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SAFETY4SEA

DeepSea: AI and Machine Learning to shape a greener future for maritime industry

New technologies need to be accessible to everyone, highlights Mr. Roberto Coustas

by Roberto Coustas
April 6, 2021
in Opinions, Smart
machine learning
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In an exclusive interview to SAFETY4SEA, Roberto Coustas, Co-Founder and CEO, DeepSea, discusses the opportunities that AI-based vessel optimisation presents for the decarbonisation of fleets and the wider shipping industry. Mr Coustas believes that by harnessing cutting-edge ML and AI and the insights that they can deliver, the world of shipping will truly become greener and more transparent.

 

SAFETY4SEA: From your perspective, what are the industry’s key challenges leading up to 2030?

Roberto Coustas: The 2020s will be a critical decade for shipping. We are seeing the sector face huge challenges, but these present immense opportunities too. The two critical priorities are decarbonisation and digitalisation – both have grown in importance following the COVID-19 pandemic, and both need to be adopted to build back better. As a technology company, we are very interested in the role of data – which is the vital link between the two.

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In shipping, many people now understand that data will drive decarbonisation. Data gives shipowners and charterers the information they need to effectively monitor and optimise their fleets. This will be even more necessary to ensure compliance with new regulations such as the Energy Efficiency Existing Ship Index (EEXI), which is pending approval at MEPC 76. But that’s only part of the story. An optimised vessel consumes less fuel, which brings commercial advantages to the bill payer, and shipowners benefit too as environmental performance is increasingly viewed as a factor that influences chartering decisions.

The path to an increasingly decarbonised and digitalised shipping sector is a complex one. As a technology entrepreneur, I know how digitalisation can help overcome the challenges that stand in the way of a greener shipping sector. It is easy to talk about digitalisation, but delivering tangible results is a lot harder – especially when budgets are tight, and a large upfront investment may not be an option. New technologies need to be accessible to everyone, and we can’t afford to exclude any players from the benefits of digital transformation. We at DeepSea aim to focus on creating solutions that are affordable and have a very clear proof of value narrative for our customers.

The key for shipowners and charterers is to use technology to make informed data-driven decisions, and make collective decisions to reduce fuel consumption, costs and emissions. Thanks to our cutting-edge AI research team, we are able to bring to market solutions to these big challenges that may have previously been considered inaccessible for a big part of the shipping community.

 

S4S: In terms of decarbonization, what should be the key priorities for the shipping industry?

R.C.: When it comes to decarbonising the shipping industry, there are a wide range of design, technical and operational measures to be considered. New research is constantly informing the debate around the future direction of the industry, but as the discussion continues, it is essential that the industry takes action right now to reduce the environmental impact of the global fleet in order to meet the set targets for 2030 and beyond. This requires on a first level assessing the current performance of the fleet with the use of technology, and then making informed smart investments in order to drive greater vessel efficiency.

It is undeniable that many owners and charterers are constrained by manual processes onboard their vessels. Most often, they still rely on noon reports for performance reporting. When DeepSea was founded, my co-founder Kostantinos Kyriakopoulos and I had the vision to enable shipowners and charterers to monitor the performance of their whole fleet in real-time – not only vessels which had made the investment in data collection from onboard the vessel.

We discovered that the answer would require years of research, but we managed to find the answer. With our first commercial product, a hardware-free AI-driven platform able to deliver accurate vessel performance insights using noon data, we realised that by combining these reports with existing AIS and weather data, we could achieve an amazing level of insight, such as models that can verify accurate reporting, predict future fuel usage and identify areas where fuel consumption can be reduced. As a result, associated emissions and costs can be greatly decreased due to the scale that this technology can reach.

 

S4S: How could AI-based vessel optimisation benefit the industry?

R.C.: Data, AI and machine learning (ML) have the power to really transform the way many owners and operators think about unlocking real value from their performance insights. These technologies provide an incredible opportunity to truly understand and improve vessel performance, because they use models trained on actual data and updated in real-time to the changing conditions of the vessel. As such, they can provide rich insights and help shipowners and charterers secure savings and generate results within an unprecedented time frame

Shipping needs smart solutions to meet the industry requirements and regulations of today and tomorrow. This includes decarbonisation and other pressing challenges, such as boosted transparency. AI-driven performance models are a critical tool to understand both how vessels are performing, and how to improve them.

Increasingly charterers are looking to compare the performance of vessels relative to their peers, seeking an end to the opaqueness that has historically defined the shipping industry. I believe the transparency unlocked by the insights that AI extracts could also create new opportunities to rethink fundamental relationships in the shipping industry, including those between ship and shore. Already our customers are bridging the gap between those onboard and their shore teams and are overall in a much stronger position to make the data-driven decisions that minimise their costs and environmental impact.

 

S4S: What is your key message to industry stakeholders in relation to a more sustainable future? 

R.C: At DeepSea we are really looking forward to continuing our work to drive shipping towards a more sustainable future. We are optimistic about the role AI and machine learning can play in shaping this new green future, but we are well aware of the challenges and opportunities that lay ahead. Our key message for stakeholders is that data will fuel decarbonisation. As it gives shipowners and charterers the information they need to monitor and optimise their fleets. This will save fuel and make a significant contribution to emissions reduction.

It is essential that the technology to deliver data-driven insights is available to every vessel. We are working to facilitate this. In the past, one significant problem has been the high costs of performance monitoring platforms, which usually require a large upfront investment. That’s why we continue to work to accelerate the accessibility of innovative technologies for all players in the industry – regardless of size and stage in their digitalisation journey. This can radically change the way in which vessel performance monitoring and optimisation is done, at a very large scale, and very quickly. But, critically, without changing the way shipowners, operators and charterers manage their businesses, but assisting them to start their digitalisation journey. This has the potential to truly help the industry to decarbonise – bit by bit – and to achieve a promising future of sustainable shipping.

 

The views presented hereabove are only those of the author and do not necessarily those of  SAFETY4SEA and are for information sharing and discussion  purposes only.

Tags: AIdecarbonizationdigitalizationfuture of shippingInterviewmachine learningoperational efficiencyperformance dataship technologysmtrends
Roberto Coustas

Roberto Coustas

Roberto Coustas was born in Athens, Greece and graduated in 2010 from Geitonas School. At the age of 17, he relocated to London and studied Mechanical Engineering at UCL, where he became familiar with Artificial Intelligence by taking extra-curricular courses from the Computer Science department. Following his interest in computing, he continued his studies at the University of Oxford, where he received his MSc in Computer Science with Distinction, specialising in Machine Learning. Working briefly as a performance engineer and technical coordinator at Danaos, he realised the potential of applying Artificial Intelligence to the shipping domain. He then co-founded DeepSea Technologies with Konstantinos Kyriakopoulos, a Cambridge University PhD, which has revolutionised vessel monitoring with the use of state of the art Artificial Intelligence technology. In 2020 DeepSea Technologies secured a 3 million euro investment from the Environmental Technology Fund (ETF), and is currently one of the fastest growing maritime startups in Europe. 

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