Modeling of Bio-Oil Production by Pyrolysis of Woody Biomass: Artificial Neural Network Approach

Year 2020, Volume: 23 Issue: 4, 1255 - 1264, 01.12.2020

Günay Özbay , Erkan Sami Kökten

https://doi.org/10.2339/politeknik.659136

Cited By: 9

Abstract

This study is dedicated to
developing a reliable artificial neural network (ANN) model to model the
pyrolysis liquid product (bio-oil). Some related parameters with the bio-oil
yield such as the pyrolysis temperature, duration, catalyst type, catalyst ratio,
particle size, proximate, and ultimate analysis of the biomass were tested. Due
to the different characteristics of different biomass types and pyrolysis
methods, only slow and intermediate pyrolysis data from woody biomass were used
in modeling. The correlation coefficients (R) were 0.992, 0.933, and 0.951 for
training, validation, and testing, respectively. In order to evaluate the
predictability of the ANN model, the predicted results were compared with the
experimental results that were not introduced before. The simulated data were
in good agreement with the experimental results indicating the reliability of
the developed model. The relative impact results revealed that the most
important parameter that affects the bio-oil yield was catalyst type (11.4%).  

Keywords

Artificial neural network, bio-oil, catalyst, modeling, pyrolysis

Supporting Institution

TÜBİTAK

Project Number

115O453

Thanks

The support from TUBITAK (The Scientific and Technological Research Council of Turkey) [Project grant number: 115O453 for this research are gratefully acknowledged.

