Yasin Arslanoğlu

Maritime transportation accounts for approximately 3.1% of global anthropogenic CO₂ emissions while enabling nearly 85% of world trade, prompting increasingly stringent regulations to decarbonize the sector. Ship-based carbon capture systems (CCS) have emerged as a promising transitional solution to support regulatory compliance and emission reduction. This study evaluates alternative carbon capture methods and technologies for maritime applications using a multi-criteria decision-making framework. Six evaluation criteria, maturity, efficiency, energy demand, cost, applicability, and safety, were identified based on the literature. Criterion weights were derived using the Analytic Hierarchy Process (AHP) with expert input and complemented by objective entropy weighting. A hybrid AHP–entropy approach was employed to balance subjective judgment with data-driven insights, while sensitivity and bounded perturbation analyses were conducted to assess robustness. The results indicate that post-combustion carbon capture is the most suitable method for shipboard application. Among the evaluated technologies, chemical adsorption ranks highest, followed by chemical looping combustion and membrane separation. The rankings remain stable under reasonable uncertainty in both weighting and performance scoring. Overall, the proposed framework provides a transparent and robust decision-support tool for monitoring CCS configurations in maritime transport.

This study proposes a novel dual-role weighting framework for dry port location selection, bridging the gap between commercial logistics efficiency and strategic disaster resilience. Designed to establish a new theoretical evaluation paradigm, the research utilizes the Fuzzy Rough SWARA (FR-SWARA) method and a 12-person expert panel to weigh a comprehensive set of 31 criteria under high-dimensional uncertainty. The findings reveal a decisive hierarchical shift, where spatial and infrastructure-related dimensions significantly outweigh traditional cost considerations. This empirical evidence substantiates the transition from ‘just-in-time’ to ‘Just-in-Case’ logistics architectures. Ultimately, the study reconceptualizes the dry port as a ‘strategic stabilizer’—a critical infrastructure node that absorbs systemic shocks and maintains supply chain continuity during diverse crises, including natural disasters and the COVID-19 pandemic. The proposed weighting framework offers a robust theoretical roadmap for policy and managerial decision-making in volatile geographies.

The infrared (IR) signature is a critical factor for operational secrecy in military vessels. Conventional IR reduction methods typically rely on cooling exhaust gases with seawater or air; however, these systems require additional energy and components, creating challenges in terms of energy efficiency and fuel security. Therefore, more efficient solutions are needed to both reduce IR signature and decrease energy dependency. Considering that nearly 30% of fuel energy is lost through exhaust, waste heat recovery offers significant potential. This study proposes a system for naval vessels that simultaneously enables waste heat recovery and IR signature reduction. Conventional-powered aircraft carriers were used as case studies, and exhaust heat was estimated using empirical methods. The proposed system operates in a cascade configuration: high-temperature heat is first converted into electricity using thermoelectric generators (TEG), while the remaining medium-grade heat is recovered through an Organic Rankine Cycle (ORC). The results show that the combined ORC-TEG system can generate approximately 1.86 MW of electrical power per gas turbine, achieving a heat-based efficiency of about 6%. This corresponds to an avoided fuel consumption of around 324 kg/h per gas turbine, offering substantial savings for long-range operations. In addition, the system significantly reduces the IR signature of the exhaust. The integration of ORC and TEG maximizes heat recovery while lowering detectability. Although TEG systems currently involve high capital costs, these are expected to decrease with ongoing technological advancements and wider adoption.

Determining the key characteristics of a ship during the concept and preliminary design phases is a critical and intricate process. In this study, we propose an alternative to traditional empirical methods by introducing a model to estimate the main particulars of diesel-powered Z-Drive harbor tugboats. This prediction is performed to determine the main particulars of tugboats: length, beam, draft, and power concerning the required service speed and bollard pull values, employing Bayesian network and non-linear regression methods. We utilized a dataset comprising 476 samples from 68 distinct diesel-powered Z-Drive harbor tugboat series to construct this model. The case study results demonstrate that the established model accurately predicts the main parameters of a tugboat with the obtained average of mean absolute percentage error values; 6.574% for the Bayesian network and 5.795%, 9.955% for non-linear regression methods. This model, therefore, proves to be a practical and valuable tool for ship designers in determining the main particulars of ships during the concept design stage by reducing revision return possibilities in further stages of ship design.