Technology will lead maritime out of a Covid-constrained world and into a greener and more sustainable future
There was much hope for a steady transition in deploying digitalisation for vessel optimisation in 2020. The coronavirus pandemic accelerated this process and has driven shipping into a much faster trajectory to technology adoption.
As predicted 12 months ago, 2020 was pivotal for maritime autonomy, digitalisation, cyber security and future shipping connectivity. It is the year shipping transitioned from a physical sector into a digitalised and connected industry.
In 2020, shipping had to tackle hard questions about cyber security after a series of successful cyber crimes. Owners, operators and managers had to work remotely with data of varying quality from their fleets under increasing pressure to reduce emissions.
Vessel optimisation became a more important aspect to shipping and technologies enabling this became increasingly adopted.
These are the key trends in maritime in 2021 and beyond, as shipping has less than 10 years to reduce emissions and carbon intensity according to IMO’s strategy.
If 2020 was the year for autonomous vessel innovation and trials, 2021 will be a year of demonstrations and commercial applications.
For commercial cross-ocean voyages, the Mayflower autonomous ship (MAS) project is a key demonstration. The Mayflower autonomous and solar-powered research vessel was launched in Plymouth, UK, after two years of design and construction, in September 2020. Its creators intend to prepare this vessel for the first-ever transatlantic crossing by an unmanned ship in 2021.
This vessel has an artificial intelligence AI Captain built by ProMare and IBM, which gives Mayflower the ability to sense, think and make decisions at sea with no onboard crew. This marine AI is underpinned by IBM’s advanced edge computing systems, automation software, computer vision technology and Red Hat open-source software. Mayflower will undergo six months of sea trials, research missions and voyages before attempting to cross the Atlantic in Q2 2021.
In Norway, the world’s first autonomous-ready commercial ship was delivered and is ready for sea trials in 2021. Yara Birkeland was delivered to Yara International in November by Norwegian shipyard Vard Brattvåg. It will undergo container loading and stability testing before sailing to a test area in Horten, Norway for further preparations for autonomous operation. Successful operation and lessons learned from the project could lay the groundwork for future autonomous vessel operations.
In offshore, 2021 will see delivery of the first autonomous vessels for survey work. Ocean Infinity is taking a lead with its Armada fleet of robotic vessels under construction. Grovfjord Mek Verksted (GMV) is building the initial fleet of 21-m vessels in Norway, ready for operation in 2021. These were designed for offshore oil, gas and renewables surveys with ultra-low emissions.
Ocean Infinity has announced plans for the next phase with a contract signed for eight 78-m optionally crewed robotic vessels. The first is expected to be launched from Vard’s shipyard in mid-2022.
Shipping companies have shown growing interest in internet of things (IoT) for equipment on their assets to remotely monitor machinery, ship condition and performance.
This is part of the industry’s digitalisation drive and an aspect of the sector’s sustainability strategy. However, there are limitations to the amount of data that can be transferred from ships to shore and on the return route.
Maritime communications providers have made great gains in recent years to ramp up bandwidth capacity and segregate ship data transmissions from general vessel communications. But there remain capacity constraints amid continuously rising bandwidth demand.
One solution comes from developing sensors with AI capabilities. These will process data at source before transferring useful information to a central collection server on a ship’s bridge or engineroom control centre.
Lux researcher Cole McCollum highlighted the potential of AI-enabled sensors in a December-published report. “Recent advancements in machine learning capabilities enable developers and operators to extract more value out of sensors,” Mr McCollum said. “This is an opportunity to create new products and improve internal processes by generating deeper insights off existing hardware.”
Shipping companies could retrofit existing machinery on vessels with AI-enabled sensors to generate data streams for condition and performance monitoring. Data would be processed on the sensor and only relevant information or changes in performance or condition would be sent to the central unit and via satellite. Mr McCollum urged companies to “consider existing sensor deployments and examine how more value can be wrung out of the data.”
Start-up companies such as Algorithmica are developing solutions that can generate more insightful data from lower-cost sensors.
There are other applications in maritime sectors for AI-enabled sensors. Mr McCollum suggested “AI-enabled sensors are powerful when coupled with automation, as in 3D printing and robotics.”
