Creating Moisture Prediction Models for Seasoned Fuelwood

Jyrki Raitila, Marja Kolström, Johanna Routa

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsProfessional

Abstract

Moisture of fuelwood affects both, profitability of supplying wood chips and the economy of running an energy plant. Most fuel wood is seasoned outdoors, therefore drying depends on the weather. Moisture changes of stored wood in different drying environments can be estimated with multivariate models. The objective of this study was to develop and validate model prototypes for estimating optimal storage times for stacked fuelwood. In addition to taking moisture samples manually, load cell based automated data recording for moisture content alteration was used successfully. The main factors considered in this study for predicting moisture changes of fuelwood were the fuelwood type, precipitation and evaporation; actual or cumulative. In practice, multivariate drying models can help optimize deliveries of fuelwood and therefore increase the efficiency of the whole bioenergy supply chain, particularly if moisture content estimation is integrated into Enterprise Resource Planning (ERP).
Original languageEnglish
Title of host publicationEuropean Biomass Conference and Exhibition Proceedings
PublisherETA-Florence Renewable Energies
ISBN (Print)978-88-89407-165
Publication statusPublished - 2016
MoE publication typeD3 Professional conference proceedings
Event24th European Biomass Conference and Exhibition, EUBCE 2016 - Amsterdam, Netherlands
Duration: 6 Jun 20169 Jun 2016

Conference

Conference24th European Biomass Conference and Exhibition, EUBCE 2016
Abbreviated titleEUBCE 2016
CountryNetherlands
CityAmsterdam
Period6/06/169/06/16

Fingerprint

fuelwood
prediction
stored wood
drying
wood drying
water content
wood chips
bioenergy
supply chain
prototypes
profitability
evaporation
storage time
weather
planning
energy
cells

Keywords

  • moisture content
  • fuelwood
  • natural drying
  • drying models
  • predicting moisture

Cite this

Raitila, J., Kolström, M., & Routa, J. (2016). Creating Moisture Prediction Models for Seasoned Fuelwood. In European Biomass Conference and Exhibition Proceedings ETA-Florence Renewable Energies.
Raitila, Jyrki ; Kolström, Marja ; Routa, Johanna. / Creating Moisture Prediction Models for Seasoned Fuelwood. European Biomass Conference and Exhibition Proceedings. ETA-Florence Renewable Energies, 2016.
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Raitila, J, Kolström, M & Routa, J 2016, Creating Moisture Prediction Models for Seasoned Fuelwood. in European Biomass Conference and Exhibition Proceedings. ETA-Florence Renewable Energies, 24th European Biomass Conference and Exhibition, EUBCE 2016, Amsterdam, Netherlands, 6/06/16.

Creating Moisture Prediction Models for Seasoned Fuelwood. / Raitila, Jyrki; Kolström, Marja; Routa, Johanna.

European Biomass Conference and Exhibition Proceedings. ETA-Florence Renewable Energies, 2016.

Research output: Chapter in Book/Report/Conference proceedingConference article in proceedingsProfessional

TY - GEN

T1 - Creating Moisture Prediction Models for Seasoned Fuelwood

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AU - Kolström, Marja

AU - Routa, Johanna

N1 - Slide presentation Project code: 104790

PY - 2016

Y1 - 2016

N2 - Moisture of fuelwood affects both, profitability of supplying wood chips and the economy of running an energy plant. Most fuel wood is seasoned outdoors, therefore drying depends on the weather. Moisture changes of stored wood in different drying environments can be estimated with multivariate models. The objective of this study was to develop and validate model prototypes for estimating optimal storage times for stacked fuelwood. In addition to taking moisture samples manually, load cell based automated data recording for moisture content alteration was used successfully. The main factors considered in this study for predicting moisture changes of fuelwood were the fuelwood type, precipitation and evaporation; actual or cumulative. In practice, multivariate drying models can help optimize deliveries of fuelwood and therefore increase the efficiency of the whole bioenergy supply chain, particularly if moisture content estimation is integrated into Enterprise Resource Planning (ERP).

AB - Moisture of fuelwood affects both, profitability of supplying wood chips and the economy of running an energy plant. Most fuel wood is seasoned outdoors, therefore drying depends on the weather. Moisture changes of stored wood in different drying environments can be estimated with multivariate models. The objective of this study was to develop and validate model prototypes for estimating optimal storage times for stacked fuelwood. In addition to taking moisture samples manually, load cell based automated data recording for moisture content alteration was used successfully. The main factors considered in this study for predicting moisture changes of fuelwood were the fuelwood type, precipitation and evaporation; actual or cumulative. In practice, multivariate drying models can help optimize deliveries of fuelwood and therefore increase the efficiency of the whole bioenergy supply chain, particularly if moisture content estimation is integrated into Enterprise Resource Planning (ERP).

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SN - 978-88-89407-165

BT - European Biomass Conference and Exhibition Proceedings

PB - ETA-Florence Renewable Energies

ER -

Raitila J, Kolström M, Routa J. Creating Moisture Prediction Models for Seasoned Fuelwood. In European Biomass Conference and Exhibition Proceedings. ETA-Florence Renewable Energies. 2016