Measurement of densities q, viscosities g, and ultrasonic speeds u has been carried out for binary mixtures of N,N-diethylaniline (N,N-DEA) with acetophenone, cyclopentanone, cyclohexanone (CH), and 2-methylcyclohexanone (Me-CH) and their pure liquids at (303.15 and 308.15) K. These experimental data have been used to calculate the excess molar volume V E , deviation in ultrasonic velocity Du, deviation in isentropic compressibility Dj s , and deviation in viscosity Dg. The variation of these properties with composition of the mixtures suggests dipole-dipole interactions and charge-transfer complex formation between N,N-diethylaniline and dipolar ketones. The magnitude of the property is found to depend on the chain length of the ketones' molecule. The viscosity data have been correlated using three equations: Grunberg and Nissan (Nature 164:799-800, 1949), Katti and Chaudhri (J Chem Eng Data 9:442-443, 1964), and Hind et al. (Trans Faraday Soc 56:328-330, 1960). These results have been fitted to the Redlich-Kister polynomial using multiparametric nonlinear regression analysis to estimate the binary coefficients and standard errors.

Experimental data on density (ρ) and speed of sound (u) at (303.15, 308.15, and 308.15) K are presented for the binary mixtures of (thiolane-1,1-dioxide + butanone), (thiolane-1,1-dioxide + pentan-2-one), (thiolane-1,1-dioxide + pentan-3-one), and (thiolane-1,1-dioxide + 4-methyl-pentan-2-one). From this data, excess molar volume and deviation in isentropic compressibility have been calculated. The Redlich−Kister polynomial equation was fitted to the experimental data.

No abstract

Crude oil is a key commodity for the economy of any country. The changes in Crude oil prices are very complex and therefore, seem to be unpredictable. The effect of increasing price and its daily fluctuations affects not only the economies and financial markets but extends to reach individuals. This is because an increase in oil prices has a direct effect on petrol prices, in addition, it also affects the prices of other goods and services. Therefore, forecasting crude oil is a very important task to reduce the impact of price fluctuations, and help investors, hedgers, and individuals to make decisions when dealing with energy markets. However, one of the main challenges of the econometric models is to forecast such a seemingly unpredictable economic series. The econometric models have not been promising when used for forecasting, particularly in the case of complex series such as oil prices. Although linear and nonlinear time series models have performed much better job in forecasting oil prices, there is yet room for an improvement. If the data generating process is nonlinear, applying linear models could result in large forecast errors. Model specification in nonlinear modeling can also be very case dependent and time-consuming. This article proposes a novel technique for forecasting crude oil price based on Neural Networks. The study adopts the data on crude oil price of West Texas Intermediate (WTI). To evaluate the performance of the model, the study employs three measures, RMSE, MAE and MAPE. In this study, the forecasting accuracy of two models of neural networks, BPN and RNN are compared with the traditional econometric model. The results reveal that the proposed method outperforms the other in terms of forecast accuracy Keywords-Neural networks, root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE),Recurrent Neural Networks(RNN),Multi-Layer Perceptron (MLP),Oil Price and ARIMA.I.