The demand for green energy continues to rise, with predictions indicating a 62-fold growth in floating wind. This offshore expansion, especially from wind, requires a corresponding increase in inspection, maintenance, and repair (IMR) operations. For offshore turbines, the marine segment accounts for half of fixed turbine O&M costs. One avenue to reduce these costs is through unmanned underwater vehicles (UUV) and automation. UUVs are becoming more specialized and automated, but most tasks remain only partially automated. It is estimated that the global energy cost in 2050 can be reduced by 0.5–0.6% from increased unmanned underwater vehicle automation, with wind contributing 0.4–0.5%. This study assesses automation levels and task frequencies across application domains, defined as types of offshore infrastructure, to calculate an automation priority score (APS), a weighted product of automation level and task frequency, indicating where economic and development benefits are. The automation level refers to a UUVs ability to operate without human intervention while performing tasks, regardless of industry use. The current highest APS is found in pipelines and jacket structures, with the lowest in wind turbines and dams. A sensitivity analysis evaluates the effects of increasing automation levels and anticipated annual growth. The study concludes that the wind sector will represent the highest automation priority in the future, providing the greatest economic incentive, especially for mooring lines. Visual inspection will increase due to AI, crawler-based UUVs will dominate circular structures IMR tasks, and autonomous underwater vehicle (AUVs) with subsea stations will handle frequent or long-range tasks.

CO2 corrosion remains a critical challenge for the safe and reliable operation of Carbon Capture, Utilization, and Storage (CCUS) infrastructure. This review summarizes CO2 corrosion implications from material selection, exposure time, CO2 phase behavior, flow conditions, and impurities such as H2O, O2, SOx, NOx, and H2S. CO2 corrosion modeling has, since early works by de Waard in 1975, expanded to a wide range of models and software tools, many of which have already been reviewed and compared. This work provides a historical timeline and a comparative summary of models and software tools to assist in selecting models for CCUS applications. Modeling approaches are classified into empirical, semi-empirical, and mechanistic categories, with their assumptions, strengths, and limitations. CO2 corrosion modeling has persistent challenges relating to data quality, data quantity, and parameter interactions, which reduce model accuracy, especially for machine learning approaches. The provided perspective emphasizes that machine learning and hybrid modeling approaches for CO2 corrosion prediction are gaining popularity, and their effectiveness is currently limited by the quality and quantity of available corrosion data. The provided opportunities include recommendations for standardized experimental procedures and hybrid modeling strategies that combine physics-based insights from mechanistic modeling approaches with data-driven machine learning approaches.

Seagrass restoration is increasingly gaining global attention, with large-scale restoration being the ultimate goal. Planting technologies play a critical role in large-scale restoration. Restoration performance comparison between planting technologies is currently limited. To facilitate comparison across seagrass species, this study proposes the following categorization for planting technologies: equipment-aided, passive dispersal, mechatronic-aided, and robotic. The respective technologies within each category are analyzed for their potential to restore large-scale areas in the sub-tidal zone. The criteria against which the technologies are being evaluated are operational feasibility, scalability, and restoration performance, each with subsequent sub-criteria. There is a distinct shortfall of shoot-based transplantation technologies, despite shoots being the preferred method for manual restoration. The comparison criteria indicate that only mechatronic-aided and robotic technologies are suitable for large-scale sub-tidal restoration. Recently, the availability of seed-based planting technologies has increased, and they have outperformed transplantation-based technologies according to the defined criteria. The trend of using seeds is expected to continue, with robotic technologies expected to provide an operational framework for large-scale restoration in the near future. Currently, mechatronic-aided technologies are better suited to large-scale restoration. However, it is predicted that, in the near future, robotic technologies will be able to collect their own donor material for seeds and perform planting themselves, thereby reducing the man-hours required for large-scale restoration.

