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    Wyświetlanie 1-2 z 2
    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:
    Comparative study for deriving stagedischarge – sediment concentration relationships using soft computing techniques
    Autorzy:
    Sihag, P.
    Sadikhani, M. R.
    Vambol, V.
    Vambol, S.
    Prabhakar, A. K.
    Sharma, N.
    Powiązania:
    https://bibliotekanauki.pl/articles/1818806.pdf
    Data publikacji:
    2021
    Wydawca:
    Stowarzyszenie Komputerowej Nauki o Materiałach i Inżynierii Powierzchni w Gliwicach
    Tematy:
    sediment load concentration
    Baitarani river
    M5P
    random forest
    ładunek osadu
    stężenie
    rzeka Baitarani
    las losowy
    Opis:
    Purpose: Knowledge of sediment load carried by any river is essential for designing and planning of hydro power and irrigation projects. So the aim of this study is to develop and evaluating the best soft-computing-based model with M5P and Random Forest regressionbased techniques for computation of sediment using datasets of daily discharge, daily gauge and sediment load at the Champua gauging site of the Upper Baitarani river basin of India. Design/methodology/approach: Last few decades, the soft computing techniques based models have been successfully used in water resources modelling and estimation. In this study, the potential of tree based models are examined by developing and comparing sediment load prediction models, based on M5P tree and Random forest regression (RF). Several M5P and RF based models have been applied to a gauging site of the Baitarani River at Odisha, India. To evaluate the performance of the selected M5P and RF-based models, three most popular statistical parameters are selected such as coefficient of correlation, root mean square error and mean absolute error. Findings: A comparison of the results suggested that RF-based model could be applied successfully for the prediction of sediment load concentration with a relatively higher magnitude of prediction accuracy. In RF-based models Qt, Q(t-1), Q(t-2), S(t-1), S(t-2), Ht and H(t-1) combination based M10 model work superior than other combination based models. Another major outcome of this investigation is Qt, Q(t-1) and S(t-1) based model M4 works better than other input combination based models using M5P technique. The optimum input combination is Qt, Q(t-1) and S(t-1) for the prediction of sediment load concentration of the Baitarani River at Odisha, India. Research limitations/implications: The developed models were tested for Baitarani River at Odisha, India.
    Źródło:
    Journal of Achievements in Materials and Manufacturing Engineering; 2021, 104, 2; 57--76
    1734-8412
    Pojawia się w:
    Journal of Achievements in Materials and Manufacturing Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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