In this work, plane tree seed-based activated carbons were characterized in detail for a variety of applications. The particularly important area of application would be in the artificial photosynthesis. After carbonization process of biomass precursor at 650°C, the resulting preliminary activated carbons were activated at various temperatures. The activated carbons were characterized by oxygen functionalities (a particularly important role has ester oxygen groups) which provide a unique microstructure. The chemical compositions of as-prepared activated carbons were analyzed through Fourier transform infrared and Raman spectra as well as gas chromatography–mass spectroscopy analysis, while morphology was observed by scanning electron microscopy analysis. Applied analysis showed that detected graphite mainly becomes uniformly nanocrystalline system. The current study also explored the applicability of carbon material obtained from plane tree seed as a potential gaseous adsorbent. The characterization showed that the tested material contains both mesopores and micropores, and this should be advantageous for the gas sorption process, since mesopores may provide low-resistant pathways for the diffusion of CO2 molecules, while the micropores are the most suitable for trapping of CO2. The sorption process analysis (including adsorption/desorption isotherms behavior) shows indication that the rate-limiting step of CO2 adsorption onto activated carbon is probably governed by diffusion-controlled process, especially at temperatures below 850°C.

To reduce pollution from ships in coastal and international navigation, shipping companies are turning to various technological solutions, mostly based on electrification and the use of alternative fuels with a lower carbon footprint. One of the alternatives to traditional diesel fuel is the use of hydrogen as a fuel or hydrogen fuel cells as a power source. Their application on ships is still in the experimental phase and is limited to smaller ships, which serve as a kind of platform for evaluating the applicability of different technological solutions. However, the use of hydrogen on a large scale as a primary energy source on coastal and ocean-going vessels also requires an infrastructure for the production and safe storage of hydrogen. This paper provides an overview of color-based hydrogen classification as one of the main methods for describing hydrogen types based on currently available production technologies, as well as the principles and safety aspects of hydrogen storage. The advantages and disadvantages of the production technologies with respect to their application in the maritime sector are discussed. Problems and obstacles that must be overcome for the successful use of hydrogen as a fuel on ships are also identified. The issues presented can be used to determine long-term indicators of the global warming potential of using hydrogen as a fuel in the shipping industry and to select an appropriate cost-effective and environmentally sustainable production and storage method in light of the technological capabilities and resources of a particular area.

Electrical power systems on hybrid-electric ferries are characterized by the intensive use of power electronics and a complex usage profile with the often-limited power of battery storage. It is extremely important to detect faults in a timely manner, which can lead to system malfunctions that can directly affect the safety and economic performance of the vessel. In this paper, a power disturbance classification method for hybrid-electric ferries is developed based on a wavelet transform and a neural network classifier. For each of the observed power disturbance categories, 200 signals were artificially generated. A discrete wavelet transform was applied to these signals, allowing different time-frequency resolutions to be used for different frequencies. Three statistical parameters are calculated for each coefficient: Standard deviation, entropy and asymmetry of the signal, providing a total of 18 variables for a signal. A neural network with 18 input neurons, 3 hidden neurons, and 6 output neurons was used to detect the aforementioned perturbations. The classification models with different wavelets were analyzed based on accuracy, confusion matrices, and other parameters. The analysis showed that the proposed model can be successfully used for the detection and classification of disturbances in the considered vessels, which allows the implementation of better and more efficient algorithms for energy management.

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An important aspect of introducing hybrid or all-electric ferries on coastlines is to analyze the supporting land-based energy infrastructure to determine if it is possible to implement charging systems that such vessels rely on. The battery energy storage systems on such vessels will need to be rapidly recharged as passengers and vehicles disembark, which means that the flow of electricity through the distribution grid will be much higher and may lead to power quality issues on the local grid. Once implemented, shore connection and battery charging systems must be safe for both people and connected equipment. The issue of implementing shore connections needs to be analyzed from a technical, economic, and legal perspective. This paper presents the challenges and problems of implementing charging stations for ferries in Croatian ports as a result of the research conducted within the project METRO - Maritime Environment-Friendly Transport Systems.