Kiln drying is the most energy-consuming operation in the industrial production of sawn timber and optimising the process is necessary to improve its efficiency. A key to achieving this is to speed up the drying, but this must not come at the cost of deteriorating the quality of the sawn timber. X-ray computed tomography (CT) can help in increasing the efficiency of the process, as it is a research tool that can be applied to study moisture transport in wood during drying. The moisture content (MC) can indeed be estimated at a sufficiently small spatial scale but many stages of image processing operations are required. This complexity has until now restricted the computation of MC distributions to only one cross-sectional plane of the sawn timber volume, consequently limiting the potential of the method.

A milestone of this PhD study work, now at its halfway point and hereby presented as a licentiate thesis, has been to develop a method based on four-dimensional CT (4DCT) for estimating the MC in each volume-element (voxel) from a repetitively CT-scanned volume of sawn timber during a drying process. The development of this method is supposed to result in a strong tool for experimental studies into the further optimisation of the industrial drying process.

An image-processing algorithm capable of efficiently estimating MC distributions from 4DCT data was successfully implemented and validated against experimental data. Further processing allows the determination of the moisture gradient (MG) as a time-dependent vector field. Initial studies carried in this doctoral project suggest that it is a relevant parameter for investigating the optimisation of the industrial drying process.

The image-processing algorithm, coded in Python software, requires limited preprocessing or parameter tuning, resulting in significantly reduced computational time compared to previous algorithms. The method has been validated, and errors were quantified by performing linear regressions between the CT-estimated MC spatially averaged at a volume of 1 cm3 and gravimetric measurements performed at the same scale. The sources of error were identified as the image processing and the density measurement uncertainty of the CT scanner. The root mean square error of the CT method for a 1 cm3 volume is 3.8 percentage points in average.

The method was used to investigate the impact of different drying schedules on the degree of checking of sawn timber. As the MG is one of the driving forces of moisture transport, thus controlling the drying rate, and it triggers cracking, it was the parameter chosen to be studied. The MG was calculated and its evolution was correlated with different drying schedules and regimes. The evolution of MG and checking in the experimental studies suggested that the models for creating kiln schedules in the industry are too conservative, i.e. the drying process can be accelerated without significantly lowering the sawn-timber quality due to cracking.

The outcome of this research will lead to a better understanding of moisture transport and drying stresses in sawn timber which will improve drying simulations, providing a strong tool for the optimisation of the industrial drying processes, and ultimately benefitting the sawmilling industry.