Skip to main content
Log in

Thermal Analysis and Cone Calorimeter Study of Engineered Wood with an Emphasis on Fire Modelling

  • Published:
Fire Technology Aims and scope Submit manuscript

Abstract

Engineered wood products (EWPs) are a group of materials having a very similar chemical composition but having different and non-uniform thermo-physical properties throughout their thickness. Such materials present a significant challenge from the pyrolysis modelling point of view. The main focus of the paper is to study and compare the differences between six EWPs—oriented strand board (OSB), plywood, particle board (PB), low-density (LDF), medium-density (MDF) and high-density (HDF) fibreboard—in terms of their pyrolysis and burning behaviour. Vertical density profiles (VDPs), thermal degradation behaviour, and burning behaviour were studied and compared. There is a considerable need for a consistent and systematic approach in estimating pyrolysis model complexity and model input parameters. A systematic method to determine the minimum level of the EWPs decomposition model complexity to reproduce the thermal degradation behaviour as measured using thermogravimetric analysis and using the set of parallel reactions was applied. EWPs were found to have similar thermal decomposition onset and range. Maximal decomposition rates were within 25%. OSB, PB, LDF and HDF decomposition can be modelled using three-step parallel reactions scheme, MDF using four parallel reactions. A set of parallel reactions cannot describe the thermal degradation behaviour of plywood. Cone calorimeter tests at heat flux levels of 20 kW/m2, 50 kW/m2 and \(80\, \hbox {kW}/\hbox {m}^{2}\) revealed that influence of the different thermo-physical properties on time to ignition and time to peak heat release rate (HRR) is not significant except LDF and HDF due to their very different density. Peak HRR varies between EWPs, which is attributed primarily to charring and different thermo-physical properties of the EWPs char. EWPs gas phase combustion parameters for the fire models were derived.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9

Similar content being viewed by others

Abbreviations

A :

Pre-exponential factor (1/s)

e :

Euler number (2.71828)

E :

Activation energy (J/mol)

\(\varDelta H_{c}\) :

Effective heat of combustion (MJ/kg)

k :

The number of points in data set (–)

L :

Thickness (mm)

m :

Mass (mg)

n :

Reaction order (–)

N :

Total number of reactions (–)

p :

Number of complexes (–)

r :

Reaction rate (1/s)

R :

Universal gas constant (8.314 J/mol/K)

t :

Time (s)

T :

Temperature (K)

\(\varDelta T\) :

Pyrolysis range (K)

y :

Mass fraction (–)

\(\alpha\) :

Conversion (–)

\(\beta\) :

Heating rate (K/s)

\(\xi\) :

Mass loss change (1/s)

\(\rho\) :

Density (\(\hbox {kg}/\hbox {m}^3\))

\(\nu\) :

Stoichiometric coefficient (–)

exp :

Experiment

j :

Reaction

p :

Peak

R :

Residue

X :

Component

0:

Initial state

CFD:

Computational fluid dynamics

DSC:

Differential scanning calorimetry

DTG:

Derivative thermogravimetric

DDTG:

Second derivative thermogravimetric

EWP:

Engineered wood product

FDS:

Fire dynamics simulator

FV:

Fitness value

HDF:

High-density fibreboard

HRR:

Heat release rate

LDF:

Low-density fibreboard

MDF:

Medium-density fibreboard

MLR:

Mass loss rate

ODE:

Ordinary differential equation

OSB:

Oriented strand board

PB:

Particle board

PTFE:

Polytetrafluoroethylene

RSD:

Relative standard deviation

SCE:

Shuffled complex evolution

STA:

Simultaneous thermal analysis

TGA:

Thermogravimetric analysis

VDP:

Vertical density profile

References

  1. Wong WCK, Dembsey NA, Alston J, Lautenberger C (2013) A multi-component dataset framework for validation of CFD flame spread models. J Fire Prot Eng 23(2):85–134

    Article  Google Scholar 

  2. Kim ME, Dembsey N (2012) Engineering guide for estimating material pyrolysis properties for fire modeling. Technical report, Worcester Polytechnic Institute

    Google Scholar 

  3. Lautenberger C, Fernandez-Pello C (2011) Optimization algorithms for material pyrolysis property estimation. In: Fire safety science proceedings of the tenth international symposium, pp 751–764

  4. Chaos M, Khan MM, Krishnamoorthy N, Ris JL, Dorofeev SB (2011) Evaluation of optimization schemes and determination of solid fuel properties for CFD fire models using the bench-scale pyrolysis tests. Proc Combust Inst 33:2599–2606

