Enhancing rock fragmentation prediction in mining operations:A hybrid GWO-RF model with SHAP interpretability

In the mining industry,precise forecasting of rock fragmentation is critical for optimising blasting processes.In this study,we address the challenge of enhancing rock fragmentation assessment by developing a novel hybrid predictive model named GWO-RF.This model combines the grey wolf optimization(GWO)algorithm with the random forest(RF)technique to predict the D_(80)value,a critical parameter in evaluating rock fragmentation quality.The study is conducted using a dataset from Sarcheshmeh Copper Mine,employing six different swarm sizes for the GWO-RF hybrid model construction.The GWO-RF model’s hyperparameters are systematically optimized within established bounds,and its performance is rigorously evaluated using multiple evaluation metrics.The results show that the GWO-RF hybrid model has higher predictive skills,exceeding traditional models in terms of accuracy.Furthermore,the interpretability of the GWO-RF model is enhanced through the utilization of SHapley Additive exPlanations(SHAP)values.The insights gained from this research contribute to optimizing blasting operations and rock fragmentation outcomes in the mining industry.

Numerical assessment of the influence of former mining activities and plasticity of rock mass on deformations of terrain surface

This paper presents the results of numerical simulations carried out to confirm the influence of former mining activities on deformation of the mining terrain.The assessment of deformation changes was carried out with the use of FLAC3 D program based on the finite difference method.Numerical calculations were carried out for the example of actual mining operations in seams 703/1-2 and 707/2 of‘‘Marcel"Coal Mine.Taking into account the influence of the model’s plastic features and the so-called activation of a higher occurring seam in conducted simulations enabled obtaining a very good description of the measured subsidence.Based on the results one may state that numerical model can be used to assess the influence of former mining activities and the direction of conducted exploitation on deformations of the mining terrain.These factors are not recognized by geometric-integral theories commonly used for predicting the influence of mining operations on the surface.The results presented in this paper confirm that the applied method of simulating the phenomenon of reactivation of post-mining goafs is correct.

Statistical approach to determination of overhaul and maintenance cost of loading equipment in surface mining

The purpose of this research was to develop a new approach in determination of overhaul and maintenance cost of loading equipment in surface mining. Two statistical models including univariate exponential regression (UER) and multivariate linear regression (MLR) were used in this study. Loading equipment parameters such as bucket capacity, machine weight, engine power, boom length, digging depth, and dumping height were considered as variables. The results obtained by models and mean absolute error rate indicate that these models can be applied as the useful tool in determination of overhaul and maintenance cost of loading equipment. The results of this study can be used by the decision-makers for the specific surface mining operations.

黄河流域煤炭开发区地下水污染成因分析及防治建议

[研究目的]黄河流域内由于煤炭资源开发导致地下水污染严重,从整体角度对流域内重点煤炭开发区地下水进行归纳总结,为其可持续健康发展和地下水资源改善提供防治建议。[研究方法]从黄河流域战略地位出发,对地下水污染成因和污染模式进行归纳总结。采用改进后的综合水质指数法对流域内九大煤炭基地的地下水进行水质评价。选用主成分分析法赋权指标,并引入改进的内梅罗污染指数法,按照WPI分级法进行评价。[研究结果]通过对黄河流域各重点断面水质评价,得到目前流域内煤炭基地水质结果中Ⅲ、Ⅳ类占比较多,水质较差。分析成因当前流域内存在高矿化度矿井水、酸性矿井水和含特殊组分矿井水污染,同时阐述污染地下水形成的浅层和深层两种污染模式。[结论]对3种矿井水采用相应防治方法,并提出膏体充填技术和微生物修复技术2种防治技术建议,通过应用实际矿山为例,印证污染防治技术能够改善由煤炭开采引发的流域内地下水污染严重的问题,以期研究结果能够对成功推动中国绿色矿山的快速发展具有参考意义。

Possibilities of Increasing the Effectiveness of Mining Methane Drainage in Conditions of Low Permeability of Coal Seams

There is very low permeability of coal seams in Polish coal mines. For this reason, pre-mining methane drainage is conducted to a small extent, which rarely brings expected results. Methane emission from roof and floor sub-economic seams has the greatest share in total methane emission to workings. Effective CMM （coal mine methane） capture is used from goaf in advance or after mining. However, due to longwall mining and ventilation systems, it is not always possible to capture methane from strata. This paper presents a method of increasing the permeability of coal seams and a method of drilling boreholes towards goaf. Initial results of the effectiveness of methane capture after applying these methods are presented.

