The document discusses a pellet distribution model that was developed to simulate how fish feed pellets disperse in ocean fish cages. The model accounts for factors like feed properties, water currents, wind, and fish behavior. It can help optimize feeding regimes and provide operators real-time information on pellet distribution. Experimental data was used to refine how the model represents pellet sinking rates and diffusion. The model may help operators better understand pellet movement throughout the entire cage volume.

1. I N C O R P O R AT I N G
f i s h far m ing t e c h no l og y
November | December 2013
Pellet distribution modelling: a tool for
improved feed delivery in sea cages
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The International magazine for the aquaculture feed industry

2. FEATURE
Pellet distribution
modelling:
a tool for
improved feed
delivery in
sea cages
by K R Skøien1,4, M O Alver1,2,3, M Føre1,2,4
T Solvang-Garten2,4, T S Aas3,4, T E Åsgård3,4
and J A Alfredsen1,4
A
bout 50 percent of the costs
related to farming of salmon and
rainbow trout are spent on feed
(Fiskeridirektoratet, 2012), and
the estimated feed loss from commercial sites
is 5-7 percent (Cromey et al., 2002; Gjøsæter
et al., 2008). This amounts to a considerable
annual economic loss for the farmer.
Lost feed may also have negative impacts
on the marine environment through accumulation of organic waste on the seabed as
well as being an attractor of wild fish. Wild
fish aggregations may influence local pollution
conditions around farms, and represent a
potential vector for the transfer of diseases
between farms (Dempster et al., 2009).
Moreover, the way the feed is delivered
in present-day large cages, and the resulting
spatiotemporal distribution of pellets inside
the cage, will influence the fish’s access to
food and may cause inter-individual competition in case of suboptimal delivery. The latter
can lead to physically damaging encounters
between fish, increased stress, less welfare
and greater spread in size across the fish
population.
Furthermore, feed utilization is highest at
high feed intake (Einen et al., 1995; Einen et
al., 1999), and thus, suboptimal feed intake
results in loss of fish biomass. Reducing feed
loss and controlling the distribution and rate
of feeding more accurately could therefore
give considerable economic, environmental
and welfare benefits for the fish farming
operation.
Factors affecting pellets
As a basis for evaluating potential actions
to improve the situation, a better understanding of the complex dynamics taking place in
the cage during feeding must be obtained.
The motion of a feed pellet is influenced by
a range of factors from the point where it
enters the feed spreader until it is either eaten
by fish or escapes the cage.
Figure 1
Some factors depend on the properties
and configuration of the feeding machinery,
and others on the physical properties of the
feed itself, as well as environmental forces
such as wind, water current and fish induced
water motion. A basic mathematical model
calculating the spatiotemporal distribution of
feed in a sea cage was developed by Alver
et al. (2004).
In the current work, this model has been
further developed and now accounts for a
more realistic representation of the physical
feed properties, the feed spreader, wind field,
water current and the feeding behaviour of
the fish.
The model, in conjunction with a suitable user interface and visualisation tools,
can hopefully serve both as a simulation
framework for developing more optimal feeding regimes and as an aid to the farmer to
control the feeding more precisely. That is, by
providing more detailed real time information
on how the feed distributes in the cage that
Figure 2
Figure 1: This particular simulation is based on a 120 m
circumference circular cage, populated by 250 000 fish with
an average weight of 1 kg. The model is run with a cell size
of 1x1x1 m, a 2 cm/s horizontal constant current field, a pellet
sink rate of 10 cm/s. Top: Pellet density at 1, 4 and 8 m depth
at 15 min. Bottom: Cross-sectional view at 5, 15 and 30 min
Figure 2: Image captured from a simulation of a combined
pellet distribution model and fish behaviour model. The black
lines are the cage structure, the black and silver lines each
represent a number of fish, and the dark cloud is feed, just
released into the cage
24 | INterNatIoNal AquAFeed | November-December 2013

3. FEATURE
These experiments were conducted to
determine the natural spread or diffusion
inherent to the pellets, and further experiments to quantify the speed and variations
in the vertical motion of pellets in the water
column are planned. The final results of
these experiments will be incorporated into
the model, and preliminary results suggest
that the physical properties of pellets have a
substantial influence on their hydrodynamic
properties.
