130 HANSA International Maritime Journal – 149. Jahrgang – 2012 – Nr. 9

Shipping Ship Operation

Getting into port on schedule – on time,

but not too early – is a difficult science

to get right when vessels can encounter any

number of variables along their voyage. Un-

til now, operators have got round this by

building buffers into their journeys, giving

them time to play for if sea conditions threat-

en to throw their schedules off course. The

practice is far from efficient however, not

least because of the fuel implications of var-

ying speeds during a long passage.

Volatile bunker prices and strict environ-

mental targets are driving technology solu-

tions to address the constraints of voyage

planning by estimating journey times and

speed requirements. In almost all cases

however, such calculations are made based

on theoretical information rather than real-

time data. This limits the extent to which a

ship’s operator and crew can be truly confi-

dent in the estimates.

Until now, that is. Eniram has invested in

software development that enables opera-

tors to capture, analyze and exploit real-

time data, adjusting performance dynami-

cally based on the latest readings. Known as

the Optimum Speed Assistant (OSA), this

tool gives operators greater control, reduc-

ing the need for a buffer and enabling vessels

to confidently maintain a consistent speed

throughout a voyage. Speed optimization of

a vessel’s voyage is the key to achieving this

control. This article takes a closer look at

what we mean by speed optimization, the

challenges surrounding it and offers a con-

sidered approach to overcoming the obsta-

cles to achieving it.

Speed optimization schemes face tough

challenges in daily vessel operations due to

strict itinerary demands and the limited ac-

curacy of available weather and sea current

forecasts. Since fuel-optimal routing is high-

ly sensitive to constraints such as just-in-time

arrival, one high-speed leg can wipe out the

accumulated fuel savings of an entire voyage.

In this article, we consider an approach

to optimal speed estimation which is based

on statistical route forecasts derived from

historical data measured on specific routes.

The optimization scheme includes penal-

ties for the undesired consequences of cer-

tain operating actions, such as excessive

acceleration, as well as rewards for taking

correct measures, such as maintaining a

consistent speed, as appropriate. We con-

ducted simulation scenarios to provide al-

ternative and improved energy efficient

speed profiles. Our study is based on analy-

zing available operational data collected

from twenty vessels. We used this data and

our extensive nautical experience to model

the environmental effects on optimal speed

profile. We found that in taking care to op-

timize speed can achieve possible 3 ± 1 %

energy savings.

1. Challenges

The challenge of route and speed optimi-

zation is often characterized by strict time

constraints and noisy signals. Recent devel-

opments in measurement systems mean it is

now possible to measure many parameters

on board a vessel and over lengthy voyages.

But it is only by analyzing this data in detail

that a clear picture into the vessel’s behavior

can be gained. Many brilliant minds and

earlier studies have helped to pinpoint the

various challenges in route optimization in-

Overcoming the challenges in vessel speed optimizationRising bunker costs and strict environmental targets are constraining voyage planning.

By estimating optimal speed and route profiles based on empirical data and statistical

models savings up to 10 % can be achieved, writes Eniram expert Jussi Pyörre

Fig. 1: Example of challen-

ges in route optimization.

Left: Different routes are

colored with extra miles

sailed through water.

Unsuccessful use of

favorable sea current

leads to increased fuel

consumption due to

the increased distance

traveled through water.

Right: Synthesis of

long-term observations

and models form a basis

for reliable sea current

forecasts suitable for

route optimizationImag

es: T

idet

ech

132 HANSA International Maritime Journal – 149. Jahrgang – 2012 – Nr. 9

Shipping Ship Operation

cluding the following notable ones:

Quality of data: This is especially an issue

in vessel energy consumption estimation.

Quality issues include data correctness

(validity), consistency, resolution, and

completeness (sufficiency).

Difficulty in accurately estimating time of

arrival: This is often subject to change and

dependent on prevailing environmental

conditions. Waves and weather impact the

speed the vessel is able to travel.

Off-design conditions for the vessel or pro-

peller: Care should be taken to optimize

propeller use to avoid excess fuel con-

sumption. For example, excessive accel-

eration can be avoided by reducing RPM

variation.

Weather forecasting limitations: Since this

is still largely based on probability rather

than accuracy, the reliability of any fore-

cast needs to be included in the evalua-

tion of the optimization results.

