Speaker
Description
Bioenergy accounts for approximately 30% of Sweden’s energy supply, of which about 80% is derived from the forest sector, e.g. in the form of logging residues such as treetops and branches. In recent years, both demand and prices for logging residues have increased in Sweden, driven by societal decarbonisation efforts and ambitions to strengthen national energy independence in response to Europe’s security situation. The moisture content in logging residues is a key payment‑determining quality attribute, as lower moisture increases energy density in the material and thus its market value. Each year, logging residues worth millions of euros are forwarded from harvesting sites and stored along forest roadsides without any possibility to monitor their moisture content. For fuel suppliers, reliable information on wood fuel stock levels and expected energy content is essential for delivery planning and for fulfilling contractual obligations. Consequently, there is a need for improved understanding of the factors influencing moisture content in wood fuel and for robust methods to estimate it.
Moisture dynamics depend on storage conditions which are strongly influenced by weather‑related factors such as precipitation, temperature, humidity and wind. While large‑scale weather datasets can explain part of this variation, local microclimatic conditions and practises at the storage site are not captured in the available datasets.
Within the Swedish Energy Agency–funded project “Improved forecasts of logging residues for precision planning”, we analysed datasets of wood chip moisture content measured at power plants for thousands of deliveries. The initial aim was to develop a data-driven prediction model to estimate moisture contents and to analyse how storage conditions and supply-chain activities, such as forwarding, could explain the observed variability and provide better decision-support. However, our results showed that forecasting moisture content based solely on available weather data is challenging due to poor representation of local site conditions. It is more effective to promote favourable drying conditions through improved planning and work practices along the supply chain. Enhanced decision-support for moisture management can increase the value of this residual assortment, improve profitability throughout the supply chain and increase overall energy efficiency.
| Keywords | bioenergy; heating-value; forwarding; decision-support |
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