HANSA 01-2021

SCHiFFStECHNiK | SHiP tECHNoloGY
Data to hand
This aggregation error can be reduced
by more frequent reporting. auto-logging
fuel consumption and speed is one
option. But retrofitting such systems
is expensive and for existing vessel the
business case is usually weak. another
option is reading and reporting consumption
on each significant speed or
weather change. This is unlikely to be
practical for crew.
The problem can however be overcome
by exploiting sensors and systems already
present on all vessels. While recording
high-frequency fuel flow data is expensive,
other data – including position,
speed through water and wind - is usually
available through the vessel’s ECdiS at
high frequency and low cost. often rPM
is available through NMEa. and speed
through water can be substituted by speed
over ground (from GPS positions) when
combined with current hindcast data.
in a previous paper, Wärtsilä Voyage
presented a sensor fusion model that used
speed over ground, forecast data, and fuel
consumption from noon reporting to create
a »virtual flow meter«. This model,
known as the Fuel Flow Model (FFM),
was verified and it proved to be capable
of removing the aggregation error from
pure noon data.
The FFM is a »grey box« model. While
a black box model would purely use data
to »learn« the relationships of various inputs
such as speed and weather to fuel consumption,
a white box model is powered by
standardized formulas that include coeffcients
depending on vessel characteristics.
one problem with the white box is
that it cannot account for vessel specifics
such as hull fouling over time. Conversely,
black box models are unable to
predict outcomes in operational conditions,
which have not been covered in
the training data if e.g., weather or operational
profile change to completely new
conditions, this makes black box model
predictions unreliable.
as a grey box model, FFM combines the
robustness of a white box approach with
the data driven vessel specifics tuning of
a black box model. input data includes
high-frequency readings of speed over
ground, heading, depth, etc. (through
ECdiS), different hindcast databases for
weather parameters, and draft and fuel
consumption measurements from noon
reports. The FFM is upsampling the noon
aggregated data (containing aggregation
error) to high frequency data (without aggregation
error)
Testing the model
Hull performance is a major aspect for ship owners and managers
a numerical study was used to verify
FFM. This involves generating realistic,
high-frequency draft and speed readings
and then computing consumption for a
generic test vessel using a simplified formula
based on speed, draft and other vessel
characteristics. The model was then
applied to consumption data aggregated
to noon periods as well as speed and draft.
The modelled high frequency consumption
output was compared to the
original generic input data. The accordance
of the model output with the
»true« data was shown to be very close
after a short learning period, indicating
that FFM can predict high frequency
(flow meter like) fuel flow which is free
from the aggregation error.
in a second phase of testing, FFM was
also able to remove large parts of error
added to the input data, representing potential
human error when inputting noon
report data. This proved that the model is
robust enough to remove significant error
from the noon report data.
in real life, the performance of the vessel
changes over time due to hull fouling. This
needs to be accounted for by the modelling,
adjusting vessel coeffcients as performance
changes. in the third phase of testing
the model was exposed to generic data
for a vessel with performance changing in
time. Six scenarios were tested, including
three with added (human noon reporting)
errors. The model was able to predict the
performance change well in all scenarios.
Outlook
The fuel readings performed by the crew
on a noon-to-noon basis are usually imprecise
due to human error. Furthermore,
speed consumption analysis based on
noon aggregated data suffers from systematical
statistical error. The Fuel Flow Model
has been proved to be able to remove both
types of errors. Thus, it provides a practical
approach to hull performance prediction
that can be applied on any vessel. The
Fuel Flow Model presented here is a simpler
version of the one underlying Wärtsilä
Voyage’s Fleet operations Solution. in
a business setting this approach has proved
to be robust and reliable. it is being used to
predict hull fouling and to check the quality
of fuel consumption reporting.
authors: Daniel Schmode,
Head of Solution advisory
Matti Antola, data Scientist
Wärtsilä Voyage
© HaNSa archive
HaNSa – international Maritime Journal 01 | 2021
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