Service speed estimations: It is difficult to

estimate the service speed obtainable by

a vessel in real weather conditions when

sailing on a given shipping route, or in-

deed to support routing decisions in

heavy seas.

Operating profile of the engine: These pro-

files are complex and are impacted by

changing engine operational characteris-

tics due to partial loading conditions or

technical degradation of the engine.

Hydrodynamics: Use of hydrodynamic

modeling while in operation is impracti-

cal and the approach of applying multi-

disciplinary design optimization is weak-

ly developed in the marine context.

Timely intervention: Operators often have

differing opinions on vessel operations

and optimum settings based on their own

experience. This means detecting small

changes in the sea conditions is difficult

to capture. On larger vessels, the control

settings of variable parameters are typi-

cally adjusted on an hourly basis rather

than within minutes. A key challenge is to

assist the operator in keeping the adjust-

ments that impact energy consumption to

a minimum while taking account of

changes in the condition of the vessel and

its environment at appropriate intervals.

User acceptance: For a system providing

operational assistance it is crucial to gain

acceptance from the operator. This in-

volves attaining some degree of confi-

dence in using the man-machine-inter-

face that informs and drives operational

decision making. It depends particularly

on ease-of-use, usefulness, and on ade-

quate support provided to onboard deci-

sion makers.

At Eniram, we have carefully considered an

approach to speed optimization that seeks

to address these highlighted challenges. Our

main approach is based on empirical data

and statistical models using this data to de-

vise new optimization scheme. This scheme

emphasizes ease-of-use and develops a mo-

del that can provide accurate prediction for

the estimated time of arrival (ETA), and

optimization to meet just-in-time arrival

needs. We emphasize transparent use of re-We emphasize transparent use of re-

liable and accurate sea current and weather

forecasts that supports onboard decision

making. Additionally, we include results

from a preliminary study we undertook to

estimate potential savings achieved using

speed profile optimization.

2. Data for speed optimization

There is variety of data measurements

impacting vessel performance that is useful

in determining speed optimization. Infor-

mation with influence includes the changing

variables relating to the condition of the ves-

sel such as its maintenance (e. g. hull dimen-

sions, coating and cleaning data) and his-

torical operational data measurements (e. g.

high frequency time series of engine load).

In order to measure hull motion accurately,

additional sensors such as inclinometers and

radars can be mounted onboard the vessel

to monitor draft dynamically. Finally, accu-

rate and reliable forecasts of sea and wind

condition accompanied with tidal and sea

current modelling also support fuel efficient

operational profile and route planning.

2.1. Historical data

Access to historical data is essential when

determining forecasts and optimizing the

accuracy of route and speed profile plan-

ning algorithms. Data measuring the vessels

operating conditions and onboard variables

Fig. 2: Surface current forecast provided by NOAA. Left: Open data service provides 144 h forecasts. Right: The service provides

forecast validation against other models, and observed data including in situ measurements

Imag

es: N

OA

A

133HANSA International Maritime Journal – 149. Jahrgang – 2012 – Nr. 9

ShippingShip Operation

combined with historical port-specific itin-

erary variations are prerequisites in deter-

mining optimization for that vessel. The

historical data includes the following var-

iables: propulsion power, RPM, location

(Fig. 1), speed, acceleration, attitude, draft,

sea-depth, use of stabilizers and wind. If not

available, sea current can be estimated using

speed over ground and other variables.

Motion modeling is based on attitude sen-

sors positioned in appropriate areas on the

vessel. In a typical vessel, several inclinome-

ters are mounted to provide data in trans-

verse and longitudinal direction. When they

are mounted fore and aft of the vessel these

sensors can also provide hull deflection es-

timates in addition to rolling and pitching

measurement. Radar data can accompany

inclinometer measurements making it pos-

sible to take into account the actual wave

patterns around the vessel.

Our approach to optimization has three

major goals: to fully use existing data; to gain

insight into real life optimization constraints

during operation in actual sea conditions;

and to increase reliability of route optimiza-

tion. This data can be used to form baselines

and reference levels. In addition, the data of

actual operational profile is used in simula-

tions to evaluate alternative profiles.

