Digitization of the Drying Process: A Study in Data Collection, Analysis, and Visualization for Improved Production Profitability in Sawmills

There are a variety of measurement systems, equipment, and digital tools in today’s sawmills developed to create conditions for a more profitable production process. However, to fully benefit from all the metrics available, it is important to be able to link the different data streams and have access to historical data. The project reported in the following report aimed to exemplify potential benefits related to the drying process that can be achieved by collecting, linking, and analyzing data from before, during, and after the drying process. The data consisted of package data from the wet sorting, electricity and energy data from the drying process, and various quality parameters from the adjustment plant where mainly moisture content and cut-offs were used in the further analysis.

After processing the data, the following could be visualized among other things: • the utilization rate of the dryers. • the difference in wet and dry dimensions for each dryer batch or product. • the energy consumption per dryer batch or product. • the cut-off per dryer batch or product.

It can also be noted that the journey towards setting up systems that automatically analyze and visualize process data can be extensive before actual results can be achieved. This is because you need to communicate with a range of equipment that has been procured without actual requirements on what the communication protocols should look like and then ensure that data from these is of usable quality.

Background: Forest geneticists typically use provenances to account for population differences in their improvement schemes; however, the historical records of the imported materials might not be very precise or well-aligned with the genetic clusters derived from advanced molecular techniques. The main objective of this study was to assess the impact of marker-based population structure on genetic parameter estimates related to growth and wood properties and their trade-offs in Norway spruce, by either incorporating it as a fixed effect (model-A) or excluding it entirely from the analysis (model-B). Results: Our results indicate that models incorporating population structure significantly reduce estimates of additive genetic variance, resulting in substantial reduction of narrow-sense heritability. However, these models considerably improve prediction accuracies. This was particularly significant for growth and solid-wood properties, which showed to have the highest population genetic differentiation (QST) among the studied traits. Additionally, although the pattern of correlations remained similar across the models, their magnitude was slightly lower for models that included population structure as a fixed effect. This suggests that selection, consistently performed within populations, might be less affected by unfavourable genetic correlations compared to mass selection conducted without pedigree restrictions. Conclusion: We conclude that the results of models properly accounting for population structure are more accurate and less biased compared to those neglecting this effect. This might have practical implications for breeders and forest managers where, decisions based on imprecise selections can pose a high risk to economic efficiency.