As carbon capture, utilization, and storage (CCUS) rapidly expands, reliable CO2 impurity monitoring is essential for verifying compliance with purity specifications. Although these specifications define which impurities must be controlled and at what levels, only a few CO2 purity requirements are typically determined on a project-by-project basis, and only a limited number are publicly available. Moreover, most specifications focus solely on specifying acceptable concentration limits, without guidance on how impurities should be measured and monitored. Fourier transform infrared (FTIR) is a promising technique for online CO2 monitoring, but variations in calibration procedures can introduce significant uncertainty into the measurements. This study addresses the lack of calibration standardization in FTIR-based CO2 impurity monitoring by evaluating how operator-dependent selection of absorption regions affects the quantification of SO2 and CO. Seven operators independently calibrated the same FTIR system, each selecting their own absorption region based on their judgment and experience with spectral interpretation. This approach was used to assess the uncertainty introduced by operator-dependent calibration choices in the absence of standardized guidelines. The results show that these subjective decisions can lead to substantial variability in the predicted concentrations, exceeding 92.6% at low impurity levels, even though all models achieved detection limits well below the relevant specification thresholds. This highlights the risk of inconsistent impurity reporting in the absence of standardized procedures. In conclusion, this study not only demonstrates the magnitude of uncertainty introduced by operator-dependent FTIR calibration but also highlights the need for a standardized calibration procedure. By doing so, the study provides a practical approach to minimizing variability in impurity measurements and enhancing the reliability of compliance verification across CCUS projects.

An issue that ROVs experience during operations is disturbances from the tether, making navigation and control more difficult as real-time measurements are not currently available. This paper proposes the development of an innovative sensor that can measure tether forces in multiple degrees of freedom. These tether forces apply an external disturbance during operation, which is difficult to model and predict. The sensor provides real-time input on the effect the tether has on the ROV, which can be utilized in feed-forward in the control system in combination with a feedback loop. There are 2 proposed designs: a 4 DOF sensor design using a plastic bottle and a 6 DOF version utilizing an aluminum cross with hollowed sections. Both designs use strain gauges to measure and determine the direction and magnitude of the force from the tether. The sensors are implemented to a modified BlueROV2 using ROS. Station-keeping tests in a harbour and test basin are done for the 4 DOF version to evaluate performance. The sensor shows potential, improving response in heave but worsening it in yaw. It removes and adds oscillations both in frequency and amplitude depending on the orientation of the waves relative to the sensor. Indicating alternative control strategies might be more suitable. The 6 DOF version is not tested on the BlueROV2. In future work, additional development is required to ensure the viability of the tether force sensor as a commercial product.

Unmanned marine vehicles are increasingly used for inspection, maintenance, and repair tasks on offshore infrastructure. To reach demands, further research is needed to enhance autonomy and operational efficiency at a reduced cost. This paper presents an experimental framework designed to enhance the autonomy of collaborative operations between unmanned surface vehicles (USVs) and unmanned underwater vehicles (UUVs), focused on the realistic replication of environmental factors. An analysis of environmental effects and existing testing facilities reveals a gap in replicating turbidity, temperature, and salinity in large-scale basins. It is concluded that designing a dedicated experimental framework for collaborative UUV and USV operations requires balancing space for surface and underwater testing while accounting for environmental and physical effects. Due to this trade-off, no universal solution exists. Instead, modular or multi-tank setups may offer the flexibility needed to adapt to specific testing requirements.

Simulation-based analysis estimating both the energy requirement of the entire carbon capture process and the purity of the recovered CO 2 is scarce. The purity of the captured CO 2 is crucial as it must meet a specification before transportation, preventing phase change and damage to the transportation system. This study conducted 31,104 simulations of a monoethanolamine carbon capture plant treating measured flue gas from an existing cement production plant. After capture, the CO 2 is treated through a deoxygenation unit followed by a compression train to fulfill specific quality specifications. Based on the sensitivity analysis, the energy consumption of the post-treatment process decreased with increased purity downstream. Despite this, the total energy consumption was not affected. Moreover, after the two-step purification the CO 2 stream was able to successfully fulfill the specification for NO x, O 2, NH 3, Ar, CO, SO 2. However, failing to meet the H 2O concentration requirements of both considered specifications and the N 2 concentration specified for ship transport. Thus, increasing the post-treatment energy cost or standard adjustments is required for future applications.

Autonomous unmanned underwater vehicles (UUVs) play a vital role in diverse underwater operations; localization is of great interest for UUVs mirroring the trend seen in self-driving surface and aerial vehicles. Unlike their land and aerial counterparts, underwater environments lack reliable Global Navigation Satellite Systems (GNSS) due to radio wave attenuation in water. Hence, alternative localization methods are imperative for both navigation and operational purposes. This study thoroughly reviews sensor technologies for underwater localization, including sonar, Doppler velocity log, cameras, and more. Different operations necessitate distinct localization accuracies and vehicle and sensor choices. Environmental factors, such as turbidity, waves, and sound disturbances, impact sensor performance. Conclusions are given on the coincidence between operational requirements and sensor specifications, with special attention to the open concerns. These considerations include aspects such as the line of sight for acoustic positioning systems and the requirement for a feature-rich environment for visual sensors. Lastly, a prediction for the future of underwater localization is given, where the tendencies indicate lower costs for sensors, making operation-specific vehicles more attractive, which aligns with an increased demand for cost-efficient autonomous offshore operations.

