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Journal Articles Neurocomputing Year : 2017

Wood moisture content prediction using feature selection techniques and a kernel method

Abstract

Wood is a renewable, abundant bio-energy and environment friendly resource. Woody biomass Moisture Content (MC) is a key parameter for controlling the biofuel product qualities and properties. In this paper, we are interested in predicting MC from data. The input impedance of half-wave dipole antenna when buried in the wood pile varies according to the permittivity of wood. Hence, the measurement of reflection coefficient, that gives information about the input impedance, depends directly on the MC of wood. The relationship between the reflection coefficient measurements and the MC is studied. Based upon this relationship, MC predictive models that use machine learning techniques and feature selection methods are proposed. Numerical experiments using real world data show the relevance of the proposed approach that requires a limited computational power. Therefore, a real-time implementation for industrial processes is feasible.
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Dates and versions

hal-04028716 , version 1 (14-03-2023)

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Hela Daassi-Gnaba, Yacine Oussar, Maria Merlan, Thierry Ditchi, Emmanuel Géron, et al.. Wood moisture content prediction using feature selection techniques and a kernel method. Neurocomputing, 2017, 237, pp.79-91. ⟨10.1016/j.neucom.2016.09.005⟩. ⟨hal-04028716⟩
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