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Prediction models for moisture content of fuel wood
Jyrki Raitila
Research output
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Contribution to conference
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Conference article
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Scientific
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INIS
moisture
100%
prediction
100%
wood fuels
100%
drying
83%
wood
33%
storage
33%
residues
33%
air
33%
piles
33%
data
16%
resources
16%
chains
16%
demand
16%
supply
16%
transport
16%
metals
16%
equilibrium
16%
sampling
16%
delivery
16%
humidity
16%
recommendations
16%
universities
16%
evaporation
16%
meteorology
16%
calorific value
16%
sorting
16%
seasonings
16%
Keyphrases
Moisture Content
100%
Fuelwood
100%
Prediction Model
100%
Drying Model
50%
Storage Time
33%
Humidity
16%
Air Temperature
16%
Drying Conditions
16%
State-of-the-art Technology
16%
Cell-based
16%
Stem Wood
16%
Logging Residues
16%
Life Sciences
16%
Natural Resources
16%
Optimal Storage
16%
Load Cell
16%
Meteorological Parameters
16%
Whole Tree
16%
Stump
16%
High Calorific Value
16%
Equilibrium Moisture Content
16%
Wood Drying
16%
Natural Life
16%
Wood Supply Chain
16%
Metal Frame
16%
Tree Stem
16%
Woodpile
16%
Agricultural and Biological Sciences
Stumps
100%
Wood Drying
100%
Natural Resources
100%
Meteorological Parameter
100%
Wood Product
100%