To validate the model, a full-scale experiment is planned at a farming site, where feed
density will be measured at points inside the
cage with the entire array of environmental
forces and fish acting upon the feed.
is commonly unavailable by traditional means,
such as subsea cameras.
The model
The model has been under development
since 2004, and since then has been expanded
from 2D to 3D, and merged with a fish behaviour and foraging model (Føre et al., 2009).
The initial distribution of pellets across the
cage surface using commercial rotary spreaders is based upon experimental data from
Oehme et al. (2012).
The model discretizes the cage into cubes,
and the transport of feed between the cubes
is calculated based on the transport equation
(Alver et al. 2004). Feed is removed from
the model when fish consume feed, or feed
escapes outside the defined cage volume.
Figure 1 shows the output from the model
across three depths and a cross section of the
cage at three different points in time.
The model is currently being enhanced
by incorporating results from pellet diffusion
experiments, where a range of different pellet sizes and densities (produced at Nofima
Feed Technology Centre, Bergen, Norway)
were released in a large tank to observe their
natural diffusion and sinking properties. Pellets
with a diameter of 3, 6, 9 and 12 mm with
low, medium and high density were tested to
cover the range of feed most frequently used
in commercial salmon farming.
Aid for the operator
Today, the feeding regime is for the most
part controlled by the fish farm operator,
which commonly possesses expert knowledge
of the location. This often results in a well-run
site with a high food utilisation (low FCR),
and minimal feed loss. There are however
substantial variations in performance between
locations.
The feeding system commonly presents
the operator with a range of numerical
displays representing various environmental
parameters, as well as live video from one or
more subsea cameras located inside the cage.
The camera adds insight to the process by
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providing information on (for example) fish
feeding behaviour and the local pellet density.
However, a camera can only capture a limited volume of the cage at a time, and displays
an unfiltered image with an array of concurrent
information. This may represent a challenge, if
for instance the operator wishes to observe
the density of feed in an area. Presence of fish
and debris in the image may then obstruct the
view, making a visual assessment of feed density difficult. Consequently, the operator must
make decisions based on limited information
from a subsection of the cage.
The pellet distribution model could be
used as a tool to illustrate the parts of the
cage that are of interest to the operator at
particular points in time. A 3D view similar
to Figure 2 can then be presented, showing
an overview of the cage containing fish and
feed, in addition to a range of environmental
parameters important for feeding.
A model with a suitable interface will have
an advantage over exclusive camera-based
control in that the entire cage can be viewed
within a single image and that the operator
can remove fish or feed from the view as
desired.
Furthermore, the model allows the view
to be zoomed and rotated to observe better
an area of interest. The model is meant to be
used in conjunction with cage cameras, and
can aid in automatic camera placement at
14/12/2012 11:23
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pg16_DFO_wolffish.
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24/8/12
12:30
Page 16
16
SCIENCE DFO
SCIENCE
SCIENCE
Farming saltwa
PY OF
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The Spotted
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The studies were
research centre’s also conducted in the
aquaculture
which allowed
facilities,
the
a large scale. farming to be done on
This zootechnical
demonstration,
the final results
will be known
of which
sometime in
2011, was
carried out in
collaboration
with Nathalie
Le François, a
researcher at
the Biodôme
de Montréal
and associate
professor at
the Université
du Québec à
Rimouski.
The first wolffish,
hatched at the
of fall 2008 in
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marin
were delivered
Maurice-Lamontagne
to the
Institute in
2009. Since
May
Figure 2: Spotted
their arrival
Photo: Arianne
Wolffish in
in the
these roughly
Savoie, Fisheries
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400 fish have tank,
and Oceans
handled very
Canada
Saltwater mariculture-aquacul
been
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researchers measure Every month, the
Quebec may
ture in
Université du
soon welcome
their growth rate,
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Québec à Rimouski
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a
as close as possible in
the Quebec
Spotted Wolffish,new
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those found in
ministry of
threatened and
to
agriculture,
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fisheries and
little-known species a
food.
operations.
farming
tastes delicious.
that
These measurements
First of all, the
compared with
In Quebec,
are
data gathered
fish that adapts Spotted Wolffish is a
commercial
in Norway
and Iceland,
fish farms
well to the conditions
currently limit
where Spotted
is kept in and
themselves to
it
have been raised
Wolffish
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freshwater fish,
farming
develops quickly to domesticate. It
for experimental
while the mariculture
commercial
industry has
and
at very
aquaculture
focused until
temperatures
low
for about 10
years now.
and is not very
very recently
on molluscs.