Predicting roof displacement of roadways in underground coal mines using adaptive neuro-fuzzy inference system optimized by various physics-based optimization algorithms

Due to the rapid industrialization and the development of the economy in each country,the demand for energy is increasing rapidly.The coal mines have to pace up the mining operations with large production to meet the energy demand.This requirement has led underground coal mines to go deeper with more difficult conditions,especially the mining hazards,such as large deformations,rockburst,coal burst,roof collapse,to name a few.Therefore,this study aims at investigating and predicting the stability of the roadways in underground coal mines exploited by longwall mining method,using various novel intelligent techniques based on physics-based optimization algorithms(i.e.multi-verse optimizer(MVO),equilibrium optimizer(EO),simulated annealing(SA),and Henry gas solubility optimization(HGSO)) and adaptive neuro-fuzzy inference system(ANFIS),named as MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSOANFIS models.Accordingly,162 roof displacement events were investigated based on the characteristics of surrounding rocks,such as cohesion,Young’s modulus,density,shear strength,angle of internal friction,uniaxial compressive strength,quench durability index,rock mass rating,and tensile strength.The MVO-ANFIS,EO-ANFIS,SA-ANFIS and HGSO-ANFIS models were then developed and evaluated based on this dataset for predicting roof displacements in roadways of underground mines.The results indicated that the proposed intelligent techniques could accurately predict the roof displacements in roadways of underground mines with an accuracy in the range of 83%-92%.Remarkably,the SA-ANFIS model yielded the most dominant accuracy(i.e.92%).Based on the accurate predictions from the proposed techniques,the reinforced solutions can be timely suggested to ensure the stability of roadways during exploiting coal,especially in the underground coal mines exploited by the longwall mining.

Simulation of clinker grinding circuits of cement plant based on process models calibrated using GA search method

Particle size distributions of obtained samples from several sampling campaigns were determined and raw data were mass balanced before being used in simulation studies.After determination of breakage function,selection function,Bond work index,residence time distribution parameters,and Whiten's model parameters for air separators and diaphragms between the two compartments of tube ball mills,performance of the circuits was simulated for given throughputs and feed particle size distribution.Whiten's model parameters were determined by GA(genetic algorithm) toolbox of MATLAB software.Based on implemented models for modeling and simulation,optimization of circuits was carried out.It increased nearly 10.5% and 15.8% in fresh feed capacity input to each tube ball mill.In addition,circulating load ratios of circuits are modified to 118% and 127% from low level of 57% and 22%,respectively,and also cut points of air separators are adjusted at 30 and 40 μm from high range of 53 and 97 μm,respectively.All applications helped in well-operation and energy consumption reduction of equipments.

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R^(2)),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

Crossing point temperature of coal

A further understanding of the self-heating of coal was obtained by investigating the crossing point temperature（CPT） of different ranks of coal.The tests were carried out using a self-designed experimental system for coal self-heating.50 g（±0.01 g） of coal particles ranging from 0.18 mm to 0.38 mm in size were put into a pure copper reaction vessel attached to the center of a temperature programmed enclosure.The temperature program increased the temperature at a rate of 0.8℃/min.Dry air was permitted to flow into the coal reaction vessel at different rates.The surrounding temperature and the coal temperature were monitored by a temperature logger.The results indicate that CPT is affected by coal rank,moisture,sulfur, and the experimental conditions.Higher ranked coals show higher CPT values.A high moisture content causes a delay phenomenon during the self-heating of the coal.Drying at 40℃decreases the effects of moisture.The reactivity of sulfur components in the coal is low under dry and low-temperature conditions. These components form a film that covers the coal surface and slightly inhibits the self-heating of the coal. The flow rate of dry air,and the heating rate of the surroundings,also affect the self-heating of the coal.The most appropriate experimental conditions for coal samples of a given weight and particle size were determined through contrastive analysis.Based on this analysis we propose that CPTs be determined under the same,or nearly the same conditions,for evaluation of the spontaneous combustion of coal.

