Thermal decomposition of rice husk: a comprehensive artificial intelligence predictive model

  • Peter Adeniyi AlabaEmail author
  • Segun I. Popoola
  • Faisal Abnisal
  • Ching Shya Lee
  • Olayinka S. Ohunakin
  • Emmanuel Adetiba
  • Matthew Boladele Akanle
  • Muhamad Fazly Abdul Patah
  • Aderemi A. A. Atayero
  • Wan Mohd Ashri Wan DaudEmail author


This study explored the predictive modelling of the pyrolysis of rice husk to determine the thermal degradation mechanism of rice husk. The study can ensure proper modelling and design of the system, towards optimising the industrial processes. The pyrolysis of rice husk was studied at 10, 15 and 20 °C min−1 heating rates in the presence of nitrogen using thermogravimetric analysis technique between room temperature and 800 °C. The thermal decomposition shows the presence of hemicellulose and some part of cellulose at 225–337 °C, the remaining cellulose and some part of lignin were degraded at 332–380 °C, and lignin was degraded completely at 480 °C. The predictive capability of artificial neural network model was studied using different architecture by varying the number of hidden neurone node, learning algorithm, hidden and output layer transfer functions. The residual mass, initial degradation temperature and thermal degradation rate at the end of the experiment increased with an increase in the heating rate. Levenberg–Marquardt algorithm performed better than scaled conjugate gradient learning algorithm. This result shows that rice husk degradation is best described using nonlinear model rather than linear model. For hidden and output layer transfer functions, ‘log-sigmoid and tan-sigmoid', and ‘tan-sigmoid and tan-sigmoid' transfer functions showed remarkable results based on the coefficient of determination and root mean square error values. The accuracy of the results increases with an increasing number of hidden neurone. This result validates the suitability of an artificial neural network model in predicting the devolatilisation behaviour of biomass.


Rice husk Thermal decomposition Artificial intelligence Neural network Pyrolysis Heating rate 



The authors acknowledge the Fundamental Research Grant Scheme (FRGS) from the University of Malaya for funding this work through Project No. “FP046-2017A”.

