1. It could be found that cellulose, hemicellulose and lignin showed a clear product tendency17-20. Cellulose pyrolysis products were mainly anhydrosugars, hemicellulose pyrolysis products were mainly acids, while lignin pyrolysis products were mainly phenols. However, the product tendency of mature biomass is still unclear. Furthermore, the quantitative correlation of pyrolysis product with biomass is rarely reported. Hence, a large sample size (20 typical biomass material containing agricultural wastes, forestry wastes and aquatic algae) was involved to explore the feedstock characteristic as well as the bio-oil composition. Simultaneously, the quantitative correlation between bio-oil feedstock and bio-oil compounds composition was established. It is believed that this study would deep the understanding of biomass material and bio-oil production. 2. Material and methods 2.1. Materials The 20 typical biomass used in this study contains 8 straw samples (peanut straw, soybean straw, cornstalk, sesame stalk, rice straw, tobacco stem, wheat straw and


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cotton stalk), 4 shell samples (peanut shell, rice husk, corncob and rape pod), 4 woody samples(camphor tree, eucalyptus, bamboo and sapele) and 4 algae samples(bloom algae, enteromorpha algae, spirulina and chlorella). Theses biomass were collected in Central China, including Hubei province, Hunan Province and Henan province. Before analysis they were dried and ground to 0.2-0.5 mm. The proximate and ultimate compositions were measured with an SDTGA-2000 analyzer (Navas Instruments, Spain) and a Vario EL II elemental (CNHS/O) analyzer (Germany). The main inorganic elements were analysed by X-Ray fluorescence (XRF, EAGLE III, EDAX Inc). The cellulose, hemicellulose and lignin content were measured with the ANKOM 2000 fiber analyzer (Macedon, NY, USA). The fiber analysis was analyzed following American Oil Chemists’ Society (AOCS) Ba 6a-05 (13). Solid biomass samples (~0.2 g) were dried at 105 ℃ for 24 h, then it was digested with neutral detergent to determine the neutral detergent fiber (NDF). After that, NDF was digested with 1 N sulfuric acid detergent determined acid detergent fiber (ADF), and subsequent digestion in 72% sulfuric acid gave a value for acid detergent lignin (ADL). The weight percentage extracted by neutral detergent (NDF) is considered to be aqueous soluble, it was recorded as extractives. The difference of the NDF–ADF percentage yields the percentage of hemicelluloses, while ADF–ADL is considered to be the percentage of cellulose. The weight remaining after the ADL procedure is considered to be lignin plus ash. Fiber analysis is generally accurate ±5%. The pyrolysis of biomass sample was performed in a fast pyrolyzer (CDS pyroprobe


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5250). The weight of sample was strictly controlled to 0.30 mg. The sample and quartz wool were loaded in the quartz tube like a sandwich, and biomass samples was located in the middle with quartz wool outside. In this study, the pyrolysis temperature was set at 550℃，the heating rate was 10000℃/s and the residence time was 10s. The residence time here in means char residence time but not for volatile vapors, it is less than a second for volatile residence time. The pyrolysis vapor was swept to gas chromatography mass spectrometry (GC/MS) promptly for species analyzing. GC/MS is Agilent HP7890 series GC) equipped with a HP-5 column (length: 30 m, internal diameter: 250µm, film thickness: 0.25µm) and a HP5975 MS detector. The injector temperature was kept at 280℃. During vapors analysis, the oven temperature was initially maintained at 40℃ for 2min, then increased to 200℃ at a heating rate of 5℃/min, then increased to 280℃ at heating rate of 10℃/min, then held for another 2 min. The mass spectrometer was operated in electron ionization (EI) mode at 70 eV. The mass spectra were obtained from m/z 20 to 400 with the scan rate of 500 Da/s. Helium (99.999%) was used as the carrier gas with a constant flow rate of 1mL/min and a split ratio of 1:80. The chromatographic peak was identified according to the NIST MS library and literature data of previous studies31. It need to pointed out the compounds in 17.26 min should be 4-vinyl phenol, rather than 2,3-dihydro-benzofuran32. Each sample was tested for at least 3 trials to confirm the repeatability. The average value of peak area% were used for analysis and the standard deviation values were also calculated. It is known that the GC/MS technique could not give the quantitative


