Unified and Heterogeneous Modeling of Water Vapor Sorption in douglas-Fir Wood with Artificial Neural Networks.

The objective of this study was firstly to investigate and understand sorption properties of earlywood, latewood, annual rings and gross wood. Secondly, to develop a heterogeneous sorption model for earlywood, latewood and annual rings by taking into consideration unified complex interactions of anatomy, chemical composition and thermodynamic parameters. Thirdly, to upscale the annual ring level model to gross wood by applying artificial neural networks (ANNs) modeling tools using dimensionally reduced inputs through dimensional analysis and genetic algorithms. Four novel physical models, namely, dynamical two-level systems (TLS) model of annual rings, sorption kinetics, sorption isotherms and TLS model of physical properties and chemical composition were derived and successfully validated using experimental data of Douglas-fir. The annual ring's TLS model was capable to generate novel physical quantities, namely, golden ring volume (GRV) and golden ring cube (GRC) to which the sorption properties are very sensitive, according to the validation tests. A new heterogeneity test criterion (HTC) was also derived. Validations of the TLS sorption models revealed new evidence showing a transient nature of sorption hysteresis in which boundary sorption isotherms asymptotically converged to a single isotherm at large time limit. A novel method for the computation of internal surface area of wood was also validated using the TLS model of sorption isotherms. The fibre saturation point prediction of the model was also found to agree well with earlier reports. The TLS model of physical properties and chemical composition was able to reveal the self-organization in Douglas-fir that gives rise to allometric scaling. The TLS modeling revealed existence of self-organizing criticality (SOC) in Douglas-fir and demonstrated mechanisms by which it is generated. Ten categories of unified ANNs Douglas-fir sorption models that predict equilibrium moisture content, diffusion and surface emission coefficients were successfully developed and validated. The network models predict sorption properties of Douglas-fir using thermodynamic variables and parameters generated by the four TLS models from chemical composition and physical properties of annual rings. The findings of this study contribute to the creation of a decision support system that would allow predicting wood properties and processing characteristics based on chemical and structural attributes. Thesis URL: https://circle.ubc.ca/handle/2429/23032