Compliance with ethical standards

Conflict of interest



  1. 1.
    Wu X, Wu Y, Wu K, Chen Y, Hu H, Yang M. Study on pyrolytic kinetics and behavior: the co-pyrolysis of microalgae and polypropylene. Bioresour Technol. 2015;192:522.PubMedCrossRefGoogle Scholar
  2. 2.
    Chen W-H, Lin B-J, Huang M-Y, Chang J-S. Thermochemical conversion of microalgal biomass into biofuels: a review. Bioresour Technol. 2015;184:314.PubMedCrossRefGoogle Scholar
  3. 3.
    Özçimen D, Karaosmanoğlu F. Production and characterization of bio-oil and biochar from rapeseed cake. Renew Energy. 2004;29(5):779.CrossRefGoogle Scholar
  4. 4.
    Li Z, Zhao W, Meng B, Liu C, Zhu Q, Zhao G. Kinetic study of corn straw pyrolysis: comparison of two different three-pseudocomponent models. Bioresour Technol. 2008;99(16):7616.PubMedCrossRefGoogle Scholar
  5. 5.
    Park HJ, Park Y-K, Dong J-I, Kim J-S, Jeon J-K, Kim S-S, Kim J, Song B, Park J, Lee K-J. Pyrolysis characteristics of Oriental white oak: kinetic study and fast pyrolysis in a fluidized bed with an improved reaction system. Fuel Process Technol. 2009;90(2):186.CrossRefGoogle Scholar
  6. 6.
    Yao X, Xu K, Liang Y. Comparing the thermo-physical properties of rice husk and rice straw as feedstock for thermochemical conversion and characterization of their waste ashes from combustion. BioResources. 2016;11(4):10549.Google Scholar
  7. 7.
    Sfakiotakis S, Vamvuka D. Development of a modified independent parallel reactions kinetic model and comparison with the distributed activation energy model for the pyrolysis of a wide variety of biomass fuels. Bioresour Technol. 2015;197:434.PubMedCrossRefGoogle Scholar
  8. 8.
    Di Blasi C. Modeling chemical and physical processes of wood and biomass pyrolysis. Prog Energy Combust Sci. 2008;34(1):47.CrossRefGoogle Scholar
  9. 9.
    Alaba PA, Sani YM, Daud WMAW. A comparative study on thermal decomposition behavior of biodiesel samples produced from shea butter over micro-and mesoporous ZSM-5 zeolites using different kinetic models. J Therm Anal Calorim. 2016;126(2):943.CrossRefGoogle Scholar
  10. 10.
    Damartzis T, Vamvuka D, Sfakiotakis S, Zabaniotou A. Thermal degradation studies and kinetic modeling of cardoon (Cynara cardunculus) pyrolysis using thermogravimetric analysis (TGA). Bioresour Technol. 2011;102(10):6230.PubMedCrossRefGoogle Scholar
  11. 11.
    Becidan M, Várhegyi G, Hustad JE, Skreiberg Ø. Thermal decomposition of biomass wastes. A kinetic study. Ind Eng Chem Res. 2007;46(8):2428.CrossRefGoogle Scholar
  12. 12.
    Várhegyi G, Bobály B, Jakab E, Chen H. Thermogravimetric study of biomass pyrolysis kinetics. A distributed activation energy model with prediction tests. Energy Fuels. 2010;25(1):24.CrossRefGoogle Scholar
  13. 13.
    Gašparovič L, Labovský J, Markoš J, Jelemenský L. Calculation of kinetic parameters of the thermal decomposition of wood by distributed activation energy model (DAEM). Chem Biochem Eng Q. 2012;26(1):45.Google Scholar
  14. 14.
    White JE, Catallo WJ, Legendre BL. Biomass pyrolysis kinetics: a comparative critical review with relevant agricultural residue case studies. J Anal Appl Pyrolysis. 2011;91(1):1.CrossRefGoogle Scholar
  15. 15.
    Alaba PA, Abbas A, Huang J, Daud WMAW. Molybdenum carbide nanoparticle: understanding the surface properties and reaction mechanism for energy production towards a sustainable future. Renew Sustain Energy Rev. 2018;91:287.CrossRefGoogle Scholar
  16. 16.
    Magela E, Silva G, Acioli PH, Pedroza AC. Estimating correlation energy of diatomic molecules and atoms with neural networks. J Comput Chem. 1997;18(11):1407.CrossRefGoogle Scholar
  17. 17.
    Balabin RM, Lomakina EI. Neural network approach to quantum-chemistry data: accurate prediction of density functional theory energies. J Chem Phys. 2009;131(7):074104.PubMedCrossRefGoogle Scholar
  18. 18.
    Urata S, Takada A, Uchimaru T, Chandra AK, Sekiya A. Artificial neural network study for the estimation of the C–H bond dissociation enthalpies. J Fluor Chem. 2002;116(2):163.CrossRefGoogle Scholar
  19. 19.
    Duan X-M, Li Z-H, Song G-L, Wang W-N, Chen G-H, Fan K-N. Neural network correction for heats of formation with a larger experimental training set and new descriptors. Chem Phys Lett. 2005;410(1–3):125.CrossRefGoogle Scholar
  20. 20.
    Wu J, Xu X. Improving the B3LYP bond energies by using the X 1 method. J Chem Phys. 2008;129(16):164103.PubMedCrossRefGoogle Scholar
  21. 21.
    Alaba PA, Popoola SI, Olatomiwa L, Akanle MB, Ohunakin OS, Adetiba E, Alex OD, Atayero AA, Daud WMAW. Towards a more efficient and cost-sensitive extreme learning machine: a state-of-the-art review of recent trend. Neurocomputing. 2019;350:70.CrossRefGoogle Scholar
  22. 22.
    Behler J. Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations. Phys Chem Chem Phys. 2011;13(40):17930.PubMedCrossRefGoogle Scholar
  23. 23.
    Mohammed IY, Abakr YA, Hui JNX, Alaba PA, Morris KI, Ibrahim MD. Recovery of clean energy precursors from Bambara groundnut waste via pyrolysis: kinetics, products distribution and optimisation using response surface methodology. J Clean Prod. 2017;164:1430.CrossRefGoogle Scholar
  24. 24.
    Mohammed IY, Abakr YA, Yusup S, Alaba PA, Morris KI, Sani YM, Kazi FK. Upgrading of Napier grass pyrolytic oil using microporous and hierarchical mesoporous zeolites: products distribution, composition and reaction pathways. J Clean Prod. 2017;162:817.CrossRefGoogle Scholar
  25. 25.
    Szumera M, Wacławska I, Sułowska J. Thermal properties of MnO2 and SiO2 containing phosphate glasses. J Therm Anal Calorim. 2016;123(2):1083.CrossRefGoogle Scholar
  26. 26.
    Noh J, Back S, Kim J, Jung Y. Active learning with non-ab initio input features toward efficient CO2 reduction catalysts. Chem Sci. 2018;9(23):5152.PubMedPubMedCentralCrossRefGoogle Scholar
  27. 27.
    Azarmi S, Oladipo A, Vaziri R, Alipour H. Comparative modelling and artificial neural network inspired prediction of waste generation rates of hospitality industry: the case of North Cyprus. Sustainability. 2018;10(9):2965.CrossRefGoogle Scholar
  28. 28.
    Betiku E, Ajala SO. Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: a case of artificial neural network vs. response surface methodology. Ind Crops Prod. 2014;53:314.CrossRefGoogle Scholar
  29. 29.
    Møller MF. A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 1993;6(4):525.CrossRefGoogle Scholar
  30. 30.
    Du Y-C, Stephanus A. Levenberg–Marquardt neural network algorithm for degree of arteriovenous fistula stenosis classification using a dual optical photoplethysmography sensor. Sensors. 2018;18(7):2322.CrossRefGoogle Scholar
  31. 31.
    Hagan MT, Menhaj MB. Training feedforward networks with the Marquardt algorithm. IEEE Trans Neural Netw. 1994;5(6):989.PubMedCrossRefGoogle Scholar
  32. 32.
    Cömert Z, Kocamaz AF. A study of artificial neural network training algorithms for classification of cardiotocography signals. Bitlis Eren Univ J Sci Technol. 2017;7(2):93.CrossRefGoogle Scholar
  33. 33.
    Popoola SI, Adetiba E, Atayero AA, Faruk N, Calafate CT. Optimal model for path loss predictions using feed-forward neural networks. Cogent Eng. 2018;5(1):1444345.CrossRefGoogle Scholar
  34. 34.
    Kuprianov VI, Arromdee P. Combustion of peanut and tamarind shells in a conical fluidized-bed combustor: a comparative study. Bioresour Technol. 2013;140:199.PubMedCrossRefGoogle Scholar
  35. 35.
    Isa KM, Daud S, Hamidin N, Ismail K, Saad SA, Kasim FH. Thermogravimetric analysis and the optimisation of bio-oil yield from fixed-bed pyrolysis of rice husk using response surface methodology (RSM). Ind Crops Prod. 2011;33(2):481.CrossRefGoogle Scholar
  36. 36.
    Mansaray K, Ghaly A. Thermal degradation of rice husks in nitrogen atmosphere. Bioresour Technol. 1998;65(1–2):13.CrossRefGoogle Scholar
  37. 37.
    Azizi K, Moraveji MK, Najafabadi HA. Characteristics and kinetics study of simultaneous pyrolysis of microalgae Chlorella vulgaris, wood and polypropylene through TGA. Bioresour Technol. 2017;243:481.PubMedCrossRefGoogle Scholar
  38. 38.
    Chen C, Ma X, He Y. Co-pyrolysis characteristics of microalgae Chlorella vulgaris and coal through TGA. Bioresour Technol. 2012;117:264.PubMedCrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2019