    Article  Google Scholar 

  5. Stoliarov SI, Li J (2016) Parametrization and validation of pyrolysis models for polymeric materials. Fire Technol 52(1):79–91

    Article  Google Scholar 

  6. Kim E, Dembsey N (2015) Parameter estimation for comprehensive pyrolysis modeling: guidance and critical observations. Fire Technol 51(2):443–477

    Article  Google Scholar 

  7. McKinnon M (2016) A generalized methodology to characterize composite materials for pyrolysis models. PhD thesis, University of Maryland

  8. Ge J, Wang R, Liu L (2016) Study on the thermal degradation kinetics of the common wooden boards. Proc Eng 135:72–82

    Article  Google Scholar 

  9. Li KY, Fleischmann CM, Spearpoint MJ (2013) Determining thermal physical properties of pyrolyzing New Zealand medium density fibreboard (MDF). Chem Eng Sci 95:211–220

    Article  Google Scholar 

  10. Li KY, Cheng X, Zhang H (2014) A simplified model on vertical density profile and shrinkage ratio of virgin and charred medium density fibreboard. Fire Mater 38:659–672

    Article  Google Scholar 

  11. Huang X, Li K, Zhang H (2017) Modelling bench-scale fire on engineered wood: effects of transient flame and physicochemical properties. Proc Combust Inst 36:3167–3175

    Article  Google Scholar 

  12. Zeinali D, Gupta A, Maragkos G, Agarwal G, Beji T, Chaos M, Wang Y, Degroote J, Merci B (2019) Study of the importance of non-uniform mass density in numerical simulations of fire spread over MDF panels in corner configuration. Combust Flame 200:303–315

    Article  Google Scholar 

  13. Antal MJ (1998) Cellulose pyrolysis kinetics: revisited. Ind Eng Chem Res 37:1267–1275

    Article  Google Scholar 

  14. Bal N, Rein G (2013) Relevant model complexity for non-charring polymer pyrolysis. Fire Saf J 61:36–44

    Article  Google Scholar 

  15. Ghorbani Z, Webster R, Lázaro M, Trouvé A (2013) Limitations in the predictive capability of pyrolysis models based on a calibrated semi-empirical approach. Fire Saf J 61:274–288

    Article  Google Scholar 

  16. Ma JF, Zhang QL (2008) Approximation property of T-S fuzzy singular systems. Control Theory Appl 25(5):837–844

    MATH  Google Scholar 

  17. Li K, Huang X, Fleischmann C, Rein G (2014) Pyrolysis of medium-density fiberboard:optimized search for kinetics scheme and parameters via a genetic algorithm driven by kissinger’s method. Energy Fuels 28:6130–6139

    Article  Google Scholar 

  18. Stark NM, Cai Z, Carll C (2010) Wood-based composite materials. In: Ross RJ (ed) Wood handbook: wood as an engineering material. Forest Products Laboratory, United States Department of Agriculture Forest Service, Madison, Wisconsin

    Google Scholar 

  19. Narayan R, Antal MJ (1996) Thermal lag, fusion, and the compensation effect during biomass pyrolysis. Ind Eng Chem Res 35:1711–1721

    Article  Google Scholar 

  20. Richter F, Rein G (2018) The role of heat transfer limitations in polymer pyrolysis at the microscale. Front Mech Eng 4:18

    Article  Google Scholar 

  21. Richter F, Rein G (2019) Heterogeneous kinetics of timber charring at the microscale. J Anal Appl Pyrol 138:1–9

    Article  Google Scholar 

  22. Back G, Beyler C, DiNenno P, Tatem P (1994) Wall incident heat flux distributions resulting from an adjacent fire. In: Fire safety science-proceedings of the fourth international symposium, pp 241–252. International Association for Fire Safety Science

  23. Babrauskas V, Mulholland G (1987) Smoke and soot data determinations in the cone calorimeter. In: Mathematical modleing of fires, ASTM STP 983, pp 83–104. American Society for Testing and Materials

  24. Matala A, Lautenberger C, Hostikka S (2012) Generalized direct method for pyrolysis kinetic parameter estimation and comparison to existing methods. J Fire Sci 30(4):339–356

    Article  Google Scholar 

  25. Rein G, Lautenberger C, Fernandez-Pello C, Torero JL, Urban DL (2006) Application of genetic algorithms and thermogravimetry to determine the kinetics of polyurethane foam in smoldering combustion. Combust Flame 146:95–108

    Article  Google Scholar 

  26. Lautenberger C, Rein G, Fernandez-Pello C (2006) The application of a genetic algorithm to estimate material properties for fire modeling from bench-scale fire test data. Fire Saf J 41:204–214

