Moisture sorption isotherms and thermodynamic properties of Oak wood (Quercus robur and Quercus canariensis): optimization of the processing parameters

Abstract

The aim of this paper was to determine the moisture desorption isotherms and essentials thermodynamic properties of two Oak wood varieties. Desorption isotherms were measured using a static gravimetric method at 50, 60, 70 and 80 °C within the range of 5–90 % relative humidity. The equilibrium moisture content decreased with increasing temperature and decreased with decreasing relative humidity at a constant temperature. The ‘Thermodynamic’ sorption equation was found to be the best for describing the experimental moisture sorption isotherms of woods within the range of temperature and water activity investigated. The Fiber saturation point, deduced from the ‘Thermodynamic’ model parameters, depends on the temperature and varying from 22.6 to 54.4 (% kg water/kg dry matter). Isosteric heat of desorption and differential entropy were calculated by applying Clausius–Clapeyron equation to the desorption data fitted by the ‘Thermodynamic’ model. The isosteric heat of desorption and the differential entropy decreased with increasing moisture content according to an exponential law equation and varying from 2.03 to 31.14 kJ/mol and from 73.98 to 4.34 J/(mol K), respectively. The linear relationship between differential enthalpy and entropy satisfied the enthalpy–entropy compensation theory. The sign of Gibbs free energy was found to be positive (+283 J/mol) and (+97 J/mol) for Quercus robur and Quercus canariensis, respectively. The isokinetic temperature was found to be greater than the harmonic temperature. Based on the enthalpy–entropy compensation theory, it could be concluded that the moisture desorption isotherm of Oak wood is a non-spontaneous and enthalpy-controlled process.

Appendix

$$\begin{aligned} {\text{s }} & = \sqrt {\frac{{\sum\nolimits_{{{\text{i}} = 1}}^{\text{nexpdata}} {({\text{X}}_{{{\text{eq}}_{\text{i}} }} - {\text{X}}_{{{\text{eq}}_{\text{cal,i}} }} )^{2} } }}{{{\text{n}}_{{{ \exp } . {\text{data}}}} - {\text{n}}_{\text{param}} }}} \\ {\text{r }} & = \sqrt {1 - \frac{{\sum\nolimits_{i = 1}^{{{\text{n}}_{\text{expdata}} }} {({\text{X}}_{{{\text{eq}}_{\text{i}} }} - {\text{X}}_{{{\text{eq}}_{\text{cal,i}} }} )^{2} } }}{{\sum\nolimits_{{{\text{i}} = 1}}^{{{\text{n}}_{\text{expdata}} }} {(\overline{{X_{\text{eq}} }} - {\text{X}}_{{{\text{eq}}_{\text{i}} }} )^{2} } }}} \\ \overline{{{\text{X}}_{\text{eq}} }} & = \frac{1}{{{\text{n}}_{{{ \exp } . {\text{data}}}} }}\sum\limits_{{{\text{i}} = 1}}^{{{\text{n}}_{\text{expdata}} }} {{\text{X}}_{{{\text{eq}}_{\text{i}} }} } \\ \end{aligned}$$