Publicação

Prediction of five softwood paper properties from its density using support vector machine regression techniques

Detalhes bibliográficos
Resumo:	Prediction paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kemel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris) and cypress species (Cupressus lusitanica, C. sempervirens e C. arizonica) beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination) with two variables: the numerical variable density and the categorical variable density.
Autores principais:	García-Gonzalo, Esperanza
Outros Autores:	Santos, António J.; Martínez-Torres, Javier; Pereira, Helena; Simões, Rogério; García-Nieto, Paulino; Anjos, O.
Assunto:	Vector machine regression Paper properties Kraft pulp Pinus pinaster Pinus sylvestris Cupressus lusitanica Cupressus sempervirens Cupressus arizonica
Ano:	2016
País:	Portugal
Tipo de documento:	artigo
Tipo de acesso:	acesso aberto
Instituição associada:	Instituto Politécnico de Castelo Branco
Idioma:	inglês
Origem:	Repositório Científico do Instituto Politécnico de Castelo Branco

Descrição
Resumo:	Prediction paper properties based on a limited number of measured variables can be an important tool for the industry. Mathematical models were developed to predict mechanical and optical properties from the corresponding paper density for some softwood papers using support vector machine regression with the Radial Basis Function Kemel. A dataset of different properties of paper handsheets produced from pulps of pine (Pinus pinaster and P. sylvestris) and cypress species (Cupressus lusitanica, C. sempervirens e C. arizonica) beaten at 1000, 4000, and 7000 revolutions was used. The results show that it is possible to obtain good models (with high coefficient of determination) with two variables: the numerical variable density and the categorical variable density.