Another option available to shipping companies and vessel owners to monitor operations in real-time without maxing out the bandwidth is using edge computing, deploying powerful computers and data storage closer to the location where it is needed, such as on ships to improve response times, provide low latency between sensor and user and save bandwidth.
By processing data on ships, owners can focus transmissions to shore providing key information on performance indicators, alerts from trend changes and condition issues, analytics insight and vital parameters.
This minimises bandwidth requirements for ship and onboard equipment monitoring, while reducing demand on people at both ends of communications. It also lowers requirements form onshore processing and cloud computing.
There are other applications for edge computing in emerging technologies for autonomous vessels and navigation. Here, processors can provide information for onboard decision support systems.
IBM’s advanced edge computing systems and automation software are an important component of AI-assisted navigation on the Mayflower autonomous ship project.
Edge computing is also scheduled to be deployed on Yara Birkeland. With more companies investing in remote monitoring and semi-autonomous shipping there will be higher demand for edge computers.
As radar’s commercial introduction in maritime transformed navigation more than 50 years ago, so lidar (light detection and ranging) will do the same.
Lidar is becoming an integral part of AI-enabled and autonomous navigation as it provides another layer of information from the environment, conditions and hazards surrounding a ship.
Kongsberg Maritime supplied lidar along with radar, AIS, cameras and infrared cameras on Yara International’s semi-autonomous container ship Yara Birkeland.
This sensor fusion will be deployed on other autonomous vessels in the future. It is already installed on tugs remotely controlled from shore in trials of autonomous navigation technology.
Sensor fusion with lidar will also be mixed with algorithms for collision avoidance, risk assessment and power optimisation to support vessel masters on manned ships in the future.
Trials are underway on a ferry in the Baltic region where information is gathered from a mixture of sensors and processed to improve ship navigation.
Tallink Grupp’s newest vessel Megastar is providing a base for practical field tests on the Helsinki-Tallinn route on the Baltic Sea. Its sensor fusion includes visual, audio, radar and lidar. These send information for processing using AI and machine-learning software. The goal is to automatically identify objects, such as navigation aids and other vessels, to improve situational awareness.
In offshore, lidar is deployed at sites for renewable energy projects to gather information on sea conditions, and offshore support vessels use lidar devices in dynamic positioning (DP). This technology is incorporated in the latest laser-based positioning sensors from Guidance Marine for DP operations.
Shipping companies, classification societies and offshore operators are beginning to invest in digital twins as they recognise the benefits.
During Riviera’s Extending intelligent monitoring of onboard machinery webinar, in September, World Maritime University (WMU) associate professor (safety and security) Dimitrios Dalaklis highlighted importance of digital twins along with IoT and cloud computing.
When combined, these solutions can contribute towards improved safety, logistics, reduced fuel costs and lower emissions. “Take the concept of a digital twin for example,” Mr Dalaklis explained. “We can now create a theoretical model and manipulate it in real time to make changes that have an almost instantaneous result in the real world.”
This optimises the decision-making process by “using highly accurate data, saving costs and having a huge impact on efficiency, both during the development stage and when the model becomes a reality,” Mr Dalaklis said.
NYK Bulkship (Asia) operations director Capt K K Mukherjee also highlighted its importance. He said the future for condition monitoring and cognitive maintenance will involve virtual reality and digital twins. These will help in repair and maintenance over a ship’s lifecycle and enable owners to identify areas that need action and improvement, he said.
Classification society DNV GL has introduced methods of class verification involving digital twins. As part of its smart vessel notation, it has introduced ways to verify vessel condition using digital technology.
DNV GL has added a chapter to its ship classification rules with three new notations covering digital features, including data-driven verification (DDV). This sets the requirements for gathering, treating and delivering collected data to ensure the quality of the data for use in a class assessment. For specified systems, the verified data can be used in the certification and classification of those systems in maritime and offshore vessels. The notation covers several different verification methods, including self-verifying systems and digital twins.
In offshore, Shell is working with Akselos on digital twin technology for structural integrity assessments of offshore assets. An early example is a structural digital twin generated of the Bonga production storage and offloading (FPSO) ship operating offshore Nigeria. Akselos is working with Shell to develop similar digital models of other offshore assets.
There are many more examples already out there and more will be coming in 2021.