Authors:
Vítor M. Magalhães
;
Giancarlo Lucca
;
Alessandro De L. Bicho
and
Eduardo N. Borges
Affiliation:
Centro de Ciências Computacionais, Universidade Federal do Rio Grande – FURG, Brazil
Keyword(s):
Moisture Content, Wood, Intelligent Systems, Machine Learning, Prediction.
Abstract:
Wood is the raw material for many manufactured goods. Charcoal, cellulose for the paper industry, laminated wood furniture, and even explosive products, such as gunpowder cotton, are possible destinations for the wood. On the other hand, the growing use of wood as a raw material has increased illegal deforestation and, as a direct consequence, it has changed the climate at a global level. The use of wood in production processes must be optimized to mitigate these adverse effects. One of the determining factors for this optimization is moisture content on wood, i.e., the ratio between the mass of water contained in the wood and dry wood mass. This article reviews the scientific literature published from 1959 to 2019 regarding the use of wood due to a better knowledge of its properties, particularly systems to explain or predict the moisture content. It contributes to the continuity of related research with the theme by ensemble the conducted studies into a single analysis.