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Published VersionCC BY V4.0, Open Access
Journal article
Evaluating the Impact of Meteorological Data Sources on Moisture Prediction Accuracy of Eucalyptus Nitens Log Pile Natural Drying Models
Croatian Journal of Forest Engineering, Vol.44(2), pp.337-346
2023
Abstract
Drying forest biomass at roadside can reduce transport costs and greenhouse gas emissions by reducing its weight and increasing its net calorific value. Drying models are required for forest supply chain analysis to determine optimum storage times considering storage costs and returns. The study purpose was to evaluate the impact of the source of meteorological data on the goodness of fit and practical application of Eucalyptus nitens log pile drying models. The study was conducted in Long Reach, NE Tasmania, Australia from the 6th of February to 6th of August 2020. Four data sources were compared: the nearest meteorological station, interpolated meteorological data, a portable weather station, and digital temperature/RH sensors. Predicted moisture content (MC) values from the only previously published E. nitens log pile drying model were also evaluated using the current study data sources as inputs.Log pile MC changes were determined from weight changes measured by placing the study logs on a steel frame bolted to load cells at each corner. As the study was based on debarked logs, dry matter losses were assumed to be negligible. Initial MC of the logs was determined by extracting samples using an electric drill and drying them until constant weight was achieved.Initial log pile drying rates were high with several daily MC losses >2%. Portable weather station data produced the best goodness of fit drying model. The second-best goodness of fit model was based on meteorological station data. From a user acceptability perspective (highest proportion of results within ±5% of measured values), the best model was based on temperature/RH sensor data. Goodness of fit measures for the temperature/RH sensor data model were poorer than for the other data sources, but still acceptable. The published E. nitens log drying model had the poorest results for goodness of fit and user acceptability.In conclusion, portable weather stations are best suited to research trials due to the expense of placing a weather station at each log pile. Drying models based on data from the nearest meteorological station or temperature/RH sensors are best suited for practical applications, such as forest supply chain analysis. Additional benefits could accrue from a forest estate-wide network of low cost temperature/RH sensors potentially supplying data to forest supply chain analysis as well as fire prediction and tree growth models.
Details
- Title
- Evaluating the Impact of Meteorological Data Sources on Moisture Prediction Accuracy of Eucalyptus Nitens Log Pile Natural Drying Models
- Authors
- Martin Strandgard (Corresponding Author) - University of the Sunshine Coast, Queensland, Forest Industries Research CentreMohammad Sadegh Taskhiri (Author) - University of TasmaniaPaul Turner (Author) - University of Tasmania
- Publication details
- Croatian Journal of Forest Engineering, Vol.44(2), pp.337-346
- Publisher
- Sveuciliste u Zagrebu, Sumarski Fakultet,University of Zagreb, Faculty of Forestry
- Date published
- 2023
- DOI
- 10.5552/crojfe.2023.1757
- ISSN
- 1848-9672; 1845-5719
- Copyright note
- © 2023 by the authors. Submitted for possible open access publication under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- Organisation Unit
- Forest Industries Research Centre; Forest Research Institute
- Language
- English
- Record Identifier
- 99980698902621
- Output Type
- Journal article
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