Thousands of tonnes of litter enter the ocean every day, posing a significant threat to marine life and ecosystems. While floating and beach litter are often in the spotlight, about 70% of marine litter eventually sinks to the seafloor, making underwater litter the largest accumulation of marine litter that often goes undetected. Plastic debris makes up the majority of ocean litter and is a known source of microplastics in the ocean. This paper focuses on the detection of ocean plastic using neural network models. Two neural network models will be trained, i.e., YOLACT and the Mask R-CNN, for the instance segmentation of underwater litter in images. The models are trained on the TrashCAN dataset, using pre-trained model weights trained using COCO. The trained neural network could achieve a mean average precision ($mAP$) of 0.377 and 0.365 for the Mask R-CNN and YOLACT, respectively. The lightweight nature of YOLACT allows it to detect images at up to six times the speed of the Mask R-CNN, while only making a comparatively smaller trade-off in terms of performance. This allows for two separate applications: YOLACT for the collection of litter using autonomous underwater vehicles (AUVs) and the Mask R-CNN for surveying litter distribution.

The cost of renewable energy technologies such as wind and solar is falling significantly over the decade and this can have a large influence on the efforts to reach sustainability. With the shipping industry contributing to a whopping 3.3% in global CO₂ emissions, the International Maritime Organization has adopted short-term measures to reduce the carbon intensity of all ships by 50% by 2050. One of the means to achieve this ambitious target is the utilisation of propulsion systems powered by sustainable energy. This review paper summarises the current state of the adoption of renewable energy and alternative fuels used for ship propulsion. Special focus is given to the means of these alternative energies in achieving the United Nations Sustainable Development Goals, in particular Goal 7 (Affordable and Clean Energy), Goal 9 (Industry, Innovation and Infrastructure) and Goal 13 (Climate Action). A state-of-the-art for various ships powered by renewable energy and alternative fuels is investigated and their technologies for mitigating carbon emissions are described. The cost for each technology found in the literature is summarised and the pros and cons of each technology are studied.

The increasing environmental concerns due to emissions from the shipping industry have accelerated the interest in developing sustainable energy sources and alternatives to traditional hydrocarbon fuel sources to reduce carbon emissions. Predominantly, a hybrid power system is used via a combination of alternative energy sources with hydrocarbon fuel due to the relatively small energy efficiency of the former as compared to the latter. For such a hybrid system to operate efficiently, the power management on the multiple power sources has to be optimised and the power requirements for different vessel types with varying loading operation profiles have to be understood. This can be achieved by using energy management systems (EMS) or power management systems (PMS) and control methods for hybrid marine power systems. This review paper focuses on the different EMSs and control strategies adopted to optimise power management as well as reduce fuel consumption and thus the carbon emission for hybrid vessel systems. This paper first presents the different commonly used hybrid propulsion systems, i.e., diesel–mechanical, diesel–electric, fully electric and other hybrid systems. Then, a comprehensive review of the different EMSs and control method strategies is carried out, followed by a comparison of the alternative energy sources to diesel power. Finally, the gaps, challenges and future works for hybrid systems are discussed.

The use of hybrid energy systems in ships has increased in recent years due to environmental concerns and rising fuel prices. This paper focuses on the development and study of a hybrid energy system using liquefied natural gas (LNG) and batteries for a tugboat. The hybrid system model is created in MATLAB/Simulink^® and uses fuel data obtained from an operational diesel-powered tugboat. The LNG–hybrid system is then subjected to testing in four distinct configurations: fixed speed, variable speed, and with and without a battery. The different configurations are compared by computing the daily fuel cost, CO₂ emissions, energy efficiency operation indicator ($EEOI$) and carbon intensity indicator ($CII$) ratings in three distinct operation cases. The analysis reveals that the use of an LNG–battery hybrid tugboat results in an average reduction of 67.2% in CO₂ emissions and an average decrease of 64.0% in daily fuel cost compared to a diesel system. An energy management system using rule-based (RB) control is incorporated to compare the daily cost and CO₂ emissions for one of the case studies. The rule-based control that requires the battery to be used and the LNG engine to be switched off at the lowest allowable minimum power based on the specific gas consumption produces the most cost-effective control strategy out of all the different control strategies tested. The result demonstrates that an additional reduction of CO₂ and daily fuel cost for LNG–battery hybrid tugboats by 23.8% and 22.3%, respectively, could be achieved with the implementation of the cost-effective strategy as compared to not having a control strategy.

This research paper explores an approach to enhancing the economic viability of the heaving wave energy converters (WECs) of both cylinder-shaped and torus-shaped devices, by integrating them with four established, floating offshore wind turbines (FOWTs). Specifically, the approach focused on the wave power performance matrix. This integration of WECs and FOWTs not only offers the potential for shared construction and maintenance costs but also presents synergistic advantages in terms of power generation and platform stability. The study began by conducting a comprehensive review of the current State-of-the-Art in co-locating different types of WECs with various foundation platforms for FOWTs, taking into consideration the semi-submersible, spar and barge platforms commonly employed in the offshore wind industry. The research took a unified approach to investigate more and new WEC arrays, totaling 20 configurations across four distinct FOWTs. The scope of this study’s assumption primarily focused on the hydrodynamic wave power performance matrix, without the inclusion of aerodynamic loads. It then compared their outcomes to determine which array demonstrated superior wave energy under the key metrics of total absorbed power, capture width, and interaction factor. Additionally, the investigation could serve to reinforce the ongoing research and development efforts in the allocation of renewable energy resources.

There is increasing interest to consider dependent failures and human errors in the offshore industry. Permanently abandoned wells dot most of the subsea environment. The nature of a well plugging and abandonment (Well P&A) run - usually the lowest-cost contractor engaged to plug several wells tapping the same reservoir makes it an ideal case study for incorporating failures based on common causes. The heavy use of operators during a cementing job also provides the case for analysis of human error in such tasks. One proposed method to analyse the above-mentioned is the use of Bayesian Belief Networks to achieve the following objectives (1) to capture better estimates of a well PA event by incorporating dependencies, and meet regulatory requirements by authorities; and (2) to use the same model to provide long term monitoring of a group of wells linked by common dependencies. This model has not only captured the dependencies of multiple variables, but also projected it in a dynamic manner to provide a risk profile for the next decade where well integrity failure is likely to happen.