Search Results (4)

Using explosive material to fragment rock masses is a common and economical method in surface mines. Nevertheless, this method can lead to some environmental problems in the surrounding regions. Flyrock is one of the most dangerous effects induced by blasting which needs to be estimated to reduce the potential risk of damage. In other words, the minimization of flyrock can lead to sustainability of surroundings environment in blasting sites. To this aim, the present study develops several new hybrid models for predicting flyrock. The proposed models were based on a cascaded forward neural network (CFNN) trained by the Levenberg–Marquardt algorithm (LMA), and also the combination of least squares support vector machine (LSSVM) and three optimization algorithms, i.e., gravitational search algorithm (GSA), whale optimization algorithm (WOA), and artificial bee colony (ABC). To construct the models, a database collected from three granite quarry sites, located in Malaysia, was applied. The prediction values were then checked and evaluated using some statistical criteria. The results revealed that all proposed models were acceptable in predicting the flyrock. Among them, the LSSVM-WOA was a more robust model than the others and predicted the flyrock values with a high degree of accuracy. Full article

Side orifices are commonly installed in the side of a main channel to spill or divert some of the flow from the source channel to lateral channels. The aim of the present study is the accurate estimation of the discharge coefficient for flow through triangular (Δ-shaped) side orifices by applying three data-driven models including support vector machine (SVM), least squares support vector machine (LSSVM) and least squares support vector machine improved by gravity search algorithm (LSSVM-GSA). The discharge coefficient was estimated by utilizing five dimensionless variables resulted from experimental data (570 runs). Five different scenarios were applied based on the input variables. The models were evaluated through several statistical indices and graphical charts. The results showed that all of the models could successfully estimate the discharge coefficient of Δ-shaped side orifices with adequate accuracy. However, the LSSVM-GSA produced the best performance for the input combination of all variables with the highest coefficients of determination (R²) and Nash–Sutcliffe efficiency (NSE), equal to 0.965 and 0.993, and the least root mean square error (RMSE) and mean absolute error (MAE), equal to 0.0099 and 0.0077, respectively. The LSSVM-GSA improved the RMSE of the SVM and LSSVM by 26% and 20% in estimating the discharge coefficient. Furthermore, the ratio of orifice crest height to orifice height (W/H) was identified as having the highest influence on the discharge coefficient of triangular side orifices among the various input variables. Full article

In this study, we solve the challenge of predicting oil recovery factor ($\mathrm{RF}$) in layered heterogeneous reservoirs after 1.5 pore volumes of water-, gas- or water-alternating-gas (WAG) injection. A dataset of ~2500 reservoir simulations is analyzed based on a Black Oil 2D Model with different combinations of reservoir heterogeneity, WAG hysteresis, gravity influence, mobility ratios and WAG ratios. In the first model MOD1, RF is correlated with one input (an effective WAG mobility ratio $M^{*}$). Good correlation (Pearson coefficient −0.94), but with scatter, motivated a second model MOD2 using eight input parameters: water–oil and gas–oil mobility ratios, water–oil and gas–oil gravity numbers, a reservoir heterogeneity factor, two hysteresis parameters and water fraction. The two mobility ratios exhibited the strongest correlation with RF (Pearson coefficient −0.57 for gas-oil and −0.48 for water-oil). LSSVM was applied in MOD2 and trained using different optimizers: PSO, GA, GWO and GSA. A physics-based adaptation of the dataset was proposed to properly handle the single-phase injection. A total of 70% of the data was used for training, 15% for validation and 15% for testing. GWO and PSO optimized the model equally well (R² = 0.9965 on the validation set), slightly better than GA and GSA (R² = 0.9963). The performance metrics for MOD1 in the total dataset were: RMSE = 0.050 and R² = 0.889; MOD2: RMSE = 0.0080 and R² = 0.998. WAG outperformed single-phase injection, in some cases with 0.3 units higher RF. The benefits of WAG increased with stronger hysteresis. The LSSVM model could be trained to be less dependent on hysteresis and the non-injected phase during single-phase injection. Full article

Wind power time series data always exhibits nonlinear and non-stationary features, making it very difficult to accurately predict. In this paper, a novel hybrid wind power time series prediction model, based on ensemble empirical mode decomposition-permutation entropy (EEMD-PE), the least squares support vector machine model (LSSVM), and gravitational search algorithm (GSA), is proposed to improve accuracy of ultra-short-term wind power forecasting. To process the data, original wind power series were decomposed by EEMD-PE techniques into a number of subsequences with obvious complexity differences. Then, a new heuristic GSA algorithm was utilized to optimize the parameters of the LSSVM. The optimized model was developed for wind power forecasting and improved regression prediction accuracy. The proposed model was validated with practical wind power generation data from the Hebei province, China. A comprehensive error metric analysis was carried out to compare the performance of our method with other approaches. The results showed that the proposed model enhanced forecasting performance compared to other benchmark models. Full article