Increasing developments in the offshore energy sector have led to demand for robotics use in inspection, maintenance, and repair maintenance tasks, particularly for the service life extension of structures. These robots experience slippage due to varying surface conditions caused by environmental factors and marine growth, leading to inconsistent traction forces and potential mission failures in single-drive systems. This paper explores control strategies and mechanical configurations both in simulation and on the physical industrial robot to mitigate slippage in offshore robotic operations, improving reliability and reducing costs. This study examines mechanical and control modifications such as multi-wheel drive (MWD), PID velocity control, and a feedback-linearized slip control system with an individual wheel disturbance observer to detect surface variations. The results indicate that a 3 WD setup with slip control handles the widest range of conditions but suffers from high control effort due to chattering effects. The simulations show potential for slip control; practically, challenges arise from low sampling rates compared to traction changes. In real-world conditions, a PID-controlled MWD system, combined with increased normal force, achieves better traction and stability. The findings highlight the need for further investigation into the mechanical design and sensor feedback, with the refinement of slip control strategies and observer design for the offshore environment.

As Carbon Capture, Utilization, and Storage (CCUS) expands, the need for safe and efficient CO2 transport becomes increasingly important. Effective monitoring of CO2 impurities is essential for maintaining system integrity and ensuring regulatory compliance across the CCUS chain. However, no standardized approach for impurity monitoring currently exists, and specifications vary depending on the project and transport method. This study identifies key stages in the CCUS process where impurity monitoring is critical. The impurity thresholds defined by the Northern Lights and Porthos projects are assessed in terms of corrosion mitigation, minimization of energy requirements, protection of human and environmental safety, and preservation of storage integrity. A conceptual framework is proposed to underscore the importance of CO2 quality compliance at points of ownership handover, where decisions are determined based on measured impurity concentrations. The evaluation of impurities shows that impurities can significantly impact transport safety, efficiency, and storage performance, justifying the need for defined concentration limits. The findings suggest a risk-informed monitoring approach, where the frequency and precision of measurement are guided by the potential impact of each impurity, with H2O highlighted as a critical case requiring high-frequency monitoring. A comparative analysis of available sensing technologies reveals that no single method can detect all impurities listed in the CO2 specifications. Instead, a combined monitoring strategy is proposed to meet specification requirements. As CCUS deployment accelerates, effective impurity monitoring will be essential to support regulatory compliance, taxation frameworks, operational control, and the development of secure and scalable CO2 transport networks.

IntroductionSubsea applications recently received increasing attention due to the global expansion of offshore energy, seabed infrastructure, and maritime activities; complex inspection, maintenance, and repair tasks in this domain are regularly solved with pilot-controlled, tethered remote-operated vehicles to reduce the use of human divers. However, collecting and precisely labeling submerged data is challenging due to uncontrollable and harsh environmental factors. As an alternative, synthetic environments offer cost-effective, controlled alternatives to real-world operations, with access to detailed ground-truth data. This study investigates the potential of synthetic underwater environments to offer cost-effective, controlled alternatives to real-world operations, by rendering detailed labeled datasets and their application to machine-learning.MethodsTwo synthetic datasets with over 1000 rendered images each were used to train DeepLabV3+ neural networks with an Xception backbone. The dataset includes environmental classes like seawater and seafloor, offshore structures components, ship hulls, and several marine growth classes. The machine-learning models were trained using transfer learning and data augmentation techniques.ResultsTesting showed high accuracy in segmenting synthetic images. In contrast, testing on real-world imagery yielded promising results for two out of three of the studied cases, though challenges in distinguishing some classes persist.DiscussionThis study demonstrates the efficiency of synthetic environments for training subsea machine learning models but also highlights some important limitations in certain cases. Improvements can be pursued by introducing layered species into synthetic environments and improving real-world optical information quality—better color representation, reduced compression artifacts, and minimized motion blur—are key focus areas. Future work involves more extensive validation with expert-labeled datasets to validate and enhance real-world application accuracy.

