Offshore wind power is an important renewable energy source and plays an essential role in optimizing the energy structure worldwide. Simultaneously, offshore wind turbine (OWT) selection is a complicated process since it concerning various variables and optimization scenarios. In this paper, a novel fuzzy Bayesian network-based model for multiple-attribute decision-making (MADM) is proposed. First of all, a three-layer decision-making framework for OWT selection is established through systematically combing previous studies, expert knowledge, and the principal component analysis (PCA) results by treating the wind turbine parameters, wind turbine economy, wind turbine reliability, and navigation safety as the attributes, and the corresponding 11 influencing factors are identified and quantified. Moreover, a triangular fuzzy number is introduced to fuzzify each influencing factor, and the belief degree for different linguistic variables corresponding to the specific influencing factor is employed in the fuzzy IF-THEN rule system. Then, the belief rule base is transformed into the Bayesian network as the conditional probability tables (CPTs), which can directly express the influence relationship of various factors and realize the integration of various influence factors to obtain the optimal scheme. Finally, the proposed model is validated by taking a case study in busy waterways in the Eastern China Sea as an example. This research provides an intuitive, feasible, and practical way for OWT selection.

The water level fluctuations in the upper reach of dam reservoirs may result in increasing occurrence probabilities of ship grounding accidents, which is especially prominent in the Three Gorges Reservoir. Grounding accidents may not only cause congestion of the traffic but also lead to loss of properties and pollution to the maritime environment. An analytical model incorporating Bayesian Network is proposed to estimate the occurrence likelihood of a ship being grounded in the fluctuating backwater zone. The proposed model comprehensively considers the characteristics of the ship's properties, organizational factors, hydrological conditions and human factors from a systematic perspective. Historical data collected from the Chongqing Maritime Safety Administration, together with incident reports, are used to develop a quantitative model. The developed model in this paper concludes that out of twenty-six factors, the area of the fluctuating backwater zone, the month, and water level are the predominant factors for the occurrence of grounding accidents in the Three Gorges Reservoir. The results can be used by maritime stakeholders to take mitigation measures for grounding accident reduction.

Collision accident accounts for the largest proportion among all types of maritime accidents, emergency decision-making is essential to reduce the consequence of such accidents. This paper proposes a novel Bayesian Network based emergency decision-making model for consequence reduction of individual ship-ship collision in the Yangtze River. The kernel of this method is to propose a three-layer decision-making framework, to develop the graphical structure for describing the accident process and to establish the conditional probability tables for the quantitative relationships. The merits of the proposed method include the intuitive representation of accident development, easy to implement, ability to deal with incomplete information and updated information. This proposed method is applied to a typical collision accident in the Yangtze River. Consequently, this paper provides a practical and novel decision-making method for collision accidents.

Pyrolysis is an effective way to convert biomass into biofuel while obtaining highly porous active carbon materials. In this study, a facile approach, involving hydrothermal and pyrolysis steps, is described for preparing hybrid metal oxide nanoparticle-embedded porous hard carbon matrices (MnO/C) from the biowaste rice husk and organometallic precursors. It was found that the pyrolysis/calcination temperature had a strong influence over the microstructure, especially over the porosity, but also over the carbon content and crystallinity of the nanocomposites; hence, the electrical properties can be controlled. Galvanostatic measurements showed that the nanocomposite obtained at 600 °C exhibited the highest charge/discharge capacity and best stability, delivering an initial discharge capacity of 1104 mA·h·g^-1 at a current density of 200 mA·g^-1, and retaining a value of 830 mA·h·g^-1 after 200 cycles, suggesting excellent cycle stability. A discharge capacity of 581 mA·h·g^-1 was obtained even at a current density as high as 2400 mA·g^-1, demonstrating superb rate capability. This outstanding electrochemical performance, ascribed to high electrochemical activity of the embedded MnO nanoparticles enhanced by electrical conductivity provided through the high surface area of the active porous carbon support, is discussed in relation to the microstructure of the nanocomposite.

The demand for energy storage systems with superior performance has led to the creation of hybrid supercapacitor device. With proper designs, the hybrid supercapacitive materials can achieve high performance while reducing the overall cost. Herein, a novel method is developed for preparing three-dimensional hierarchical graphitic carbon nanocomposites with highly dispersed mixed Co-Ni oxide nanoparticles (Co-Ni-O/3DG) by a facile one-pot process involving carbonization and subsequent oxidation of metal ion doped biopolymer precursors. The mixed metal nanoparticles produced during carbonization enabled a top-down preparation of 3D graphitic carbon nanosheets. The nanocomposites were fully characterized and showed excellent electrochemical performance supported by the DFT calculation. Specific capacitance of 1586 F·g ^-1 was achieved (current density 1.0 A·g ^-1 ), with capacitance retention of 94.5% after 10 000 cycles demonstrating exceptional cycling stability. In an asymmetric full-cell system using a Co-Ni-O/3DG positive electrode, high energy densities of 32.8-54.7 Wh kg ^-1 associated with very high power densities of 11358-748.6 W kg ^-1 were obtained, comparable to the most advanced contemporary supercapacitive materials, while they also possesses an improved cyclability as well as using biosourced staring materials, underlining the electrode's potential application in hybrid supercapacitor devices.

Offshore wind farms have developed fast as an environmentally friendly source of energy. The submarine power cable of the offshore wind farm, used for connecting power generation devices to onshore equipment, may have a significant impact on navigation safety and is prone to being damaged (e.g. caused by fishing or emergency anchoring by ships) when adopting unfavorable routing. This paper proposes a fuzzy evidential reasoning method for submarine power cable routing selection of the offshore wind farm by comprehensively considering the conditions for cable laying and its influence on maritime safety. The kernel of this approach is to establish a three-layer decision-making framework after fuzzification of the input variables, to derive the belief rule base, and to obtain the optimal routing from the submarine power cable candidates using evidential reasoning and index value. The proposed approach is applied to a real routing selection problem of a submarine power cable for an offshore wind farm in Zhejiang Province of China. The resulting choice corresponds to the discussions in a workshop unanimously.