In recent years, Multi-purpose Offshore Platforms (MPOP) have emerged as a novel solution to address the increasing global food and energy demand. Beyond the conceptual and qualitative analysis, this paper develops a hybrid quantitative framework to evaluate the life-cycle performance of co-located Wave Energy Converters (WECs) and offshore aquaculture (AQ) systems. The framework integrates hydrodynamic numerical simulations and probabilistic reliability analysis into a System Dynamics (SD) model to simulate complex subsystem interactions and quantify the system productivity and economic feasibility under environmental and operational uncertainty. A case study in Southern Tasmania demonstrates that the upstream WEC farm effectively reduces the incoming wave heights by up to 23%, mitigating aquaculture mooring tensions of the downstream salmon farm by 18%. This protection effect translates into an economic benefit that the co-located configuration achieves a 30.7% reduction in Life Cycle Costs (LCC) compared to the stand-alone configuration. Furthermore, the MPOP demonstrates the robust power capacity, ensuring a continuous off-grid power supply despite long-term component degradation and fluctuating aquaculture power demand. The results validate the MPOP concept as a commercially viable solution for sustainable blue economy development and provide a comprehensive simulation and decision-making tool for exploring future offshore multi-sector cooperation.

This study investigates the use of novel, highly conductive polyaniline (PANI) coated gravels as a substrate in electroactive wetlands. PANI coating was synthesized via a cost-effective method using ammonium persulphate and aniline, achieving production cost of $0.55/kg. The coated gravels were incorporated into two electroactive wetlands: CW-MFC with PANI-coated substrate (CW-MFC-PC) and electroactive CW with PANI-coated substrate (EAW-PC), compared with control CWs using normal gravel. Successful coating was confirmed using Raman spectroscopy and SEM-EDX, and antibacterial activity was assessed to ensure substrate viability. High pollutant removal was achieved, with COD removal of 98.77 f 0.55 % (EAW-PC), 98.50 f 0.70 % (CW-MFC-PC), and 96.27 f 1.77 % (CW). Ammonium and phosphate removals reached 25.51 f 9.23 % and 31.48 f 16.11 % in EAW-PC, respectively, surpassing control. High conductivity enabled substantial voltage generation of 755 mV in CW-MFC-PC. These results highlight PANI-coated gravels as a low-cost, multifunctional substrate enhancing both treatment efficiency and bioelectricity recovery.

Pitting corrosion-fatigue remains a dominant degradation mechanism in offshore and marine steel structures. Conventional approaches for modelling pitting corrosion-fatigue often fail to capture the stochastic, localised progression of pit-induced fatigue under complex environmental and loading conditions. This study focuses on recent advancements in data-driven methodologies, particularly machine learning (ML) and hybrid models. ML frameworks, ranging from neural networks to ensemble learning, show considerable promise in modelling non-linear, high-dimensional relationships among corrosion, fatigue, and environmental parameters. Hybrid models, which integrate domain knowledge through physics-based features or mechanistic coupling, further enhance predictive accuracy and robustness while offering improved interpretability. This study highlights both opportunities and persistent challenges in the field, including the scarcity of standardised, high-fidelity datasets; difficulties in generalising models across different steel grades and exposure conditions; and the limited interpretability of certain ML algorithms. This study emphasises the development of intelligent, adaptable models to support improved structural health monitoring and asset management in offshore and marine environments.

Austenitic stainless steels such as AISI 316L are widely used in offshore renewable energy structures due to their favourable strength-to-weight ratio and corrosion resistance. However, prolonged exposure to chloride-rich marine environments promotes localised pitting corrosion, which significantly influences structural integrity and fatigue life. The stochastic nature of pit morphology limits the reliability of conventional corrosion severity classification methods based on deterministic thresholds or expert judgment. This study proposes an automated machine learning (AutoML) framework for objective corrosion severity classification of AISI 316L based on pit morphology. Key morphological features, including pit depth, metal loss, and pit diameter obtained from optical profilometry, were used as model inputs. Unsupervised 𝑘-means clustering was first applied to identify intrinsic groupings in the morphology space and to define three physically interpretable severity classes: mild, moderate, and severe. These cluster-derived labels were then used to train a supervised multiclass classifier using the Tree-based Pipeline Optimisation Tool (TPOT) AutoML framework. The optimal AutoML pipeline achieved a classification accuracy of 87.5%, with balanced precision and recall across all severity classes. The results demonstrate that the proposed data-driven methodology reduces subjectivity in corrosion severity assessment and provides a robust and reproducible framework for corrosion evaluation in offshore renewable energy infrastructure.

