Research review of material prediction and quality control of heat-treated wood

Abstract

As an environmental-friendly wood physical modification method, wood heat treatment can not only improve the dimensional stability and biological durability but also effectively improve wood color, and is widely used in the functional improvement of fast-growing wood. Some representative research achievements on heat treatment technology are discussed. The traditional processes of wood heat treatment are summarized, and the future research is prospected. The present research on wood heat treatment mainly focuses on the following aspects: (1) the influence mechanism of high temperature heat treatment on such properties as wood dimensional stability, color and crystallization; (2) effects of high temperature environment on the content of the main chemical components, the volatile evaporations and degradation process of wood extraction; (3) changes and reactivity of phenolic hydroxyl and surface free radicals in lignin; (4) effects of heat treatment on wood permeability, paint film adhesion and durability. On this basis, the correlation between changes of wood characteristics (mass or surface chromaticity index) before and after heat treatment and heat treatment intensity is further analyzed. The characteristic parameters of wood such as mass loss rate, color difference of treated wood and the ratio of oxygen to carbon elements are important parameters to predict the properties of heat-treated wood. The future research on wood heat treatment should focus on the relation of micromechanical properties at the cell level and the macro-mechanical properties, and the development of catalysts to reduce the energy consumption of wood heat treatment. The biological durability, environmental and mechanical properties of heat-treated wood can be predicted rapidly and accurately based on the content or proportion of the main elements, surface color and other characteristics. On the other hand, the main parameters of heat treatment process are determined by using the prediction model according to the application environment and requirements, which can provide reference for the follow-up study. [Ch, 48 ref.]