Impact of Different Time Interval Bases on the Accuracy of Meteorological Data Based Drying Models for Oak (Quercus L.) Logs Stored in Piles for Energy Purposes

Natural drying of fuel wood is a feasible option to increase resource efficiency in biomass based energy supply. Meteorological data based drying models are the state-of-the-art to monitor the drying progress. The continuous weighing approach is used to gain data for developing these models. The aim of this study was to investigate the drying performance of oak (Quercus L.) logs stored in piles for energy purposes and assess the effect of model time interval base on the accuracy of meteorological data based drying models. The log pile’s moisture content dropped from initial 38.9% on February 1, 2013 to 24.8% on October 21, 2013, resulting in a total reduction of 14.1%. At the end, moisture content was distributed evenly within the logs and total dry matter losses were low (2.4%). From load and meteorological data, models were developed including 10-minute, hourly, daily and monthly time interval bases. Model performance was validated by comparing the model estimates to the basic observation. Models proved to be very accurate in estimating moisture content change. Compared to the observation, the hourly time interval based model was the most accurate option (mean deviation of 0.10 ±0.13%), while the least accurate option (10-min interval; 1.49 ±1.29%) was still reasonably accurate. Daily and monthly time interval based models are most suitable for use in the forest industry, as they are accurate, while requiring less extensive and detailed input data than models based on hourly or 10-minute time interval.