Abstract
| Original language | English |
|---|---|
| Number of pages | 91 |
| Publication status | Published - 2014 |
| MoE publication type | D4 Published development or research report or study |
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Keywords
- fuel wood drying
- modelling
- load cells
- machine vision technology
- near infrared spectroscopy
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A prediction model prototype for estimating optimal storage duration and sorting : D2.2. / Erber, G.; Routa, J.; Wilhelmsson, L.; Raitila, Jyrki; Toiviainen, Maunu; Riekkinen, J.
2014. 91 p.Research output: Book/Report › Report › Professional
TY - BOOK
T1 - A prediction model prototype for estimating optimal storage duration and sorting
T2 - D2.2
AU - Erber, G.
AU - Routa, J.
AU - Wilhelmsson, L.
AU - Raitila, Jyrki
AU - Toiviainen, Maunu
AU - Riekkinen, J.
PY - 2014
Y1 - 2014
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 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 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
BT - A prediction model prototype for estimating optimal storage duration and sorting
ER -