Optimized functional linked neural network for predicting diaphragm wall deflection induced by braced excavations in clays

Deep excavation during the construction of underground systems can cause movement on the ground,especially in soft clay layers.At high levels,excessive ground movements can lead to severe damage to adjacent structures.In this study,finite element analyses(FEM)and the hardening small strain(HSS)model were performed to investigate the deflection of the diaphragm wall in the soft clay layer induced by braced excavations.Different geometric and mechanical properties of the wall were investigated to study the deflection behavior of the wall in soft clays.Accordingly,1090 hypothetical cases were surveyed and simulated based on the HSS model and FEM to evaluate the wall deflection behavior.The results were then used to develop an intelligent model for predicting wall deflection using the functional linked neural network(FLNN)with different functional expansions and activation functions.Although the FLNN is a novel approach to predict wall deflection;however,in order to improve the accuracy of the FLNN model in predicting wall deflection,three swarm-based optimization algorithms,such as artificial bee colony(ABC),Harris’s hawk’s optimization(HHO),and hunger games search(HGS),were hybridized to the FLNN model to generate three novel intelligent models,namely ABC-FLNN,HHO-FLNN,HGS-FLNN.The results of the hybrid models were then compared with the basic FLNN and MLP models.They revealed that FLNN is a good solution for predicting wall deflection,and the application of different functional expansions and activation functions has a significant effect on the outcome predictions of the wall deflection.It is remarkably interesting that the performance of the FLNN model was better than the MLP model with a mean absolute error(MAE)of 19.971,root-mean-squared error(RMSE)of 24.574,and determination coefficient(R^(2))of 0.878.Meanwhile,the performance of the MLP model only obtained an MAE of 20.321,RMSE of 27.091,and R^(2)of 0.851.Furthermore,the results also indicated that the proposed hybrid models,i.e.,ABC-FLNN,HHO-FLNN,HGS-FLNN,yielded more superior performances than those of the FLNN and MLP models in terms of the prediction of deflection behavior of diaphragm walls with an MAE in the range of 11.877 to 12.239,RMSE in the range of 15.821 to 16.045,and R^(2)in the range of 0.949 to 0.951.They can be used as an alternative tool to simulate diaphragm wall deflections under different conditions with a high degree of accuracy.

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines.To evaluate the quality of blasting,the size of rock distribution is used as a critical criterion in blasting operations.A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage.Therefore,this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters,as well as the efficiency of blasting operation in open mines.Accordingly,a nature-inspired algorithm(i.e.,firefly algorithm-FFA)and different machine learning algorithms(i.e.,gradient boosting machine(GBM),support vector machine(SVM),Gaussian process(GP),and artificial neural network(ANN))were combined for this aim,abbreviated as FFA-GBM,FFA-SVM,FFA-GP,and FFA-ANN,respectively.Subsequently,predicted results from the abovementioned models were compared with each other using three statistical indicators(e.g.,mean absolute error,root-mean-squared error,and correlation coefficient)and color intensity method.For developing and simulating the size of rock in blasting operations,136 blasting events with their images were collected and analyzed by the Split-Desktop software.In which,111 events were randomly selected for the development and optimization of the models.Subsequently,the remaining 25 blasting events were applied to confirm the accuracy of the proposed models.Herein,blast design parameters were regarded as input variables to predict the size of rock in blasting operations.Finally,the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting.Among the models developed in this study,FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks.The other techniques(i.e.,FFA-SVM,FFA-GP,and FFA-ANN)yielded lower computational stability and efficiency.Hence,the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.

Novel Soft ComputingModel for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models

This study considered and predicted blast-induced ground vibration(PPV)in open-pit mines using bagging and sibling techniques under the rigorous combination of machine learning algorithms.Accordingly,four machine learning algorithms,including support vector regression(SVR),extra trees(ExTree),K-nearest neighbors(KNN),and decision tree regression(DTR),were used as the base models for the purposes of combination and PPV initial prediction.The bagging regressor(BA)was then applied to combine these base models with the efforts of variance reduction,overfitting elimination,and generating more robust predictive models,abbreviated as BA-ExTree,BAKNN,BA-SVR,and BA-DTR.It is emphasized that the ExTree model has not been considered for predicting blastinduced ground vibration before,and the bagging of ExTree is an innovation aiming to improve the accuracy of the inherently ExTree model,as well.In addition,two empirical models(i.e.,USBM and Ambraseys)were also treated and compared with the bagging models to gain a comprehensive assessment.With this aim,we collected 300 blasting events with different parameters at the Sin Quyen copper mine(Vietnam),and the produced PPV values were also measured.They were then compiled as the dataset to develop the PPV predictive models.The results revealed that the bagging models provided better performance than the empirical models,except for the BA-DTR model.Of those,the BA-ExTree is the best model with the highest accuracy(i.e.,88.8%).Whereas,the empirical models only provided the accuracy from 73.6%–76%.The details of comparisons and assessments were also presented in this study.