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analysis of each compound33. However, the peak area of a compound is considered linear with its quantity, and the peak area% is linear with its content. Therefore, the change of peak area% revealed the selectivity change and the change of peak area reveled the yield change31, 34. 3. Results and discussion 3.1 Characteristic of biomass samples Table 2 lists the proximate and ultimate results of 20 biomass samples. Most biomass samples contained high volatile content but low fixed carbon as seen in Table 2. The H/C - O/C diagram of biomass composition were plotted in Fig.1 with typical coal samples as comparison. Comparing with coal, biomass samples have a high H/C ratio and O/C ratio, as the high H and O content, thus it leading to the lower heating value of biomass in comparison with coal35. However, the higher oxygen in biomass make it possible to produce high valued oxygen-containing compounds such as furfural and levoglucosanone36. Biomass is environmental friendly energy resources for most samples containing lower nitrogen and sulfur, and it might be a potential clean alternative to the fossil fuels. But algae sample showed different property, the spirulina and chlorella contained higher nitrogen content. It may cause potential environmental issue, so special attention should be paid to the two kinds of biomass during the conversion and utilization process. With respected to variant origin, feedstock showed different property. The woody biomass showed high volatile and low ash characteristic, as all woody biomass samples contained volatile higher than 80% and ash lower than 6%. Except for the


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feedstock type, the growth environment, harvesting way and transport type would also influence the ash content

37

. The algae biomass contained higher ash and nitrogen

content. The bloom algae and enteromorpha algae contained more than 30% ash and the spirulina and chlorella contained more than 5% of nitrogen. Since the calcium and potassium in biomass ash were believed to have a catalyst effect on biomass pyrolysis 38, the main inorganic species of biomass were tested and the results were listed in Table S1 as oxides. It could be found that the straw and shell contains high K2O and it mainly because the agricultural plant absorbed much K-rich fertilizer. The rice husk ash contained as high as 93.36% of SiO2. The woody biomass contained high CaO. The algae ash contained higher Cl as they grown in Cl salt rich water. The fiber analysis result is plotted in Fig.2 and Table 1s. It could be seen that the straw, shell and wood biomass contained high cellulose, hemicellulose and lignin while low extracts. Cellulose, hemicellulose and lignin covers up to 70% of most samples. While the tobacco stems show special property with high extracts. And the algae biomass contained higher lipid extracts and hemicellulose, but lower cellulose and lignin. 3.2 The bio-oil distribution The typical GC/MS total ion chromatography for biomass fast pyrolysis vapors is shown in Fig.3 and the corresponding peaks, time, chemical names, and molecular weight are listed in Table S2. Over 150 peaks were detected by GC/MS, only 29 peaks from 1.8min to 30min and the area percent more than 0.5% were judged to be crucial


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and selected for analysis. According to the functional group, these compounds were divided into chemical groups, namely furans (furfural, 5-hydroxymethylfurfural), ketones (hydroxy-acetaldehyde, 1-hydroxy-2-propanone, 2-propanone), short-chain acids (acetic acids)/fatty acids, phenols (2-methoxy-phenol, 2,6-dimethoxy-phenol, 2-methoxy-4-vinylphenol, creosol), hydrocarbons (hexadecane), esters (acetic acid methyl

ester),

cyclopentenes

(2-cyclopenten-1-one,

2-hydroxy-)

and

n-compounds(2,2-diethyl-3-methyl- oxazolidine). Fig.4 shows bio-oil distribution for different type biomass. It can be found that ketones and short chain acids were the main compounds of straw biomass pyrolysis. For straw sample, the ketone content is quite high, and it is over 20%, especially for rice stalk, which reaches ~40%. It might be attributed to the high cellulose and hemicellulose content in straw samples

