Accurate quantification of atmospheric emissions from port service vessels remains challenging in many developing regions because detailed monitoring systems and advanced atmospheric-dispersion models are often unavailable. This study presents a bottom-up framework for estimating emissions and assessing their potential transport toward adjacent terrestrial environments, using the Port of Mohammedia (Morocco) as a case study. Operational data collected from seven service vessels during 2024, including fuel consumption, vessel activity, and engine characteristics, were combined with standard emission factors to estimate annual emissions of carbon dioxide (CO2), nitrogen oxides (NOx), sulfur dioxide (SO2), particulate matter (PM2.5), and particulate matter (PM10). A quality-control procedure was implemented to identify anomalous records and improve inventory reliability. Annual emissions were estimated at 961.24 t CO2, 15.20 t NOx, 0.55 t SO2, 0.26 t PM2.5, and 0.31 t PM10, with substantial temporal variability driven by vessel activity and fuel consumption. Two vessels alone contributed 65.8
The present study investigates wave interaction with two Semi-trapezoidal Porous Breakwaters (STPB) through detailed experimental and soft-computing-based analyses. The hydrodynamic behaviour of the STPB is examined in a wave flume by considering different structural parameters, including variations in porosity and the relative placement of the breakwaters. The influence of these parameters on wave reflection, transmission, and energy dissipation characteristics is systematically analysed under regular wave conditions. The hydrodynamic parameters obtained experimentally are further utilized to develop predictive models for assessing the performance of the porous breakwaters. To enhance prediction capability and reduce the dependency on time-consuming experimental investigations, advanced soft computing techniques such as Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Adaptive Neuro-Fuzzy Inference Systems (ANFIS) are employed. The experimental dataset is divided into training and testing subsets for model development and validation. The predictive performance of the developed models is evaluated using statistical indicators including the coefficient of correlation, root mean square error (RMSE), and scatter index (SI). The importance of the present study lies in the combined experimental and comparative soft-computing-based investigation of STPB, which has received limited attention in previous coastal engineering studies. The study not only develops efficient predictive models for hydrodynamic characteristics of porous breakwaters but also compares the effectiveness of ANN, SVM, and ANFIS approaches in modelling wave-structure interaction problems. The research contributes toward the development of computationally efficient tools for predicting the performance of submerged porous coastal structures in both near-shore and deep-water regions. The findings provide valuable information for the design, optimization, and sustainable application of porous breakwaters for coastal protection and wave energy management.
The enforcement of the IMO MARPOL Annex VI global sulphur cap has accelerated the widespread adoption of exhaust gas cleaning systems (EGCS) as a primary compliance technology in marine transport. Despite extensive evaluation of their sulphur removal efficiency, limited understanding exists regarding the internal operational dynamics, transient behaviour, and environmental risk characteristics of EGCS under real ship operating conditions. This study presents a comprehensive, high-frequency, data-driven analysis of an open-loop EGCS installed on a commercial vessel, based on approximately 12,000 time-synchronised observations collected from five diesel generators and 58 physically measured operational variables. The dataset captures authentic shipboard behaviour across inactive, nominal, high-load, and transient operating regimes, encompassing hydraulic demand, energy intensity, pressure dynamics, safety signals, and transient disturbances. A composite environmental risk index integrating flow stress, energy intensity, transient intensity, and pressure effects is formulated to provide a unified, regulation-relevant metric. Results demonstrate that environmental risk is highly non-uniform in time and is dominated by high-load and transient operation rather than average conditions. Risk contributions are unevenly distributed across diesel generators, with a small subset accounting for the majority of high-risk events. A constrained machine-learning model accurately predicts composite environmental risk and reveals physically consistent driver hierarchies, while time-resolved analysis confirms clear early-warning capability. This work advances EGCS environmental assessment from static compliance verification toward predictive environmental assurance, providing a directly implementable framework for real-time shipboard monitoring.
Deep water and ultra-deep water oil exploration require overcoming several technical challenges, particularly those associated with riser design. In the search for solutions to enable the use of rigid steel risers at great depth, the use of buoyancy devices that adjust the curvature of the riser has been extensively studied. This configuration reduces the tension and wear of the riser by decoupling the movement of the floating unit from the contact point of the riser with the seabed, thus minimizing the structural load, leading to improved operational efficiency and extended system lifespan. This paper proposes a procedure to determine and evaluate the static configuration of the riser structure in the lazy wave regime, using practical input parameters such as the total length, depth, position of the well relative to the floating unit, and arrangement of buoyancy devices. An analytical approach based on the catenary equations is conducted, and a numerical approach is carried out using a finite element model developed in ANSYS Mechanical. The results obtained from both approaches show a consistent correlation and provide a solid theoretical foundation for future dynamic analyses of risers in the lazy wave configuration.
This paper gives a comparative numerical analysis of different propeller boss cap fin (PBCF) geometries using computational fluid dynamics (CFD) software. Previous experimental and numerical studies on PBCF applications are first summarized, highlighting their reported impact on ship propulsion and propeller efficiency. The incentive for the study comes from industry demand and regulatory requirements for new builds to be more environmentally friendly and to reduce fuel consumption. A propulsion system with small ship propeller and PBCF has been studied, seven propeller cap models and nine PBCF models were designed and evaluated through CFD open water test simulations. The numerical setup represents standard open water test conditions, and the results are compared against those of the initial propeller without PBCF. The initial propeller model is five blades fixed pitch propeller designed for 55 m passenger ship. Thrust, torque, and propeller efficiency are evaluated and compared to analyse the impact of PBCF geometry on propeller performance. The results indicate that the open water efficiency gains are small comparable to the estimated numerical uncertainty, which indicates that energy savings with PBCF on small passenger vessels are insignificant. Therefore, detailed flow field analyses are performed in the region behind propeller to examine changes in vortex structure and velocity distribution induced by the fins. Those numerical results provide the basis for future research of PBCF impact on noise and vibration reduction rather than achieving high efficiency gains.
