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Your search keyword '"García-Nieto, Paulino J."' showing total 7 results
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7 results on '"García-Nieto, Paulino J."'

2. A Hybrid Algorithm for the Assessment of the Influence of Risk Factors in the Development of Upper Limb Musculoskeletal Disorders

Author

Busto Serrano, Nélida M., García Nieto, Paulino J., Suárez Sánchez, Ana, Sánchez Lasheras, Fernando, Riesgo Fernández, Pedro, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, de Cos Juez, Francisco Javier, editor, Villar, José Ramón, editor, de la Cal, Enrique A., editor, Herrero, Álvaro, editor, Quintián, Héctor, editor, Sáez, José António, editor, and Corchado, Emilio, editor

Published
2018
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6. Using Apparent Density of Paper from Hardwood Kraft Pulps to Predict Sheet Properties, based on Unsupervised Classification and Multivariable Regression Techniques.

Author

Anjos, Ofélia, García-Gonzalo, Esperanza, Santos, António J. A., Simões, Rogério, Martínez-Torres, Javier, Pereira, Helena, and García-Nieto, Paulino J.

Subjects
HARDWOODS, ACACIA, SULFATE pulping process, RAW materials, MULTIVARIATE analysis, MATHEMATICAL models
Abstract

Paper properties determine the product application potential and depend on the raw material, pulping conditions, and pulp refining. The aim of this study was to construct mathematical models that predict quantitative relations between the paper density and various mechanical and optical properties of the paper. A dataset of properties of paper handsheets produced with pulps of Acacia dealbata, Acacia melanoxylon, and Eucalyptus globulus beaten at 500, 2500, and 4500 revolutions was used. Unsupervised classification techniques were combined to assess the need to perform separated prediction models for each species, and multivariable regression techniques were used to establish such prediction models. It was possible to develop models with a high goodness of fit using paper density as the independent variable (or predictor) for all variables except tear index and zero-span tensile strength, both dry and wet. [ABSTRACT FROM AUTHOR]

Published
2015
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