Preliminary
sensitive to
In other parts
changes in
results from
the salinity
Mont-Joli show
of the
saltwater fish
of the water.
Spotted Wolffish
a growth rate
farms are located world,
slightly less
that
the ocean. Doing
can
right in
than that observed is
densities, something be farmed in high
so significantly
Norway, a
farming costs
reduces
in
that is crucial
country that
the profitability
and makes
for
has had
considerable
profitable.
of an aquaculture
them
experience in
operation (see
In Quebec,
farming the
species; thus,
Figure
installing
aquaculture equipment
rearing
though the Spotted 2). As well, even
in the ocean
Maurice-Lamontagne conditions at the
dicey prospect
Wolffish does
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reproduce spontaneously
because of ice
Institute still
not
some room for
winter. Previously,
cover in
have
in captivity,
improvement.
new generations
experiments
Feeding poses
can
farming saltwater
with
year using captive be produced every
one of the
fish in tanks
challenges for
the need for
obtaining optimal biggest
technical expertiserevealed
not forget another broodstock. And let’s
in farmed
as the high
important quality
as well
Spotted Wolffish.growth
cost
fish possesses:
this
commercial feed
it tastes great.
however, research of production. Today,
The
used
Aside from these
advances are
intended for salmonids until now was
the potential
showing
obvious advantages,
of the Spotted
it is important
and has not
modified. The
Wolffish.
to find out
This new mariculture
feed has too much been
species grows
how this
candidate was
wolffish that are
in captivity
fat and
first noticed
so that its
fed this type of
in the early 2000s.
potential benefit
to
food tend
to Quebec’s aquaculture
time, teams
develop
At the
industry can
liver
from the
abnormalities.
Researchers
be properly
Lamontagne
Mauricealso question
assessed. For
that reason, Denis
Institute in
whether it
offers enough
Mont-Joli,
Quebec, collected
Chabot,
protein for the
the Maurice-Lamontagne a researcher at
their first Spotted
needs of the
particular
Wolffish as part
Institute,
species. Another
approached
of the research
the feed does
problem:
by the Société was
they were
projects
not float and
développement
conducting
de
sinks to the
bottom of the
with the
de
tank, which is
(SODIM) to carry l’industrie maricole
problematic
when it comes
tests using water
to feeding fish
tanks.
raised in high
INTERNATIONAL
densities. Ideally, species
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4. FEATURE
GRAPASisland:Layout 1
30/8/13
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Page 1
8 – 10 April 2014 . Bangkok International Trade & Exhibition Centre (BITEC), Bangkok, Thailand
Asia’s premier rice &
flour milling and grain
processing exhibition
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points of interest determined by
the model.
A model-based 3D view could
represent an intuitive way of presenting process information to
the operator, and could be a
valuable tool in the daily feeding
operations.
As well as providing the base
for a 3D view, the model might
also predict future feed distribution patterns throughout the
cage volume. This could be used
to give an early warning of feed
loss, enabling adjustments to be
made to the quantity and temporal release of feed. Output
from the model could also be
used to estimate and report
parameters that are difficult to
observe directly, such as feed
loss.
Scientific use
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26 | INterNatIoNal AquAFeed | November-December 2013
In addition to the value of
active use in ongoing production,
the model may allow farmers
and scientists to test a range of
scenarios before deployment on
the farm.
Different environmental conditions, feeding regimes, spreaders and feed types can all be
run through the model, and the
results evaluated to determine
the effects on feed loss, and feed
densities throughout the cage.
