| 1. |
- Adamopoulos, Stergios, et al.
(författare)
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Predicting the Properties of Corrugated Base Papers Using Multiple Linear Regression and Artificial Neural Networks
- 2016
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Ingår i: Drewno. - 1644-3985. ; 59:198, s. 61-72
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Tidskriftsartikel (refereegranskat)abstract
- The difficulty in predicting the properties and behaviour of paper products produced using heterogeneous raw materials with high percentages of recovered fibres poses restrictions on their efficient and effective use as corrugated packaging materials. This work presents predictive models for the mechanical properties of corrugated base papers (liner and fluting-medium) from fibre and physical property data using multiple linear regression and artificial neural networks. The most significant results were obtained for the prediction of the tensile strength of liners in the cross direction from the origin (wood type, pulp method) of the fibres using linear regression, and the prediction of the compressive strength of fluting-medium in the longitudinal (machine) direction, according to the short-span test, using a neural network with one hidden layer with 6 neurons, with coefficients of determination at 95.14% and 99.28%, respectively
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| 2. |
- Nilsson, Daniel, et al.
(författare)
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The importance of accurate measurement of comminuted logging residues’ moisture contents for small-scale forest owners
- 2016
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Ingår i: Drewno. - 1644-3985. ; 59:198, s. 99-110
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Tidskriftsartikel (refereegranskat)abstract
- Bioenergy from logging residues is an important contributor to Swedish energysupplies. Thus, accurate measurements of delivered logging residues’ energycontents are very important for both sellers and buyers. Deliveries’ energycontents are highly correlated with their moisture contents, and thus aredetermined in southern Sweden (and elsewhere) by measuring their masses andmoisture contents. There is insufficient knowledge, however, about the variation inmoisture content within and between deliveries, and hence the minimum numberof samples needed to obtain the required precision. Thus, these variations wereexamined in detail in the presented study. Nested analysis of the variance of theacquired data shows that at least nine samples are required to obtain estimates ofa delivery’s moisture content with a 3% margin of error. For high volume trade,such as that between forest companies and the energy-conversion industry,current measurement practices are sufficiently accurate. For private forest ownersmaking single deliveries, however, higher precision is required as inaccuratemeasurements can strongly affect prices.
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