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The Baltic Dry Index (BDI) is a robust indicator in the shipping sector in terms of global economic activities, future world trade, transport capacity, freight rates, ship demand, ship orders, etc. It is hard to forecast the BDI because of its high volatility and complexity. This paper proposes an artificial neural network (ANN) approach for BDI forecasting. Data from January 2010 to December 2016 are used to forecast the BDI. Three different ANN models are developed: (i) the past weekly observation of the BDI, (ii) the past two weekly observations of the BDI, and (iii) the past weekly observation of the BDI with crude oil price. While the performance parameters of these three models are close to each other, the most consistent model is found to be the second one. Results show that the ANN approach is a significant method for modeling and forecasting the BDI and proving its applicability. [ABSTRACT FROM AUTHOR]

In this study, the cyclic variability of a diesel engine using diesel fuel and butanol–diesel fuel blends is modeled using an artificial neural network (ANN) method. The engine was operated with ten different engine speeds and full load conditions using six different n-butanol–diesel fuel blends. The coefficient of variation (COV) of the indicated mean effective pressure (IMEP), which is a well-accepted evaluation method, was used to assess the cyclic variability for 100 sequential engine cycles. Results indicated that adding n-butanol to diesel fuel caused an increase. Moreover, the COV imep values exhibited a decreasing trend with an increase in the engine speed for each fuel. The experimental results were used to train the ANN. The ANN network was trained with Levenberg – Marquardt (LM) and Scaled Conjugate Gradient (SCG) algorithms. After training the ANN, it was found that the coefficient of determination (R 2 ) values were in the range of between 0.737 and 0.9677, the mean-absolute-percentage error (MAPE) values were smaller than 8.7 and the mean-square error values (MSE) were smaller than 0.042. The predictions of the developed ANN model showed reasonable consistency with the experimental results. [ABSTRACT FROM AUTHOR]