Identification of Optimal Trim Configurations to improve Energy Efficiency in Ships

Perera, Lokukaluge P.; Mo, Brage; Kristjánsson, Leifur A.

doi:10.1016/j.ifacol.2015.10.291

Cited by 17 publications

(10 citation statements)

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“…Therefore, the initial mean and covariance values selection should be appropriately; the initial mean and covariance values are from the previous statistical distributions approximated (see Figure 2). The estimated mean and variance values for each Gaussian distribution in Figure 2 are: (6) One should note that slight variations among the initial and final means values and considerable variations among the initial and final covariance values are in these results observed. These same observations are also under the results in Figure 7 noted.…”

Section: Mean and Covariance Valuesmentioning

confidence: 75%

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Marine Engine-Centered Data Analytics for Ship Performance Monitoring

Perera

2017

Journal of Offshore Mechanics and Arctic Engineering

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This study proposes marine engine centered data analytics as a part of the ship energy efficiency management plan (SEEMP). The SEEMP enforces various emission control measures to improve ship energy efficiency by considering vessel performance and navigation data. The proposed data analytics is developed in the engine-propeller combinator diagram (i.e., one propeller shaft with a direct drive main engine). Three operating regions from the initial data analysis are under the combinator diagram noted to capture the shape of these regions by the proposed data analytics. The data analytics consists of implementing Gaussian mixture models (GMMs) to classify the most frequent operating regions of the main engine. Furthermore, the expectation maximization (EM) algorithm calculates the parameters of GMMs. This approach, also named data clustering algorithm, facilitates an iterative process for capturing the operating regions of the main engine (i.e., in the combinatory diagram) with the respective mean and covariance matrices. Hence, these data analytics can monitor ship performance and navigation conditions with respect to engine operating regions as a part of the SEEMP. Furthermore, development of advanced mathematical models for ship performance monitoring within the operational regions (i.e., data clusters) of marine engines is expected.

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Section: Mean and Covariance Valuesmentioning

confidence: 75%

“…Furthermore, the proposed data analytics at the ship operation phase can identify optimal engine-propulsion conditions of vessels [6].…”

Section: Introduction Energy Efficiency Management Planmentioning

confidence: 99%

Marine Engine-Centered Data Analytics for Ship Performance Monitoring

Perera

2017

Journal of Offshore Mechanics and Arctic Engineering

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“…Many papers related to energy efficiency improvement in ships have been published [8][9][10][11][12][13] using energy criteria (first law) for performance assessment. Some others are addressing the performance of the ship's propulsion [14][15] considering both the energy and the exergy criteria.…”

Section: Related Workmentioning

confidence: 99%

“…The engine characteristics are essentially expressed by the thermal efficiency (η) and brake-specific fuel oil consumption Sfoc. While the thermal efficiency is determined as the ratio of brake power to the fuel energy rate of the control volume, Sfoc is defined as a measure of how much fuel is consumed in 1 h to obtain 1 kW brake power and they are, respectively, denoted as in eqns (9) and 10:…”

Section: Energy Flowmentioning

confidence: 99%

Analy sis of container ship energy systems

Aijjou¹,

Bahatti²,

Raihani³

2020

Int. J. EQ

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Nowadays energy efficiency is a global concern for the maritime organization and business. Rising operating costs, mainly fuel prices and stricter environmental regulations are forcing the shipping industry to find more effective ways of designing and operating the ships in an energy-efficient manner. An analysis of the energy system of ships and understanding the different energy flows can contribute to better energy management and efficiency in the ships. Keeping this objective in mind, the energy systems of typical medium-sized container ships are analysed in this paper based on the vessel's operating data and equipment parameters collected by the crew over three months. The study focuses on the energy and exergy balances of the main components. It has been shown that more than 80% of total energy consumption is caused by the propulsion plant, while the electric power generation accounts for 14-17%. Up to 60% of the energy supply is lost to the environment through the cooling system, radiation, friction and exhaust from diesel engines. Exergy losses caused by the exhaust gas and heat transfer are other contributors. Roughly 6% of the fuel exergy input is lost due to heat transfer. The exergy lost through the exhaust gas flue mainly from the main engine is estimated at 12% of the total exergy input. There is considerable potential for waste heat recovery on container vessels. However, to improve the overall energy efficiency, a techno-economic study has to be carried out on waste heat recovery solutions for each category of vessels and trading mode.

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“…In [23], data such as wind speed and direction, average draft, trim, main engine power, shaft speed and fuel consumption are analyzed and several higher fuel consumption trends under these parameter variations are noted. The optimal trim configuration is identified with respect to the fuel consumption rates.…”

Section: Energy Efficiency Improvement / Unapređenje Energetske Učinkmentioning

confidence: 99%

Veliki skupovi podataka u pomorskoj industriji

Mirović¹,

Miličević²,

Obradovic³

2018

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SummaryMaritime industry is a complex system that requires a quick adaptation to changing conditions and in which decision-making needs to take into account a large number of parameters. As navigation systems become more advanced, there is a significant amount of ship performance and navigation data generated. Big Data analytics tools make it possible to analyze these large quantities of data in order to gain the insight that supports decision-making. This paper gives an overview of the applications of Big Data in maritime industry, specifically in logistics optimization, safety and energy efficiency improvement, as well as the challenges that systems involving Big Data face. SažetakPomorska industrija je složeni sustav koji zahtijeva brzu prilagodbu u promjenjivim uvjetima u kojima je potrebno uzeti u obzir velik broj parametara prilikom donošenja odluka. Napretkom navigacijskih sustava, generira se znatna količina podataka o performansama broda i navigaciji. Analitički alati za velike skupove podataka omogućuju analizu tih podataka kako bi se dobilo razumijevanje potrebno za podršku donošenju odlukâ. Ovaj članak daje pregled primjene velikih skupova podataka u pomorstvu, posebno u optimizaciji logistike, sigurnosti i poboljšanju energetske učinkovitosti, kao i izazove s kojima se suočavaju sustavi koji koriste velike skupove podataka. KEY

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Identification of Optimal Trim Configurations to improve Energy Efficiency in Ships

Cited by 17 publications

References 1 publication

Marine Engine-Centered Data Analytics for Ship Performance Monitoring

Marine Engine-Centered Data Analytics for Ship Performance Monitoring

Analy sis of container ship energy systems

Veliki skupovi podataka u pomorskoj industriji

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