Advertisement

Biomass Conversion and Biorefinery

, Volume 8, Issue 3, pp 647–657 | Cite as

Utilisation of a waste biomass, walnut shells, to produce bio-products via pyrolysis: investigation using ISO-conversional and neural network methods

  • Tanveer Rasool
  • Vimal Chandra Srivastava
  • M. N. S. Khan
Original Article

Abstract

This study was conducted to investigate the kinetic, thermodynamics and the reaction mechanism of pyrolysis of native walnut shells of Kashmir, India. Thermal degradation experiments were performed at three heating rates of 10, 25, and 50 K min−1 to calculate the kinetic and thermodynamic parameters, using iso-conversional Kissinger-Akahira-Sunrose (KAS) and Ozawa-Flynn-Wall (OFW) models. The reaction mechanism was predicted by applying Coats-Redfern (CR) method. Moreover, an artificial neural network (ANN) simulation was used to obtain best fit points after comparing the experimental data with the predicted data points. Average activation energy was calculated from the thermogravimetric study was found to be in the range of 146.03–148.89 kJ mol−1, while the Gibbs free energy (ΔG) value for walnut shells was found to be ~180 kJ mol−1. The most appropriate degradation mechanism was found to be based on diffusion and chemical reaction for the temperature range under study. The broad characterisation along with the values of thermodynamic parameters show that the walnut shells can be used as an economical as well as eco-friendly bio-energy feed-stock for pyrolysis. The reaction mechanism of thermal degradation of walnut shells was found to be consisting of two broader zones based on conversion achieved, zone I (0.2 ≤ α ≤ 0.4) and zone II (0.4 ≤ α ≤ 0.8).

Keywords

Thermal degradation Waste biomass Kinetics Thermodynamics Artificial neural network 

Abbreviations

Ao

Pre-exponential factor, s−1

ANN

Artificial neural network

CR

Coats-Redfern method

D1–5

Diffusion-based mechanisms

DTA

Differential thermal analysis

DTG

Differential thermogravimetry

Et

Activation energy, kJ mol−1

F1–5

Chemical reaction-based mechanism

HHV

High heating value, MJ g−1

k (T)

