The mining industry faces a number of challenges that promote the adoption of new technologies. Big data, which is driven by the accelerating progress of information and communication technology, is one of the promising technologies that can reshape the entire mining landscape. Despite numerous attempts to apply big data in the mining industry, fundamental problems of big data, especially big data management (BDM), in the mining industry persist. This paper aims to fill the gap by presenting the basics of BDM. This work provides a brief introduction to big data and BDM, and it discusses the challenges encountered by the mining industry to indicate the necessity of implementing big data. It also summarizes data sources in the mining industry and presents the potential benefits of big data to the mining industry. This work also envisions a future in which a global database project is established and big data is used together with other technologies (i.e., automation), supported by government policies and following international standards. This paper also outlines the precautions for the utilization of BDM in the mining industry.

Big data management in the mining industry

This study aims to identify the suitability of hybridizing the firefly algorithm (FA), genetic algorithm (GA), and particle swarm optimization (PSO) with two well-known data-driven models of support vector regression (SVR) and artificial neural network (ANN) to predict blast-induced ground vibration. Here, these combinations are abbreviated using FA–SVR, PSO–SVR, GA–SVR, FA–ANN, PSO–ANN, and GA–ANN models. In addition, a modified FA (MFA) combined with SVR model is also proposed in this study, namely, MFA–SVR. The feasibility of the proposed models is examined using a case study, located in Johor, Malaysia. Then, to provide an objective assessment of performances of the predictive models, their results were compared based on several well known and popular statistical criteria. According to the results, the MFA–SVR with the coefficient of determination (R2) of 0.984 and root mean square error (RMSE) of 0.614 was more accurate model to predict PPV than the PSO–SVR with R2 = 0.977 and RMSE = 0.725, the FA–SVR with R2 = 0.964 and RMSE = 0.923, the GA–SVR with R2 = 0.957 and RMSE = 1.016, the GA–ANN with R2 = 0.936 and RMSE = 1.252, the FA–ANN with R2 = 0.925 and RMSE = 1.368, and the PSO–ANN with R2 = 0.924 and RMSE = 1.366. Consequently, the MFA–SVR model can be sufficiently employed in estimating the ground vibration, and has the capacity to generalize.

A new design of evolutionary hybrid optimization of SVR model in predicting the blast-induced ground vibration

Recent developments in smart mining technology have enabled the production, collection, and sharing of a large amount of data in real time. Therefore, research employing machine learning (ML) that utilizes these data is being actively conducted in the mining industry. In this study, we reviewed 109 research papers, published over the past decade, that discuss ML techniques for mineral exploration, exploitation, and mine reclamation. Research trends, ML models, and evaluation methods primarily discussed in the 109 papers were systematically analyzed. The results demonstrated that ML studies have been actively conducted in the mining industry since 2018, mostly for mineral exploration. Among the ML models, support vector machine was utilized the most, followed by deep learning models. The ML models were evaluated mostly in terms of their root mean square error and coefficient of determination.

Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation

Flyrock is one of the most important environmental and hazardous issues in mine blasting, which can affect equipment and people, and may lead to fatal accidents. Therefore, prediction and minimization of this phenomenon are crucial to many rock removal projects. This study was organized in two sections. The first section is related to evaluation and selection of the most effective parameters of flyrock through the use of random forest technique. This resulted in the exclusion of “maximum charge per delay” from being used as input variable. The remaining input variables, i.e., hole diameter, hole depth, burden-to-spacing ratio, stemming, and powder factor, were utilized to develop the probabilistic prediction model using the Bayesian network (BN) technique. The learning and structure type of the BN model were maximum likelihood and tree augmented naive Bayes, respectively. Many perfect probabilities were observed in the BN model for flyrock occurrence. The hole diameters between 97.5 and 127.5 mm appeared in four perfect probability conditions, which show that this hole diameter range is the most influential parameter. In addition, the combination of hole diameter and hole depth yielded three perfect probability predictions, suggesting that this factor combination is also influential on flyrock distance prediction. The results of this study can be used for optimum design of blasting pattern parameters for flyrock prediction.

https://link.springer.com/content/pdf/10.1007/s11053-019-09611-4.pdf

Random Forest and Bayesian Network Techniques for Probabilistic Prediction of Flyrock Induced by Blasting in Quarry Sites

