The offshore de-oiling process is a vital part of current oil recovery, as it separates the profitable oil from water and ensures that the discharged water contains as little of the polluting oil as possible. With the passage of time, there is an increase in the water fraction in reservoirs that adds to the strain put on these facilities, and thus larger quantities of oil are being discharged into the oceans, which has in many studies been linked to negative effects on marine life. In many cases, such installations are controlled using non-cooperative single objective controllers which are inefficient in handling fluctuating inflows or complicated operating conditions. This work introduces a model-based robust H ∞ control solution that handles the entire de-oiling system and improves the system’s robustness towards fluctuating flow thereby improving the oil recovery and reducing the environmental impacts of the discharge. The robust H ∞ control solution was compared to a benchmark Proportional-Integral-Derivative (PID) control solution and evaluated through simulation and experiments performed on a pilot plant. This study found that the robust H ∞ control solution greatly improved the performance of the de-oiling process.
Methanol, as one of the significant green fuel candidates for the combustion engines, can be produced from Power to X and biomass production. However, compression ignition (CI) of pure methanol in a combustion engine is impractical due to its low cetane rating. The strategy has gained little attention in the past, but is possible if the methanol is premixed with a fuel additive (ignition improver). In order to optimize and understand additivated methanol combustion, a phenomenological spray/packet combustion model is developed in this work. The model is used to calibrate an Arrhenius-type ignition delay equation for CI engine using additivated methanol, and the resulting calibrated ignition delay parameter is 2.14. The procedure involves to compare the modeled and experimental combustion rate profiles that are derived from a small marine CI engine by burning methanol with 3.5 % and up to 7.5 % kg/kg fuel additive. The present work finds that the phenomenological diesel combustion model methodology can be used with good accuracy, to simulate combustion rate profiles of additivated methanol in a CI engine. The model is, furthermore, able to indicate intermediate variables such as burning packet speeds, air mass, droplet mass, air/fuel equivalence ratio, and burning packet temperature for different packets of combustion.
Melting Arctic sea ice, shore ice, and permafrost are changing costs and benefits to transport routes between Atlantic and Pacific oceans, and more generally, for maritime economic activity in the Arctic. We investigate the potential for development of Arctic ports from a logistics (demand) and an infrastructural (supply) point of view that directly incorporates local concerns. This approach broadens the scope of the discussion from existing analyses that focus primarily on the ways in which global forces, exerted through resource extraction or trans-polar shipping, impact the Arctic.
The sea ice in the Arctic has shrunk significantly in the last decades. The transport pattern has as a result partly changed with more traffic in remote areas. This change may influence on the risk pattern. The critical factors are harsh weather, ice conditions, remoteness and vulnerability of nature. In this paper, we look into the risk of accidents in Atlantic Arctic based on previous ship accidents and the changes in maritime activity. The risk has to be assessed to ensure a proper level of emergency response. The consequences of incidents depend on the incident type, scale and location. As accidents are rare, there are limited statistics available for Arctic maritime accidents. Hence, this study offers a qualitative analysis and an expert-based risk assessment. Implications for the emergency preparedness system of the Arctic region are discussed.
This article examines the theoretical and practical implications of artificial intelligence (AI) integration in supply chain management (SCM). AI has developed dramatically in recent years, embodied by the newest generation of large language models (LLM) that exhibit human-like capabilities in various domains. However, SCM as a discipline seems unprepared for this potential revolution, as existing perspectives do not capture the potential for disruption offered by AI tools. Moreover, AI integration in SCM is not only a technical but also a social process, influenced by human sensemaking and interpretation of AI systems. This article offers a novel theoretical lens called the AI Integration (AII) framework, which considers two key dimensions: the level of AI integration across the supply chain and the role of AI in decision-making. It also incorporates human meaning-making as an overlaying factor that shapes AI integration and disruption dynamics. The article demonstrates that different ways of integrating AI will lead to different kinds of disruptions, both in theory and practice. It also discusses the implications of AI integration for SCM theorizing and practice, highlighting the need for cross-disciplinary collaboration and sociotechnical perspectives.
This study addresses a critical gap in environmental assessments by focusing on petrochemical port operations, an area traditionally overlooked in life cycle assessments (LCAs) of material supply chains. This study investigates various methods of loading for 22 petrochemical products i.e., gas, liquid, container, tanker, and bulk loading; at the biggest petrochemical port in the world situated in the Persian Gulf with a loading capacity of 35 MMt/yr. Twelve scenarios were developed to enhance environmental efficiency based on hotspots defined in LCAs of port loading operations of petrochemicals in their present state. Scenarios 1 through 5 consider electricity savings of 2%–10%, scenarios 6 through 10 consider renewable photovoltaic energy mix of 10%–50%, and scenarios 11 and 12 consider no flaring and rejection of ash waste from ships.
