Full-text resources of PSJD and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl


Preferences help
enabled [disable] Abstract
Number of results
2014 | 16 | 1 | 110-116

Article title

Linking process variables and newsprint properties in Mazandaran Wood and paper Industries


Title variants

Languages of publication



Pulp and paper industries have provided great research opportunities to control systems. The objective of this study was to investigate the relationships between 80 process variables of CMP tower and stock preparation, and 17 newsprint quality properties in Mazandaran Wood and Paper Industries (MWPI). After the preparation of two suitable data series considering the time needed for pulp to paper, the relations between process dependent and newsprint independent variables were determined using partial least squares (PLS) regression. As a result, two PLS models were developed. The first model with 4 latent vectors categorized and related CMP tower variables and the second one, through 8 latent vectors connected stock preparation variables with paper properties. PLS regression coefficients determined how much the most influencing process variables impact each paper properties









Physical description


1 - 03 - 2014
25 - 03 - 2014


  • Behbahan Khatam Alanbi University of Technology, Natural Resources Department, Behbahan, Iran,
  • Gorgan Agricultural Science and Natural Resources, Department of Pulp and Paper Technology, Gogan, Iran


  • 1. Schweiger, C.A. & Rudd, J.B. (1994). Prediction and control of paper machine using adaptive technologies in process modeling. TAPPI J. 77(11), 201-208.
  • 2. Abdi, H. (2007). Partial Least Square Regression (PLS- -Regression). Encyclopedia of Measurement and Statistics. Thousand Oaks, USA.
  • 3. Bjorkstrom, A. (2007). Regression methods and their interconnections. Technical report, Stockholm University, Sweden.
  • 4. Farshadfar, E. (2007). Basis and Methods of Multivariate Statistics (2end ed.). Taghbostan Press, Razi university.
  • 5. Fridén, H. & Tano, K. (2005). Using PLS models with both controlled and uncontrolled X variables for” Waht if...” prediction. In The 9th Scandinavian Symposium on Chemometrics, Reykjavik, Iceland 2005-09-30. Ornsköldsvik: NPI.
  • 6. Suwannarangsee, S., Bunterngsook, B., Arnthong, J., Paemanee, A., Thamchaipenet, A., Eurwilaichitr, L. & Champreda, V. (2012). Optimisation of synergistic biomass-degrading enzyme systems for effi cient rice straw hydrolysis using an experimental mixture design. Bioresource Technol. 119, 252-261.[WoS]
  • 7. Kallioinen, M., Huuhilo, T., Reinikainen, S.P., Nuortila- -Jokinen, J. & Mänttäri, M. (2006). Examination of membrane performance with multivariate methods: A case study within a pulp and paper mill fi ltration application. Chemometr Intell. Lab. 84(1), 98-105.
  • 8. Lahtinen, K. & Kuuipalo, J. (2008). Statistical prediction model for water vapour barrier of extrusion-coated paper. TAPPI J. 9(2008), 8-15.
  • 9. Mercangoz, M. & Doyle, F.J. (2006). Model-based control in the pulp and paper industry. Control Systems, Ieee. 26(4), 30-39. DOI: 10.1109/MCS.2006.1657874.[Crossref]
  • 10. Broderick, G., Paris, J., Valade, J.L. & Wood, J. (1995). Applying latent vector analysis to pulp characterization. PAP Puu-Pup Tim. 77(6/7), 410-418.
  • 11. Broderick, G., Paris, J., Valade, J.L. & Wood, J. (1996). Linking the fi ber characteristics and handsheet properties of a high-yield pulp. TAPPI J. 79(1), 161-169.
  • 12. Grage, H. (2004). A statistical analysis of data from the production line at the Munksund paper mill. Technical report, Lund Institute of Technology, Sweden.
  • 13. Nordstrom, F., Lindstrom, T. & Holst, J. (2005). Statistical models for on-line monitoring quality properties. Technical report, Lund Institute of Technology.
  • 14. Ortiz-Cordova, M.H.A., Orccotoma, J.B.J. & Begin, B.P.J. (2006). MATHEMATICAL MODELS-Analysis of paper strength variability in an integrated newsprint mill. Pulp Pap- -Canada. 107(10), 37-43.
  • 15. Wold, S. (1995). PLS for multivariate linear modeling. Chemometric methods in molecular design 2, 195-218.
  • 16. Van der Voet, H. (1994). Comparing the Predictive Accuracy of Models Using a Simple Randomization Test. Chemometr Intell. Lab. 25, 313-323.
  • 17. Jones, G.L. (1993). Modeling a corrugating-medium paper machine for improved edgewise compressive strength, TAPPI J. 76(7), 122-129.

Document Type

Publication order reference


YADDA identifier

JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.