Evaluation of energy efficiency potentials from generator operations on vessels

Yiğit, Kenan

doi:10.1016/j.energy.2022.124687

Cited by 3 publications

(4 citation statements)

References 27 publications

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“…When the generator load is known, the fuel consumption can be calculated. In this way, fuel consumption can be optimized, and steps can be taken toward greater energy efficiency [65]. Knowing the fuel consumption can also enable the detection of a malfunction or an error in the generator system when the consumption differs from the normal state, i.e., when the energy efficiency changes.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

et al. 2023

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Energy efficiency and operational safety practices on ships have gained more importance due to the rules set by the International Maritime Organization in recent years. While approximately 70% of the fuel consumed on a commercial ship is utilized for the propulsion load, a significant portion of the remaining fuel is consumed by the auxiliary generators responsible for the ship’s onboard load. It is crucial to comprehend the impact of the electrical load on the ship’s generators, as it significantly assists maritime operators in strategic energy planning to minimize the chance of unexpected electrical breakdowns during operation. However, an appropriate handling mechanism is required when there are massive datasets and varied input data involved. Thus, this study implements data-driven approaches to estimate the load of a chemical tanker ship’s generator using a 1000-day real dataset. Two case studies were performed, namely, single load prediction for each generator and total load prediction for all generators. The prediction results show that for the single generator load prediction of DG1, DG2, and DG3, the decision tree model encountered the least errors for MAE (0.2364, 0.1306, and 0.1532), RMSE (0.2455, 0.2069, and 0.2182), and MAPE (17.493, 5.1139, and 7.7481). In contrast, the deep neural network outperforms all other prediction models in the case of total generation prediction, with values of 1.0866, 2.6049, and 14.728 for MAE, RMSE, and MAPE, respectively.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

et al. 2023

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“…is can indirectly cause di erent amounts of fuel consumption, fuel cost, and emissions for the same amount of onboard electrical power generation. In [13], the energy e ciency of a number of diesel, chemical, ro-ro and barge vessels was evaluated. e annual fuel consumption and cost, and the potential emissions that may occur from possible load sharing applications of the generator sets on the vessel were estimated.…”

Section: Energy Ef Ciency Potentials Of the Generator Operationsmentioning

confidence: 99%

“…e results showed that running the generators at high capacity instead of low capacity could meet the onboard electrical power demand with less fuel. When generators run at 90% of their capacity instead of 40%, the minimum savings in terms of fuel consumption, fuel cost, CO2 and NOx emissions is about 7% [13]. is ratio should be at least 5% in the case of SOx.…”

Section: Energy Ef Ciency Potentials Of the Generator Operationsmentioning

confidence: 99%

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Electrical design for efficiency: technical and operational measures for optimizing the use of electrical power on ships

Sanabria Vargas,

Vergara Pestana,

Mendoza Iglesias

et al. 2023

cienc. tecnol. buques

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There is currently a growing concern in the shipping industry about energy consumption and environmental impacts. According to the International Maritime Organization's (IMO) energy efficiency guidelines, today's ships must have an energy efficiency management plan to reduce the CO2 emission and other pollutants. In this article, a bibliographic review of methodologies for the optimization of energy consumption on ships is carried out, starting from the identification of sources of energy losses, to the implementation of technical and operational measures that contribute to their improvement, making a qualitative evaluation of the identified methodologies. Sources of energy losses associated with equipment and auxiliary systems are analyzed, as well as opportunities for improvement in the use of electrical energy through the implementation of intelligent energy management systems, high efficiency motors, and lighting. The technical and operational energy efficiency measures described above demonstrate the importance of their implementation from the early stages of the ship's electrical design, as well as monitoring energy consumption during its life cycle, to improve energy efficiency on board.

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Evaluation of the factors causing container lost at sea through fuzzy-based Bayesian network

Öztürk

2024

Regional Studies in Marine Science

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Evaluation of energy efficiency potentials from generator operations on vessels

Cited by 3 publications

References 27 publications

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

A Data-Driven Approach for Generator Load Prediction in Shipboard Microgrid: The Chemical Tanker Case Study

Electrical design for efficiency: technical and operational measures for optimizing the use of electrical power on ships

Evaluation of the factors causing container lost at sea through fuzzy-based Bayesian network

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