Synergistic wood drying modeling based on multiple-scale anatomical and physical attributes

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Our over all focus is to understand the complex nature of water sorption and transport mechanisms in wood as well as its thermal characteristics under convective and dielectric heating. This knowledge will help with the rigorous derivation of reliable mathematical and computational models to predict the convective and dielectric heating/drying history of various wood products thus, providing the industry with a decision tool that will help to minimize process times, energy consumption, costs and product defects. Our next 5-year objective is to investigate the characteristic dimensions that can physically describe sorption properties of annual rings, determine the prevailing fractal dimensions with their scaling behaviors and develop coarse grained wood drying prediction model using universal scaling relations in the wood-water system and artificial neural networks computational tools. Drying parameters prediction model will be developed for dimension lumber from physical and anatomical properties of annual rings by analysing, identifying and describing fractal dimensions that will form characteristic lengths individually or in combination. This study will be comprised of two phases, namely experimental and analytical modeling. The first phase will involve density profile measurement, microscopic characterization of radial, tangential and cross sectional surfaces, moisture sorption study at annual ring level, fluid flow and wood porous structure studies, coupled heat and mass transfer experiments, mono and multifractal annual rings image and sorption properties scaling analysis. In the second phase, the major characteristic lengths will be identified, analyzed and fractal dimensions determined after which follows the derivation and development of wood drying scaling parameters and fractal based upscaling that will be concluded by development artificial neural networks drying parameters prediction model. The model will enable fractal based intelligent optimization of the wood drying operations