34

. The short chain acid for peanut straw,

soybean straw, sesame straw and cotton stalk were about 20%, it might explain the low pH value and strong corrosive in bio-oil

39

. Higher furan derivatives (19.46%)

was achieved for cornstalk, as the higher holocellulose content in cornstalk particles, while phenol part reached ~30% for cotton stalk. But, tobacco stems showed very different property for producing ~45% N-contained compounds. It might mainly derived from the cracking of nicotine in tobacco residue 40. With respected to shell samples, similar compounds showed for bio-oil that of straw but higher phenols (~20%) content. And this may be caused by the similar composition of shell and straw biomass, while the higher phenols was attributed to the higher lignin content 41. The bio-oil distribution of woody biomass is shown in Fig.4


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(c). The main components are still phenols, ketones and acids, however, the woody biomass produced the highest phenols, it might be attributed to the highest lignin content in woody biomass. For eucalyptus, the phenol content was higher up to 31%, it is quite high. It is worth to noting that the woody bio-oil contains higher N-compounds, but not many nitrogen was shown for woody material, it need to confirm the potential mechanism in the coming future. Comparing with other type of samples, algae biomass showed very different property of pyrolysis bio-oil and fatty acids were the main components. Most algae samples produced higher than 30% of fatty acids and it even reached 45% for bloom algae. It may be caused by the high lipid in algae and their decomposition attributed to the high fatty acids. There were also some hydrocarbon, which might be attributed to cracking of fatty acids. The enteromorpha (EP) algae showed higher furan yield (28%), it might be attributed to the higher Cl and P content showed in EP sample 42. 3.3 The correlation of bio-oil property with the biomass The composition of bio-oil vary with biomass feedstock, however, it showed strong relationship with the material property. The correlation was analyzed with linear fit method, and the correlation coefficient at P-values at significance level of 0.05 was listed in Table 3. The absolute value higher than 0.7 means that there is an obvious linear correlation, and the corresponding formula could be used to predict the bio-oil composition. When it is between 0.5 and 0.7 means that there is a correlation but may not be linear correlation, when it is below 0.5 means the correlation is not obvious. It could be found that phenols vs lignin, short-chain acids vs ash, hydrocarbons vs


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cellulose show an obvious linear correlation. The ketones vs cellulose and furans vs holocellulose have a correlation but they may not be linear correlation. However, the other compounds such as the cyclopendentes and esters show a weak correlation with the material characteristic. The specific correlation of several compounds is plotted in Fig.5. In Fig.5 (a), the phenols vs lignin shows a positive linear correlation. The phenols increased linearly with the lignin increasing. And it indicates that these phenols may formed by the lignin cracked

43

. The base composition of lignin were syringyl, guaiacyl and

p-hydroxylphenyl units. And they were connected by the C-O-C bond. According to our previous lignin pyrolysis mechanism study

44

, their energy barrier was relative

low and the bata-O-4 bond is easily to break with phenols evolved out. Although containing low lignin content, the corn stalk, rice straw and wheat straw also produced high phenols. It might be attributed by the higher cellulose content (~40%) and the small molecular polymerization coming from the cellulose decomposition may also produce phenols 45. Ketones shows a correlation with cellulose, but no correlation with hemicellulose and lignin, and the correlation is plotted in Fig.5 (b). The ketones increased with the cellulose increasing. It might related to the cracking pathway of cellulose, as the bond between the C2 and C3 is longer than the other bonds of the glucose ring so it is easy to break down45. Hence, the glucose ring is easy to break at C1–O and C2–C3, with two-carbon (C1-C2) and four-carbon (C3–C6) fragments formed. The two carbon fragment would form the hydroxyl-acetaldehyde, while the four carbon fragment