Introduction to the Special Issue on Soft Computing Techniques in Materials Science and Engineering

Soft computing(SC)refers to the ability of a digital computer or robot to perform functions that are normally associated with intelligent individuals,such as reasoning and problem-solving.An example of this would be a project aimed at creating systems capable of reasoning,discovering meaning,generalising,or learning from past experience.Science and engineering problems that are both non-linear and complex can be solved using these methodologies.It has been proven that these algorithms can be used to solve numerous real-world problems.The techniques outlined can be used to increase the accuracy of existing models/equations,or they can be used to propose a newmodel that can address the problem.

Relative floatability as a criterion for evaluating the separation performance of phosphate from iron

This work was aimed to study the relative floatability of phosphate flotation by means of kinetic analysis.The relative floatability is important to determine how selectively the phosphate is separated from its impurities. The effects of pulp pH, solid content, reagents dosage(depressant, collector and co-collector) and conditioning time were investigated on the ratio of the modified rate constant of phosphate to the modified rate constant of iron(relative floatability). The results showed that a large dosage of depressant associated with a low value of collector resulted in a better relative floatability. Increasing the co-collector dosage, conditioning time and pH increased the relative floatability up to a certain value and thereafter resulted in diminishing the relative floatability. Meanwhile, the results indicated that increment of solid concentration increased the relative floatability in range investigated. It was also found that that maximum relative floatability(16.05) could be obtained in pulp pH, 9.32, solid percentage, 30,depressant dosage, 440 g/t, collector dosage, 560 g/t, co-collector dosage, 84.63 g/t and conditioning time,9.43 min.

Proposing a novel geo-structural model for Torbat-e-Jam-Fariman plain(Northeast of Iran),based on Geomorphic indices calculation,conjugating the field evidences

There are various faults in northern and southern margins of Torbat-e-Jam-Fariman plain which show the probability of enormous earthquake in the future.In present study the geomorphic indices contain Asymmetry Function(Af),Sinuosity of mountain front(Smf),Valley floor index(Vf),Hypsometric index(Hi),Mean Axial slope of channel index(MASC)and Drainage Basin Shape(Bs),have been utilized to determine the relative tectonic activity index(IAT)to recognize,eventually,the geo-structural model of the study area.Faults and folds control the geo-structural activities of the study area,and the geomorphic indices are being affected in consequence of their activities.The intensity of these activities is different throughout the plain.There are many geomorphic evidences,related to active transform fault which are detectable all over the study area such as deviated rivers,quaternary sediments transformation,fault traces.Therefore,recognition of geo-structural model of the study area is extremely vital.Field study,then,approved the results of geomorphic indices calculation in determining the geo-structural model of the study area.Results depicted that the geostructural model of the study area is a kind of Horsetail splay form which is in accordance to the relative tectonic activity of the study area.Based on the above mentioned results it can be predicted that the splays are the trail of Neyshabour fault.

基于基因表达编程和粒子群优化鲁棒混合计算模型的深部地下矿井岩爆分类

在深部地下采矿中,岩爆因具有许多不利影响(如对人员、设备、隧道/地下矿山工作面和开采周期等的影响)而被视为不确定性风险。由于其不确定性的特征,对岩爆趋势的准确预测和分类具有一定难度,且已有研究成果较少。提出一种基于基因表达编程(GEP)和粒子群优化(PSO)的鲁棒混合计算模型GEP-PSO,用于预测和分类深部开口的岩爆趋势,提高了预测和分类的准确性。在建立GEP-PSO模型的过程中,评估GEP模型中不同数量的基因(1~4)和连接功能(例如,加法、提取、乘法和除法)。收集246次岩爆发生的地质和施工因素,用于建立岩爆分类的GEP-PSO模型;应用处理数据集缺失值的技术改进数据集的属性;用相关矩阵选取潜在输入参数的特征;建立13个混合GEP-PSO模型,得到各模型的精度。结果表明:在GEP结构中具有3个基因和乘法连接函数的GEP-PSO模型具有最高的准确度(80.49%)。将获得的最佳GEP-PSO模型的结果与基于相同数据集开发的各种已有模型进行比较,结果表明,选择的GEP-PSO模型结果优于已有模型,表明提出的GEP-PSO模型在岩爆等级的预测和分类方面的准确性显著提高,可以应用于深开挖工程中,以准确预测和评估岩爆敏感性。

