Mika Aalto, Olli-Jussi Korpinen, Tapio Ranta. (2019). Feedstock availability and moisture content data processing for multi-year simulation of forest biomass supply in energy production. Silva Fennica vol. 53 no. 4 article id 10147. https://doi.org/10.14214/sf.10147

Keywords: bioenergy; simulation; forest resources; data analysis; geographic information system

Highlights: A method for allocating forest biomass availability for a multi-year simulation model was developed; The possibility to take the quality change of feedstock into account by moisture estimations was studied; A method to estimate weather data for moisture estimation equations with fewer parameters was presented.

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Simulation and modeling have become more common in forest biomass studies. Dynamic simulation has been used to study the supply chain of forest biomass with numerous different models. A robust predictive multi-year model requires biomass availability data, where annual variation is included spatially and temporally. This can be done by using data from enterprises, but in some cases relevant data is not accessible. Another option is to use forest inventory data to estimate biomass availability, but this data must be processed in the correct form to be utilized in the model. This study developed a method for preparing forest inventory data for a multi-year simulation supply model using the theoretical availability of feedstock. Methods for estimating quality changes during roadside storage are also presented, including a possible parameter estimation to decrease the amount of data needed. The methods were tested case by case using the inventory database “Biomass Atlas” and weather data from a weather station in Mikkeli, Finland. The data processing method for biomass allocation produced a reasonable quantity of stands and feedstock, having a realistic annual supply with variation for the demand point. The results of the study indicate that it is possible to estimate moisture content changes using weather data. The estimations decreased the accuracy of the model and, therefore, estimations should be kept minimal. The presented data preparation method can generate a supply of forest biomass for the simulation model, but the validity of the data must be ensured for correct model behavior.

Aalto, Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Laboratory of Bioenergy, Lönnrotinkatu 7, FI-50100 Mikkeli, Finland https://orcid.org/0000-0002-7768-1145 E-mail: mika.aalto@lut.fi
Korpinen, Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Laboratory of Bioenergy, Lönnrotinkatu 7, FI-50100 Mikkeli, Finland E-mail: olli-jussi.korpinen@lut.fi
Ranta, Lappeenranta-Lahti University of Technology LUT, School of Energy Systems, Laboratory of Bioenergy, Lönnrotinkatu 7, FI-50100 Mikkeli, Finland https://orcid.org/0000-0001-5464-5136 E-mail: tapio.ranta@lut.fi

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