A prediction model prototype for estimating optimal storage duration and sorting

G. Erber, J. Routa, L. Wilhelmsson, Jyrki Raitila, Maunu Toiviainen, J. Riekkinen, L. Sikanen

Research output: Book/ReportReport

Abstract

The objective of this study was to develop prototypes for estimating the optimal storage time and sorting of fuel wood. Drying trials employing the state of the art technology of load cell based metal frames were carried out by University of Natural Resources and Life Sciences (BOKU), METLA and Skogsforsk. A reference trial employing traditional pile sampling was carried out by VTT. Easily applicable drying models for logging residues, whole trees, stem wood and stumps were developed. A large variety of meteorological parameters can be used for model input. Parameters ranged from basic data like relative air humidity and air temperature to more complex parameters like evaporation and equilibrium moisture content of fuel wood. Fuel wood drying models can improve the fuel wood supply chain by helping the supplier find and choose those wood piles that are drier and thus with a higher calorific value for delivery. It enables supplier to deliver fuel wood which better meets the demands of the customers. Transport can be optimized by these models too. The drying models can also be used to formulate recommendations concerning seasoning of residues and optimum storage times for different assortment, species and drying conditions. An outlook on future application and further research needs was provided. Machine vision technology for sorting of fuel wood by quality and particle size, as well as for assessing the volume of a delivered fuel wood load was tested by VTT and JAMK. INFRES partners provided chip samples from all over Europe for testing. RGB images proved to work very well when identifying shapes and sizes of chips. If odd particles have the same colour as woody material, RGB images could not identify them. Measuring wood chip loads with a time-of-flight (TOF) camera rendered the most promising results. The average error was about 10%. Compared to visible light technology, near infrared (NIR) spectroscopy proved to be much more accurate in determining fuel wood moisture content and detecting foreign objects. Technology based on visible light is not able to work online (moving chips). To the contrary, NIR technology proved to work online and therefore could be used at a power plant or fuel wood terminal where wood chips are moved with a conveyer. However, NIR technology has other challenges such as not being able to give reliable moisture information with regard to frozen materials. Furthermore, an outlook on future research needs was provided.
Original languageEnglish
PublisherMetla
Number of pages76
ISBN (Print)978-951-40-2476-4
Publication statusPublished - 2014
MoE publication typeD4 Published development or research report or study

Publication series

SeriesMetlan työraportteja - Working Papers of the Finnish Forest Research Institute
ISSN1795-150X

Fingerprint

Wood fuels
Sorting
Drying
Wood
Moisture
Infrared radiation
Wood products
Calorific value
Near infrared spectroscopy
Natural resources
Air
Supply chains
Computer vision
Piles
Atmospheric humidity
Power plants
Evaporation
Cameras
Particle size
Sampling

Keywords

  • fuel wood drying
  • modelling
  • load cells
  • machine vision technology
  • near infrared spectroscopy

Cite this

Erber, G., Routa, J., Wilhelmsson, L., Raitila, J., Toiviainen, M., Riekkinen, J., & Sikanen, L. (2014). A prediction model prototype for estimating optimal storage duration and sorting. Metla. Metlan työraportteja - Working Papers of the Finnish Forest Research Institute
Erber, G. ; Routa, J. ; Wilhelmsson, L. ; Raitila, Jyrki ; Toiviainen, Maunu ; Riekkinen, J. ; Sikanen, L. / A prediction model prototype for estimating optimal storage duration and sorting. Metla, 2014. 76 p. (Metlan työraportteja - Working Papers of the Finnish Forest Research Institute).
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Erber, G, Routa, J, Wilhelmsson, L, Raitila, J, Toiviainen, M, Riekkinen, J & Sikanen, L 2014, A prediction model prototype for estimating optimal storage duration and sorting. Metlan työraportteja - Working Papers of the Finnish Forest Research Institute, Metla.

A prediction model prototype for estimating optimal storage duration and sorting. / Erber, G.; Routa, J.; Wilhelmsson, L.; Raitila, Jyrki; Toiviainen, Maunu; Riekkinen, J.; Sikanen, L.

Metla, 2014. 76 p. (Metlan työraportteja - Working Papers of the Finnish Forest Research Institute).

Research output: Book/ReportReport

TY - BOOK

T1 - A prediction model prototype for estimating optimal storage duration and sorting

AU - Erber, G.

AU - Routa, J.

AU - Wilhelmsson, L.

AU - Raitila, Jyrki

AU - Toiviainen, Maunu

AU - Riekkinen, J.

