Investigation of wood properties that influence the final moisture content of air-dried sugi (Cryptomeria japonica) using principal component regression analysis
Sugi (Cryptomeria japonica D. Don) lumber is known to have a large variability in final moisture content (MCf) and is difficult to dry. This study investigated the variability in MCf of sugi in relation to wood properties. The wood property variables included initial moisture content (MCi), basic density (BD), annual ring orientation (ARO), annual ring width (ARW), heartwood ratio (HR) and CIE L* color (L*). Sugi samples were cut from flat-sawn lumbers and air-dried; a principal component regression (PCR) model for predicting MCf was developed with the wood property variables. The wood properties that contributed to the prediction of MCf were evaluated by PCR analysis. Significant positive regression coefficients of the PCR model were observed in the MCi, BD, ARO and HR, whereas negative ones in the L*. There were no significant regression coefficients in the ARW. These results suggest that the MCi, BD, ARO and HR had a positive influence, the L* had a negative influence, and the ARW had little influence on the MCf of air-dried sugi wood. This finding is in line with the general view on the drying characteristics in relation to these wood properties.
Neuro-fuzzy Hybrid System for Monitoring Wood Moisture Content During Drying
The heterogeneous behavior of wood during drying is a process difficult to control. The objective of this study was to evaluate the accuracy of the neuro-fuzzy hybrid system for monitoring wood moisture during drying. Eucalyptus urophylla x Eucalyptus grandis samples (2 x 2 x 4 cm) were saturated and dried in climatic chamber for 15 days. Basic density was determined by the dry mass/saturated volume ratio. Two neuro-fuzzy systems were developed to monitor wood moisture, the first based on the genetic material and drying period and the second based on basic density and drying period. The drying rate of wood samples was higher at the initial period and all reached equilibrium moisture content after 15 days. Density showed relationship with wood moisture during the study period. Both systems have the potential to monitor moisture, however, neuro-fuzzy system based on basic density and drying period showed better results and is therefore more suitable.
The high-temperature drying of softwood boards: a kiln-wide model
Characteristic drying curves are normally determined from laboratory experimental data to specify the drying behaviour in a commercial-scale dryer. By contrast in this paper, we obtain the drying kinetics from the numerical results of a more rigorous mathematical model. In our example, we use a model developed for the high-temperature drying of a single softwood board to describe the drying behaviour of a stack of boards in a kiln. Analysis of the mechanism of moisture movement has led us to identify three stages in the drying of a sapwood board and two for a heartwood board. The drying kinetics can be represented by a dual characteristic curve covering the two falling-rate periods, to yield simplified expressions for the drying kinetics. These expressions can be coupled with mass and energy balances over a control volume to give equations which can be solved numerically to determine the humidity, temperature and moisture-content profiles in the airflow direction within a timber kiln. This two-step modelling procedure provides a computationally efficient way of analysing conditions in any batch dryer