Reaction rate constant

Kb

Boltzman constant, 1.381 × 10−23 J K−1

KAS

Kissinger-Akahira-Sunrose

MSE

Mean square error

OFW

Ozawa-Flynn-Wall method

o

Output values

R1–5

Contacting geometry-based mechanism

t

Target values

α

Conversion

∆G

Gibbs free energy, kJ mol−1

∆H

Change in enthalpy, kJ mol−1

Notes

References

  1. 1.
    Abnisa F, Arami-Niya A, Daud WMAW, Sahu JN, Noor IM (2013) Utilization of oil palm tree residues to produce bio-oil and bio-char via pyrolysis. Energy Convers Manag 76:1073–1082CrossRefGoogle Scholar
  2. 2.
    Ningbo G, Baoling L, Aimin L, Juanjuan L (2015) Continuous pyrolysis of pine sawdust at different pyrolysis temperatures and solid residence times. J Anal App Pyrol 114:155–162CrossRefGoogle Scholar
  3. 3.
    Mukherjee A, Das P, Minu K (2014) Thermogravimetric analysis and kinetic modelling studies of selected agro-residues and biodiesel industry wastes for pyrolytic conversion to bio-oil. Biomass Conv Bioref 4:259–258CrossRefGoogle Scholar
  4. 4.
    Pradhan RR, Garnaik PP, Regmi B, Dash B, Dutta A (2017) Pyrolysis kinetics of sal (Shorea robusta) seeds. Biomass Conv Bioref 7:237–247CrossRefGoogle Scholar
  5. 5.
    Acikalm K (2011) Thermogravimetric analysis of walnut shell as pyrolysis feedstock. J Therm Anal Calorim 105:145–150CrossRefGoogle Scholar
  6. 6.
    Demirbas A (2006) Effect of temperature on pyrolysis products from four nut shells. J Anal Appl Pyrol 76:285–289CrossRefGoogle Scholar
  7. 7.
    Yuan HR, Liu RH (2007) Study on pyrolysis kinetics of walnut shell. J Therm Anal Calorim 89-3:983–986CrossRefGoogle Scholar
  8. 8.
    Cai JM, Wu WX, Liu RH, Huber GW (2013) A distributed activation energy model for the pyrolysis of lignocellulosic biomass. Green Chem 15:1331–1340CrossRefGoogle Scholar
  9. 9.
    Wu W, Mei Y, Zhang L, Liu R, Cai J (2014) Effective activation energies of lignocellulosic biomass pyrolysis. Energ Fuel 28:3916–3923CrossRefGoogle Scholar
  10. 10.
    Irdem SD, Parparita E, Vasile C, Uddin MA, Yanik J (2014) Steam reforming of tar derived from walnut shell and almond shell gasification on red mud and iron-ceria catalysts. Energ Fuel 28:3808–3813CrossRefGoogle Scholar
  11. 11.
    Kalogirou SA (2003) Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust Sci 29:515–566CrossRefGoogle Scholar
  12. 12.
    Ahmad MS, Mehmood MA, Taqvi STH, Elkamel A, Liu C, Xu J, Rahimuddin SA, Gull M (2017) Pyrolysis, kinetics analysis, thermodynamics parameters and reaction mechanism of Typha latifolia to evaluate its bioenergy potential. Bioresour Technol 245:491–501CrossRefGoogle Scholar
  13. 13.
    Yıldız Z, Uzun H, Ceylan S, Topcu Y (2016) Application of artificial neural networks to co-combustion of hazelnut husk–lignite coal blends. Bioresour Technol 200:42–47CrossRefGoogle Scholar
  14. 14.
    Sharma RM, Kour K, Singh B, Yadav S, Kotwal N, Rana JC, Anand R (2014) Selection and characterization of elite walnut (Juglans regia L.) clone from seedling origin trees in North Western Himalayan region of India. Aust J Crop Sci 8(2):257–262Google Scholar
  15. 15.
    Sharma R (2012) Area and production database of fruit crops. Directorate of hortic state department of horticulture. Jammu and Kashmir government, Srinagar, IndiaGoogle Scholar
  16. 16.
    Uzun BB, Yaman E (2014) Thermogravemetric characteristics and kinetics of scrap tyre and juglans regia shell co-pyrolysis. Waste Manage Res 32(10):961–970CrossRefGoogle Scholar
  17. 17.
    Akahira T, Sunrose T (1969) Transactions of Joint Convention of Four Electrical Institutes. 246Google Scholar
  18. 18.
    Flynn JH, Wall LA (1966) A quick, direct method for the determination of activation energy from thermogravimetric Data. J Polym Sci Pol Lett 5:323–328CrossRefGoogle Scholar
  19. 19.
    Ozawa T (1965) A new method of analyzing thermogravimetric data. Bull Chem Soc Japan 38:1881–1886CrossRefGoogle Scholar
  20. 20.
    Chen D, Zhou J, Zhang Q (2014) Effects of torrefaction on the pyrolysis behaviour and bio-oil properties of rice husk by using TG-FTIR and Py-GC/MS. Energ Fuel 28:5857–5863CrossRefGoogle Scholar
  21. 21.
    Doyle CD (1965) Series approximations to the equations of thermo-gravimetric data. Nature 207:290–291CrossRefGoogle Scholar