Mining legacies are a global driver of heavy metal contamination in freshwater systems, yet many stream condition indices underestimate ecological risks by excluding toxic metals. This study introduces the Mining-Impacted Stream Condition index (MISC) as a transferable framework for incorporating metals into ecological assessments. Its robustness was evaluated in Victoria, Australia, a region with an extensive gold-mining legacy. Using 25 years of monitoring data, this first state-wide assessment examined seven toxic metals (As, Cr, Pb, Ni, Zn, Hg, Cu). Exceedances of ecological thresholds were widespread, observed in 99 % of Cr, 84 % of Cu, 81 % of Hg, 80 % of Pb, and 75 % of Zn samples. Comparative evaluation with the state's Index of Stream Condition (ISC) across 2010, 2016, and 2022 showed systematically lower MISC scores, with reductions of up to 38 % in mining catchments and 42 % of rivers assessed downgraded to poorer ecological categories. Despite overall declines in metal concentrations, heavy metals remained the dominant contributors (similar to 35 %) to ecological degradation. By explicitly integrating heavy metals, the MISC framework provides a more sensitive and comprehensive tool for evaluating river health. Its transferability makes it relevant for mining and industrial catchments worldwide, where conventional indices risk underestimating ecological impairment. The framework thus supports more accurate ecological classification and better-informed remediation strategies in regions facing long-term contamination legacies.

Offshore renewable energy systems (ORESs) have substantial potential to decarbonize industries and clean energy generation. However, they face challenges, including high costs and risks due to uncertain and hazardous marine environments. To address this challenge, this study presents a multi-objective design and optimization framework for a hybrid standalone ORES powering industrial and domestic green electricity and hydrogen loads simultaneously. The hybrid ORES model integrates offshore wind (OWF) and floating solar (FPV) farms with large-scale battery energy storage systems (BESS) and hydrogen storage systems (HSS) as a firming technology. The framework optimises three conflicting objectives, including minimising cost and potential energy waste and maximising system reliability, subject to various technical and economic constraints. To regulate optimal energy flow and protect ORES components, a simple and efficient power management strategy is presented and utilized. Five recent state-of-the-art multi-objective metaheuristics are applied to solve the hybrid ORES design and obtain Pareto solutions. The comparative analysis utilises widely employed Pareto front hypervolume (HV) metric statistics and Friedman's rank test. A case sensitivity analysis is conducted to evaluate the model's robustness, reliability, and effectiveness. Validation of the methodology is conducted through a real-world case study in Australia's offshore region, showcasing its capacity to supply clean energy to industrial and domestic loads. Results indicate SHAMODE-WO's superior diversity and convergence traits in Pareto-optimal sets, with SHAMODE closely trailing. Furthermore, the average Friedman rank across all three cases designates SHAMODE-WO as the top performer. The proposed framework can facilitate decision-makers in addressing complex multi-objective ORES optimization problems and choosing optimal solutions.

This study investigates the performance and reliability of a Multi-Purpose Offshore Platform (MPOP) system integrating a Wave Energy Converter (WEC) farm and an offshore aquaculture farm. A coupled numerical approach, combining OrcaFlex and SNL-SWAN, was adopted to simulate wave-WEC interactions and wave propagation over large downstream areas. The study highlights the influence of WEC arrangements and wave directions on energy production. The energy requirements of the aquaculture system were modelled under Australian environmental conditions. System reliability was evaluated using failure rate data and scenario simulations via the system dynamics (SD) model. Results show that minor and major failures minimally affect system availability, while critical failures can lead to significant power shortfalls. In an extreme scenario, where all WECs fail, the aquaculture farm would require over 400 MWh of external energy support. This research provides insights into optimizing MPOP configurations and highlights the importance of incorporating reliability and failure scenarios to ensure continuous energy and food production in offshore environments.

Offshore oil platforms face severe fire hazards due to large hydrocarbon inventories, necessitating effective fire protection strategies. This study integrates hydrocarbon fire simulation with fire protection assessment to evaluate the structural response and mitigation measures. Using CFD-based fire simulations in Pyrosim, the study analyzes temperature distribution, flame zones, and critical thermal effects, incorporating wind influence. A parametric assessment is conducted on active (sprinklers, CO2 suppression, ventilation) and passive (fireproof cladding) fire protection measures to determine their effectiveness in reducing fire-induced risks. Additionally, the study introduces pseudo-emergency Response Planning Guidelines (pseudo-ERPG) to define safety zones based on temperature contours, extending beyond traditional hazard distance evaluations. The results highlight the effectiveness of integrated fire protection strategies in mitigating structural damage and improving offshore fire safety. While this is a preliminary step, it provides a foundation for future advancements in offshore fire risk assessment and emergency response planning.

