This study performed a technical–economic analysis for ship-based ammonia transportation to investigate the feasibility of international ammonia transportation. Ammonia is considered to be a vital hydrogen carrier, so the international trade in ammonia by ship will considerably increase in the future. This study proposed three scenarios for transporting ammonia from the USA, Saudi Arabia, and Australia to South Korea and employed an 84,000 m³ class ammonia carrier. Not only traditional very low sulfur fuel oil (VLSFO)/marine diesel oil (MDO) but also LNG and ammonia fuels were considered as propulsion and power generation fuels in the carrier. A life-cycle cost (LCC) model consisting of capital expenditure (CAPEX) and operational expenditure (OPEX) was employed for the cost estimation. The results showed that the transportation costs depend on the distance. The unit transportation cost from the USA to South Korea was approximately three times higher than that of Australia to South Korea. Ammonia fuel yielded the highest costs among the fuels investigated (VLSFO/MGO, LNG, and ammonia). When using ammonia fuel, the unit transportation cost was approximately twice that when using VLSFO/MDO. The fuel costs occupied the largest portion of the LCC. The unit transportation costs from Australia to South Korea were 23.6 USD/ton-NH3 for the LVSFO/MDO fuel case, 31.6 USD/ton-NH3 for the LNG fuel case, and 42.9 USD/ton-NH3 for the ammonia fuel case. This study also conducted a sensitivity analysis to investigate the influence of assumptions, including assumed parameters.

This study analysed a novel re-liquefaction system integrated with a fuel supply system (FSS) for an LPG carrier to conventional systems. The re-liquefaction system and FSS were installed independently in a conventional LPG carrier, while those systems were combined in the novel system. The condensed LPG in the re-liquefaction system was directly transferred to the FSS without the cooling and expansion process in the novel system. 84,000 m³ LPG carrier equipped with a 10 MW engine at normal continuous rating (NCR) was selected as a target ship. Aspen HYSYS ver.12.1 was employed for process simulation. The results showed that the energy consumption for the novel system was reduced by 38%. The energy for re-liquefaction was decreased because the flow rate recirculated was decreased, and the energy for FSS was reduced as the temperature of the stream supplied to the FSS was relatively high in the novel system. A sensitivity analysis was conducted to investigate the effect of the parameters on the results. The investigated parameters were LPG compositions, seawater temperature, compressor efficiency, and pump efficiency. The energy consumption for the system was significantly different depending on the LPG composition, and the energy consumption was changed by 2.5% for conventional systems and 0.9% for the novel systems with the variation of 4 °C seawater temperature. The energy for the novel system was reduced by 2.8% for conventional systems and 2.3% for the novel systems with the 5% increment of compressor efficiency, whereas pump efficiency had little effect on the results.

A rapid identification of oil would facilitate a prompt response and efficient removal in the event of an oil spill. Traditional chemical methods in oil fingerprinting have limitations in terms of both time and cost. This study considers machine learning models that can be applied immediately upon measurement of oil density and viscosity. The main objective was to compare models generated from various combinations of features and data. Under five different algorithms, the resulting models were evaluated in terms of their feasibility, advantages, and limitations (FAL). The extra tree (ET) and histogram-based gradient boosting (HGB) models, which incorporated physical features, their rates of change, and environmental features, were found to be the most accurate, achieving 88.55% and 88.41% accuracy, respectively. The accuracy of the models was further enhanced by adjusting the features. In particular, incorporating the rate of change in oil properties led to an enhancement in the accuracy of ET to 92.83%. However, further inclusion of secondary features led to a reduction in accuracy. The effect of input imprecision was analyzed. A 10% of inherent error reduced the accuracy of the HGB model to 60%. Comparing these FAL, machine learning can be a simple, rapid, and cost-effective auxiliary for forensic analysis in diverse spill environments.

Recovery modeling and countermeasures for oil spilled at sea have been extensively researched, but research remains insufficient on recovery potential estimation methods. It is required to access the mechanical recovery potential by considering the relationship between oil behavior, environmental conditions, and the performance of clean-up activities. Two response-planning models were developed in this study. One is a spatially uniform recovery model for estimating recovery potential that reflects weathering, oil properties, and equipment efficiency. The other is a spatially nonuniform recovery model that considers not only the above characteristics but also local thickness reduction by skimming. A comparison between the two models and an analysis of their effects on response was carried out through the calculation using an accident scenario. It is possible to analyze the effect of the thin slicks, natural dissipation, and the quantification of deployable skimming systems with the spatially nonuniform recovery model. Finally, we analyzed interrelationships among residual oil volume on the sea, response time, and the number of skimming systems.