Comparative regression models on experimental designing the Kraft pulping conditions of Anatolian black pine (Pinus nigra Subsp. Pallasiana) wood

SUMMARY Comparative Regression Models on Experimental Designing of the Kraft Pulping Conditions of Anatolian Black Pine {Pinus nigra subsp. pallasiana) Wood In this study, 27 kraft-cooking experiments were made from Anatolian Black Pine wood chips, changing systematically the most effective cooking parameters on pulp properties. Each experiment was duplicated for computing the standard errors. Then pulps converted to papersheets and some chemical, physical and mechanical properties of pulps and sheets were determined. Factorial experimental design (FED) and central composite design (CCD) were chosen to set the regression models. In factorial design, results of total 27 pulping experiments to examine the influence of independent cooking variables such as sulphidity ratio, H-factor, active alkali ratio and beating revolution on pulp and sheet properties were used. Whereas, in central composite design, results of 15 in the 27 total pulping experiments and results of 25 in the total 81 papermaking experiments were used. The results were fitted to a second order polynomial function and prediction ability of the equations derived both FED and CCD methods were compared. As a result, predicting of pulp properties such as kappa number, viscosity, total yield, screened yield, holocellulose and alpha-cellulose content with equations derived from CCD methods gave satisfactory results. But it is determined that prediction ability of CCD method for breaking length, burst index and stretch of papersheets was remained low. According to results of factorial experimental design applied on kraft pulping conditions of Crimean pine wood, optimum pulp and paper properties were obtained at 45% sulphidity charge, 1600 H-factor, 18% active alkali ratio and 600 rev. beating degree level. Key Words: Anatolian Black Pine, Pinus nigra subsp. pallasiana, Kraft Pulping, H- factor, G-factor, Regression Models, Central Composite Design, Factorial Experimental Design VII