scholarly journals Application of Cascade Forward Backpropagation Neural Networks for Selecting Mining Methods

2022 ◽  
Vol 14 (2) ◽  
pp. 635
Author(s):  
Ahmed M. A. Shohda ◽  
Mahrous A. M. Ali ◽  
Gaofeng Ren ◽  
Jong-Gwan Kim ◽  
Mohamed Abd-El-Hakeem Mohamed
Keyword(s):  
Neural Networks ◽  
Experimental Testing ◽  
Hanging Wall ◽  
Computer Applications ◽  
Foot Wall ◽  
Mining Method ◽  
Mining Operations ◽  
Backpropagation Neural Networks ◽  
Mining Methods ◽  
Selection Of

Decision-making is very important in many fields, such as mining engineering. In addition, there has been a growth of computer applications in all fields, especially mining operations. One of these application fields is mine design and the selection of suitable mining methods, and computer applications can help mine engineers to decide upon and choose more satisfactory methods. The selection of mining methods depends on the rock-layer specification. All rock characteristics should be classified in terms of technical and economic concerns related to mining rock specifications, such as mechanical and physical properties, and evaluated according to their weights and ratings. Methodologically, in this study, the criteria considered in the University of British Columbia (UBC) method were used as references to establish general criteria. These criteria consist of general shape, ore thickness, ore plunge, and grade distribution, in addition to the rock quality designation (ore zone, hanging wall, and foot wall) and rock substance strength (ore zone, hanging wall, and foot wall). The technique for order of preference by similarity to ideal solution (TOPSIS) was adopted, and an improved TOPSIS method was developed based on experimental testing and checked by means of the application of cascade forward backpropagation neural networks in mining method selection. The results provide indicators that decision makers can use to choose between different mining methods based on the total points given to all ore properties. The best mining method is cut and fill stopping, with a rank of 0.70, and the second is top slicing, with a rank of 0.67.

Hierarchical fuzzy-AHP-based multi-criteria decision making approach for selection of underground metal mining method

2021 ◽  
Vol 15 (3) ◽  
pp. 405-420
Author(s):  
Bhanu Chander Balusa ◽  
Amit Kumar Gorai
Keyword(s):  
Decision Making ◽  
Underground Mining ◽  
Fuzzy Ahp ◽  
Hanging Wall ◽  
Ore Deposits ◽  
Mining Method ◽  
Metal Mining ◽  
Extrinsic Factors ◽  
Ore Deposit ◽  
Mining Methods

In the last few decades, many underground mining methods were proposed for extractions of ores. The decision-making for selecting the most suitable mining method for a typical ore depost depnds on various intrinsic and extrinsic factors (intrinsic – dip, shape, thickness, depth, grade distribution, RMR (rock mass rating) and RSS (rock substance strength) of ore, hanging wall, footwall, and extrinsic – recovery, dilution, safety, productivity, flexibility, capital). The present study aims to develop a hierarchical Fuzzy-AHP (FAHP) model for choosing the most suitable underground mining method for an ore deposit. The structure of the proposed hierarchical FAHP model consists of five levels. The level-1 of the hierarchy defines two variables (intrinsic factors and extrinsic factors). These are further classified into quantitative or qualitative nature of variable (listed in level-2). The criteria, sub-criteria, and mining method variables are listed respectively in Level 3, Level 4, and Level 5. For each level of the hierarchy, a fuzzy pair-wise comparison matrices are developed using the corresponding levels’ listed variables. These matrices at each level are subsequently used to determine the local and global weights of each variable. The global weights are used for prioritizing the different mining methods. The proposed hierarchical FAHP model was validated by considering the field data of two different ore deposits in India. The results showed that the most appropriate mining method predicted from the decision-making model and the adopted mining method for extracting the ore deposit are same in two case studied mines.

scholarly journals Application of Fuzzy Analytic Hierarchy Process to Underground Mining Method Selection

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 192 ◽  
Author(s):  
Sanja Bajić ◽  
Dragoljub Bajić ◽  
Branko Gluščević ◽  
Vesna Ristić Vakanjac
Keyword(s):  
Decision Making ◽  
Expert Knowledge ◽  
Underground Mining ◽  
Optimal Solution ◽  
Multiple Criteria ◽  
Final Decision ◽  
Mining Method ◽  
Multiple Criteria Optimization ◽  
Mining Methods ◽  
Selection Of