Slope stability of increasing height and expanding capacity of south dumping site of Hesgoula coal mine:a case study

This paper is devoted to improve the containment capacity of the Hesgoula south dumping site.The general geology of the dumping site was obtained through geological surveys.Physico-mechanical properties of silty clay and bedrock layers that have a large impact on the stability of the dump were measured by direct shear tests and triaxial tests in laboratory.Then ultimate bearing capacity of the substrate were analyzed and calculated.This paper proposed three capacity expansion and increase plans and used GeoStudio software for comparison.Through computation of the stability of the dump site slope after capacity expansion and increase for each plan,the capacity expansion plan was determined.The capacity expansion and increase plan will solve the problem of the current insufficient containment capacity of the Hesgoula south dumping site,which is of great significance for saving mine transportation costs,improving work efficiency,and reducing grassland occupation.

A practical framework for performance-based reliability analysis of subway stations based on a faultestructure combined system

It is necessary to pay particular attention to the uncertainties that exist in an engineering problem to reduce the risk of seismic damage of infrastructures against natural hazards.Moreover,certain structural performance levels should be satisfied during strong earthquakes.However,these performance levels have been only well described for aboveground structures.This study investigates the main uncertainties involved in the performance-based seismic analysis of a multi-story subway station.More than 100 pulse-like and no pulse-like ground motions have been selected.In this regard,an effective framework is presented,based on a set of nonlinear static and dynamic analyses performed by OpenSees code.The probabilistic seismic demand models for computing the free-field shear strain of soil and racking ratio of structure are proposed.These models result in less variability compared with existing relations,and make it possible to evaluate a wider range of uncertainties through reliability analysis in Rtx software using the Monte Carlo sampling method.This work is performed for three different structural performance levels(denoted as PL1ePL3).It is demonstrated that the error terms related to the magnitude and location of earthquake excitations and also the corresponding attenuation relationships have been the most important parameters.Therefore,using a faultestructure model would be inevitable for the reliability analysis of subway stations.It is found that the higher performance level(i.e.PL3)has more sensitivity to random variables than the others.In this condition,the pulse-like ground motions have a major contribution to the vulnerability of subway stations.

Geological Hazards Occurred on the Road Connecting Vashlijvari-Lisi (M. Machavariani Street) in Tbilisi

The capital of Georgia—Tbilisi has a very convenient location and is a node of the transit corridor. Along with natural-geological conditions, its complexity is due to the rapid demographic growth of the city in a highly “sensitive” area of the geological environment and the pressure of high engineering and agricultural activities. In Tbilisi, it is observed almost all types of geological hazards, including landslide-gravitational, suffosion, debris/mudflows, river bank erosion and inundation were caused by groundwater. These hazards cause high damages to the residential houses and other infrastructure facilities. Most importantly and most tragically is that these kind of negative geological events are often accompanied by human casualties. The study discusses the geological processes developed in March 2021 in the corridor of the Vashlijvari-Lisi road (Machavariani Street). The information obtained from the study, reflects the triggering factors of the geological hazards, also damages caused by them, and provides recommendations for short-term and long-term protective measures that should ensure the sustainable operation of the road and other infrastructure facilities.

Utilizing of Methane from Polish Hard Coal Mines

The methane capturing and its utilization is of great importance for the safety in Polish coal mines. The present study deals with methods which are used to capture and utilize it in hard coal mines. It is of great importance to know that at present, ethane recovered in operating mines is obtained only through drainage systems whose implementation is enforced by health and safety regulations. Because of the fact that the amount of methane released in hard coal mines is expected to rise in years to come, as the methane content of coal seams increases with depth, heavy emphasis should be placed on methane recovery and the practical applications of the captured gas. If mine gas was officially recognised as a primary source for producing ＂environmentally friendly electricity＂, would open the perspectives of increasing methane utilization. In addition, the mining industry would gain an incentive to intensify methane recovery. It would be possible to carry out additional work focused on methane drainage from excavations which are not operated. Also, the costs of methane drainage could be included in the costs of energy production, which would undoubtedly have a positive effect on the profitability of mining companies.