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might form the hydroxyl-acetone firstly, then acetaldehyde. However, the four kinds of woody biomass containing high cellulose produce relative lower ketones. This might because of the lower ash content in the woody biomass, while the ash play as a catalyst and promote ring open reaction of glucose 38. No obvious correlation is found between short-chain acids and biomass fiber components (cellulose, hemicellulose and lignin). It indicated that short-chain acids are mainly be formed from the secondary pyrolysis of biomass volatile, and it is difficult to predict the content according biomass main composition. However, a strong relation is found between short-chain acids and ash content, and the yield decreased with ash content increasing as is shown in Fig.5 (c). Acetyl was believed to be precursor of acetic acid. During biomass pyrolysis the acetyl would easy to form acetic acids or carbon dioxide by deacetylation. The alkali metal in biomass ash has a catalysis effect to promote deactylation to form carbon dioxide. In this way, high ash content leads to acids decrease46, 47. The correlation of furans vs cellulose, hemicellulose and lignin were not obvious, but it with holocellulose were obvious (0.61). And the specific correlation is plotted in Fig.5 (d). The furans increased linearly with the holocellulose content. Matthew et al 48 pointed out that furans come from glucose directly without any intermediate using both CPMD simulation and thin-film cellulose pyrolysis. Binder et al49 pointed as a kind of hemicellulose composition, hexose is a sources of 5-hydroxymethylfurfural. So high amount holocellulose leads to high furans, since both cellulose and hemicellulose is possible to form furans. The hydrocarbons didn’t show positive relation with biomass


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components, but showed positive correlation with extracts (0.75). It means that the hydrocarbons was not formed by the three main compounds decomposition directly, but it may be formed extracts cracking48. The algae produce high hydrocarbons, since it contains higher grace50. The correlation coefficient of N-compounds vs nitrogen content was 0.62 and it was plotted in Fig.5 (f). The N-compounds increased with the nitrogen content increasing. However, most biomass material contained trace nitrogen, and it caused the low content of N-compounds in bio-oil. While, for algae sample, the N content was relatively high, and the N-compounds was about 20%. But tobacco stem showed very special point as the only 3%, but the N-compounds in liquid oil was up to 45%. It mainly becaused that high nicotine content in tobacco stem, which cracked and released out during the pyrolysis process

51

. Our former research52 has

pointed out nitrogen in biomass was exist as protein around 70%, the rest of them was as nitrate. These protein was easy to produce interactive reaction with other composition to produce N-containing compounds during pyrolysis. 4. Conclusion Strong correlation was found between the bio-oil compounds and biomass property. The phenols increased linearly with lignin content increasing, the ketones increased with the cellulose content increasing, the furans increased with the holocellulose content increasing and the N-compounds increased with the nitrogen content increasing. However, the short-chain acids decreased linearly with ash content increasing and the hydrocarbons decreased linearly with the cellulose increasing. The woody biomass produced high phenols, the straw biomass produced high ketones, the


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shell biomass produced high furans and the algae biomass produced high fatty acids. Specially, the enteromorpha algae produced high furans while the tobacco stems produced high N-compounds. Acknowledgements The authors wish to express their great appreciation of the financial support from the National Basic Research Program of China (973 Program: 2013CB228102), the National Nature Science Foundation of China (51622604 and 51406061), the Special Fund for Agro-scientific Research in the Public Interest (201303095), the Fundamental Research Funds for the Central Universities and the technical support from Analytical and Testing Center in Huazhong University of Science & Technology (http://atc.hust.edu.cn). Appendix A. Supplementary maerial：： Supplementary data associated with this article can be found, in the online version. Reference 1.

