Skip to main content
Log in

An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process

  • Original Paper
  • Published:
Clean Technologies and Environmental Policy Aims and scope Submit manuscript

Abstract

Volatile organic compounds refer to a large class of carbon-based chemicals capable of evaporating easily into the air at room temperature. Formaldehyde is one of the best known volatile organic compounds, and long-term exposure to formaldehyde emission from wood-based building products in indoor air may cause many adverse health effects. This paper presents an implementation of artificial neural networks for modeling the formaldehyde emission from particleboard as a wood-based product based on wood-glue moisture content, density of board and pressing temperature, with the experimental data collected from Petinarakis and Kavvouras (Wood Res 51(1):31–40, 2006). With the constructed model, formaldehyde emission of particleboard could be predicted successfully, and the intermediate formaldehyde emission values not obtained from experimental investigation could be predicted for different combinations of manufacturing parameters. The results proved that the artificial neural network is a promising technique in predicting the formaldehyde emission from particleboard. In this regard, the findings of this study will help the manufacturing industries in obtaining the intermediate values of the formaldehyde emission without performing further experimental activity. The model thus may save time, reduce the consumption of experimental materials and design costs.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  • Ahmadi M, Naderpour H, Kheyroddin A (2014) Utilization of artificial neural networks to prediction of the capacity of CCFT short columns subject to short term axial load. Arch Civ Mech Eng 14:510–517

    Article  Google Scholar 

  • Behera SK, Meher SK, Park HS (2015) Artificial neural network model for predicting methane percentage in biogas recovered from a landfill upon injection of liquid organic waste. Clean Technol Environ Policy 17:443–453

    Article  CAS  Google Scholar 

  • Bilhan O, Emiroglu ME, Kisi O (2011) Use of artificial neural networks for prediction of discharge coefficient of triangular labyrinth side weir in curved channels. Adv Eng Softw 42:208–214

    Article  Google Scholar 

  • Canakci A, Varol T, Ozsahin S (2015) Artificial neural network to predict the effect of heat treatment, reinforcement size, and volume fraction on AlCuMg alloy matrix composite properties fabricated by stir casting method. Int J Adv Manuf Technol 78:305–317

    Article  Google Scholar 

  • Ceylan I (2008) Determination of drying characteristics of timber by using artificial neural networks and mathematical models. Drying Technol 26:1469–1476

    Article  CAS  Google Scholar 

  • Chakraborty S, Chowdhury S, Saha PD (2013) Artificial neural network (ANN) modeling of dynamic adsorption of crystal violet from aqueous solution using citric-acid-modified rice (Oryza sativa) straw as adsorbent. Clean Technol Environ Policy 15:255–264

    Article  CAS  Google Scholar 

  • Chen CR, Ramaswamy HS, Marcotte M (2007) Neural network applications in heat and mass transfer operations in food processing. WIT Trans State-of-the-art in Sci Eng 13:39–59

    Article  Google Scholar 

  • Elshorbagy A, Corzo G, Srinivasulu S, Solomatine DP (2010) Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology. Part 1: concepts and methodology. Hydrol Earth Syst Sci 14:1931–1941

    Article  Google Scholar 

  • EN 717-3 (1994) Wood-based panels-determination of formaldehyde release. Part 3: Formaldehyde release by the flask method

  • Esteban LG, Fernandez FG, de Palacios P, Romero RM, Cano NN (2009a) Artificial neural networks in wood identification: the case of two juniperus species from the Canary Islands. IAWA J. 30(1):87–94

    Article  Google Scholar 

  • Esteban LG, Fernandez FG, de Palacios P (2009b) MOE prediction in Abies pinsapo Boiss. timber: application of an artificial neural network using non-destructive testing. Comput Struct 87:1360–1365

    Article  Google Scholar 

  • Ghaffari A, Abdollahi H, Khoshayand MR, Bozchalooi IS, Dadgar A, Tehrani MR (2006) Performance comparison of neural network training algorithms in modeling of bimodal drug delivery. Int J Pharm 327:126–138

    Article  CAS  Google Scholar 

  • Huang HY, Haghighat F (2002) Modelling of volatile organic compounds emission from dry building materials. Build Environ 37:1127–1138

    Article  Google Scholar 

  • Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and econometric time series. Neurocomputing 10:215–236

    Article  Google Scholar 

  • Kecebas A, Yabanova I, Yumurtaci M (2012) Artificial neural network modeling of geothermal district heating system thought exergy analysis. Energy Convers Manage 64:206–212

    Article  Google Scholar 

  • Khayet M, Cojocaru C (2013) Artificial neural network model for desalination by sweeping gas membrane distillation. Desalination 308:102–110

    Article  CAS  Google Scholar 

  • Liu Z, Yea W, Little JC (2013) Predicting emissions of volatile and semivolatile organic compounds from building materials: a review. Build Environ 64:7–25