Green hydrogen is gaining prominence as a sustainable fuel to decarbonize hard-to-electrify industries and complement renewable energy growth. Among clean hydrogen production technologies, seawater-based PEM electrolysis systems hold substantial promise. However, implementing offshore PEM electrolysis systems faces significant challenges in ensuring long-term availability due to technological infancy and harsh environmental conditions. Ensuring safe and reliable operation is therefore critical to advancing global sustainability goals. While existing research has primarily focused on component-level techno-economic feasibility, limited attention has been given to system-level safety and availability analysis, particularly for offshore renewable-powered seawater-based PEM electrolysis systems. This study addresses this gap by conducting a comprehensive availability analysis of containerized plug-and-play PEM systems in offshore environments. A Bayesian Network model is employed, incorporating Fault Tree Analysis and Reliability Block Diagram approaches, for failure and availability analysis at the system level. A maintenance decision support tool using Influence diagram is developed to analyse different maintenance planning strategies impact on system availability improvement. A case study incorporating industrial modular PEM model is utilized to analyse the developed model effectiveness. The study identifies 81 availability states, with the hydrogen generation subsystem being the most critical to system performance. Comparative analysis shows that applying redundancy across all subsystems improves availability by 18.54 % but reduces Expected Utility by 4.94 %. The optimal strategy involves redundancy for seawater purification, cooling, and monitoring subsystems, with preventive maintenance for hydrogen generation, achieving a maximum EU of 5.29 x 106. This framework supports decision-makers in evaluating system availability under uncertain offshore conditions, optimizing maintenance strategies, and ensuring resilience for large-scale H2 production.

Offshore aquaculture industries face significant challenges in securing reliable and environmentally friendly energy sources. Among the possible solutions, wave energy converters (WECs) show a promising solution to transfer the technology for creating sustainable operations for remotely accessible fish farms. However, ideally integrating them into the aquaculture floating vessels necessitates a careful and thorough design approach. This paper presents a comprehensive framework for evaluating the performance of the AquaPower platform (APP) concept vessel in supporting offshore fish farm operations. Drawing inspiration from established WEC principles, this concept merges a floating platform with a tensioned mooring lines integrated with power take off systems connected to a moored vessel. The framework addresses crucial aspects, including power generation, structural resilience, and mitigation of mooring fatigue-induced deterioration, which are essential for optimizing the APP’s performance. To enhance evaluation the reliability of the structure, the framework introduces a robust surrogate model based on Bayesian data analysis. This enables the assessment of mooring asset reliability and projected lifespan for real-time monitoring. The practical demonstration of this framework investigated through a case study for designing and evaluating the APP, effectively highlighting its potential as a feasible wave energy solution for the progress of offshore aquaculture towards blue economy technology. This paper’s primary aim is to contribute to affirming the feasibility and viability of the APP concept as an effective and sustainable wave energy remedy for offshore aquaculture. The results of this study can be applied to other contexts, demonstrating the framework’s ability to enhance the dependability of various offshore energy structures, including floating wind turbines, and extend their operational lifespan.

The integration of emerging offshore renewable energy systems, transportation, and other facilities through implementing a multipurpose offshore platform (MPOP) presents itself as a potential solution for achieving environmental and socio-economic sustainability in the realm of offshore renewable energy. Currently, development of MPOP layouts is conceptual and mainly relies on the design specifications of other offshore platforms. From sustainability and economic viability perspectives, the safety and reliability of such platforms has significant uncertainty. In this paper, a novel framework for designing MPOP layouts is proposed incorporating inherent safety principles and social and regulatory considerations to address the reliability and safety of such platforms. The applicability of the methodology is demonstrated with a case study. The case study results show that the framework effectively enhances the platform's operational safety reducing potential safety costs with an inherently safer layout. The developed framework can help in developing layout plans for novel and complex offshore facilities at early design stages.

The transition to a sustainable future with hydrogen as a key energy carrier necessitates a comprehensive understanding of the safety aspects of hydrogen including liquid hydrogen (LH₂). Hence, this study presents a detailed computational fluid mechanics analysis to explore accidental LH₂ leakage and dispersion in a hydrogen refuelling station under varied conditions which is essential to prevent fire and explosion. The correlated impact of influential parameters including wind direction, wind velocity, leak direction, and leak rate, were analysed. The study shows that hydrogen dispersion is significantly impacted by the combined effect of wind direction and surrounding structures. Additionally, the leak rate and leak direction have a significant effect on the development of the flammable cloud volume (FCV), which is critical for estimating the explosion hazards. Increasing wind velocity from 2 to 4 m/s at a constant leak rate of 0.06 kg/s results in an 82% reduction in FCV. The minimum FCV occurs when leak and wind directions oppose at 4 m/s. The most critical situation concerning FCV arises when the leak and wind directions are perpendicular, with a leak rate of 0.06 kg/s and a wind velocity of 2 m/s. These findings can aid in the development of optimised sensing and monitoring systems and operational strategies to reduce the risk of catastrophic fire and explosion consequences.