The expansion of Carbon Capture, Utilization, and Storage (CCUS) highlights the growing need for carbon dioxide (CO2) pipeline transportation. While pure CO2 is non-corrosive, impurities such as H2O and NO2 create a corrosive environment that risks pipeline integrity. This study investigates how H2O and NO2 concentrations, along with temperature, influence corrosion under CO2 pipeline conditions. The investigation was performed in an autoclave setup emulating a linear velocity of 0.96 m/s at 100 bar and temperatures of 5 degrees C and 25 degrees C, testing X52 and GR70, and a more corrosion-resistant 9Cr alloy. The results indicated that the presence of NO2 elevated the corrosion rate compared to scenarios without. Low H2O concentration led to a corrosion rate of up to five times higher at 5 degrees C, compared to at 25 degrees C, in the presence of NO2. Low to moderate corrosion was observed for the carbon steels without NO2 and with 70 ppmv H2O at both temperatures. Reducing the H2O concentration below 70 ppmv and removing NO2, while SO2 and O2 are present, will only result in low to moderate corrosion in the carbon steel CO2 pipeline. The corrosion rate for X52 and GR70 was 0.065 mm/y and 0.016 mm/y higher or 5 and 3 times greater, respectively, at 5 degrees C compared to 25 degrees C. The study concludes that H2O should be maintained below 70 ppmv and NO2 should be eliminated to prevent severe corrosion. Emphasizing the importance of CO2 specification compliance and the need for further research into CO2 compositions that align with the specifications.

The complexity and nonlinearity of components in large-scale thermal facilities have resulted in a lack of recognized energy management models, and simple rule-based energy management strategies are still the main approach, which reduces their operating efficiency. In this study, a dynamic programming (DP) method for globally optimal power distribution and operation mode decision for two-by-one combined cycle gas turbine is proposed. First, the energy management model of system is established. Then the drum pressure of the heat recovery steam generator, which indicates the thermal energy storage of the system, is chosen as the state variable, while the control variables are the gas turbine power, turbine power and operation mode. In addition, the system response time is considered to re-evaluated the mode switch command. The simulation results show that the DP optimizes the thermal storage management, which allows the gas turbine to run in the high-efficiency operating range for a longer time. The DP-based strategy saves 6.25%, 5.89%, and 4.92% of fuel at initial drum pressure 8MPa, 9MPa, and 10MPa, respectively, compared to the rule-based strategy. The results of this study can be used as a benchmark to evaluate online energy management strategies in future work.

A crucial component for unmanned underwater vehicles (UUVs), including remotely operated vehicles (ROVs) and autonomous underwater vehicles (AUVs), are the thrusters, which, in addition, are sensitive to damage during operations in harsh environments. This paper presents a study on the impact of incipient faults on the performance of thruster propellers used in offshore operations. The study evaluates the reduction in propeller performance due to wear and tear under realistic working conditions. The study employs a combination of experimental data analysis and signal processing techniques, including fast Fourier transforms and harmonics analysis, to identify faults and assess their severity. The results show that worn propellers can be identified through 5th-order harmonics and rotational velocity changes. The paper concludes with a proposal for future research using a model-based approach to enhance fault detection capabilities further.

The high penetration of renewable energy can potentially change the role of combined cycle gas turbines (CCGT) from load suppliers to flexible suppliers. However, the slow process in the dynamic modeling of the CCGT complete system restricts the design of its best flexible operation strategy. Therefore, this paper adopts the composite modeling method to establish a complete nonlinear dynamic model of a 2 x 1 CCGT including two gas turbines, two three-pressure heat recovery steam generators (HRSG), one steam turbine, and one heat exchanger. This model can reflect the dynamic characteristics of pressure and flow in the bottom cycle due to the changes in operating mode, which can guide the design of the flexible operation control strategy of the 2 x 1 CCGT. To verify the model's accuracy, the simulated data were compared with the field data from a 900 MW cycle with the same configuration. The results show that the established model can accurately reflect the changing trend of variables during mode switching. The range errors of essential variables such as gas turbine (GT) power is 3.8 %, GT outlet temperature is 1.4 %, high-pressure drum pressure is 4.5 %, and the range errors of other variables are below 5 %, proving the fidelity of the established control model. The dynamic model established in this paper will be the basis for model-based controller design which can handle multi-objective optimization and constraints in flexible operations. Moreover, this model will be used to guide the design of multi-mode control and energy management strategies.