  22. 22.
    Damartzis T, Vamvuka D, Sfakiotakis S, Zabaniotou A (2011) Thermal degradation studies and kinetic modelling of cardoon (Cynaracardunculus) pyrolysis using thermogravimetric analysis (TGA). Bioresour Technol 102(10):6230–6238CrossRefGoogle Scholar
  23. 23.
    Ceylan S, Topcu Y (2014) Pyrolysis kinetics of hazelnut husk using thermo-gravimetric analysis. Bioresour Technol 156:182–188CrossRefGoogle Scholar
  24. 24.
    Mythili R, Venkatachalam P, Subramanian P, Uma D (2013) Characterization of bio residues for bio oil production through pyrolysis. Biresourc Technol 138:71–78CrossRefGoogle Scholar
  25. 25.
    Toshniwal P, Srivastava VC (2017) Existence of synergistic effects during co-pyrolysis of petroleum coke and wood pellet. Intl J Chem React Eng 20160046:1–12Google Scholar
  26. 26.
    El-Sayed SA, Mostafa ME (2015) Kinetic parameters determination of biomass pyrolysis fuels using TGA and DTA Techniques. Waste Biomass Valori 6:401–415CrossRefGoogle Scholar
  27. 27.
    Ferreira CIA, Calisto V, Cuerda-Correa EM, Otero M, Nadais H, Esteves VI (2016) Comparative valorisation of agricultural and industrial bio wastes by combustion and pyrolysis. Bioresour Technol 218:918–925CrossRefGoogle Scholar
  28. 28.
    Kilic M, Kirbiyik C, Cepeliogullar O, Putun AE (2013) Adsorption of heavy metal ions from aqueous solutions by bio-char, a by-product of pyrolysis. Appl Surf Sci 283:856–862CrossRefGoogle Scholar
  29. 29.
    Dandekar A, Baker RTK, Vannice MA (1998) Characterization of activated carbon, graphitized carbon fibres and synthetic diamond power using TPD and DRIFTS. Carbon 36:1821–1831CrossRefGoogle Scholar
  30. 30.
    Hanafiah MAKM, Ngah WSW, Zolkafly SH, Teong LC, Majid ZA (2012) Acid Blue 25 adsorption on base treated Shorea dasyphylla sawdust: Kinetic, isotherm, thermodynamic and spectroscopic analysis. J Environ Sci 24:261–268CrossRefGoogle Scholar
  31. 31.
    Yang J, Fang F, Zhou J (2013) Effect of microstructure and surface chemistry in liquid-phase adsorptive nicotine by almond-shell-based activated carbon. Chinese Sci Bull 58(30):3715–3720CrossRefGoogle Scholar
  32. 32.
    Grube M, Lin J, Lee P, Kokorevicha S (2006) Evaluation of sewage sludge-based compost by FT-IR spectroscopy. Geoderma 130:324–333CrossRefGoogle Scholar
  33. 33.
    Mehmood MA, Ye G, Luo H, Liu C, Malik S, Afzal I, Xu J, Ahmad MS (2017) Pyrolysis and kinetic analyses of camel grass (Cymbopogon schoenanthus) for bioenergy. Bioresour Technol 228:18–24CrossRefGoogle Scholar
  34. 34.
    Braga RM, Melo DM, Aquino FM, Freitas JC, Melo MA, Barros JM, Fontes MS (2014) Characterization and comparative study of pyrolysis kinetics of the rice husk and the elephant grass. J Therm Anal Calorim 115(2):1915–1920CrossRefGoogle Scholar
  35. 35.
    Kim YS, Kim YS, Kim SH (2010) Investigation of thermodynamic parameters in the thermal decomposition of plastic waste–waste lube oil compounds. Environ Sci Technol 44:5313–5317CrossRefGoogle Scholar
  36. 36.
    Xu Y, Chen B (2013) Investigation of thermodynamic parameters in the pyrolysis conversion of biomass and manure to biochars using thermogravimetric analysis. Bioresource Technol 146:485–493CrossRefGoogle Scholar
  37. 37.
    Vlaev L, Georgieva V, Genieva S (2007) Products and kinetics of non-isothermal decomposition of vanadium (IV) oxide compounds. J Therm Anal Calorim 88:805–812CrossRefGoogle Scholar
  38. 38.
    Turmanova SC, Genieva S, Dimitrova A, Vlaev L (2008) Non-isothermal degradationkinetics of filled with rise husk ash polypropene composites. Express Polym Lett 2:133–146CrossRefGoogle Scholar
  39. 39.
    Maia AAD, de Morais LC (2016) Kinetic parameters of red pepper waste as biomass to solid biofuel. Bioresour Technol 204:157–163CrossRefGoogle Scholar
  40. 40.
    Khawam A, Flanagan DR (2006) Solid-state kinetic models: basics and mathematical fundamentals. J Phys Chem B 110:17315–17328CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Chemical EngineeringNational Institute of Technology SrinagarSrinagarIndia
  2. 2.Department of Chemical EngineeringIndian Institute of Technology RoorkeeRoorkeeIndia

Personalised recommendations