An international standard on hull and
propeller performance – ISO 19030 – is
currently coming into force. It looks
likely to be followed by other standards
on performance measurement systems for
other sections of ships and for marine installations. The standard itself will also
be constantly improved so that performance criteria will become tighter.
The introduction of the standard
by the Geneva-based International
Organisation for Standardisation (ISO)
shows now much the trend to exploiting
Big Data to apply uniform quality specifications to coatings and similar products
is gathering pace.
There is growing pressure for improvements in energy performance in
particular by improving the quality and
maintenance of hulls and propellers.
Around 10 percent of the world shipping
fleet’s costs and greenhouse gas (GHG)
emissions stems from poor hull and propeller performance with the main culprits
being biofouling and mechanical damage,
according to the Marine Environment
Protection Committee (MEPC), a branch
of the United Nations’ International
Maritime Organisation (IMO).
Owners and operators of ships see Big
Data as an opportunity to raise the energy efficiency of vessels in order to meet
targets for both energy consumption and
for CO2 emissions.
Classification organizations, which
audit ships to categorize their quality and
efficiency for insurance and other purposes, are wanting to take advantage of
the rising amount of data on the performance of vessels, especially in areas like
anti-corrosion.
Coatings companies and performance
monitoring specialists have been introducing monitoring systems with different
levels of data detail.
AkzoNobel, for example, launched
four years ago its Intertrac service for
helping ship owners and operators to
measure the energy consumption and
CO2 emissions of their vessels with an
emphasis on the performance of anti-
fouling coatings of which it is a producer.
This was based on data from a satellite-
controlled Automatic Identification
System (AIS) for tracking vessels. Then
last year the company brought out
Intertrac Vision, a scheme which provides
more detailed data.
“AIS data provides the time and loca-
tion of the vessel only,” explained Michael
Hindmarsh, AkzoNobel’s Intertrac Vision
manager. “We align this position data
with other databases some of which use
‘Big Data’ to calculate the fouling chal-
lenge and fouling risk of a vessel trading
in that location at that point in time.”
On the other hand Intertrac Vision
uses algorithms that has been developed
from 3. 5 billion datapoints to predict the
hull performance. “It is not a hull perfor-
mance analysis tool,” said Hindmarsh.
“The prediction algorithms take into
account the fouling challenge, the ves-
sel type, the vessel activity, average hull
roughness and fouling control perfor-
mance. There is no requirement for the
crew to collect data.”
DNV GL, which said it is a market
leader among service providers in Big
Data-scale monitoring of ships perfor-
mances, runs a scheme under which a
ships’ crew do the data gathering.
“(What is measured ) includes data
on changing environmental conditions
to which vessels are exposed, such as
wind, different temperatures, degrees of
calmness and hull roughness, salt content,
and chemical properties of sea water,”
said Braun. “All of these factors can in-
fluence the demand for power by a ship.”
“A ship’s crew should be recording at
least 3-4 data values per day,” he contin-
ued. “This is equivalent to using Big Data
to make comparisons of the effectiveness
of different coatings on different vessel
types when exposed to different environ-
mental conditions.”
With anti-corrosion coatings, the
trend has been to move on site some of
the sophisticated testing done in labora-
tories with imaging and related equip-
ment. A major means of recording on site
data is through sensors. But a big chal-
lenge is that the sensors themselves can
become corroded.
The ISO started the procedure of
drawing up a hull and propeller per-
formance standard three years ago
with Svend Soeyland of Nordic Energy
Research acting as convener of the
working group and Geir Axel Oftedahl
of Jotun project manager.
Over 50 experts and observers from
shipping companies and associations,
ship builders, coatings producers, per-
formance monitoring companies and
NGOs participated in seven meetings of
the group. A ballot on the final standard
took place in March.
“This standard is intended for all
stakeholders that are striving to apply
a rigorous, yet practical way of measur-
ing the changes in hull and propeller
performance,” Soeyland and Oftedahl
told the Hull Performance & Insight
Conference 2016 in Italy earlier this
year in a joint paper.
“It will also provide much needed
transparency for buyers and sellers of
technologies and services intended to im-
prove hull and propeller performance,”
they added. “Finally, it will make it easier
for the same buyers and sellers to enter
into performance based-contracts.”
The scope of ISO 19030 includes
sensor requirements, measurement
procedures and methods of calculat-
ing performance indicators in main-
tenance, repair and retrofit activities.
One of the indicators is comparisons
between the performance of, for exam-
ple, coatings at different times between
dry docking maintenance.
Ship owners and operators have been
already preparing supply and other con-
tracts based on the new standard, ac-
cording to Soeyland and Oftedahl. But
many of them will have first to be in a
position to adopt performance monitor-
ing systems.
“Only a minority of ship owners and
operators are using Big Data-type perfor-
mance monitoring systems,” said Braun.
“We estimate that around 80 to 85 per-
cent are not into performance manage-
ment yet; five percent of global fleet is
using our Navigator Insight/ECO Insight,
eight percent are using the systems of nu-
merous competitors and three percent
are doing the monitoring themselves.”
With data standards likely to be become
even stricter as Big Data becomes more
prevalent, it could be some time before performance monitors system are applied by
a large proportion of world shipping. CW
