Piece of wood with formulas on the sides

Project: Predicting the water vapor sorption of wood with machine learning methods

The water vapor sorption behavior of wood is a fundamental property that affects nearly all of its properties such as heat capacity, thermal conductivity, strength, dimensions, and resistance against decay. Prediction of the water sorption of wood with the existing numerical models is limited and their validity is under question, mainly due to the heterogeneity of wood structure (physically and chemically) at the cell wall level. This project aims at evaluating the applicability of machine learning methods for predicting the sorption behavior of wood species.

Project information

Project manager
Reza Hosseinpourpia
Other project members
Deliang Dai
Participating organizations
Linnaeus University
Linnaeus University
June 2022 - December 2022
Forestry and wood technology (Department of forestry and wood technology, Faculty of technology)
Knowledge Environment
Advanced Materials

More about the project

The project is part of the research in the Linnaeus Knowledgement Environment Advanced Materials.