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Tytuł artykułu

Predicting the properties of corrugated base papers using multiple linear regression and artificial neural networks

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The difficulty in predicting the properties and behaviour of paper products produced using heterogeneous raw materials with high percentages of recovered fibres poses restrictions on their efficient and effective use as corrugated packaging materials. This work presents predictive models for the mechanical properties of corrugated base papers (liner and fluting-medium) from fibre and physical property data using multiple linear regression and artificial neural networks. The most significant results were obtained for the prediction of the tensile strength of liners in the cross direction from the origin (wood type, pulp method) of the fibres using linear regression, and the prediction of the compressive strength of fluting-medium in the longitudinal (machine) direction, according to the short-span test, using a neural network with one hidden layer with 6 neurons, with coefficients of determination at 95.14% and 99.28%, respectively.
Rocznik
Strony
61--72
Opis fizyczny
Bibliogr. 40 poz., rys., tab.
Twórcy
  • Linnaeus University, Department of Forestry and Wood Technology, Växjo, Sweden
  • Technological Educational Institute of Thessaly, Department of Wood and Furniture Design and Technology, Karditsa, Greece
autor
  • Institute for Research and Technology (IRETETH), Center for Research and Technology – Hellas (CERTH), Volos, Greece
autor
  • Technological Educational Institute of Thessaly, Department of Wood and Furniture Design and Technology, Karditsa, Greece
Bibliografia
  • Abubakr S.M., Scott G.M., Klungness J.H. [1995]: Fiber fractionation as a method of improving handsheet properties after repeated recycling. Tappi Journal 78 (5): 123-126
  • Adamopoulos S., Martinez E., Ramirez D. [2007]: Characterization of packaging grade papers from recycled raw materials through the study of fibre morphology and composition. Global NEST Journal 9 (1): 20-28
  • Adamopoulos S., Oliver, J.-V. [2006]: Fiber composition of packaging grade papers as determined by the Graff “C” staining test. Wood and Fiber science 38 (4): 567-575
  • Adamopoulos S., Passialis C., Voulgaridis E., Oliver Villanueva J.-V. [2014]: Grammage and structural density as quality indexes of packaging grade paper manufactured from recycled pulp. Drewno 57 (191): 145-151
  • Adamopoulos S., Passialis C., Voulgaridis E. [2009]: Fibre characteristics of papers used in European corrugated packaging industry. ATIP 63 (4): 14-21
  • Adamopoulos S., Voulgaridis E., Passialis C. [2013]: Morphology and identification of fibre furnish components of papers used in the production of corrugated board. Celuloză şi Hârtie 62 (3): 3-10
  • Avijit D. [1995]: The current state of paper recycling, a global review. IPPTA 7 (4): 1-12
  • Batchelor W. [1999]: Refining and the development of fibre properties. Nordic Pulp and Paper Journal 14 (4): 285-291
  • Bishop C.M. [1995]: Neural networks for pattern recognition, Oxford University Press
  • Brancato A.A. [2008]: Effect of progressive recycling on cellulose fiber surface properties. PhD Thesis, Georgia Institute of Technology, USA, 147 pp
  • CEPI Containerboard [2012]: European Corrugated Base Papers List. Brussels, Belgium, 22 pp
  • Ciesielski K. and K. Olejnik K. [2014]: Application of neural networks for estimation of paper properties based on refined pulp properties. Fibres and Textiles in Eastern Europe 22: 126-132
  • Cybenko G. [1989]: Approximation by superpositions of a sigmoidal function. Mathematics of control. Signals and Systems 2 (4): 303-314
  • Ellis R., Sedlachek K. [1993]: Recycled versus virgin-fiber characteristics: A comparison. TAPPI Press, Atlanta, GA: 7-19
