Owners could save 20% by investing in data monitoring, analytics and machine learning
Owners of medium range product tankers could increase earnings by US$1,000/day per ship by optimising vessel speed, according to Lean Marine chief executive Mikael Laurin. This adds up to US$365,000 per year or US$3.65M for a fleet of 10 tankers.
He said owners can reduce fuel consumption by around 20% by optimising propulsion, during his presentation at Riviera Maritime Media’s Tanker Shipping & Trade Conference in London on 27 November 2019.
To achieve these reductions, owners need to monitor and analyse tanker performance more frequently than using a daily report. “If you want to learn more you need to be able to measure it,” said Mr Laurin.
This means owners need to invest in onboard sensors, data transmission and analytics to understand how affective they are for fleet performance and reducing operating costs and emissions.
“If you can measure it, you can manage it,” said Mr Laurin. “You need the right tools on board and to educate people on how to use the technology.”
He thinks big data analytics should also include artificial intelligence and machine learning to identify more performance information to reduce operating costs further.
“We can learn more and can compare performance against other ships, fleets and owners,” he said.
Fuel efficiency should be considered when planning and executing voyages. Software and applications for speed optimisation, weather routeing, trim optimisation and managing cargo heating and cooling can also be used for voyage planning.
During voyage execution, ship masters should follow the planned optimised route, follow best practice, optimise autopilot settings and slow turn to minimise resistance. Following advised speed, minimising variations in shaft power, maintaining continuous data logging to receive decision support from shore and keeping engines and propellers running in optimal conditions will all help to save fuel.
After the voyage, managers need to review performance, analyse and compare routes and analyse hull, propeller and engine performance.
Also at the conference, Greensteam head of performance management Jonas Frederiksen said machine learning can enable shipping companies to identify areas to reduce fuel consumption and greenhouse gas (GHG) emissions.
Shipowners and operators will be tasked with reducing GHG emissions by 40% over the next 10 years and to reach IMO’s 50% target by 2050.
“Machine learning is a viable strategy for vessel optimisation,” he said. Machine learning will help owners to identify how to optimise trim, reduce speeds and manage hull fouling.
“It is very important to get a clear view of where to target GHG reductions,” said Mr Frederiksen. “Machine learning gives owners clarity on GHG reductions and getting higher fuel savings.”
He said machine learning is a “zero-capex and zero off-hire investment” for owners to target 5% GHG emission reductions.
“Machine learning and vessel optimisation plays a vital role in reaching GHG reduction goals,” said Mr Frederiksen.
Data collected from ships, meteorology, ocean conditions and AIS data indicates where and why a vessel uses excess fuel. These insights allow the crew to adjust operations, minimising fuel waste, reducing costs and cutting emissions.
Machine learning identifies the relationship between factors affecting vessel fuel efficiency. GreenSteam’s machine learning platform also measures complementary emissions reduction technologies. Models of vessels are continually refreshed and improved with the benefit of each new day’s data.
Some of the largest fuel consumption savings come from minimising hull fouling. According to Safinah Group general manager Carl Barnes at the TST Conference, slime and weed fouling can increase drag up to 10% and shell fouling up to 40%. He said water temperature, illumination, nutrient levels and ship speed and idle time will influence fouling levels. This can be minimised depending on the hull coating.
Mr Barnes said data collected and processed during hull voyages enables owners to determine which coatings are suitable. Analysed data should include vessel activity and speed, hull and propeller performance and the condition of the hull on arrival.
Drydocking data also needs to be analysed, to help owners to decide when and which yard to use for hull recoating.
Another major contributor to reducing fuel consumption and emissions is implementing voyage planning and execution technology. Shipmanager Anglo-Eastern turned to Wärtsilä for technology to support voyage planning and execution, plus engine performance and fuel efficiency monitoring.
It is using Wärtsilä’s Fleet Operations Solution technology to optimise voyages, for weather routeing, for monitoring engine, hull and propeller performance, speed management and overall fuel cost reduction.
Singapore-headquartered Thome Group is implementing OneOcean’s unified platform across its fleet to combine its e-navigational, regulatory and environmental requirements. Thome intends to use OneOcean to optimise vessel voyages using PassageManager, Regs4Ships to keep seafarers up-to-date with the latest regulations and EnviroManager to reduce waste and emissions.
OneOcean was formed by the merger of UK-based ChartCo with Canada-headquartered Marine Press. This brought together ECDIS software, weather information and data for regulatory compliance in one platform.
Another merger in Q4 2019 produced a smart navigation platform for optimising vessel voyages.
Japanese electronic navigation supplier Cornes Chart Group acquired UK-headquartered Global Navigation Systems to expand its e-navigation offering. The deal followed Cornes’ purchase of Singapore-based Safe Navigation earlier in 2019 and forms a global platform for integrated maritime electronic navigation technology.
Arista Shipping has started using weather and ocean condition data to improve vessel voyages and ship performance.
The Greek operator of dry bulk cargo ships is using ABS’ analytics of historic meteorological and ocean data to improve vessel efficiency and route planning. It is using ABS’ Metocean Hindcast data application for smarter shipping operations, processing more than a decade of Metocean Hindcast model data for weather routeing and fuel-efficient voyage planning.
Royston is collaborating with Tidetech to overlay metocean data on an electronic fuel management system (EFMS) for more accurate vessel tracking and route planning.
This enables high-resolution data from Tidetech global datasets on environmental conditions to be displayed on the map dashboard of EFMS enginei. Information is available on board ships and onshore through a dedicated portal, assisting ship operators to improve fuel management and emissions control through enhanced route planning.