- 6.1 Introduction
- 6.2 Material and Methods
- 6.3 Statistical Analysis and Methods
- 6.4 Results and Discussion
- 6.5 Conclusions
- 6.6 Literature Cited
- 6.7 List of Tables
- 6.8 List of Figures
Wood density was found to be negatively related to most strength properties of MDF panels (Nelson 1973; Woodson 1976b). It is easier to make good bonded panels at a specific target panel density with low-density (LD) wood than with high-density (HD) wood due to higher compaction ratios (panel density divided by wood density) resulted from the former (Maloney 1993; Hsu 1997).
According to a study by Nelson (1973), strength properties and linear expansion (LE) of hardboards were positively correlated to pulp pH. Water absorption (WA) was also related to pulp pH; linear stability was positively related to fiber length. Yet, Nelson (1973) found that thickness stability was not related to any wood or fiber property studied. Therefore, fiber morphological and chemical properties, fiber composition and fiber strength are considered to be basic characteristics influencing fiberboard panel properties (Jones 1960; Johns et al. 1985; Myers 1987; Suchsland and Woodson 1990; Maloney 1993; Moss and Retulainen 1995; Hsu 1997; Groom et al. 1999).
The influence of fiber strength on fiberboard quality is dependant on panel density. It has little influence in the case of low density fiberboard (LDF), but its influence becomes significant in MDF. Fiber strength can be the predominant factor for high density fiberboard (HDF) (≥900 kg/m3) (Jones 1960). In LDF, failures usually occur in the bonds between fibers. In HDF panels, the contact between fibers is more intimate and thus failures occur within the fiber itself (Suchsland and Woodson 1990).
Fiber length is a critical factor determining the properties of paper (Dinwoodie 1965) and MDF (Suchsland and Woodson 1990). Longer fibers have the tendency to yield higher bulk density. Nelson (1973) found that low bulk density was associated with high panel strength. Fiber length and fiber orientation in the board are also correlated. Shorter fibers are more likely to deviate from the horizontal compared to longer fibers (Suchsland and Woodson 1990). Thus, shorter fibers could be disadvantageous since the contact area between fibers is diminished.
The pH and buffering capacity of the fibers are important parameters for resination. If urea-formaldehyde (UF) resin is used, higher fiber pH and lower buffering capacity are desirable (Nelson 1973; Johns and Niazi 1980; Johns et al. 1985; Maloney 1993; Hsu 1997). A too low pH may eventually cause pre-cure during the initial phase of hot-pressing, while buffering capacity influences the cure rate of UF resin.
Fiber composition refers to the percentages of fines, fiber bundles, whole and broken fibers of the pulp (Myers 1987). Low percentages of fiber bundles and fines are desirable as the former destroy the uniformity of fiber distribution and the latter consume more resin (Woodson 1976a; Moss and Retulainen 1995; Groom et al. 1999; Barnes 2002).
A high level of refining energy spent on wood chips, flakes, or shavings may lead to reduction in fiber length and production of debris (Clark 1978; Myers, 1987; Kure and Dahlqvist 1998; Kure et al. 1999). Steam time and pressure, disc diameter, plate pattern, loading rate, gap between the grinding plates, and rotation speed of the plates also affect fiber composition and size distribution.
The previous reviews show that the impact of the various parameters on panel properties is complex. The objective of this study was to investigate the relationships between MDF panel properties and fiber characteristics. MDF panels were fabricated from eleven raw fiber materials. Fiber samples were taken from the eleven materials for wood characterization. To eliminate the influence of the processing parameters, a constant hot-pressing program was used for the manufacturing of the panels.
Hybrid poplar ( Populus spp.) material came from a clonal trial established by the Forest Research Branch of the Québec Ministry of Natural Resources in St-Ours, Southern Québec, Canada in 1993. The poplar trees were planted at a 1.5×3.5 m spacing in this site that is a part of the Champlain marine deposit with rich salty-clay soil (40 % clay). A systematic thinning was carried out in the spring of 1996 and the thinning led to a spacing of 2.5×3.0 m (Zhang et al. 2003). Four trees from each of hybrid poplar ( P. maximowiczii and P. balsamifera ) clones 915303, 915311, and 915313 were harvested from this site in December 2002 at the age of 10 years.
Larch material was a mix of two individual species ( Larix gmelinii and Larix sibirica ) with an approximate proportion of 4:1. The trees were collected from an exotic larch plantation grown in Northern Ontario, Canada through a commercial thinning. The trees were 10-15 years old, and thus were composted of juvenile wood. In total, seventy sample trees were collected and transported to Forintek Canada Corp. for this study as well as a lumber study.