This paper introduces a novel control design focused on enhancing the operational safety and efficiency of inspection, maintenance, and repair (IMR) operations conducted by autonomous remotely operated vehicles. Specifically, we propose using a safeguarding controller based on an adaptive control barrier function (CBF) incorporating safety properties previously identified in the literature. This approach permits temporary safe set violations, using an integrative penalty term to strengthen safety measures when necessary. A nominal nonlinear controller inside the safety bounds is proposed to ensure good reference tracking. The proposed controller is demonstrated in a simulation case study where an ROV (remotely operated vehicle) is set to clean an offshore monopile. The simulation includes unknown time-varying and step-like disturbances caused by water waves, the tether, ocean currents, and the high-pressure water jet. The proposed control law can react to the disturbances, and the ROV never leaves the defined safe set.

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An efficient cyclic cold storage system plays a crucial role in improving the performance of a Liquid Air Energy Storage system. Packed bed cold storage (PBCS) systems, widely researched and employed, demonstrate thermal stability, a broad operational temperature range, and cost-effectiveness. This study developed a two-dimensional transient cyclic solid-PBCS system using a porous media thermal non-equilibrium model, which was implemented in ANSYS Fluent software. During the cold charging and discharging periods, cryogenic nitrogen and nitrogen at ambient temperature were used as working fluids to exchange heat with the solid cold storage medium in the PBCS. Temperature distributions were analyzed during the cold charging, cold discharging, and the consecutive cyclic cold charging-discharging modes. The study systematically examined the impact of mass flow rate and the number of cold storage cycle on key system performance parameters, including heat exchange capacity and cyclic cold exergy efficiency. The results indicated that mass flow rate significantly affects the number of cycles required to reach a steady-state cyclic PBCS system, with higher mass flow rates necessitating more cycles. Optimal cyclic performance of the solid PBCS can be achieved at a moderate superficial mass flow rate. At a superficial mass flow rate of 0.32 kg s(-1) m(-2), the cyclic cold charging exergy efficiency exceeds 99 % and cyclic cold discharging exergy efficiency can over 68 %. The stability of multiple consecutive cold storage cycles in the solid PBCS system is critical to the overall performance of LAES. These findings on the influence of PBCS cold storage cycles and fluid mass flow on system cyclic performance provide valuable theoretical guidance for the practical operation of PBCS cycles.

Transportation of CO2 is essential for multiple applications in Carbon Capture, Utilisation and Storage (CCUS), e.g., for utilisation in methanol production, enhanced oil recovery, or permanent storage. Currently, the CCUS industry is still in its fancy, and the transportation regulation is still defined from project to project, where the existing quality specifications are tailored to the specific storage or utilisation site. It is estimated that transportation accounts for ∼25% of the total costs of a CCUS project, and commercialisation cannot be achieved with an infrastructure of high-grade steel together with high purity CO2. The current transportation infrastructure is based on point-to-point transport, where it is believed that it will be challenging to upscale CCUS without a common quality standard. This leaves a knowledge gap in the design, operation, and investment of CO2 transportation. This study includes an evaluation of the challenges that halt the progression in CO2 transportation based on a survey of the literature. Analysing the benefit of establishing an international quality standard for CO2 transportation for CCUS to become a global industry. A detailed description of the initiative policies within CCUS along with the challenges associated with designing the CO2 transportation infrastructure, which arises when chemical reactions form corrosive or scaling compounds. As a result, this study proposes a future action plan to make CO2 transport more feasible by forming a common CO2 quality specification and a material selection based on CO2 quality.

Gathering real-world high-quality data from underwater environments is cost-intensive, as is labeling this data for machine learning. Given this, synthetic data represents a possible solution that delivers ground-truth training data. Nevertheless, rendering and modeling of underwater environments are challenging due to several factors, including attenuation, scattering, and turbidity. The focus of this study is on the creation of a simulated underwater environment constructed for the purposes of simulating marine growth on offshore structures. The main requirement is the creation of renderings of sufficient quality and quantity with respect to the representation of marine-species distribution and intra-class variation, and sufficiently accurate recreation of lighting and turbidity (Jerlov water type) conditions underwater. Underwater rendering has been implemented using Blender, with a CAD model of an actual offshore installation and combined with marine growth from 2D/3D scanned and hand-modeled entities. The proposed approach provides for the generation of synthetic images usable for training computer vision models in marine-growth inspection applications as well as other related underwater applications. This has been demonstrated in a case study, wherein the utility of the rendered dataset has been briefly demonstrated in a neural network marine-growth segmentation task, targeting native fouling species for North-sea installations with a pixel-level fouling coverage. The produced renderings are available as a dataset of 1038 scene renders, using varying poses and randomized representative marine growth; each render includes RGB images, ground-truth segmentation masks, water-free RGB images, and depth information. In future work, the expansion with additional species and objects in other oceanic and coastal environments is envisioned.