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

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.Search in Google Scholar

2. Abdi, H. (2007). Partial Least Square Regression (PLS- -Regression). Encyclopedia of Measurement and Statistics. Thousand Oaks, USA.Search in Google Scholar

3. Bjorkstrom, A. (2007). Regression methods and their interconnections. Technical report, Stockholm University, Sweden.Search in Google Scholar

4. Farshadfar, E. (2007). Basis and Methods of Multivariate Statistics (2end ed.). Taghbostan Press, Razi university.Search in Google Scholar

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.Search in Google Scholar

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, 252-261.10.1016/j.biortech.2012.05.098Search in Google Scholar

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), 98-105.10.1016/j.chemolab.2006.04.015Search in Google Scholar

8. Lahtinen, K. & Kuuipalo, J. (2008). Statistical prediction model for water vapour barrier of extrusion-coated paper. TAPPI J. 9(2008), 8-15.Search in Google Scholar

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.2006.1657874.10.1109/MCS.2006.1657874Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

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.Search in Google Scholar

13. Nordstrom, F., Lindstrom, T. & Holst, J. (2005). Statistical models for on-line monitoring quality properties. Technical report, Lund Institute of Technology.Search in Google Scholar

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.Search in Google Scholar

15. Wold, S. (1995). PLS for multivariate linear modeling. Chemometric methods in molecular design 2, 195-218.Search in Google Scholar

16. Van der Voet, H. (1994). Comparing the Predictive Accuracy of Models Using a Simple Randomization Test. Chemometr Intell. Lab. 25, 313-323.10.1016/0169-7439(94)85050-XSearch in Google Scholar

17. Jones, G.L. (1993). Modeling a corrugating-medium paper machine for improved edgewise compressive strength, TAPPI J. 76(7), 122-129. Search in Google Scholar