2.2. Sea current forecasts

Successful speed optimization relies heav-

ily on accurate forecasts. Generally speaking,

forecasts made on a global scale and pro-

vided by international centers do not take

into account all of the specific characteris-

tics of the local areas. This is also true of

global sea current models when predicting

conditions in coastal environments.

One increasingly attractive approach in

dealing with this type of flaw in forecasting

data is to use open data to construct current

fields along the route. Fig. 2 gives an exam-

ple of 144 hour surface current forecasts

provided by the US Government National

Fig. 3: Example leg from Cozumel in Mexico to the Port of Miami in the US: Recorded generator power and mean load measurements are used to generate empirical engine models

So

urc

e:

En

ira

m

Fig. 4: Historical operational data. Left: Based on measured current profiles along the fixed route, optimal speed through water profile can be estimated. Right: Distribution median (red) forms a basis for constrained route optimization

So

urc

e:

En

ira

m

134 HANSA International Maritime Journal – 149. Jahrgang – 2012 – Nr. 9

Shipping Ship Operation

Oceanic and Atmospheric Administration

(NOAA).

As with all environmental data, appropri-

ate quality procedures have to be imple-

mented so that the sea current model and

forecast can be verified as well as any uncer-

tainties of the computations quantified. The

example in Fig. 2 validates NOAA forecasts

against in-situ measurements. The model

validation is thus done against other mod-

els, satellite data, automatic weather station

and measurements by vessel operating in

the sea area.

Another example of available current

forecasting services is given in Fig. 1. The

example shows Gulf Stream forecast based

on synthesis of long-term observations and

models. The commercial service is based on

separate tidal and sea current modeling as

well as benchmarking against best available

measurements.

Finally, the level of accuracy of the fore-

cast products must be used as fully as pos-

sible in speed optimization schemes.

3. Speed optimization formula

In our observation, keeping to the sched-

uled arrival time, navigating restricted speed

areas and shallow waters as well as other

seafaring conditions have a direct impact on

speed optimization. These affecting factors

should be collected into baselines and used

to provide unbiased performance estima-

tion and benchmarking. We also advocate

modeling the different operational and en-

vironmental effects separately.

Speed optimization helps to solve numer-

ically the speed distribution during the voy-

age in a way that minimizes the amount of

fuel consumed. In addition to avoiding ex-

cessive speed, the fuel consumption of the

vessel can be reduced by continually moni-

toring any changes in engine load and weath-

er conditions and making the necessary

engine load adjustments as changes are

detected. This is why accurate information

about the state of the vessel and its environ-

mental surroundings are important in main-

taining an efficient operation.

Since engine load is expressed in terms of

power, engine RPM and torque are the key

variables used to monitor fuel consump-

tion. Fig. 3 shows an example of measured

fuel consumption. This data can be used to

build empirical engine models suitable for

speed optimization. Additional factors such

as density and caloric value of the fuel may

be used to obtain more accurate modeling.

Eniram’s approach to optimizing vessel

consumption uses statistical models based

on measured operational and hydrodynam-

ic models. The data is collected for extended

periods during the operation so the statisti-

cal model can be updated to reflect cumu-

lated data or any improved accuracy, as a

result of possible upgrades such as hull coat-

ings and retro-fits.

In practice the possibility to create spe-

cific empirical model depends on availabil-

ity of accurate data. Fortunately, in many

cases it is sufficient to model only the most

significant components affecting the fuel

consumption.

4. Implementation

The Eniram system for optimizing the

operations of the vessel relies on receiving

signals from the onboard automation sys-

tem and sensors and applies historical data,

baselines and regression models. Simulation

of optimal settings for the operational pa-

rameters is performed using a multi-disci-

plinary design analysis and optimization

framework which enables complex mode-

ling of various disciplines, with design vari-

ables, objective function and constraints.

The defined baselines we use serve as a

basis for measuring vessel performance and

offer frameworks for operational compari-

son such as calculations of leg, voyage or

specific sea area benchmarks at individual

vessel level or at fleet level.

Historical measurement mapped into the

baseline vector doesn’t just indicate what

variables are constrained or impossible (e. g.

maximum speed or trim), but also provide

information on the actual preferred combi-

nation of control variables (e. g. service

speed in specific sea area in specific weath-

er conditions).