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23. Chen, D.; Li, Y.; Cen, K.; Luo, M.; Li, H.; Lu, B., Pyrolysis polygeneration of poplar wood: Effect of heating rate and pyrolysis temperature. Bioresource technology 2016, 218, 780-788. 24. Mullen, C. A.; Boateng, A. A., Chemical composition of bio-oils produced by fast pyrolysis of two energy crops. Energy Fuels 2008, 22, (3), 2104-2109. 25. Mani, T.; Murugan, P.; Mahinpey, N., Pyrolysis of oat straw and the comparison of the product yield to wheat and flax straw pyrolysis. Energy & Fuels 2011, 25, (7), 2803-2807. 26. Yanik, J.; Kornmayer, C.; Saglam, M.; Yüksel, M., Fast pyrolysis of agricultural wastes: Characterization of pyrolysis products. Fuel Processing Technology 2007, 88, (10), 942-947. 27. Maddi, B.; Viamajala, S.; Varanasi, S., Comparative study of pyrolysis of algal biomass from natural lake blooms with lignocellulosic biomass. Bioresource technology 2011, 102, (23), 11018-11026. 28. Chen, Y.; Yang, H.; Wang, X.; Chen, W.; Chen, H., Biomass pyrolytic polygeneration system: Adaptability for different feedstocks. Energy & Fuels 2016, 30, (1), 414-422. 29. Mullen, C. A.; Boateng, A. A.; Hicks, K. B.; Goldberg, N. M.; Moreau, R. A., Analysis and comparison of bio-oil produced by fast pyrolysis from three barley biomass/byproduct streams. Energy & Fuels 2009, 24, (1), 699-706. 30. Oasmaa, A.; Solantausta, Y.; Arpiainen, V.; Kuoppala, E.; Sipilä, K., Fast pyrolysis bio-oils from wood and agricultural residues. Energy & Fuels 2009, 24, (2), 1380-1388. 31. Lu, Q.; Yang, X.-c.; Dong, C.-q.; Zhang, Z.-f.; Zhang, X.-m.; Zhu, X.-f., Influence of pyrolysis temperature and time on the cellulose fast pyrolysis products: Analytical Py-GC/MS study. Journal of Analytical and Applied Pyrolysis 2011, 92, (2), 430-438. 32. Qu, Y.-C.; Wang, Z.; Lu, Q.; Zhang, Y., Selective production of 4-vinylphenol by fast pyrolysis of


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herbaceous biomass. Industrial & Engineering Chemistry Research 2013, 52, (36), 12771-12776. 33. Rencoret, J.; del Río, J. C.; Nierop, K. G.; Gutiérrez, A.; Ralph, J., Rapid Py-GC/MS assessment of the structural alterations of lignins in genetically modified plants. Journal of Analytical and Applied Pyrolysis 2016. 34. Wang, S.; Guo, X.; Liang, T.; Zhou, Y.; Luo, Z., Mechanism research on cellulose pyrolysis by Py-GC/MS and subsequent density functional theory studies. Bioresource Technology 2012, 104, 722-728. 35. Moghtaderi, B.; Meesri, C.; Wall, T. F., Pyrolytic characteristics of blended coal and woody biomass. Fuel 2004, 83, (6), 745-750. 36. Petrus, L.; Noordermeer, M. A., Biomass to biofuels, a chemical perspective. Green chemistry 2006, 8, (10), 861-867. 37. Vassilev, S. V.; Baxter, D.; Andersen, L. K.; Vassileva, C. G., An overview of the composition and application of biomass ash. Part 1. Phase–mineral and chemical composition and classification. Fuel 2013, 105, 40-76. 38. Patwardhan, P. R.; Satrio, J. A.; Brown, R. C.; Shanks, B. H., Influence of inorganic salts on the primary pyrolysis products of cellulose. Bioresource technology 2010, 101, (12), 4646-4655. 39. Xiu, S.; Shahbazi, A., Bio-oil production and upgrading research: A review. Renewable and Sustainable Energy Reviews 2012, 16, (7), 4406-4414. 40. Peng, C.; Zhang, G.; Yue, J.; Xu, G., Pyrolysis of lignin for phenols with alkaline additive. Fuel Processing Technology 2014, 124, 212-221. 41. Shen, D.; Gu, S., The mechanism for thermal decomposition of cellulose and its main products. Bioresource technology 2009, 100, (24), 6496-6504.


