LAPSE:2018.0887
Published Article
LAPSE:2018.0887
Adaptive Procurement Guidelines for Automatic Selection of Renewable Forest Energy Sources within a Sustainable Energy Production System
Teijo Palander, Kalle Kärhä
November 27, 2018
An automatic forest-stand selection method was developed that integrates the procurement of profitable energy sources within a sustainable energy production system. We tested the method using a forest harvester simulator. We found that site-specific estimates of forest characteristics are important when predicting the potential of an energy-wood stand as a renewable energy source. Further, tree parameters can be combined with automatic stem measurements from a multiple-tree harvester to predict the energy-wood biomass in the stands. The selection process uses data from profitability studies. The selection process also uses environmental criteria to ensure that sufficient soil organic matter is left behind and to protect the soil against erosion. The integrated system of the harvester automatically adapts the system’s models and stand-selection rules to account for various site-specific stand parameters. Predicting the profitable and environmentally acceptable yield of stand biomass has great potential in sustainable forest resource management, but managers must decide whether the operational procurement guidelines provided by the stand-selection method is acceptable under their local real-world wood procurement conditions.
Keywords
automation, energy-wood, profitable, renewable, sustainable
Suggested Citation
Palander T, Kärhä K. Adaptive Procurement Guidelines for Automatic Selection of Renewable Forest Energy Sources within a Sustainable Energy Production System. (2018). LAPSE:2018.0887
Author Affiliations
Palander T: Faculty of Science and Forestry, University of Eastern Finland, P.O. Box 111, Joensuu FI-80101, Finland
Kärhä K: Stora Enso Wood Supply Finland, P.O. Box 309, Helsinki FI-00101, Finland [ORCID]
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Journal Name
Energies
Volume
9
Issue
3
Article Number
E155
Year
2016
Publication Date
2016-03-03
Published Version
ISSN
1996-1073
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Original Submission
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PII: en9030155, Publication Type: Journal Article
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LAPSE:2018.0887
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doi:10.3390/en9030155
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Nov 27, 2018
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CC BY 4.0
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[v1] (Original Submission)
Nov 27, 2018
 
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Nov 27, 2018
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Original Submitter
Calvin Tsay
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