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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. Six anomaly detection algorithms were developed based on the acquired data. A threshold averaging algorithm over a period based on the averaged maximum/minimum thresholds detected anomalies 2.5 h before the hydraulic accumulator failure. In the support vector machine (SVM) and XGBoost model that distinguish normal and abnormal pulsating pressure data, the SVM model had an accuracy of 0.8571 on the test set and the XGBoost model had an accuracy of 0.8857. In a convolutional neural network (CNN) and CNN autoencoder model trained with normal and abnormal pulsating pressure images, the CNN model had an accuracy of 0.9714, and the CNN autoencoder model correctly detected the 8 abnormal images out of 11 abnormal images. The long short-term memory (LSTM) autoencoder model detected 36 abnormal data points in the test set.
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Redes Neurales de la Computación , Máquina de Vectores de Soporte , Factores de Tiempo , AlgoritmosRESUMEN
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. Furthermore, the difference in the exhaust gas temperatures originating from the different engine and fuel types can affect the nanostructure of the soot emitted from marine diesel engines.
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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. In the Republic of Korea, the use of LNG has been implemented in various sectors, including public transport buses, domestic applications, power generation, and in huge marine engines. Therefore, a proper, flexible, and safe transport system should be put in place to meet the high demand. In this work, finite element analysis (FEA) was performed on a domestically developed 40 ft ISO LNG tank using Ansys Mechanical software under low- and high-cycle conditions. The results showed that the fatigue damage factor for all the test cases was much lower than 1. The maximum principal stress generated in the 40 ft LNG ISO tank container did not exceed the yield strength of the calculated material (carbon steel). Maximum principal stress of 123.2 MPa and 107.61 MPa was obtained with low-cycle and high-cycle analysis, respectively, which is 50.28% less than the yield strength of carbon steel. The total number of cycles was greater than the total number of design cycles, and the 40 ft LNG ISO tank container was satisfied with a fatigue life of 20 years.
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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). To predict the amount of coastal litter per meter, recurrent neural network (RNN)-based models were used. Neural basis expansion analysis for interpretable time series forecasting (N-BEATS) and neural hierarchical interpolation for time series forecasting (N-HiTS), an improved model of N-BEATS recently announced, were used for comparison with RNN-based models. When predictive performance and trend followability were evaluated, overall N-BEATS and N-HiTS outperformed RNN-based models. Furthermore, we found that average of N-BEATS and N-HiTS models yielded better results than using one model.
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Redes Neurales de la Computación , Plásticos , Predicción , República de Corea , Factores de Tiempo , Estaciones del Año , Plásticos/análisis , Monitoreo del Ambiente , Residuos/análisis , PlayasRESUMEN
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). These products have a carbon black morphology and were transformed into highly crystalline carbon structures through a simple heat treatment. These new structured materials showed reversible capacities of 544, 538, 510, 485, 451 and 395 mA h g-1 at C-rates of 0.1, 0.2, 0.5, 1.0, 2.0 and 5.0C, respectively, and excellent rate performance. These findings were the result of a combination hierarchical pores ranging from the meso- to macroscale and the high capacitive charge storage behavior of the soot. The results of this study prove that annealed soot with a unique multilayer graphite structure shows promising electrochemical performance suitable for the production of low-cost, high-performance LIB anode materials.
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[This corrects the article DOI: 10.1039/D0RA07195A.].
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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. Thus, the treated waste soot acquires the shape of CB but has a much smaller specific surface area. When the treated soot is used as a conductive material in lithium ion battery (LIB) half cells, the Coulombic efficiency of the entire anode is improved significantly owing to its low specific surface area; the electrochemical performance of the LIB is considerably enhanced compared to that of conventional conductive materials. Thus, polluting soot generated in marine propulsion can be transformed into a new class of CB with a unique structure by simple heat treatment; this soot can also be used as an inexpensive conductive material to enhance the LIB performance.
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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. The graphitized soot showed a high discharge capacity and an excellent cycle life, with a reversible capacity of 260 mAhg-1 even after 150 cycles at a rate of 1 C. This study demonstrates that the annealed soot with a unique graphitic multilayer structure has an electrochemical performance that renders it suitable as a candidate for the production of low-cost anode materials for use in LIBs.