When a baseline is shown transparent -

ly to the operator, a clear understanding of

typical route profile can be achieved. While

baseline can be seen as a collection of static

and historically averaged leg specific data,

actual optimization is done interactively

just before and during the voyage. Thus

real-time data complements the baseline

and enables dynamic optimization. For ex-

ample, recommended speed is adjusted ac-

cording to the changing location at sea to

guarantee arrival in time. Use of historical

measurements to define baselines relates to

a long tradition of collecting data from ship-

ping routes. Abundant data exists listing

statistical long-term parameters of winds

and waves on shipping routes. Wave size

characterization might be contained in a

typical atlas in a specific sea area and for a

given season.

Fig. 5: »Example of squat model«. For this cruise ship

shallow water effect is measured starting from 130 m

water depth. Color indicates vessel speed. For example,

for a vessel running at 20 kn speed and 30 m depth the

energy consumption increases 20 %

Source: Eniram

135HANSA International Maritime Journal – 149. Jahrgang – 2012 – Nr. 9

ShippingShip Operation

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Fig. 4 presents a case where historical op-

erational data for a specific route is used as

a prerequisite condition for determining

optimal speed profile.

5. Conclusions

Fuel prices compounded by environmen-

tal targets are forcing fleet operators to

think harder about how they calculate and

manage speed. While aiming for just-in-

time arrivals into port is one way of increas-

ing efficiency, if speed management isn’t

fine-tuned as part of this strategy any finan-

cial gains could be wiped out. The addi-

tional fuel required to support variances in

speed is significant enough to undermine

even the best thought-out voyage optimiza-

tion plans.

To maximize fuel efficiency in today’s

climate, operators need to be able to main-

tain a more constant performance – what-

ever the prevailing conditions – by making

appropriate adjustments using a combina-

tion of real-time readings and historical

information about the known performance

of a vessel in a variety of conditions.

Having confirmed with our customers

that there is a real market need we wanted

to come up with a solution, based on our

existing data collection technology and ana-

lytical skills that would help to improve fuel

efficiency using speed optimization.

Our initial study, based on operational

data from twenty vessels modeling the en-

vironmental effect and simulation of opti-

mal speed profiles, suggests possible energy

savings of 3 ± 1 %. This estimate is in accord-

ance with values of 1–5 % which have been

reported elsewhere. We have found that in

many cases, if a vessel has operated with

significant time margins to ensure arrival in

time or due to ETA, savings up to 10 % can

be achieved.

We have presented an approach for esti-

mating optimal speed and route profiles

based on historical data measured on a spe-

cific route. The approach, compensating

varying depth, sea currents and weather

conditions, has been born out of need to

provide ease-of-use guidance to vessel op-

erators in transparent way while minimiz-

ing the probability for error in optimal

speed profile.

Our optimization scheme addresses many

of the challenges expressed earlier. We have

introduced a scheme that is based on:

constraint,

an example),

optimization,

-

er and verification modeling performance

using actual data.

The advantage of this data intensive ap-

proach is that accumulated leg data can be

used by the crew and fleet operators to cap-

ture detailed factors to enable efficient op-

erating.

Vessel performance management and

voyage optimization is being increasingly

made easier and more accurate with the

help of technology. Collecting, interpreting

and using both real-time and historic data

is important in getting the most out of any

system. For this reason, operators and their

crews need to become more ready to em-

brace solutions and data analytics tools

available. While it requires something of a

real leap to let technology decide a vessel’s

best speed, the potential for thousands of

dollars in bunker savings on each individu-

al voyage should be a strong incentive.

Our speed optimization decision support

tool (OSA) delivers the necessary guidance

and decision support to a ship’s crew to help

maintain the most consistent and fuel-effi-

cient speed, for the given route, conditions

and port arrival needs.

Author:

Jussi Pyörre

Vice President Technology

Eniram Ltd., Helsinki/Finland

info@eniram.fi

Note: This article is based on a technical paper written by Te-ro Ilus and Aatos Heikkinen and presented at 11th Internati-onal Conference on Computer Applications and Information Technology in the Maritime Industries (COMPIT ’12) in Liège/Belgium. The full conference paper, which goes into more detail regarding the simulations and methodology, can be obtained by contacting: info@eniram.fi