The geomechanical properties of rock, including shear strength (SS) and uniaxial compressive strength (UCS), are very important parameters in designing rock structures. To improve the accuracy of SS and UCS prediction, this study presented an evolving support vector regression (SVR) using Grey Wolf optimization (GWO). To examine the feasibility and applicability of the SVR-GWO model, the differential evolution (DE) and artificial bee colony (ABC) algorithms were also used. In other words, the SVR hyperparameters were tuned using the GWO, DE, and ABC algorithms. To implement the proposed models, a comprehensive database accessible in an open-source was used in this study. Finally, the comparative experiments such as root mean square error (RMSE) were conducted to show the superiority of the proposed models. The SVR-GWO model predicted the SS and UCS with RMSE of 0.460 and 3.208, respectively, while, the SVR-DE model predicted the SS and UCS with RMSE of 0.542 and 5.4, respectively. Furthermore, the SVR-ABC model predicted the SS and UCS with RMSE of 0.855 and 5.033, respectively. The aforementioned results clearly exhibited the applicability as well as the usability of the proposed SVR-GWO model in the prediction of both SS and UCS parameters. Accordingly, the SVR-GWO model can be also applied to solving various complex systems, especially in geotechnical and mining fields.

Evolving support vector regression using Grey Wolf optimization; forecasting the geomechanical properties of rock

Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.

ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting

The rapid development of industry keeps increasing the demand for energy. Coal, as the main energy source, has a huge level of consumption, resulting in the continuous generation of its combustion byproduct coal fly ash (CFA). The accumulated CFA will occupy a large amount of land, but also cause serious environmental pollution and personal injury, which makes the resource utilization of CFA gradually to be attached importance. However, given the variability of the amount of CFA generation, predicting it in advance is the basis to ensure effective disposal and rational utilization. In this study, CFA generation was taken as the target variable, three machine learning (ML) algorithms were used to construct the model, and four evaluation indices were used to evaluate its performance. The results showed that the DNN model with the R = 0.89, R2 = 0.77 on the testing set performed better than the traditional multiple linear regression equation and other ML algorithms, and the feasibility of DNN as the optimal model framework was demonstrated. Applying this model framework to the engineering field enables managers to identify the next step of the disposal method in advance, so as to rationally allocate ways of recycling and utilization to maximize the use and sales benefits of CFA while minimizing its disposal costs. In addition, sensitivity analysis further explains ML’s internal decisions and verifies that coal consumption is more important than installed capacity, which provides a certain reference for ensuring the rational utilization of CFA.

/pdf/comparison-and-determination-of-optimal-machine-learning-13me013h.pdf

Comparison and Determination of Optimal Machine Learning Model for Predicting Generation of Coal Fly Ash

Blood glucose (BG) control is important for diabetics to avoid hyperglycemia and hypoglycemia events in their daily life. And the forecasting of BG becomes one of the most important sections of the blood glucose control system. Accurate prediction makes patients can react to the abnormal values in advance so that precisely controlling their BG level can be realized. In this study, we propose a novel method to predict BG levels for 30 min of prediction horizon (PH) with a hybrid deep learning model, which integrates multi-layer perceptron, stacked bidirectional gated recurrent unit (Bi-GRU) based recurrent neural network (RNN), and the attention mechanism (AM). Firstly, after pre-processing, the real patient data (in this work, the history BG only) as the input is transmitted to the network used for training and testing. Subsequently, the stacked Bi-GRU layers are used for extracting temporal characteristics of time sequence data, the AM is used for paying more attention to the temporal information which has higher relevance with outputs by assigning weights and then multi-layer perceptron for rapid gradient descent and calculating the regression output of our neural network (in this work, the predict BG). Finally, some important criteria of predictive evaluation are used to demonstrate the effect of our method.

A Hybrid Deep Learning Model for the Blood Glucose Prediction

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ORELM: A Novel Machine Learning Approach for Prediction of Flyrock in Mine Blasting

Comparison and Determination of Optimal Machine Learning Model for Predicting Generation of Coal Fly Ash

A Hybrid Deep Learning Model for the Blood Glucose Prediction