Authors and Affiliations

  • Peter Adeniyi Alaba
    • 1
    Email author
  • Segun I. Popoola
    • 2
  • Faisal Abnisal
    • 3
  • Ching Shya Lee
    • 1
    • 4
    • 5
  • Olayinka S. Ohunakin
    • 6
    • 7
  • Emmanuel Adetiba
    • 2
    • 8
  • Matthew Boladele Akanle
    • 2
  • Muhamad Fazly Abdul Patah
    • 1
  • Aderemi A. A. Atayero
    • 2
  • Wan Mohd Ashri Wan Daud
    • 1
    Email author
  1. 1.Department of Chemical Engineering, Faculty of EngineeringUniversity of MalayaKuala LumpurMalaysia
  2. 2.Department of Electrical and Information EngineeringCovenant UniversityOtaNigeria
  3. 3.Department of Chemical Engineering, Faculty of EngineeringKing Abdulaziz UniversityRabighSaudi Arabia
  4. 4.University of MalayaKuala LumpurMalaysia
  5. 5.UMR5503 Laboratoire de Génie Chimique (LGC)ToulouseFrance
  6. 6.The Energy and Environment Research Group (TEERG), Mechanical Engineering DepartmentCovenant UniversityOtaNigeria
  7. 7.Faculty of Engineering and the Built EnvironmentUniversity of JohannesburgJohannesburgSouth Africa
  8. 8.HRA, Institute for Systems ScienceDurban University of TechnologyDurbanSouth Africa

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