The paper proposes a problem-solving approach in the area of underground mining, related to the evaluation and selection of the optimal mining method, employing fuzzy multiple-criteria optimization. The application of fuzzy logic to decision-making in multiple-criteria optimization is particularly useful in cases where not enough information is available about a given system, and where expert knowledge and experience are an important aspect. With a straightforward objective, multiple-criteria decision-making is used to rank various mining methods relative to a set of criteria and to select the optimal solution. The considered mining methods represent possible alternatives. In addition, various criteria and subcriteria that influence the selection of the best available solution are defined and analyzed. The final decision concerning the selection of the optimal mining method is made based on mathematical optimization calculations. The paper demonstrates the proposed approach as applied in a case study.

scholarly journals Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1213
Author(s):  
Ahmed Aljanad ◽  
Nadia M. L. Tan ◽  
Vassilios G. Agelidis ◽  
Hussain Shareef
Keyword(s):  
Neural Networks ◽  
Particle Swarm Optimization ◽  
Solar Irradiance ◽  
Particle Swarm ◽  
Time Interval ◽  
Time Intervals ◽  
Swarm Optimization ◽  
Backpropagation Neural Networks ◽  
Proposed Model ◽  
Short Time

Hourly global solar irradiance (GSR) data are required for sizing, planning, and modeling of solar photovoltaic farms. However, operating and controlling such farms exposed to varying environmental conditions, such as fast passing clouds, necessitates GSR data to be available for very short time intervals. Classical backpropagation neural networks do not perform satisfactorily when predicting parameters within short intervals. This paper proposes a hybrid backpropagation neural networks based on particle swarm optimization. The particle swarm algorithm is used as an optimization algorithm within the backpropagation neural networks to optimize the number of hidden layers and neurons used and its learning rate. The proposed model can be used as a reliable model in predicting changes in the solar irradiance during short time interval in tropical regions such as Malaysia and other regions. Actual global solar irradiance data of 5-s and 1-min intervals, recorded by weather stations, are applied to train and test the proposed algorithm. Moreover, to ensure the adaptability and robustness of the proposed technique, two different cases are evaluated using 1-day and 3-days profiles, for two different time intervals of 1-min and 5-s each. A set of statistical error indices have been introduced to evaluate the performance of the proposed algorithm. From the results obtained, the 3-days profile’s performance evaluation of the BPNN-PSO are 1.7078 of RMSE, 0.7537 of MAE, 0.0292 of MSE, and 31.4348 of MAPE (%), at 5-s time interval, where the obtained results of 1-min interval are 0.6566 of RMSE, 0.2754 of MAE, 0.0043 of MSE, and 1.4732 of MAPE (%). The results revealed that proposed model outperformed the standalone backpropagation neural networks method in predicting global solar irradiance values for extremely short-time intervals. In addition to that, the proposed model exhibited high level of predictability compared to other existing models.

scholarly journals A New Hyper-Parameter Optimization Method for Power Load Forecast Based on Recurrent Neural Networks

Algorithms ◽  
2021 ◽  
Vol 14 (6) ◽  
pp. 163
Author(s):  
Yaru Li ◽  
Yulai Zhang ◽  
Yongping Cai
Keyword(s):  
Neural Networks ◽  
Parameter Optimization ◽  
Recurrent Neural Networks ◽  
Critical Role ◽  
Optimization Method ◽  
Bayesian Optimization ◽  
Power Load ◽  
Gradient Calculation ◽  
Truncated Newton ◽  
Selection Of

The selection of the hyper-parameters plays a critical role in the task of prediction based on the recurrent neural networks (RNN). Traditionally, the hyper-parameters of the machine learning models are selected by simulations as well as human experiences. In recent years, multiple algorithms based on Bayesian optimization (BO) are developed to determine the optimal values of the hyper-parameters. In most of these methods, gradients are required to be calculated. In this work, the particle swarm optimization (PSO) is used under the BO framework to develop a new method for hyper-parameter optimization. The proposed algorithm (BO-PSO) is free of gradient calculation and the particles can be optimized in parallel naturally. So the computational complexity can be effectively reduced which means better hyper-parameters can be obtained under the same amount of calculation. Experiments are done on real world power load data,where the proposed method outperforms the existing state-of-the-art algorithms,BO with limit-BFGS-bound (BO-L-BFGS-B) and BO with truncated-newton (BO-TNC),in terms of the prediction accuracy. The errors of the prediction result in different models show that BO-PSO is an effective hyper-parameter optimization method.

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