• Energy Research

Rapeseed is considered as one of the most important oilseed crops in the world. Vegetable oil obtained from rapeseed is a valuable raw material for the food and energy industry as well as for industrial applications. Compared to other vegetable oils, it has a lower concentration of saturated fatty acids (5%–10%), a higher content of monounsaturated fatty acids (44%–75%), and a moderate content of alpha-linolenic acid (9%–13%). Overall, rapeseed is grown in all continents on an industrial scale, so there is a growing need to predict yield before harvest. A combination of quantitative and qualitative data were used in this work in order to build three independent prediction models, on the basis of which yield simulations were carried out. Empirical data collected during field tests carried out in 2008–2015 were used to build three models, QQWR15_4, QQWR31_5, and QQWR30_6. Each model was composed of a different number of independent variables, ranging from 21 to 27. The lowest MAPE (mean absolute percentage error) yield prediction error corresponded to QQWR31_5, it was 6.88%, and the coefficient of determination R2 was 0.69. As a result of the sensitivity analysis of the neural network, the most important independent variable influencing the final rapeseed yield was indicated, and for all the analyzed models it was “The kind of sowing date in the previous year” (KSD_PY).

Wood obtained from freshly cut trees has a high moisture content and therefore its mass per volume unit is higher than the mass of dry wood. The natural drying is a simple and effective method of weight (moisture content) reduction, making a better use of the vehicle's payload, and thus reducing costs, including environmental costs. The conclusion is based on simulations and analyzes attempting to evaluate different solutions. As evidenced, the drying process of logs depends on numerous factors, which entails attempts at development of an optimal logging method, especially concerning energy wood, allowing for maximum value growth in as little time as possible. The suggestions cover various forms of logging (of whole trees, long timber), arranging (in piles, bundles), storing (in stands, by the roadside near cutting areas), and protection. Combined methods such as storing under canvas for winter only or storing in cutting areas and later in piles at the exit road also work in practice, but require more funds because their cost is determined by the number of stages composing the whole process. In case of wood, development of an optimal and simultaneously universal method used to prepare the lumber for transport and its delivery with consideration of as little environmental impact as possible is complicated. The numerous variables, some of which cannot be controlled or affected, create a considerable scientific problem as well. The authors covered in the presented literature review often stress the role of specific weather conditions accompanying an experiment or its location, the storage method, or lumber dimensions in the drying process. Storage time is perceived as equally important, but it is a derivative more or less determined by the previous variables. Previous studies of drying at the cutting area have been focused mainly on energy wood and less on industrial wood. They analysed the results of long−term storage and did not stress the potential advantages of short−term drying. Furthermore, we do not have sufficient information on species, including forest trees, which have a lower share in the volume of the logged wood. Advanced research can help establish the minimum mass (moisture content) with considerable impact on supply chain effectiveness improvement or evaluated various storing and drying methods. We reviewed reports explaining the impact of various factors on natural drying of wood, models and storage effects, and analyzing potential economic and environmental benefits. Sylwan 161 (11): 898-908