References

• 1. Boateng A. A., Mullen C. A., Goldberg N. M., Hicks K. B., Devine T. E., Lima I. M., and McMurtrey A. J. E., “Sustainable production of bioenergy and bio-char from the straw of high‐biomass soybean lines via fast pyrolysis”, Environmental Progress & Sustainable Energy, 29(2): 175-183, (2010) 2. Branca C., Galgano A., Blasi C., Esposito M., and Di Blasi C., “H2SO4-catalyzed pyrolysis of corncobs”, Energy & Fuels, 25(1): 359-369, (2011) 3. Özbay G., Özçifçi A., and Karagöz S., “Catalytic pyrolysis of waste melamine coated chipboard”, Environmental Progress & Sustainable Energy, 32(1): 156-161, (2013)
• 4. Murata K., Liu, Y., Inaba M., and Takahara I., “Catalytic fast pyrolysis of jatropha wastes”, Journal of Analytical and Applied Pyrolysis, 94: 75-82, (2012.) 5. Özbay G., Özçifçi A., and Kokten E. S., “The pyrolysis characteristics of wood waste containing different types of varnishes”, Turkish Journal of Agriculture and Forestry, 40(5): 705-714, (2016) 6. Bennett N. M., Helle S. S., and Duff S. J., “Extraction and hydrolysis of levoglucosan from pyrolysis oil”, Bioresource technology, 100(23): 6059-6063, (2009) 7. Bridgwater A. V., “Review of fast pyrolysis of biomass and product upgrading”, Biomass and bioenergy, 38: 68-94, (2012) 8. Ikura M., Stanciulescu M., and Hogan E., “Emulsification of pyrolysis derived bio-oil in diesel fuel”, Biomass and bioenergy, 24(3): 221-232, (2003) 9. Kan T., Strezov V., and Evans,T. J., “Lignocellulosic biomass pyrolysis: A review of product properties and effects of pyrolysis parameters”, Renewable and Sustainable Energy Reviews, 57: 1126-1140, (2016) 10. Lenz V., and Ortwein A., “Smart Biomass Heat–heat from solid biofuels as an integral part of a future energy system based on renewables”, Chemical Engineering and Technology, 40(2): 313-322, (2017) 11. Ghatak M. D., and Ghatak A., “Artificial neural network model to predict behavior of biogas production curve from mixed lignocellulosic co-substrates”, Fuel, 232: 178-189, (2018) 12. Madhu P., Matheswaran M. M., and Periyanayagi G., “Optimization and characterization of bio-oil produced from cotton shell by flash pyrolysis using artificial neural network”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 39(23): 2173-2180, (2017). 13. Sunphorka S., Chalermsinsuwa B., and Piumsomboon, P., “Artificial neural network model for the prediction of kinetic parameters of biomass pyrolysis from its constituents”, Fuel, 193: 142-158, (2017) 14. Cao H., Xin Y., and Yuan Q., “Prediction of biochar yield from cattle manure pyrolysis via least squares support vector machine intelligent approach”, Bioresource technology, 202: 158-164, (2016) 15. Chen X., Zhang H., Song Y., and Xiao R., “Prediction of product distribution and bio-oil heating value of biomass fast pyrolysis”, Chemical Engineering and Processing-Process Intensification, 130: 36-42, (2018) 16. Abnisa F., Arami-Niya A., Daud W. W., Sahu J. N., and Noor I. M., “Utilization of oil palm tree residues to produce bio-oil and bio-char via pyrolysis”, Energy conversion and management, 76: 1073-1082, (2013) 17. Angın D., and Egrisogut Tiryaki A., “Application of response surface methodology and artificial neural network on pyrolysis of safflower seed press cake”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 38(8): 1055-1061, (2016) 18. Pinto F., Paradela F., Gulyurtlu I., and Ramos A. M., “Prediction of liquid yields from the pyrolysis of waste mixtures using response surface methodology”, Fuel processing technology, 116: 271-283, (2013) 19. Phichai K., Pragrobpondee,P., Khumpart,T., and Hirunpraditkoon S., “Prediction heating values of lignocellulosics from biomass characteristics”, Int. J. Chem. Mol. Nucl. Mater. Metall. Eng, 7: 214-217. (2013) 20. Çepelioğullar Ö., Mutlu İ., Yaman S., and Haykiri-Acma H., “A study to predict pyrolytic behaviors of refuse-derived fuel (RDF): artificial neural network application”, Journal of Analytical and Applied Pyrolysis, 122: 84-94, (2016). 21. Mikulandrić R., Lončar D., Böhning D., Böhme R., and Beckmann M., “Artificial neural network modelling approach for a biomass gasification process in fixed bed gasifiers”, Energy conversion and management, 87: 1210-1223, (2014) 22. Puig-Arnavat M., Hernández J. A., Bruno J. C., and Coronas A., “Artificial neural network models for biomass gasification in fluidized bed gasifiers”, Biomass and bioenergy, 49: 279-289, 2013 23. Lim C. H., Mohammed I. Y., Abakr Y. A., Kazi F. K., Yusup S., and Lam H. L., “Novel input-output prediction approach for biomass pyrolysis”, Journal of cleaner production, 136: 51-61, (2016) 24. Merdun H., Sezgin I. V., “Modeling of pyrolysis product yields by artificial neural networks”, International Journal of Renewable Energy Research (IJRER), 8(2): 1178-1188, (2018) 25. Sun Y., Liu L., Wang Q., Yang X., and Tu X., “Pyrolysis products from industrial waste biomass based on a neural network model”, Journal of analytical and applied pyrolysis, 120: 94-102, (2016) 26. Wang H., Wang X., Cui Y., Xue Z., and Ba Y., “Slow pyrolysis polygeneration of bamboo (Phyllostachys pubescens): Product yield prediction and biochar formation mechanism”, Bioresource technology, 263: 444-449, (2018) 27. Aydinli B., Caglar A., Pekol S., and Karaci A., “The prediction of potential energy and matter production from biomass pyrolysis with artificial neural network”, Energy Exploration and Exploitation, 35(6): 698-712, (2017) 28. Karaci A., Caglar A., Aydinli B., and Pekol S., “The pyrolysis process verification of hydrogen rich gas (H–rG) production by artificial neural network (ANN)”, International Journal of Hydrogen Energy, 41(8): 4570-4578., (2016). 