  • El-Sebakhy E.A. [2006]: Artificial Neural Networks, Probabilistic Networks, Support Vector Machines, Adaptive-Neuro Fuzzy Systems, and Functional Networks. Elsevier Science, Saudi Arabia
  • European Commission [1994]: European Parliament and Council Directive 94/62/EC of 20 December 1994 on packaging and packaging waste. Official Journal, L365, 31 December 1994, 10-23
  • European Commission [2004]: European Parliament and Council Directive 2004/12/EC of 11 February 2004 amending Directive 94/62/EC on packaging and packaging waste. Official Journal, L47, 18 February 2004, 26-31
  • European Commission [2005]: European Parliament and Council Directive 2005/20/EC of 9 March 2005 amending Directive 94/62/EC on packaging and packaging waste. Official Journal, L70, 16 March 2005, 17-18
  • FEFCO [2012]: European database for corrugated board life cycle studies. FEFCO, Brussels, Belgium
  • Gianeswhar M., Hart D., Scott W. [2000]: The development of mathematical models for predicting sizing, strength and opacity on the Miami University pilot paper machine. In: 2000 TAPPI Papermakers Conference, Atlanta, GA. TAPPI Press
  • Howard R.C., Bichard W.J. [1992]: The basic effect of recycling on pulp properties. Journal of Pulp and Paper Science 18 (4): 151-159
  • Ilvessalo-Pfäffli M.-S. [1995]: Fiber Atlas: Identification of Papermaking Fibers. Springer-Verlag, Berlin
  • Inc D. [2015]: STATISTICA Neural Networks. http://software.dell.com/products/statistica
  • Ince P. [2004]: Fiber resources. Pages 877–883 in J. Burley, J. Evans, and J.A. Youngquist, eds. Encyclopedia of Forest Sciences, Vol. 2. Elsevier Academic Press, Oxford, UK
  • Kim H., Shen X., Rao M., Zurcher J. [1993]: Quality prediction by neural network for pulp and paper processes. Canadian Conference on Electrical and Computer Engineering. Vancouver, BC, pp. 104-107
  • Lou W., Nakai S. [2001]: Application of artificial neural networks for predicting the thermal inactivation of bacteria: a combined effect of temperature, pH and water activity. Food Research International 34 (7): 573-579
  • Nazhad M.M. [2005]: Recycled fibre quality-A review. Journal of Industrial and Engineering Chemistry 11 (3): 314-329
  • Nazhad, M.M., Paszner L. [1994]: Fundamentals of strength loss in recycled paper. Tappi 77 (9): 171-179
  • Nieminen P., Kärkkäinen T., Luostarinen K., Muhonen J. [2011]: Neural prediction of product quality based on pilot paper machine process measurements. 10th International Conference “Adaptive and Natural Computing Algorithms”, ICANNGA 2011, Ljubljana, Slovenia, pp. 240-249
  • Olejnik K., Ciesielski K. [2004]: Neural network model of pulp refining process. Inzynieria Chemiczna i Procesowa 25[3]: 1411-1416
  • Scharcanski J., Dodson C. [1997]: Neural network model for paper forming process. IEEE Transactions on Industry Applications 33[3]: 826-839
  • Virtanen Y., Nilsson S. [2013]: Environmental impacts of waste paper recycling. International Institute for Applied Systems Analysis. Earthscan Publications, London, UK
  • List of standards
  • ISO 187:1990 Paper, board and pulps. Standard atmosphere for conditioning and testing, and procedure for monitoring the atmosphere and conditioning of samples
  • ISO 534:2005 Paper and board. Determination of thickness, density and specific volume
  • ISO 536:2012 Paper and board. Determination of grammage
  • ISO 1924-3:2005 Paper and board. Determination of tensile properties. Part 3: Constant rate of elongation method [100 mm/min]
  • ISO 9895:2008 Paper and board. Compressive strength. Short-span test
  • ISO 1974:2012 Paper. Determination of tearing resistance. Elmendorf method
  • ISO 9184-1:1990 Paper, board and pulps. Fibre furnish analysis – Part 1: General method
  • ISO 9184-3:1990 Paper, board and pulps. Fibre furnish analysis – Part 3: Herzberg staining test
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-d9f31f3b-8b1c-4319-b124-97b49bd2986b
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