Energy-efficient shipping: An application of big data analysis for optimizing engine speed of inland ships considering multiple environmental factors

Energy efficiency of inland ships is significantly influenced by navigational environment, including wind speed and direction as well as water depth and speed. The complexity of the inland navigational environment makes it rather difficult to determine the optimal speeds under different environmental conditions to achieve the best energy efficiency. Route division according to the characteristics of these environmental factors could provide a good solution for the optimization of ship engine speed under different navigational environments. In this paper, the distributed parallel k-means clustering algorithm is adopted to achieve an elaborate route division by analyzing the corresponding environmental factors based on a self-developed big data analytics platform. Subsequently, a ship energy efficiency optimization model considering multiple environmental factors is established through analyzing the energy transfer among hull, propeller and main engine. Then, decisions are made concerning the optimal engine speeds in different segments along the path. Finally, a case study on the Yangtze River is performed to validate the present optimization method. The results show that the proposed method can effectively reduce energy consumption and CO2 emissions of ships.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

Adaptive neural sliding mode control for heterogeneous ship formation keeping considering uncertain dynamics and disturbances

This paper investigates the formation keeping problem of heterogeneous ships with underactuated inputs, uncertain dynamics, and environmental disturbances. The control objective is to make the heterogeneous followers keep the desired formation while tracking a leader. To solve the problem effectively, a novel virtual leader–follower formation scheme considering the ship heterogeneity is proposed by utilizing the backstepping method, adaptive neural network, and adaptive control law. The stability of the formation control system is proved based on Lyapunov's direct method where all tracking errors are guaranteed to be uniformly ultimately bounded. Finally, simulations and comparisons are conducted to verify the effectiveness of the proposed control law.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

Dynamic optimization of ship energy efficiency considering time-varying environmental factors

Nowadays, optimization of ship energy efficiency attracts increasing attention in order to meet the requirement for energy conservation and emission reduction. Ship operation energy efficiency is significantly influenced by environmental factors such as wind speed and direction, water speed and depth. Owing to inherent time-variety and uncertainty associated with these various factors, it is very difficult to determine optimal sailing speeds accurately for different legs of the whole route using traditional static optimization methods, especially when the weather conditions change frequently over the length of a ship route. Therefore, in this paper, a novel dynamic optimization method adopting the model predictive control (MPC) strategy is proposed to optimize ship energy efficiency accounting for these time-varying environmental factors. Firstly, the dynamic optimization model of ship energy efficiency considering time-varying environmental factors and the nonlinear system model of ship energy efficiency are established. On this basis, the control algorithm and controller for the dynamic optimization of ship energy efficiency (DOSEE) are designed. Finally, a case study is carried out to demonstrate the validity of this optimization method. The results indicate that the optimal sailing speeds at different time steps could be determined through the dynamic optimization method. This method can improve ship energy efficiency and reduce CO2 emissions effectively.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

A novel bi-level distributed dynamic optimization method of ship fleets energy consumption

The optimization of ship energy consumption is attracting a great deal of attention, as societies seek to save energy and reduce emissions. Shipping companies are more concerned with the energy consumption of a ship fleet, as opposed to that of a single ship. Because the energy consumption of a fleet is influenced by multiple factors including environmental factors, port operations and transport demands, an improvement in a single ship's energy consumption does not necessarily mean that the overall energy consumption of a fleet is good. In addition, those factors are usually varying over time, making it hard to optimize the fleet's energy consumption by methods that do not consider these time-varying factors. Therefore, a bi-level distributed dynamic optimization method based on distributed model predictive control is proposed. Moreover, an upper-level optimization model for fleet operational decision-making and a lower-level dynamic optimization model of fleet energy consumption are established. Based on these, a control algorithm for the dynamic optimization of fleet energy consumption is developed. Finally, a case study is carried out to demonstrate the effectiveness of the method. It can further reduce the energy consumption of each ship by at least 1.1% and about 6.8% for the whole fleet.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

A novel method for joint optimization of the sailing route and speed considering multiple environmental factors for more energy efficient shipping

Energy saving and emission reduction have attracted a great deal of attention in the maritime industry. The optimization of a ship's energy efficiency can reduce energy consumption and CO2 emissions effectively. However, most of the available studies only focus on either the sailing speed or route optimization, and the interaction between speed and route under the influence of multiple environmental factors was not accounted properly. In this paper, a novel joint optimization method of the sailing route and speed, which considers the interaction between route and speed as well as multiple environmental factors, is proposed to fully exploit the energy efficiency's potential. Moreover, a joint optimization model of the sailing route and speed, which is based on an energy consumption model that considers multiple environmental factors, is established. Next, a solution algorithm for the joint optimization model is investigated in order to achieve joint decision-making with regard to the sailing route and speed. Finally, a case study is conducted that demonstrates the effectiveness of the proposed method. The results show that the proposed method can achieve the optimal sailing route and speed under complex environmental conditions, as well as a reduction in fuel consumption and CO2 emissions of about 4%.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

