Mathematical Modelling of Marine Power Plants with Thermochemical Fuel Treatment

Cherednichenko, Oleksandr; Serbin, Serhiy; Tkach, Mykhaylo; Kowalski, Jerzy; Chen, Daifen

doi:10.2478/pomr-2022-0030

Cited by 6 publications

(4 citation statements)

References 22 publications

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“…To solve this problem, Vakili et al [166] developed an energy management framework to reduce air emissions in shipbuilding. In addition, optimizing the ship design [167][168][169], using advanced techniques and applying post-treatment methods for SOx and NOx reduction [170][171][172][173][174][175][176][177], using low or free-carbon fuels (hydrogen, ammonia) [178][179][180][181][182][183][184][185], and/or developing methods for recovering the waste heat from large marine diesel engines [186][187][188][189], using renewable energy and energy storage technologies in ships, port, and vehicles in port [85,[190][191][192] could also be potential solutions to reduce pollutant emission from maritime and port actions. Although the above studies propose to solve this pollution by different methods, they are all towards sustainability for the environment, especially a specific environment such as seaports.…”

Section: Discussionmentioning

confidence: 99%

Application of IoT Technologies in Seaport Management

Nguyen

et al. 2023

JOIV : Int. J. Inform. Visualization

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Seaports have a very important role in connecting freight by sea. Goods transported through seaports in the world are increasing day by day to meet human needs. This increases the pressure to apply more technologies for better port management. The world's seaports in the 4th generation, the generation in which seaports enhance connectivity to form a large network, have shown high efficiency when applying technology to port management. This optimizes port operations and connects port information into a network that improves productivity and reduces loading and unloading times. Today, the Internet of Things is the foundation for technologies to manage and optimize operations in various fields. It is considered by scientists to be a highly influential technology in the “4.0” era. The Internet of Things (IoT) technology directly affects the activities and processes of loading and unloading goods at seaports. Modern IoT-based port management technologies such as Radio Frequency Identification (RFID) and Dedicated Short Range Communications (DSRC) are contributing to the increased speed and safe movement of goods through seaports. The application of IoT in port management has become an inevitable trend and will be presented in this article. In the next generation, seaports tend to develop into smart ports based on rapidly developing technology platforms such as IoT, blockchain, and cloud computing. Smart port development also poses many issues to be resolved, including environmental issues. In this paper, the authors present some solutions to develop smart ports in an environmentally friendly manner.

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Section: Discussionmentioning

confidence: 99%

Application of IoT Technologies in Seaport Management

Nguyen

et al. 2023

JOIV : Int. J. Inform. Visualization

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“…The working environment is harsher, especially for a marine two-stroke diesel engine which generates a higher possibility of interference from vibration data. Cherednichenko et al [15] undertook the physical modelling of thermochemical fuel treatment processes. Varbanets et al [16] pointed out that mathematical simulation of the engine was helpful in identifying malfunctions and predicting the effects of a malfunction on the performance of the engine.…”

Section: Number Of Dual Fuel Diesel Enginesmentioning

confidence: 99%

Hierarchical Multiscale Fluctuation Dispersion Entropy for Fuel Injection System Fault Diagnosis

Shi

Yan

2023

Polish Maritime Research

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Marine electronically controlled (ME) two-stroke diesel engines occupy the highest market share in newly-built ships and its fuel injection system is quite different and important. Fault diagnosis in the fuel injection system is crucial to ensure the power, economy and emission of ME diesel engines, so we introduce hierarchical multiscale fluctuation dispersion entropy (HMFDE) and a support matrix machine (SMM) to realise it. We also discuss the influence of parameter changes on the entropy calculation’s accuracy and efficiency. The system simulation model is established and verified by Amesim software, and then HMFDE is used to extract a matrix from the features of a high pressure signal in a common rail pipe, under four working conditions. Compared with vectorised HMFDE, the accuracy of fault diagnosis using SMM is nearly 3% higher than that using a support vector machine (SVM). Experiments also show that the proposed method is more accurate and stable when compared with hierarchical multiscale dispersion entropy (HMDE), hierarchical dispersion entropy (HDE), multiscale fluctuation dispersion entropy (MFDE), multiscale dispersion entropy (MDE) and multiscale sample entropy (MSE). Therefore, the proposed method is more suitable for the modelling data. This research provides a new direction for matrix learning applications in fault diagnosis in marine two-stroke diesel engines.

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“…This can help decrease downtime and lower the chances of mechanical breakdowns while at sea. In addition, machine learning algorithms can be used to optimize vessel routing and speed profiles by utilizing real-time data on fuel [68]- [71], weather forecasts, and traffic patterns [72]- [76], leading to a decrease in fuel consumption, greenhouse gas emissions, and operating costs. A typical application spectrum is depicted in Fig.…”

Section: Introductionmentioning

confidence: 99%

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

Vu,

Le,

et al. 2024

JOIV : Int. J. Inform. Visualization

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This review article looks at the developing field of artificial intelligence and machine learning in maritime and marine environment management. The marine industry is increasingly interested in applying advanced AI and ML technologies to solve sustainability, efficiency, and regulatory compliance issues. This paper examines maritime and marine AI and ML applications using a deep literature review and case study analysis. Modeling ship fuel consumption, which impacts the environment and operating expenses, is a top responsibility. The study demonstrates that ML approaches such as Random Forest and Tweedie models can estimate ship fuel use. Statistical analysis demonstrates that the Random Forest model beats the Tweedie model regarding accuracy and consistency. For the training and testing datasets, the Random Forest model has high R2 values of 0.9997 and 0.9926, indicating a solid match. Low Root Mean Square Error (RMSE) and average absolute relative deviation (AARD) suggest that the model accurately reflects fuel use variability. While still performing well, the Tweedie model has lower R2 values and higher RMSE and AARD values, suggesting reduced accuracy and precision in fuel consumption prediction. These findings provide light on the potential applications of artificial intelligence and machine learning in maritime and marine environment management. Advanced analytics enables decision-makers to analyze fuel consumption patterns better, increase operational efficiency, and decrease environmental impact, thus improving maritime sustainability.

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Mathematical Modelling of Marine Power Plants with Thermochemical Fuel Treatment

Cited by 6 publications

References 22 publications

Application of IoT Technologies in Seaport Management

Application of IoT Technologies in Seaport Management

Hierarchical Multiscale Fluctuation Dispersion Entropy for Fuel Injection System Fault Diagnosis

An insight into the Application of AI in maritime and Logistics toward Sustainable Transportation

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