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Influence of heartwood on wood density and pulp properties explained by machine learning techniques
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Resumo(s)
The aim of this work is to develop a tool to predict some pulp properties e.g., pulp yield,
Kappa number, ISO brightness (ISO 2470:2008), fiber length and fiber width, using the sapwood and
heartwood proportion in the raw-material. For this purpose, Acacia melanoxylon trees were collected
from four sites in Portugal. Percentage of sapwood and heartwood, area and the stem eccentricity
(in N-S and E-W directions) were measured on transversal stem sections of A. melanoxylon R. Br.
The relative position of the samples with respect to the total tree height was also considered as an input
variable. Different configurations were tested until the maximum correlation coefficient was achieved.
A classical mathematical technique (multiple linear regression) and machine learning methods
(classification and regression trees, multi-layer perceptron and support vector machines) were tested.
Classification and regression trees (CART) was the most accurate model for the prediction of pulp
ISO brightness (R = 0.85). The other parameters could be predicted with fair results (R = 0.64–0.75) by
CART. Hence, the proportion of heartwood and sapwood is a relevant parameter for pulping and
pulp properties, and should be taken as a quality trait when assessing a pulpwood resource
Descrição
Palavras-chave
Acacia melanoxylon heartwood pulp properties multiple linear regression CART multi-layer perceptron support vector machines
Contexto Educativo
Citação
Editora
MDPI