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42. Shen, D.; Gu, S.; Luo, K.; Wang, S.; Fang, M., The pyrolytic degradation of wood-derived lignin from pulping process. Bioresource technology 2010, 101, (15), 6136-6146. 43. Piskorz, J.; Radlein, D.; Scott, D. S., On the mechanism of the rapid pyrolysis of cellulose. Journal of Analytical and Applied pyrolysis 1986, 9, (2), 121-137. 44. Chen, L.; Ye, X.; Luo, F.; Shao, J.; Lu, Q.; Fang, Y.; Wang, X.; Chen, H., Pyrolysis mechanism of β O 4 type lignin model dimer. Journal of Analytical and Applied Pyrolysis 2015, 115, 103-111. 45. Fahmi, R.; Bridgwater, A. V.; Donnison, I.; Yates, N.; Jones, J., The effect of lignin and inorganic species in biomass on pyrolysis oil yields, quality and stability. Fuel 2008, 87, (7), 1230-1240. 46. Wang, S.; Guo, X.; Wang, K.; Luo, Z., Influence of the interaction of components on the pyrolysis behavior of biomass. Journal of Analytical and Applied Pyrolysis 2011, 91, (1), 183-189. 47. Kabel, M. A.; van den Borne, H.; Vincken, J.-P.; Voragen, A. G.; Schols, H. A., Structural differences of xylans affect their interaction with cellulose. Carbohydrate Polymers 2007, 69, (1), 94-105. 48. Mettler, M. S.; Mushrif, S. H.; Paulsen, A. D.; Javadekar, A. D.; Vlachos, D. G.; Dauenhauer, P. J., Revealing pyrolysis chemistry for biofuels production: Conversion of cellulose to furans and small oxygenates. Energy & Environmental Science 2012, 5, (1), 5414-5424. 49. Binder, J. B.; Raines, R. T., Simple chemical transformation of lignocellulosic biomass into furans for fuels and chemicals. Journal of the American Chemical Society 2009, 131, (5), 1979-1985. 50. Demirbaş, A., Oily products from mosses and algae via pyrolysis. Energy Sources, Part A 2006, 28, (10), 933-940. 51. Chen, H.; Si, Y.; Chen, Y.; Yang, H.; Chen, D.; Chen, W., NO x precursors from biomass pyrolysis: Distribution of amino acids in biomass and Tar-N during devolatilization using model compounds. Fuel


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Table. Captions Table 1 Recent study on the pyrolysis characteristic of different feedstock Table 2 Proximate and ultimate analysis results of different kinds of biomass Table 3 Correlation coefficient of characteristic and bio-oil composition


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Energy & Fuels

Table 1 Recent study on the pyrolysis characteristic of different feedstock feedstock

pyrolysis characteristic

correlation

cellulose

levoglucosan was the main product of cellulose pyrolysis

medium correlation

cellulose

high crystallinity cellulose produce high yield of anhydro-sugars

medium correlation

hemicellulose

hardwood hemicellulose mainly produced furfural and acids while softwood hemicellulose mainly produced 5-hydroxymethylfurfural and anhydro sugars

medium correlation

lignin

vanillin and 2-methoxy-4-methyl phenol were the most abundant monomeric products from lignin pyrolysis

medium correlation

corncob produced high phenols, 2-furanmethanol, 2-cyclopentanedione

weak correlation

poplar wood

poplar wood produced high carbonyls in bio-oil mainly including:ketones and aldehydes

weak correlation

poplar wood

higher pyrolysis temperature and heating rate contributed to obtain non-condensable gas

weak correlation

alfalfa drove bio-oil contained higher nitrogen, water, and aromatic hydrocarbons than switchgrass bio-oil

weak correlation

corncob

switchgrass and alfalfa


Re f 17

18

19

20

21

22

23

24

Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

oat straw, wheat straw and flax straw

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flax straw produce carbon-rich bio-char, oat straw provide high yield bio-oil

weak correlation

both the corncob, oreganum stalks and straw produced highly oxygenated bio-oil

weak correlation

corncobs, woodchips, rice husk,lyngbya and cladophora

comparing with lignocellulosic feedstock algae biomass produced nitrogen rich bio-oil