To prioritize these scenarios based on environmental efficiency gains, a comprehensive LCA-GRM hybrid model has been introduced. This integrated model combines life cycle assessment and gray relational modeling, providing a robust framework for evaluating and ranking the scenarios. The Best Worst Method (BWM) is implemented for weighing multiple environmental criteria, contributing to informed decision-making.
The findings underscore the substantial impact of electricity consumption and gas flaring in petrochemical port operations, prompting the identification of the 'no flaring' scenario (S11) as the most preferred option. Implementing this scenario could lead to significant reductions in climate change impacts (22.14%), ozone formation and human health impacts (16.73%), and photochemical oxidant formation (15.98%).
The study's significance lies in emphasizing the environmental implications of port operations and urging policymakers to integrate port impacts into broader supply chain assessments. We advocate for targeted strategies to enhance electricity efficiency and reduce gas flaring in petrochemical ports, aligning with global sustainability goals. The Comprehensive LCA-GRM hybrid approach offers valuable insights for decision-makers involved in the global transportation of goods through ports.
This paper introduces a resilience assessment methodology for sustainable autonomous maritime transport networks developed by the European project entitled “Advanced, Efficient, and Green Intermodal Systems” (AEGIS). This problem being addressed in this paper concerns the investigation of threats, incidents, and risks in an autonomous- and sustainable shipping context, and the research question is the development of both preventive measures and reactive actions to maintain an acceptable level of operational constraints. The paper's methodology aids in designing sustainable logistics systems for highly automated waterborne transport, identifying threats and barriers to mitigate event consequences, thereby facilitating a seamless green transition. To examine the usability, this methodology is applied in a case study for cargo transportation, where we in this paper consider the maritime corridor between Trondheim and Rotterdam. The findings encompass the spectrum of possible actions to prevent and mitigate unwanted events and enhance resilience and flexibility. This can be used as a tool to respond to unwanted threats, enhance safety, and introduce new strategies. These results are deemed important as resilience is one of the prerequisites for the development of a sustainable transport system. This is true both for the companies that are engaged in the operation of such systems and for policymakers.
The space occupied by traditional and new human-based marine uses at sea is expanding, creating a need for developing methods to assess interactions between co-located uses in maritime spatial planning (MSP). However, no clear terminology for use-use interactions exists. Thus, an analytical framework for spatial decision support tools (DSTs) to assess use-use interactions is deduced from literature. Four spatial-temporal links are found to either alone or together constitute use-use interactions: location links, environmental links, technical links, and user attraction links. It is found to be important for DSTs to support co-location management in MSP by iteratively through the MSP process 1) spatially-temporally locate spatial-temporal links constituting use-use interactions, 2) list conflicts and synergies of the located use-use interactions, and 3) weight the conflicts and synergies. With this analytical framework, two types of DSTs are analysed for their ability to include co-location; matrix- and ranking-based DSTs to detect conflicts and synergies and space allocating DSTs to avoid/minimise conflicts and optimise synergies. Whereas the first group of tools categorise or rank use-use combinations, the latter group use information about which multi-use combinations are possible as pre-existing knowledge, and thus the two groups of DSTs can advantageously be used together. A discrepancy is found between the co-location framework and the DSTs. It is argued that future tools could work on removing this discrepancy by considering the spatial-temporal links of use-use interactions, strengthen the focus on synergies, as well as prioritize ranking of synergies and conflicts over binary approaches that only evaluate spatial compatibility.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.
A practical estimation methodology of the mean added resistance in irregular waves is shown, and the present paper provides statistical analyses of estimates for ships in actual conditions. The study merges telemetry data of more than 200 in-service container vessels with ocean re-analysis data from ERA5. Theoretical estimates relying on spectral calculations of added resistance are made for both long- and short-crested waves and are based on a combination of a parametric expression for the wave spectrum and a semi-empirical formula for the added resistance transfer function. The theoretical estimates are compared to predictions from an indirect calculation of added resistance relying on shaft power measurements and empirical estimates of the remaining resistance components. Overall, the comparison reveals a bias in bow oblique waves and higher sea states of the spectral estimates as well as the large variance of the empirically derived predictions — particularly in beam-to-following waves. One of the study’s main findings, confirming previous studies but based on a much larger dataset than in earlier similar studies, is that added resistance assessment based on in-service data is complex due to significant associated uncertainties.