As MDF mills in eastern Canada usually rely on mixed chips of black spruce, jack pine and balsam fir (S-P-F), a mixed sample of the S-P-F wood chips was collected as a reference from a composite panel mill in Québec, Canada.
ASTM (D 2395-02) method B (2004) was followed for wood density determination. Wood chips collected from each sample were oven-dried at 103 ± 2 oC until constant weight was achieved. The oven-dry weight of the wood chips was taken using a scale. Then the oven-dried chips were soaked in cold water until saturated. The volume of the chips was determined by volume displacement. Density of the wood chips was based on oven-dry weight of wood chips divided by green volume. A large number of wood chips was measured for each sample to ensure that the value was representative. Basic wood density of the eleven samples is listed in Table 6-3.
Wood base buffering capacity was calculated by multiplying the ml of NaOH solution required to raise the starting pH of the wood extract to pH 7 by the normality of the solution (Johns and Niazi 1980). First, wood chips from each sample were ground to powder. Then, 25 g of oven-dried wood powder was added to 200 ml of distilled water for refluxing (20 min). The mixture was filtered through a filter paper using a vacuum and washed several times. The extract was diluted to 500 ml and cooled down before titrating. 50 ml of extract solution was pipetted into a 150 ml beaker and the pH was measured. The extract solution was then titrated to pH 7 with nominal 0.025 N NaOH and a titration curve in 0.1 ml steps was created. The means of four measurements are presented in Table 6-3.
For fiber pH and base buffering capacity, 10 g oven-dried fiber was refluxed and the same procedure as described above was applied. Table 6-3 presents the average of four measurements.
Fiber quality was evaluated using a HiRes Fiber Quality Analyzer (FQA) at the ‘Centre specialisé en pates et papiers’ (Université du Québec à Trois-Riviéres, Canada). Fiber quality parameters evaluated for each sample include fiber coarseness, arithmetic mean length, length weighted mean length, arithmetic mean width, arithmetic fine percentage and length weighted fine percentage are given in Table 6-3.
The Bauer-McNett Classifier was used to determine fiber size distribution according to the procedures described in Tappi T 233 cm-95 (1995). Four screens of mesh size 3.240 mm2, 0.828 mm2, 0.281 mm2, and 0.017 mm2 were used. 10 g (oven-dry weight) of fibers were poured into the first compartment. The water flow rate was 11.355 L/min. A 20 min motor running period was employed. The fiber pads were collected and dried at 105 oC to a constant weight. The measurement was replicated three times for each sample and the values were averaged (Table 6-3).
Dummy variables were created as follows:
z1 = 1if subject is in black spruce 1-20 age zone
Then the eleven wood species or types can be identified by the values of the dummy variables z1, z2, …, z10.
z1 = 1, z2, z3, …, z10 = 0: black spruce 1-20 year-old wood
z2 = 1, z1, z3, …, z10 = 0: black spruce 21-40 year-old wood
z3 = 1, z1, z2, …, z10 = 0: black spruce over 40 year-old wood
z4 = 1, z1, z2, …, z10 = 0: black spruce top logs
z5 = 1, z1, z2, …, z10 = 0: black spruce mid logs
z6 = 1, z1, z2, …, z10 = 0: black spruce butt logs
z7 = 1, z1, z2, …, z10 = 0: hybrid poplar clone 915303
z8 = 1, z1, z2, …, z10 = 0: hybrid poplar clone 915311
z9 = 1, z1, z2, …, z10 = 0: hybrid poplar clone 915313
z10 = 1, z1, z2, …, z9 = 0: two individual larch species
z1, z2, …, z10 = 0: S-P-F wood chips
The multiple linear model for the relationships between response and predictor variables took the form:
Y = b0 + bix1~17 + bjz1~10 + bkx1~17z1~10 + … + ε
where: Y is the response variable, MOR, MOE, IB, LE, TS, or WA;
x1, x2, …, x17, and z1, z2, …, z10 are the predictor variables;
b0 is the intercept, bi, bj, and bk are the regression coefficients (i = 1, …, 17; j = 18, …, 27; k = 28, …, 197);
ε is the error term.
Statistical Analysis System (SAS) (1990) software was used for data analysis. Since there were a number of predictor variables, the multivariate model candidate selection was based on two criteria, stepwise selection and model R square. Thus, preliminary predictor variables selected by the stepwise procedure were listed. These predictor variables were then screened by model R square selection criterion.