weak correlation

cotton stalks, rapeseed stalks, tobacco stems, rice husks, and bamboo

bamboo produced phenol-rich bio-oil, tobacco stems provide N-compounds rich bio-oil

weak correlation

barley straw，barley hulls and distiller 's grains

containing high lipids the distiller 's grains produce high basic organic-nitrogen compounds

weak correlation

corncob, oreganum stalks and straw

Pine dust, euca, barley straw, rape the ash, O/C ratio and volatile in biomass material showed an obvious correlation straw, timothy hay and reed canary with organic yield grass Weak correlation: showed the product distribution Medium correlation: discussed the products distribution tendencies Strong correlation: explored the quantitative correlation


strong correlation

25

26

27

28

29

30

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Energy & Fuels

Table 2 Proximate and ultimate analysis results of different kinds of biomass Sample

Proximate analysis (wt.%), d Ultimate analysis (wt.%), d

Ad FCd Vd C H O* N S peanut straw 10.29 13.94 75.76 49.00 7.05 30.59 2.74 0.32 soybean straw 8.22 15.65 76.12 48.84 6.81 35.11 0.81 0.21 cornstalk 10.36 18.37 71.27 48.59 6.28 33.27 1.31 0.18 sesame stalk 9.02 16.85 74.13 49.16 6.59 34.06 0.85 0.31 straw rice straw 14.41 13.80 71.78 47.55 6.51 30.34 0.94 0.25 tobacco stem 23.90 7.32 68.72 40.89 6.23 27.20 3.07 0.39 wheat straw 10.76 13.35 75.90 48.22 6.62 33.05 1.06 0.31 cotton stalk 3.14 16.32 80.54 47.12 6.25 42.59 0.57 0.20 peanut shell 3.83 20.95 75.23 51.25 6.44 36.70 1.54 0.24 rice husk 17.05 15.55 67.39 49.04 6.53 26.85 0.36 0.16 shell corncob 3.53 16.93 79.54 48.56 6.50 40.66 0.58 0.16 rape pod 12.22 12.84 74.93 48.24 6.69 30.97 1.18 0.69 camphor tree 2.41 13.69 83.90 50.69 6.35 40.15 0.25 0.15 eucalyptus 3.32 13.93 82.75 51.19 6.40 38.53 0.39 0.16 woody bamboo 5.47 7.75 86.78 53.32 6.64 34.21 0.20 0.17 sapele 0.90 16.09 83.01 51.29 6.28 41.29 0.11 0.13 bloom alage 37.46 2.74 59.79 35.93 7.59 16.67 4.35 0.40 enteromorpha algae 30.11 5.33 64.57 37.97 6.21 21.33 2.66 3.54 alage spirulina 5.47 5.87 88.67 49.79 7.43 24.70 11.58 0.65 chlorella 6.25 10.81 82.94 56.80 8.24 20.19 7.41 0.65 *the oxygen content was determined by difference