The System-Theoretic Process Analysis (STPA) method is a useful approach to analyze system safety, however, it still has inadequate capabilities for quantitative safety analysis. To overcome this limitation, an integrated methodology was investigated for quantitative safety analysis of the complex socio-technical system that combines a system-theoretic approach and numerical simulation. In the proposed methodology, STPA method is utilized to discover potential unsafe control actions (UCAs) and corresponding causes based on the operational principle of the target system from systemic perspective. Moreover, the consequences of identified UCAs can be quantified and certain safety constraints also were improved to prevent UCAs using numerical simulations. The blind shear ram preventers (BSRPs) as complex system, with tightly interacting diverse subsystems or components are, is employed to illustrate the applicability of the methodology. The results verified that the proposed methodology could effectively evaluate potential hazards and quantify the analysis results. These results will be helpful for the design and operations of the BSRP system in deepwater drilling activities. The developed methodology also has a more general application to safety analyses in other process industries.

This study develops a methodology using a system dynamics approach to analyse human error probability (HEP) within the context of hydrogen fuelling station (HFS) maintenance, mainly focusing on the dynamic nature of performance shaping factors (PSFs) over time. The developed model offers a comprehensive understanding of how diverse human factors dynamically influence task performance, yielding critical insights for safety management strategies to study 8-hour day shifts in maintenance activity. The study explores the intricate relationship between time-dependent PSFs and human error probability, highlighting that as the number of maintenance tasks rises, so does the potential for increased fatigue levels, subsequently elevating HEP. Introducing breaks during work emerges as a promising intervention to mitigate task-related fatigue, reducing HEP. Careful break implementation is necessary to prevent shifts from extending, which could inadvertently raise HEP. This research has implications extending beyond HFS, benefiting industries where operational safety and efficiency are paramount. Future studies can build upon these findings, exploring additional interventions and diverse work scenarios to advance our understanding of human performance and error prevention strategies, ultimately fostering safer and more productive work environments. The integration of system dynamics and the insights gained contribute significantly to hydrogen safety and offer a robust foundation for future investigations in this field.

About 60% of marine vessels' power is consumed to overcome friction resistance between the hull and water. Air lubrication can effectively reduce this resistance and lower fuel consumption, and consequently emissions. This study aims to analyze the use of a gas-injected liquid lubrication system (GILLS) to reduce friction resistance in a real-world scenario. A 3D computational fluid dynamics model is adopted to analyse how a full-scale ship (the Sea Transport Solutions Designed Catamaran ROPAX ferry) with a length of 44.9 m and a width of 16.5 m is affected by its speed and draught. The computational model is based on a volume of fluid model using the k-omega shear stress transport turbulence model. Results show that at a 1.5 m draught and 20 knots cruising speed, injecting 0.05 kg/s of compressed air into each GILLS unit reduces friction resistance by 10.45%. A hybrid model of natural air suction and force-compressed air shows a friction resistance reduction of 10.41%, which is a promising solution with less required external power. The proposed technique offers improved fuel efficiency and can help to meet environmental regulations without engine modifications.

Uncertainty presents a challenge in assessing risks, often resulting in outcomes that diverge from reality. System Hazard Identification, Prediction and Prevention (SHIPP), as one of the emerging risk assessment methods, aims to predict and effectively prevent accidents. This study aims to enhance the prediction potential of the SHIPP method by reducing uncertainty by combining Z-numbers and intuitionistic fuzzy logic. The experts' opinions and confidence levels regarding the prior probability of basic events (BEs) were measured using Intuitionistic Z-numbers (IZN). Subsequently, the SHIPP method utilized the obtained results and the actual data on unusual events in the industry to determine the posterior probability of barrier failure and consequences. The practical application of the developed methodology was demonstrated by selecting spherical tanks containing LPG. The results indicated that employing IZN to estimate the prior probability of BEs reduces uncertainty in determining the posterior probability of barrier failure and subsequent consequences. Consequently, enhancing the predictive accuracy of the SHIPP method in estimating the likelihood of unusual events will significantly improve the quality of risk management.

Corrosion is widely known to be a major cause of the failures in process facilities. Prediction of corrosion damage is therefore essential for industries to manage the availability of their assets. This research aims to investigate the application of supervised machine learning methods for the classification of pitting corrosion damage. Several machine learning classifiers, namely ensemble methods, support vector machine (SVM), K-nearest neighbours, and the decision tree are used to classify the extent of pitting corrosion damage in corroded steel samples. To simulate the corrosion of the steel samples, a series of laboratory experiments were conducted. After processing the results using appropriate statistical methods, the corrosion data was used to train the machine learning models. The trained models can predict the class of corrosion damage with acceptable accuracy using the material and environmental specifications of the samples. Additionally, a discussion on the selection of machine learning techniques which classify corrosion damage using a risk-based approach is provided. With their optimal accuracy and lower risk of misclassification, the SVM and AdaBoost models perform better than the other studied models.