A novel dynamical collaborative optimization method of ship energy consumption based on a spatial and temporal distribution analysis of voyage data

It is of significant importance to optimize the energy consumption of ships in order to improve economy and reduce CO2 emissions. However, the energy use of ships is affected by a series of navigational environmental parameters, which have certain spatial and temporal differences and variability. Therefore, the dynamic collaborative optimization method of sailing route and speed, which fully considers the spatial and temporal distribution characteristics of those factors, is of great importance. In this paper, the spatial and temporal distribution characteristics of the environmental factors and their related ship energy consumption profiles are first analyzed. Subsequently, a ship energy consumption model considering various environmental factors is established to realize the prediction of energy use of ships within the navigation region. Then, a novel dynamic collaborative optimization algorithm, which adopts the Model Predictive Control (MPC) strategy and swarm intelligence algorithm, is proposed, to further improve the ship's energy consumption optimization. Finally, a case study is conducted to demonstrate the effectiveness of the proposed method. The results show that the newly developed dynamic collaborative optimization method, which fully considers the continuously time-varying characteristics of environmental and operational parameters, could effectively reduce the energy consumption in comparison to the original operational mode. In addition, the adoption of the MPC strategy produces better performance results compared to the optimization method without the MPC strategy.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Transport Engineering and Logistic

Expression and functional characterization of a gene associated with retinoid-interferon-induced mortality 19 (GRIM-19) from orange-spotted grouper (Epinephelus coioides)

GRIM-19 is a nuclear encoded subunit of complex I that has been implicated in apoptosis. The protein participates in multiple functions including the innate immune response. GRIM-19 has been studied in humans and other mammals; however, fish GRIM-19 has not been well characterized. In this study, a new GRIM-19 gene, EcGRIM-19, was isolated from the orange-spotted grouper (Epinephelus coioides) cDNA library, which was constructed following LPS treatment. EcGRIM-19 is a 582-bp gene that encodes a 144-amino acid protein. The gene is a true ortholog of mammalian GRIM-19. EcGRIM-19 exhibits ubiquitous and constitutive expression in the different tissues of the orange-spotted grouper. The expression levels of EcGRIM-19 are altered in the gill, spleen, kidney and liver after induction with LPS. The subcellular localization analysis demonstrated that the EcGRIM-19 protein is localized predominantly in the mitochondria. In addition, amino acids 30-50 of the protein are responsible for the mitochondrial localization of EcGRIM-19. The caspase assay demonstrated that the overexpression of GRIM-19 enhanced the cellular sensitivity to interferon(IFN)-beta- and retinoic acid (RA)-induced death in HeLa cells. The data presented in this study are important for further understanding the EcGRIM-19 gene function in fish. (c) 2012 Elsevier Ltd. All rights reserved.GRIM-19 is a nuclear encoded subunit of complex I that has been implicated in apoptosis. The protein participates in multiple functions including the innate immune response. GRIM-19 has been studied in humans and other mammals; however, fish GRIM-19 has not been well characterized. In this study, a new GRIM-19 gene, EcGRIM-19, was isolated from the orange-spotted grouper (Epinephelus coioides) cDNA library, which was constructed following LPS treatment. EcGRIM-19 is a 582-bp gene that encodes a 144-amino acid protein. The gene is a true ortholog of mammalian GRIM-19. EcGRIM-19 exhibits ubiquitous and constitutive expression in the different tissues of the orange-spotted grouper. The expression levels of EcGRIM-19 are altered in the gill, spleen, kidney and liver after induction with LPS. The subcellular localization analysis demonstrated that the EcGRIM-19 protein is localized predominantly in the mitochondria. In addition, amino acids 30-50 of the protein are responsible for the mitochondrial localization of EcGRIM-19. The caspase assay demonstrated that the overexpression of GRIM-19 enhanced the cellular sensitivity to interferon(IFN)-beta- and retinoic acid (RA)-induced death in HeLa cells. The data presented in this study are important for further understanding the EcGRIM-19 gene function in fish. (c) 2012 Elsevier Ltd. All rights reserved