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    Wyświetlanie 1-3 z 3
    Tytuł:
    Reduction of CO2 emissions from offshore combined cycle diesel engine-steam turbine power plant powered by alternative fuels
    Autorzy:
    Olszewski, Wojciech
    Dzida, Marek
    Nguyen, Van Giao
    Cao, Dao Nam
    Powiązania:
    https://bibliotekanauki.pl/articles/34613948.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    ship power plants
    alternative marine fuel
    greenhouse gases
    CO2 emissions
    combined power system
    diesel engines
    steam turbine
    gas turbine
    Opis:
    Diverse forms of environmental pollution arise with the introduction of materials or energy that exert adverse effects on human health, climate patterns, ecosystems, and beyond. Rigorous emission regulations for gases resulting from fuel combustion are being enforced by the European Union and the International Maritime Organization (IMO), directed at maritime sectors to mitigate emissions of SOx, NOx, and CO2. The IMO envisions the realisation of its 2050 targets through a suite of strategies encompassing deliberate reductions in vessel speed, enhanced ship operations, improved propulsion systems, and a transition towards low and zero-emission fuels such as LNG, methanol, hydrogen, and ammonia. While the majority of vessels currently depend on heavy fuel or low-sulphur fuel oil, novel designs integrating alternative fuels are gaining prominence. Technologies like exhaust gas purification systems, LNG, and methanol are being embraced to achieve minimised emissions. This study introduces the concept of a high-power combined ship system, composed of a primary main engine, a diesel engine, and a steam turbine system, harnessing the energy contained within the flue gases of the main combustion engine. Assumptions, constraints for calculations, and a thermodynamic evaluation of the combined cycle are outlined. Additionally, the study scrutinises the utilisation of alternative fuels for ship propulsion and their potential to curtail exhaust emissions, with a specific focus on reducing CO2 output.
    Źródło:
    Polish Maritime Research; 2023, 3; 71-80
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Using Artificial Neural Networks for predicting ship fuel consumption
    Autorzy:
    Nguyen, Van Giao
    Sakthivel, Rajamohan
    Rudzik, Krzysztof
    Kozak, Janusz
    Sharma, Prabhakar
    Pham, Nguyen Dang Khoa
    Nguyen, Phuoc Quy Phong
    Nguyen, Xuan Phuong
    Powiązania:
    https://bibliotekanauki.pl/articles/32918813.pdf
    Data publikacji:
    2023
    Wydawca:
    Politechnika Gdańska. Wydział Inżynierii Mechanicznej i Okrętownictwa
    Tematy:
    artificial neural network
    fuel management
    marine engine
    ship fuel consumption
    energy efficiencys
    Opis:
    In marine vessel operations, fuel costs are major operating costs which affect the overall profitability of the maritime transport industry. The effective enhancement of using ship fuel will increase ship operation efficiency. Since ship fuel consumption depends on different factors, such as weather, cruising condition, cargo load, and engine condition, it is difficult to assess the fuel consumption pattern for various types of ships. Most traditional statistical methods do not consider these factors when predicting marine vessel fuel consumption. With technological development, different statistical models have been developed for estimating fuel consumption patterns based on ship data. Artificial Neural Networks (ANN) are some of the most effective artificial methods for modelling and validating marine vessel fuel consumption. The application of ANN in maritime transport improves the accuracy of the regression models developed for analysing interactive relationships between various factors. The present review sheds light on consolidating the works carried out in predicting ship fuel consumption using ANN, with an emphasis on topics such as ANN structure, application and prediction algorithms. Future research directions are also proposed and the present review can be a benchmark for mathematical modelling of ship fuel consumption using ANN.
    Źródło:
    Polish Maritime Research; 2023, 2; 39-60
    1233-2585
    Pojawia się w:
    Polish Maritime Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Identifying Key Parameters Influencing Soil Quality at Various Depths in Tram Chim National Park, Dong Thap Province, Vietnam
    Autorzy:
    Giao, Nguyen Thanh
    Nhien, Huynh Thi Hong
    Dan, Truong Hoang
    Ni, Duong Van
    Powiązania:
    https://bibliotekanauki.pl/articles/2202379.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    soil quality
    national park
    organic matter
    alkaline soil
    multivariate analysis
    Opis:
    This study used multivariate statistics including cluster analysis (CA) and principal component analysis (PCA) to evaluate the variability and key indicators causing changes in soil quality in Tram Chim National Park, Dong Thap province, Vietnam. Soil samples were collected in the dry season at the habitats of Ischaemum rugosum (CM), Panicum repens (CO), Nelumbo nucifera (LS), Eleocharis dulcis (NO), Oryza rufipogon (LM), Rice field (RL), Melaleuca cajuputi (T) in two layers: A (0–20 cm) and B (20–40 cm). The parameters of pH, total nitrogen (TN), total phosphorus (TP), total acidity (TA), organic matter (OM), total iron (Fe) and exchanged aluminum (Al3+) were used to assess soil quality. The results showed that soil pH was low in both A and B layers. Fe and Al were both high, and the concentrations of these metals in layer A were higher than those in layer B. The OM content was medium while the TN and TP levels were very low. Most of the soil quality indicators tended to decrease with the depth (except for TA). The results of CA analysis showed that there was almost no major change in soil quality between the two soil layers; however, the soil quality in rice field habitat was different from other habitats. The cause may be due to human impact in adding fertilizers/pesticides during farming practices. The PCA results showed at least five influencing factors, explaining 99.7% and 99.9% of soil quality variation in A and B layers. The Al3+, TA, OM, and TP parameters had the main influence on the soil quality of layer A. Meanwhile, the pH, Al3+, TA, TN, Fet indicators had influence on the soil quality of layer B. These indicators need to be future surveyed to assess the evolution of soil quality in the study area.
    Źródło:
    Journal of Ecological Engineering; 2023, 24, 2; 81--91
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-3 z 3

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