AU - Sikanen, L.

N1 - Project code: 77745

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N2 - The objective of this study was to develop prototypes for estimating the optimal storage time and sorting of fuel wood. Drying trials employing the state of the art technology of load cell based metal frames were carried out by University of Natural Resources and Life Sciences (BOKU), METLA and Skogsforsk. A reference trial employing traditional pile sampling was carried out by VTT. Easily applicable drying models for logging residues, whole trees, stem wood and stumps were developed. A large variety of meteorological parameters can be used for model input. Parameters ranged from basic data like relative air humidity and air temperature to more complex parameters like evaporation and equilibrium moisture content of fuel wood. Fuel wood drying models can improve the fuel wood supply chain by helping the supplier find and choose those wood piles that are drier and thus with a higher calorific value for delivery. It enables supplier to deliver fuel wood which better meets the demands of the customers. Transport can be optimized by these models too. The drying models can also be used to formulate recommendations concerning seasoning of residues and optimum storage times for different assortment, species and drying conditions. An outlook on future application and further research needs was provided. Machine vision technology for sorting of fuel wood by quality and particle size, as well as for assessing the volume of a delivered fuel wood load was tested by VTT and JAMK. INFRES partners provided chip samples from all over Europe for testing. RGB images proved to work very well when identifying shapes and sizes of chips. If odd particles have the same colour as woody material, RGB images could not identify them. Measuring wood chip loads with a time-of-flight (TOF) camera rendered the most promising results. The average error was about 10%. Compared to visible light technology, near infrared (NIR) spectroscopy proved to be much more accurate in determining fuel wood moisture content and detecting foreign objects. Technology based on visible light is not able to work online (moving chips). To the contrary, NIR technology proved to work online and therefore could be used at a power plant or fuel wood terminal where wood chips are moved with a conveyer. However, NIR technology has other challenges such as not being able to give reliable moisture information with regard to frozen materials. Furthermore, an outlook on future research needs was provided.

AB - The objective of this study was to develop prototypes for estimating the optimal storage time and sorting of fuel wood. Drying trials employing the state of the art technology of load cell based metal frames were carried out by University of Natural Resources and Life Sciences (BOKU), METLA and Skogsforsk. A reference trial employing traditional pile sampling was carried out by VTT. Easily applicable drying models for logging residues, whole trees, stem wood and stumps were developed. A large variety of meteorological parameters can be used for model input. Parameters ranged from basic data like relative air humidity and air temperature to more complex parameters like evaporation and equilibrium moisture content of fuel wood. Fuel wood drying models can improve the fuel wood supply chain by helping the supplier find and choose those wood piles that are drier and thus with a higher calorific value for delivery. It enables supplier to deliver fuel wood which better meets the demands of the customers. Transport can be optimized by these models too. The drying models can also be used to formulate recommendations concerning seasoning of residues and optimum storage times for different assortment, species and drying conditions. An outlook on future application and further research needs was provided. Machine vision technology for sorting of fuel wood by quality and particle size, as well as for assessing the volume of a delivered fuel wood load was tested by VTT and JAMK. INFRES partners provided chip samples from all over Europe for testing. RGB images proved to work very well when identifying shapes and sizes of chips. If odd particles have the same colour as woody material, RGB images could not identify them. Measuring wood chip loads with a time-of-flight (TOF) camera rendered the most promising results. The average error was about 10%. Compared to visible light technology, near infrared (NIR) spectroscopy proved to be much more accurate in determining fuel wood moisture content and detecting foreign objects. Technology based on visible light is not able to work online (moving chips). To the contrary, NIR technology proved to work online and therefore could be used at a power plant or fuel wood terminal where wood chips are moved with a conveyer. However, NIR technology has other challenges such as not being able to give reliable moisture information with regard to frozen materials. Furthermore, an outlook on future research needs was provided.

KW - fuel wood drying

KW - modelling

KW - load cells

KW - machine vision technology

KW - near infrared spectroscopy

M3 - Report

SN - 978-951-40-2476-4

T3 - Metlan työraportteja - Working Papers of the Finnish Forest Research Institute

BT - A prediction model prototype for estimating optimal storage duration and sorting

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ER -

Erber G, Routa J, Wilhelmsson L, Raitila J, Toiviainen M, Riekkinen J et al. A prediction model prototype for estimating optimal storage duration and sorting. Metla, 2014. 76 p. (Metlan työraportteja - Working Papers of the Finnish Forest Research Institute).