The predictor variables entered in the model might be highly correlated (multicolinearity), which can result in unstable estimates and high standard errors (Draper 1980). To avoid this, the selected predictor variables were checked for multicolinearity using syntax “MODEL response variable=predictor variable/VIF COLLIN”. The calculation of the variance inflation factor (VIF) is to regress a specific predictor variable on other predictor variables in order to check multicolinearity of that variable. Generally, if the VIF value is higher than 10, attention should be paid to this variable (Belsley et al. 1980).
MOR = 39.8 - 0.509 arith_fine
The MOR model indicates that arithmetic fine fiber percentage has a negative effect on panel bending strength. If the pulp contains a high proportion of fines and the resin content remains constant, the fines consume more resin due to their large surface area, and resin available for the whole fibers is diminished. As a result, and as explained in the introduction, the fiber to fiber bonding system of the panel is consequently weakened.
MOE = 2.51×103 – 409 _200_ + 45.6 tanwood_pH + 479 z5
The MOE model indicates that very small particles that can pass through a 200 mesh screen had a negative effect on panel MOE. MOE was also positively related to tangent wood pH. According to the feature of the tangent function, wood pH would affect MOE positively if wood pH fell in the ranges of 1/2π – π, π – 3/2π, 3/2π – 2π or 2π – 5/2π. π, 3/2π, 2π, and 5/2π are critical values for wood pH in determining its effect on MOE. If wood pH is equal to π or 2π, there is no effect of wood pH on MOE; if wood pH approaches to the values of 3/2π or 5/2π, the effect of wood pH on MOE becomes instantly great. In addition, the presence of the dummy variable z5 indicates that an unknown wood fiber characteristic significantly influenced panel MOE and it belongs to black spruce mid logs.
IB = 3.48 – 0.0509 arith_fine + 0.405 _200_ + 0.304 z7 – 0.383 fiber_pH
Internal bond strength is linearly and negatively related to arithmetic fine fiber percentage. However, it is positively related to the percentage of very small particles (<0.017 mm2) of the pulp. Moreover, IB is negatively affected by fiber pH. This seems to be contradictory with the previous findings (Johns and Niazi 1980; Johns et al. 1985; Maloney 1993; Hsu 1997). The negative correlation between IB and fiber pH might be attributed to the relatively low temperature for panel consolidation used in this study. The dummy variable z7 appearing in the model implies that other unknown wood fiber characteristics belonging to hybrid poplar clone 915303 influenced IB strength significantly.
LE = 0.209 + 0.0899 z10 – 0.244 quadwood_density
The model indicates that LE was negatively related to wood density. Nelson (1973) found that linear stability is positively related to fiber length. Here, arithmetic mean fiber length was not selected for the model as it was found not to be related to linear expansion.
TS = 44.8 – 38.7 arith_length
The TS model indicates that there is a negative and linear relationship between panel thickness swell and arithmetic mean fiber length.
WA = 158 – 0.00530 cubfiber_width
The predicted relationship between WA and arithmetic mean fiber width is curvilinear. The overall effect of arithmetic mean fiber width on panel WA is negative meaning that larger fiber width is associated with smaller panel WA.
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Table 6-2 Panel bending properties, internal bond strength and dimensional stability.
Table 6-3 Measured wood and fiber characteristics.
Table 6-4 Predictor/response variables and transformed predictor variables.
Table 6-5 Summary of stepwise model selection for MOR, MOE, IB, LE, TS, and WA.
Table 6-6 Multiple linear regression model parameter estimates for MOR, MOE, IB, LE, TS, and WA.Refining speed, cooker pressure, retention time and temperature were 2500 rpm, 7.5 bar, 3 min and 160 oC for all types of material.
* Moisture content after resin and wax blending. Values in parenthesis represent standard deviations.
LE was determined by the linear variation with changing RH from 50 % to 80 % as the percentage change in length based on the length measured at 50 % RH. TS and WA were obtained by calculating the variation in thickness and weight following 24 h water soaking divided by the thickness and weight measured on the specimens equilibrated at 22 oC and 65 %.Values in parentheses represent standard deviations.
Numbers in columns ‘>3.240 mm2’, ‘0.828-3.240 mm2’, ‘0.281-0.828 mm2’, and ‘0.017-0.281 mm2’ were the percentages of fibers retained on the screens with mesh sizes of 3.240 mm2, 0.828 mm2, 0.281 mm2, and 0.017 mm2. Numbers in column ‘<0.017 mm2’ were the percentages of fibers passed through the screen with mesh size of 0.017 mm2.
|Parameter Estimates||Variance Inflation|
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