Toward sustainable clean ports: Smoke opacity measurement based on semantic segmentation and edge device.

To overcome the limitations of manually determining the opacity of smoke emitted from ships, we present a novel framework using artificial intelligence and an edge device. Four deep learning models trained with black and white smoke images for semantic segmentation were compared and analyzed. It was confirmed that two of these models, namely the novel W-Net and DeepLab v3+m exhibited superior performance compared with the conventional models.

Analysis and forecasting of national marine litter based on coastal data in South Korea from 2009 to 2021.

In this study, statistical analysis and forecasting were performed using coastal litter data of Korea. The analysis indicated that rope and vinyl accounted for the highest proportion of coastal litter items. The statistical analysis of the national coastal litter trends revealed that the greatest concentration of litter was observed during summer months (June-August).

Fatigue Analysis of a 40 ft LNG ISO Tank Container.

The demand for Liquefied natural gas (LNG) has rapidly increased over the past few years. This is because of increasingly stringent environmental regulations to curb harmful emissions from fossil fuels. LNG is one of the clean energy sources that has attracted a great deal of research.

Anomaly Detection Based on Time Series Data of Hydraulic Accumulator.

Although hydraulic accumulators play a vital role in the hydraulic system, they face the challenges of being broken by continuous abnormal pulsating pressure which occurs due to the malfunction of hydraulic systems. Hence, this study develops anomaly detection algorithms to detect abnormalities of pulsating pressure for hydraulic accumulators. A digital pressure sensor was installed in a hydraulic accumulator to acquire the pulsating pressure data.

Correction: Application of soot discharged from the combustion of marine gas oil as an anode material for lithium ion batteries.

[This corrects the article DOI: 10.1039/D0RA07195A.].

Application of soot discharged from the combustion of marine gas oil as an anode material for lithium ion batteries.

Many studies have recently investigated the characteristics of combustion products emitted from ships and onshore plant facilities for use as energy sources. Most combustion products that have been reported until now are from heavy oils, however, no studies on those from light oils have been published. This study attempted to use the combustion products from the light oils from naval ships as anode materials for lithium ion batteries (LIBs).

Conversion of Black Carbon Emitted from Diesel-Powered Merchant Ships to Novel Conductive Carbon Black as Anodic Material for Lithium Ion Batteries.

Waste soot generated from diesel engine of merchant ships has ≥ 2 µm agglomerates consisting of 30-50 nm spherical particles, whose morphology is identical to that of carbon black (CB) used in many industrial applications. In this study, we crystallized waste soot by heat treatment to transform it into a unique completely graphitic nano-onion structure, which is considerably different from that of commercial conductive CB. While commercial CB has a large specific surface area because of many surface micropores generated due to quenching by water-spraying in the production process, the heat-treated waste soot has a smooth micropore-free surface.

Recycling Waste Soot from Merchant Ships to Produce Anode Materials for Rechargeable Lithium-Ion Batteries.

In this study, the waste soot generated by ships was recycled to produce an active material for use in lithium-ion batteries (LIBs). Soot collected from a ship was graphitized by a heat treatment process and used as an anode active material. It was confirmed that the graphitized soot was converted into a highly crystalline graphite, and was found to form carbon nano-onions with an average diameter of 70 nm.

Carbon Nanostructure of Diesel Soot Particles Emitted from 2 and 4 Stroke Marine Engines Burning Different Fuels.

Diesel soot particles were sampled from 2-stroke and 4-stroke engines that burned two different fuels (Bunker A and C, respectively), and the effects of the engine and fuel types on the structural characteristics of the soot particle were analyzed. The carbon nanostructures of the sampled particles were characterized using various techniques. The results showed that the soot sample collected from the 4-stroke engine, which burned Bunker C, has a higher degree of order of the carbon nanostructure than the sample collected from the 2-stroke engine, which burned Bunker A.