Energy & Fuels

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48

Table 3 Correlation coefficient of characteristic and bio-oil composition compounds phenols ketones short-chain acids furans esters hydrocarbons cyclopendentens N-compounds cellulose 0.79 0.65 -0.25 0.07 -0.25 -0.76 0.38 -0.39 hemicellulose -0.04 -0.01 0.00 0.33 -0.13 0.09 0.22 -0.17 lignin 0.82 0.19 0.55 -0.23 0.01 -0.54 0.02 -0.20 holocellulose 0.68 0.53 0.06 0.61 -0.29 -0.61 0.45 -0.44 cellulose and lignin 0.81 0.56 0.45 -0.05 -0.18 -0.78 0.28 -0.37 hemicellulose and lignin 0.48 0.10 0.60 0.09 -0.11 -0.43 0.22 -0.35 cellulose+hemicellulose+lignin 0.80 0.53 0.44 0.10 -0.24 -0.75 0.38 -0.45 extracts -0.80 -0.53 -0.44 -0.10 0.24 0.75 -0.38 0.45 ash -0.66 -0.27 -0.76 0.22 -0.29 0.21 -0.14 0.25 volatile 0.39 -0.01 0.41 -0.26 0.41 0.08 -0.07 -0.29 Fc 0.74 0.62 0.36 0.00 -0.01 -0.56 0.39 -0.45 H/C -0.83 -0.45 -0.25 0.13 0.00 0.53 -0.30 0.16 O/C 0.71 0.47 0.36 0.04 -0.28 -0.63 0.31 -0.09 Oxygen 0.82 0.49 0.48 0.00 -0.11 -0.63 0.31 -0.23 Nitrogen -0.59 -0.57 -0.25 -0.31 0.37 0.76 -0.30 0.62 K 0.16 0.13 0.27 0.28 -0.07 -0.29 0.32 0.21 Ca -0.07 -0.01 0.37 -0.58 0.35 0.35 -0.13 0.10 *the under line means the strong correlation coefficient


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Energy & Fuels

Fig. captions Fig.1. Van Krevelen diagram for H/C and O/C of biomass and coal. Fig. 2. Fiber analysis results of different kinds of biomass. Fig.3. Total ion chromatography of bamboo drove bio-oil. Fig. 4. Bio-oil compounds distribution of (a) straw (b) shell (c) algae (d) woody. Fig. 5. Correlation of bio-oil and feedstock characteristic (a) phenols vs lignin (b) ketones vs cellulose (c) short-chain acids vs ash (d) furan vs holocellulose (e) hydrocarbon vs extracts (f) N-compounds vs nitrogen.


Energy & Fuels

2.0

1.5 Peat Lignite

H/C

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1.0 Coal Straw biomass Shell biomass Woody biomass Alage biomass

Bituminous coal

0.5 Anthracite

0.0 0.0

0.2

0.4

0.6

0.8

O/C

Fig.1 Van Krevelen diagram for H/C and O/C of biomass and coal


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Energy & Fuels

Fig.2 Fiber analysis results of different kinds of biomass


Energy & Fuels

1.0x107 Bamboo

Hydroxy-acetaldehyde

8.0x106

O

4-vinylphenol

HO

Intensity

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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2-Methoxy-4-vinylphenol

6.0x106 Acetic acids

4.0x10

2-methoxy-phenol

6

2,6-dimethoxy-phenol

2.0x106 0.0 0

10

20

30

40

Time(min)

Fig.3 Total ion chromatography of bamboo drove bio-oil


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Energy & Fuels

Fig.4 Bio-oil compounds distribution of (a) straw (b) shell (c) algae (d) woody


Energy & Fuels

Straw Shell Woody Algae

(a)

40

20 10

20

Y=-5.04+1.13X R=0.82

0


(b)

40

Ketones%

Phenols%

30

Y=4.44+0.51X R=0.65

0

-10 0

10

20

30

0

20

Lignin%

40

60

50

75

Cellulose%

30

Straw Shell Woody

24

(d)

30

Furans%

Short-chain acids%

(c)

18

12

20

Straw Shell Woody Algae Y=-15.77+0.39X R=0.61

10

Y=21-0.45X R=-0.76

6

0 0

0

5

10

15

20

25

0

25

Ash%

Holocellulose%

25

Y=1.09+0.21X R=0.75

40

N-compounds%


(e) 20

Hydrocarbons%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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15 10

5


(f)

20

Y=7.38+1.02X R=0.62

0

0 0

15

30

45

60

0

3

Extracts(%)

6

9

12

Nitrogen%

Fig.5 Correlation of bio-oil and feedstock characteristic (a) phenols vs lignin (b) ketones vs cellulose (c) short-chain acids vs ash (d) furan vs holocellulose (e) hydrocarbon vs extracts (f) N-compounds vs nitrogen


Correlation of Feedstock and Bio-oil Compound ... - ACS Publications

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