Further work
The model has been verified
with respect to a limited number
of parameters. Further improvements must be made, and if
necessary further properties of

5. FEATURE
Gjøsæter J., Otterå, H., Slinde, E., Nedreaas, K., Ervik,
the fish and environment need to be taken waste solids from marine cage farms. Aquaculture
214 (1-4), 211 - 239.
A. 2008. Effekter av spillfôr på marine organismer.
into account.
Kyst og Havbruk, 52-55.
As development continues, the physi- Dempster T., Uglem, I., Sanchez-Jerez, P., FernandezJover, D., Bayle-Sempere, J.,
Oehme M., Aas T. S., Sørensen M., Lygren I., Åsgård
cal realism of the mathematical model is
T. 2012. Feed pellet
expected to increase. For the simulations Nilsen, R., Bjørn, P. A. 2009. Coastal salmon farms
to be accurate, a certain number of meas- attract large and persistent aggregations of wild
distribution in a sea cage using pneumatic
fish: and ecosystem effect. Marine Ecology Progress
urements from the cage are required. It is Series 385, 1-14.
feeding system with rotor spreader. Aquacultural
envisioned that for online use, the model will
Engineering 51, 44 -52, ISSN 0144-8609, 10.1016/j.
Einen, O., Holmefjord, I., Åsgård, T., Talbot, C.
be fed with basic environmental parameters, 1995. Auditing nutrient discharges from fish
aquaeng.2012.07.001.
such as wind and current. A number of pellet farms: theoretical and practical considerations.
sensors might also be deployed throughout Aquaculture Research 26, 701-713.
More InforMatIon:
the cage. The model will then use these Einen, O., Mørkøre, T., Thomassen, M. S. 1999.
Email: kristoffer.rist.skoien@itk.ntnu.no
measurements to predict feed distribution Feed ration prior to slaughter – a potential tool
This work is part of the Centre for Research-based
for managing product quality of Atlantic salmon
across the entire volume.
Innovation in Aquaculture Technology - CREATE,
(Salmo salar). Aquaculture 178, 149-169.
It is emphasised that focus is currently
hosted by SINTEF Fisheries and Aquaculture
1Norwegian University of Science and
on the model itself, and not on the visualisa- Fiskeridirektoratet (Directorate of Fisheries) 2012.
Technology, Norway
tion and user interface needed to provide a www.fiskeridir.no
2SINTEF Fisheries and Aquaculture AS, Norway
more useful presentation for the operator. Føre M., Dempster T., Alfredsen, J. A., Oppedal, F.
3Nofima, Norway
Hopefully, further development of these fea- 2009. Modelling of Atlantic salmon (Salmo salar
4Centre for Research-based Innovation in
L.) behavior in sea cages: A Lagrangian approach.
tures can be undertaken in future projects.
Aquaculture Technology, SFI, SINTEF Sealab, Norway
Aquaculture 288, 196-204.
The model will shed more light
VICTAMisland:Layout 1 30/8/13 14:22 Page 1
on whether the traditional methods
of pellet delivery, such as passive
rotational spreaders in a fixed location within the cage, represent a
satisfactory solution for feed distribution in sea cages, or if better
performance could be achieved by
employing more advanced spreaders with the ability to actively control the rate and area where feed is
released into the cage.
Summary
The development of a mathematical model of feed distribution
in sea cages is well underway and
takes into account environmental
factors, the spreader, physical pellet
properties and foraging fish. The
model with a user interface might
be valuable as a tool during production, combining the knowledge
of the operator with an extensive
but simple process overview for
best results.
The model can also be used as
a simulation tool for testing new
equipment and adjusting variables before deployment. This
will possibly lead to an improved
feeding regime with economic,
environmental and welfare benefits.
References
Alver M. O., Alfredsen J. A., Sigholt,
T. 2004. Dynamic modelling of pellet
distribution in Atlantic salmon (Salmo
salar L.) cages, Aquacultural Engineering,
Volume 31, Issues 1–2, Pages
51-72, ISSN 0144-8609, 10.1016/j.
aquaeng.2004.01.002.
Cromey, C. J., Nickell, T. D., Black, K.
D. 2002. Depomod-modelling the
deposition and biological effects of
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Asia’s largest exhibition
and conferences for
animal feed, aquafeed
and petfood production
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