Prediction of Five Softwood Paper Properties from its Density using Support Vector Machine Regression Techniques

Predicting paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kernel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris) and cypress species (Cupressus lusitanica, C. sempervirens, and C. arizonica) beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination) with two variables: the numerical variable density and the categorical variable species.

[1]  Nelson Durán,et al.  Modification of fibre surfaces during pulping and refining as analysed by SEM, XPS and ToF-SIMS , 2003 .

[2]  Ronei J. Poppi,et al.  Determination of Quality Parameters in Moist Wood Chips by Near Infrared Spectroscopy Combining PLS-DA and Support Vector Machines , 2013 .

[3]  Hui Juan Zhang,et al.  Modeling Wood Density of Larch by Near-Infrared Spectrometry and Support Vector Machine , 2011 .

[4]  Helena Pereira,et al.  Influence on pulping yield and pulp properties of wood density of Acacia melanoxylon , 2012, Journal of Wood Science.

[5]  M. Safaei,et al.  Basic effects of pulp refining on fiber properties--a review. , 2015, Carbohydrate polymers.

[6]  Rogério Simões,et al.  Papermaking potential of Acacia dealbata and Acacia melanoxylon , 2006 .

[7]  Jihong He,et al.  A new method for determining the relative bonded area , 2005 .

[8]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[9]  T. Maloney,et al.  Internal Fibrillation in Never-dried and Once-dried Chemical Pulps , 2002 .

[10]  Rogério Simões,et al.  Effect of Acacia Melanoxylon Fibre Morphology on Papermaking Potential , 2011 .

[11]  O. Anjos,et al.  Characterization of Cypress Wood for Kraft Pulp Production , 2014 .

[12]  Leena Paavilainen,et al.  Conformability, flexibility and collapsibility of sulphate pulp fibres , 1993 .

[13]  Vladimir Vapnik,et al.  An overview of statistical learning theory , 1999, IEEE Trans. Neural Networks.

[14]  Martin A. Hubbe,et al.  PAPER’S APPEARANCE: A REVIEW , 2008 .

[15]  Helena Pereira,et al.  Morphological, mechanical, and optical properties of cypress papers , 2014 .

[16]  R. Kibblewhite,et al.  Hardwood market kraft fibre and pulp qualities , 1991 .

[17]  R. S. Seth,et al.  The reinforcing properties of softwood kraft pulps. , 1990 .

[18]  E. García-Gonzalo,et al.  Using Apparent Density of Paper from Hardwood Kraft Pulps to Predict Sheet Properties, based on Unsupervised Classification and Multivariable Regression Techniques , 2015 .

[19]  M. Clerc.  Standard Particle Swarm Optimisation From 2006 to 2011 , 2012 .

[20]  Hannu Paulapuro,et al.  Interfiber bonding and fiber segment activation in paper , 2007, BioResources.

[21]  Laurence R. Schimleck,et al.  Kernel regression methods for the prediction of wood properties of Pinus taeda using near infrared spectroscopy , 2009, Wood Science and Technology.

[22]  Pilar Diaz,et al.  Effect of cellulase-assisted refining on the properties of dried and never-dried eucalyptus pulp , 2002 .