Download Fuel wood quality optimization through drying models and

Fuel wood quality optimization through
drying models and sorting tools
Bioenergy from forests is a source of energy where
often the quality, in terms of moisture content, is
unknown until delivery to the plant. INFRES develops
methods to effectively estimate moisture content of
fuel wood piles in the field and for sorting of chipped
material at the plant.
If a supplier of fuel wood has a larger number of piles,
the main question is to which he should go to get
material of demanded quality. At the plant, the main
question is how to effectively assess the quality of the
material provided by different suppliers. INFRES aims
to provide answers to these questions by
collaboration of research institutions on European
level.
Fuel wood drying models
The idea behind fuel wood drying models is to estimate the
actual moisture content of fuel wood piles by
meteorological data and the pile’s moisture content at the
start. To develop these models, the state of the art
technology of load cell based metal frames was employed.
Logs are piled in these frames and any reduction in load
weight is considered to result from drying. A
meteorological station is placed nearby and meteorological
data like wind speed and direction, air temperature,
relative air humidity, precipitation and solar radiation are
recorded.
Drying experiments
The experiments took place at Austria, Finland and
Sweden. Log wood, whole tree and logging residue drying
were studied. In total, 31 pile drying circles were studied
from 2012 to 2014. It was possible to derive multiple linear
models based on causal weather variables and
specifications of wood material (type of material, initial
moisture content and freshness). Weather conditions
below the freezing point including snow coverage have to
be separated from conditions above the freezing point
including drying and wetting by rainfall. The implications of
snow coverage can be different, strongly depending on the
pile volume in relation to the volume of covering snow. In
addition, microclimatic conditions in the storage location
affect drying performance remarkably. When using these
drying models, this has to be taken into account. Dry
matter losses (caused by microbial activity, most commonly
fungal attacks) can be a very important issue, especially
when storing whole trees and logging residues. Self‐heating
of logging residues and whole tree piles can inflict
significant dry matter losses. Drying models need to
incorporate these effects. Otherwise drying performance is
overestimated.
Fuel wood sorting
As the use of fuel wood chips is constantly increasing in
Europe, a need to monitor quality and systematize
sampling methods has arisen. New automated or semi‐
automated measuring methods and technologies are
needed for measuring and monitoring wood chip quality
more efficiently.
Experimental setup with metal frames based on load cells
load with fuel wood for observing drying performance.
Optimization through models
Moisture content models could improve the whole fuel
supply chain by helping the supplier find and choose those
wood piles that are drier and thus have a higher calorific
value for delivery. Also, different boilers require different
types of fuel. All in all, this kind of multivariate drying
model would help optimize deliveries of fuel wood and
therefore increase the efficiency of the whole fuel wood
supply chain. In order to make this moisture calculating
even more user friendly, all data should be easily and
automatically acquired and fed into a suitable fuel wood
management system, preferably integrated into an existing
fuel procurement database. The drying models can be also
be used to formulate recommendations concerning
seasoning of residues at harvesting objects, optimal storage
time for fresh versus seasoned residues and stem wood
depending on time of harvesting normal, best or worst case
weather conditions, different kind of wood materials e.g.
species, branches, tops, small or large diameter stem
wood, effects of different piling principles, pile geometry,
etc.
Observed and modelled drying curves for fuel wood piles.
Machine vision technology
Machine vision technology is widely used in many industrial
processes. Camera technology is constantly developing, yet
prices of suitable cameras are quite moderate. The main
goals of these tests and the study was to determine how
machine vision technology could be used in monitoring fuel
wood quality (distinguishing wood chip types, determining
moisture differences and detecting impurities or
determining the particle size of wood chips, measuring fuel
wood loads). Both visible light and near infrared technology
were tested.
Potential applications of machine vision
technology
In total, 12 different samples of fuel wood chips in terms of
moisture content and tree species were provided by the
INFRES partners. It proved that RGB cameras are suitable
for determining shapes (and therefore size categories) and
identifying impurities, but are not suitable for online
monitoring of moving fuel wood chips. Measuring wood
chip loads with a time‐of‐flight (TOF) camera rendered the
most promising results. Volumes measured with this
camera corresponded quite well with manually measured
volume values. Near infrared (NIR) spectroscopy proved to
be much more accurate in determining moisture and
detecting foreign materials among wood chips. The most
obvious strengths of NIR spectroscopy are its ability to
accurately measure moisture content and detect foreign
particles in an online setting. Therefore, near infrared
cameras could be used at a power plant or fuel wood
terminal where wood chips are moved with a conveyer.
Experimental setup for testing machine vision technology
Particle size determination using the machine vision system.
Contact information
Gernot Erber, BOKU University of University of Natural
Resources and Life Sciences, Vienna
[email protected]
Tel. +43 1 47654 4302
August 2014
The full report “A prediction model prototype for
estimating optimal storage duration and sorting” available
at www.infres.eu or same titled working paper of the
Finnish Forest Research Institute, available at www.metla.fi
The research of the INFRES project has received funding from the
European Union Seventh Framework Programme (FP7/2012-2015] under
grant agreement n°311881. The sole responsibility for the content of this
flyer lies with the authors. It does not necessarily reflect the opinion of the
European Communities. The European Commission is not responsible for
any use that maybe made of the information contained therein.