A statistical analysis of dissolving timber pulp properties using linear mixed models.
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The main focus of the study was to understand the behaviour of seven timber genotypes based on seven chemical properties observed during the chemical pulping process with the prime objective of developing methods of grouping different timber genotypes into compatible groups of timber that can be optimally processed together. Four related statistical methods were used in analysing the data and each had a specific objective. The random coefficients model was used to investigate how the genotypes evolve over the processing stages and it was discovered that the rates of change of the chemical properties studied depended on their initial readings at the beginning of processing. This trend applied for all seven genotypes of pulping trees studied. The important results that came out of fitting the random coefficient model to the data is that the higher the raw stage readings (initial values) the higher the rates of change in the chemical properties over the processing stages. The changes were either increases or decreases in the chemical property studied. The random coefficient model was also used to suggest a rudimental mixing index for the different genotypes based on the average ranking of their slope parameters (rates of change) for the seven variables studied. It was found, for example, that the genotypes GUA and GUW are the least mixable ones. Piecewise linear regression models were used to identify important variables when classifying genotypes and it was generally found that viscosity is not a very useful variable in the classification of genotypes. Using piecewise linear regression models together with kernel density estimation a mixing index (scale) was developed that can be used to determine which genotypes are the most mixable for chemical processing. A coparison of the random coefficient and the piecewise linear regression models shows that the two models yielded very similar conclusions on what genotypes are most mixable during processing. Joint modelling was used to analysis the correlations between evolutions of different chemical properties studied. The various levels of correlations between these variables were discussed. The main limitation of the joint modelling method was its computational challenges because of the many parameters that need to be estimated at the same time.