29. Perez L. B., and Cortez L. A. B., “Potential for the use of pyrolytic tar from bagasse in industry”, Biomass and Bioenergy, 12(5): 363-366, 1997 30. George J., Arun P., and Muraleedharan C., “Assessment of producer gas composition in air gasification of biomass using artificial neural network model”, International Journal of Hydrogen Energy, 43 (20): 9558-9568, (2018) 31. Akhtar J., and Amin N. S., “A review on operating parameters for optimum liquid oil yield in biomass pyrolysis”, Renewable and Sustainable Energy Reviews, 16 (7): 5101-5109, (2012) 32. Guedes R. E., Luna A. S., and Torres A. R., “Operating parameters for bio-oil production in biomass pyrolysis: A review”, Journal of Analytical and Applied Pyrolysis, 129: 134-149, (2018) 33. Qiang L., Wen-Zhi L., and Xi-Feng Z., “Overview of fuel properties of biomass fast pyrolysis oils”, Energy Conversion and Management, 50(5): 1376-1383, (2009-a). 34. Chen M. Q., Wang J., Zhang M. X., Chen M. G., Zhu X. F., Min F. F., and Tan Z. C., “Catalytic effects of eight inorganic additives on pyrolysis of pine wood sawdust by microwave heating”, Journal of Analytical and Applied Pyrolysis, 82 (1): 145-150, (2008) 35. Özbay G., “Catalytic pyrolysis of pine wood sawdust to produce bio-oil: effect of temperature and catalyst additives”, Journal of Wood Chemistry and technology, 35(4): 302-313, (2015) 36. Goyal H. B., Seal D., and Saxena R. C., “Bio-fuels from thermochemical conversion of renewable resources: a review”, Renewable and sustainable energy reviews, 12 (2): 504-517, (2008) 37. Qiang L., Wen-Zhi L., Dong Z., and Xi-Feng Z., “Analytical pyrolysis–gas chromatography/mass spectrometry (Py–GC/MS) of sawdust with Al/SBA-15 catalysts”, Journal of Analytical and Applied Pyrolysis, 84(2): 131-138, (2009-b) 38. Zhao N., and Li B. X., “The effect of sodium chloride on the pyrolysis of rice husk”, Applied energy, 178: 346-352, (2016) 39. Öztemel E., “Yapay Sinir Ağları”. Papatya Press, (in Turkish), İstanbul, (2006) 40. Garson G. D. “Interpreting neural-network connection weights”, AI expert, 6(4): 46-51, (1991) 41. ASTM D 4442-92 (ASTM Standarts), “Standard Test Methods for Direct Moisture Content Measurement of Wood and Wood-Base Materials”, (2003) 42. ASTM E 897-88 (ASTM Standarts), “Standard test method for volatile matter in analysis sample refuse derived fuel”, (2004) 43. ASTM D 1102-84 (ASTM Standarts), “Standard test method for ash in wood”, (1983) 44. Bewick V., Cheek L., and Ball J., “Statistics review 7: Correlation and regression”, Critical care, 7(6): 451., (2003) 44. Rutkowski P., “Chemical composition of bio-oil produced by co-pyrolysis of biopolymer/polypropylene mixtures with K2CO3 and ZnCl2 addition”, Journal of Analytical and Applied Pyrolysis, 95, 38-47, (2012) 45. Zhang H. Xiao R., Huang H., and Xiao G., “Comparison of non-catalytic and catalytic fast pyrolysis of corncob in a fluidized bed reactor”, Bioresource Technology, 100(3): 1428-1434, (2009) 46. Haykiri-Acma H., “The role of particle size in the non-isothermal pyrolysis of hazelnut shell”, Journal of analytical and applied pyrolysis, 75 (2): 211-216, (2006) 47. Isahak W. N. R. W., Hisham M. W., Yarmo M. A., and Hin T. Y. Y., “A review on bio-oil production from biomass by using pyrolysis method”, Renewable and sustainable energy reviews, 16 (8): 5910-5923, (2012) 48. Tsai W. T., Lee M. K., and Chang Y. M., “Fast pyrolysis of rice husk: Product yields and compositions”, Bioresource technology, 98(1): 22-28, (2007) 49. Sulaiman W. R. W., and Lee E. S., “Pyrolysis of eucalyptus wood in a fluidized-bed reactor”, Research on Chemical Intermediates, 38(8): 2025-2039, (2012). 50. Abnisa F., Daud W. W., and Sahu J. N., “Optimization and characterization studies on bio-oil production from palm shell by pyrolysis using response surface methodology”, Biomass and Bioenergy, 35(8): 3604-3616, (2011) 51. Lee Y., Park J., Ryu C., Gang K. S., Yang W., Park Y. K., and Hyun S., “Comparison of biochar properties from biomass residues produced by slow pyrolysis at 500 ˚C”, Bioresource technology, 148: 196-201, (2013) 52. Wu W., and Qiu K., “Vacuum co-pyrolysis of Chinese fir sawdust and waste printed circuit boards. Part I: Influence of mass ratio of reactants”, Journal of analytical and applied pyrolysis, 105: 252-261, (2014) 53. Chen D., Liu D., Zhang H., Chen Y., and Li Q., “Bamboo pyrolysis using TG–FTIR and a lab-scale reactor: Analysis of pyrolysis behavior, product properties, and carbon and energy yields”, Fuel, 148: 79-86, (2015) 54. Doumer M. E., Arízaga G. G. C., da Silva D. A., Yamamoto C. I., Novotny E. H., Santos J. M., and Mangrich A. S., “Slow pyrolysis of different Brazilian waste biomasses as sources of soil conditioners and energy, and for environmental protection”, Journal of analytical and applied pyrolysis, 113: 434-443, (2015). 55. Klaigaew K., Wattanapaphawong P., Khuhaudomlap N., Hinchiranan N., Kuchontara P., Kangwansaichol K., and Reubroycharoen P., “Liquid phase pyrolysis of giant leucaena wood to bio-oil over NiMo/Al2O3 catalyst”, Energy Procedia, 79: 492-499, (2015) 56. Özbay G., Kılıc Pekgözlü A., and Ozcifci A., “The effect of heat treatment on bio-oil properties obtained from pyrolysis of wood sawdust”, European journal of wood and wood products, 73(4): 507-514, (2015) 57. Özbay G., “Pyrolysis of Firwood (Abies bornmülleriana Mattf.) Sawdust: Characterization of Bio-Oil and Bio-Char”, Drvna industrija, 66(2): 105-114 (2015) 58. Gómez N., Rosas J. G., Cara J., Martínez O., Alburquerque J. A., and Sánchez M. E., “Slow pyrolysis of relevant biomasses in the Mediterranean basin. Part 1. Effect of temperature on process performance on a pilot scale”, Journal of cleaner production, 120: 181-190, (2016) 59. Halim S. A., and Swithenbank J., “Characterisation of Malaysian wood pellets and rubberwood using slow pyrolysis and microwave technology”, Journal of analytical and applied pyrolysis, 122: 64-75, (2016) 60. Moralı U., Yavuzel N., and Şensöz S., “Pyrolysis of hornbeam (Carpinus betulus L.) sawdust: Characterization of bio-oil and bio-char”, Bioresource technology, 221: 682-685, (2016)