    Article  CAS  Google Scholar 

  • Mafakheri E, Tahmasebi P, Ghanbari D (2012) Application of artificial neural networks for prediction of coercivity of highly ordered cobalt nanowires synthesized by pulse electrodeposition. Measurement 45:1387–1395

    Article  Google Scholar 

  • Nemli G (2002) Factors affecting the production of E1 type particleboard. Turk J Agric For 26:31–36

    Google Scholar 

  • Ozsahin S (2013) Optimization of process parameters in oriented strand board manufacturing with artificial neural network analysis. Eur J Wood Prod 71:769–777

    Article  Google Scholar 

  • Petinarakis JH, Kavvouras PK (2006) Technological factors affecting the emission of formaldehyde from particleboards. Wood Res. 51(1):31–40

    CAS  Google Scholar 

  • Pham DT, Sagiroglu S (2000) Neural network classification of defects in veneer boards. Proc Inst Mech Eng Part B: J Eng Manuf 214(3):255–258

    Article  Google Scholar 

  • Rumelhart D, Hinton G, Williams R (1986) Learning representations by backpropagation errors. Nature 323:533–536

    Article  Google Scholar 

  • Salem MZM, Böhm M, Srba J, Beránková J (2012) Evaluation of formaldehyde emission from different types of wood-based panels and flooring materials using different standard test methods. Build Environ 49:86–96

    Article  Google Scholar 

  • Shenk JS, Westerhaus MO (1996) Calibration the ISI way. In: Davies AMC, Williams PC (eds) Near infrared spectroscopy. NIR Publications, Chichester

    Google Scholar 

  • Srisaeng P, Baxter GS, Wild G (2015) Forecasting demand for low cost carriers in Australia using an artificial neural network approach. Aviation 19(2):90–103

    Article  Google Scholar 

  • Tabarsa T, Ashori A, Gholamzadeh M (2011) Evaluation of surface roughness and mechanical properties of particleboard panels made from bagasse. Compos B 42:1330–1335

    Article  Google Scholar 

  • Taghiyari HR, Ghorbanali M, Tahir PMD (2014) Effect of the improvement in thermal conductivity coefficient by Nano-Wollastonite on physical and mechanical properties in Medium-Density Fiberboard (MDF). Bioresources 9(3):4138–4149

    Article  CAS  Google Scholar 

  • Tahmasebi P, Hezarkhani A (2010) Application of adaptive neuro-fuzzy inference system for grade estimation; Case Study, Sarcheshmeh Porphyry Copper Deposit, Kerman, Iran. Aust J Basic Appl Sci 4(3):408–420

    CAS  Google Scholar 

  • Xiong J, Liu C, Zhang Y (2012) A general analytical model for formaldehyde and VOC emission/sorption in single-layer building materials and its application in determining the characteristic parameters. Atmos Environ 47:288–294

    Article  CAS  Google Scholar 

  • Xu Y, Zhang YP (2004) A general model for analyzing single surface VOC emission characteristics from building materials and its application. Atmos Environ 38:113–119

    Article  Google Scholar 

  • Xu X, Yu ZT, Hu YC, Fan LW, Tian T, Cen KF (2007) Nonlinear fitting calculation of wood thermal conductivity using neural networks. Zhejiang Univ Press 41(7):1201–1204

    Google Scholar 

  • Yang H, Cheng W, Han G (2015) Wood modification at high temperature and pressurized steam: a relational model of mechanical properties based on a neural network. Bioresources 10(3):5758–5776

    CAS  Google Scholar 

  • Zhang G, Ptuwo BE, Hu MY (1998) Forecasting with ANN: the state of the art. Int J Forecast 14:35–62

    Article  CAS  Google Scholar 

  • Zhang J, Cao J, Zhang D (2006) ANN-based data fusion for lumber moisture content sensors. Trans Inst Meas Contr 28(1):69–79

    Article  CAS  Google Scholar 

  • Zielinska S, Kepczynska E (2013) Neural modeling of plant tissue cultures: a review. BioTechnologia 94(3):253–268

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors would like to thank Dr. Joseph H. Petinarakis and Dr. P. K. Kavvouras from National Agricultural Research Foundation, Institute of Mediterranean Forest Ecosystems and Forest Products Technology, Forest Research Institute, Athens, Greece, for obtaining the database used in the paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sebahattin Tiryaki.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akyüz, İ., Özşahin, Ş., Tiryaki, S. et al. An application of artificial neural networks for modeling formaldehyde emission based on process parameters in particleboard manufacturing process. Clean Techn Environ Policy 19, 1449–1458 (2017). https://doi.org/10.1007/s10098-017-1342-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10098-017-1342-0

Keywords

Navigation