    Article  Google Scholar 

  27. Lautenberger C, Fernandez-Pello C (2009) A model for the oxidative pyrolysis of wood. Combust Flame 156:1503–1513

    Article  Google Scholar 

  28. Hasalová L, Ira J, Jahoda M (2016) Practical observations on the use of shuffled complex evolution (SCE) algorithm for kinetic parameters estimation in pyrolysis modeling. Fire Saf J 80:71–82

    Article  Google Scholar 

  29. Li K, Pau DS, Zhang H (2016) Pyrolysis of polyurethane foam: optimized search for kinetic properties via simultaneous K–K method, genetic algorithm and elemental analysis. Fire Mater 40:800–817

    Article  Google Scholar 

  30. Lyon RE, Safronova N, Oztekin E (2011) A simple method for determining kinetic parameters for materials in fire models. In: Fire safety science proceedings of the tenth international symposium, pp 765–778

  31. McGrattan K, Hostikka S, Floyd J, Vanella M (2018) Fire dynamics simulator, technical reference guide: verification, vol 2. National Institute of Standards and Technology, Gaithesburg

    Google Scholar 

  32. Duan QY, Sorooshian S, Gupta VK (1993) Shuffled complex evolution approach for effective and efficient global minimization. J Optim Theory Appl 76:501–521

    Article  MathSciNet  Google Scholar 

  33. Duan Q, Sorooshian S, Gupta VK (1994) Optimal use of the sce-ua global optimization method for calibrating watershed models. J Hydrol 158:265–284

    Article  Google Scholar 

  34. Grønli MG, Varhegyi G, Di-Blasi C (2002) Thermogravimetric analysis and devolatilization kinetics of wood. Ind Eng Chem Res 41:4201–4208

    Article  Google Scholar 

  35. Yang H, Yan R, Chen H, Lee DH, Zheng C (2007) Characteristics of hemicellulose, cellulose and lignin pyrolysis. Fuel 86:1781–1788

    Article  Google Scholar 

  36. Ding Y, Ezekoye OA, Lu S, Wang C, Zhou R (2017) Comparative pyrolysis behaviors and reaction mechanisms of hardwood and softwood. Energy Convers Manag 132:102–109

    Article  Google Scholar 

  37. Fateh T, Rogazme T, Luche J, Jabouille F (2013) Kinetic and mechanism of the thermal degradation of a plywood by using thermogravimetry and fourier-transformed infrared spectroscopy analysis in nitrogen and air atmosphere. Fire Saf J 58:25–37

    Article  Google Scholar 

  38. Richter F, Rein G (2017) Pyrolysis kinetics and multi-objective inverse modelling of cellulose at the microscale. Fire Saf J 91:191–199

    Article  Google Scholar 

  39. Bal N, Rein G (2015) On the effect of inverse modelling and compensation effects in computational pyrolysis for fire scenarios. Fire Saf J 72:68–76

    Article  Google Scholar 

  40. Patel P, Hull TR, Stec AA, Lyon RE (2011) Influence of physical properties on polymer flammability in the cone calorimeter. Polym Adv Technol 22:1100–1107

    Article  Google Scholar 

  41. Matala A (2008) Estimation of solid phase reaction parameters for fire simulation. Master’s thesis, Helsinky University of Technology

  42. Lautenberger C (2007) Generalized pyrolysis model for combustible solids. PhD thesis, University of California

  43. Stoliarov SI, Safronava N, Lyon RE (2009) The effect of variation in polymer properties on the rate of burning. Fire Mater 33:257–271

    Article  Google Scholar 

  44. Stoliarov SI, Crowley S, Walters RN, Lyon RE (2009) Prediction of the burning rates of charring polymers. Fire Mater 33:257–271

    Article  Google Scholar 

  45. Li K, Mousavi M, Hostikka S (2017) Char cracking of medium density fibreboard due to thermal shock effect induced pyrolysis shrinkage. Fire Saf J 91:165–173

    Article  Google Scholar 

  46. Ragland KW, Aerts DJ (1991) Properties of wood for combustion analysis. Bioresour Technol 37:161–168

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge financial support from the Specific University Research (MSMT No 21-SVV/2018) fund of the Ministry of Education Youth and Sport of the Czech Republic. The authors would like to acknowledge financial support by the Czech Science Foundation (project GACR no. 19-22435S).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiří Ira.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ira, J., Hasalová, L., Šálek, V. et al. Thermal Analysis and Cone Calorimeter Study of Engineered Wood with an Emphasis on Fire Modelling. Fire Technol 56, 1099–1132 (2020). https://doi.org/10.1007/s10694-019-00922-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10694-019-00922-9

Keywords

Navigation