Artificial neural network procedures were used to predict the combustible value （i.e. 100-Ash） and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation...Artificial neural network procedures were used to predict the combustible value （i.e. 100-Ash） and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of collector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.展开更多
In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occu...In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.展开更多
According to the nonlinear and time dependent features of the ventilation systems for coal mines, a neural network method is applied to control the ventilator for coal mines in real time. The technical processes of co...According to the nonlinear and time dependent features of the ventilation systems for coal mines, a neural network method is applied to control the ventilator for coal mines in real time. The technical processes of coal mine ventilation system are introduced, and the principle of controlling a ventilation fan is also explained in detail. The artificial neutral network method is used to calculate the wind quantity needed by work spots in coal mine on the basis of the data collected by the system, including ventilation conditions, environmental temperatures, humidity, coal dust and the contents of all kinds of poisonous and harmful gases. Then the speed of ventilation fan is controlled according to the required wind which is determined by an overall integration of data. A neural network method is presented for overall optimized solution or the genetic algorithm of simulated annealing.展开更多
Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizati...Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.展开更多
Forecast is very important for preventing and controlling the disaster of spontaneous combustion （sponcom）. Gaseous products of coal, such as carbon monoxide, ethylene, propane and hydrogen, are commonly used as ind...Forecast is very important for preventing and controlling the disaster of spontaneous combustion （sponcom）. Gaseous products of coal, such as carbon monoxide, ethylene, propane and hydrogen, are commonly used as indicators to reflect its status quo of sponcom in coal mines. Nevertheless, since the corresponding relationship between the temperature and the indicators is non-linear and can＇t be depicted with simple mathematical formula, it is very difficult to diagnose and forecast coal sponcom by monitoring indicator gases＇ distribution. A forward feeding 3-layer artificial neural network （ANN） model is employed to express the corresponding relation between temperature and index gases of coal sponcom more accurately. A large amount of data from programmed temperature oxidation experiments were employed to train the network to gain the connection strength between nerve cells and to accomplish the model. It proved in real coal productions that the ANN model can forecast coal sponcom accurately.展开更多
It always adopts the direct hierarchy analysis to value the exploitation conditions of surface mining areas. This way has some unavoidable shortcomings because it is mainly under the aid of experts and it is affected ...It always adopts the direct hierarchy analysis to value the exploitation conditions of surface mining areas. This way has some unavoidable shortcomings because it is mainly under the aid of experts and it is affected by the subjective thinking of the experts. This paper puts forwards a new approach that divides the whole exploitation conditions into sixteen subsidiary systems and each subsidiary system forms a neural network system. The whole decision system of exploitation conditions of surface mining areas is composed of sixteen subsidiary neural network systems. Each neural network is practiced with the data of the worksite, which is reasonable and scientific. This way will be a new decision approach for exploiting the surface mining areas.展开更多
A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, ro...A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.展开更多
In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in ...In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.展开更多
In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Ba...In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.展开更多
This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows t...This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows that the set up neural network model has high accuracy and decision making benefit.展开更多
This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the st...This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the structure of the three\|layer neural network, its learning process, its operating algorithm to realize the evaluation of mine design schemes in a computer and a practical example is also involved in it.展开更多
Blasting is the live wire of mining and its operations,with air overpressure(AOp)recognised as an end product of blasting.AOp is known to be one of the most important environmental hazards of mining.Further research i...Blasting is the live wire of mining and its operations,with air overpressure(AOp)recognised as an end product of blasting.AOp is known to be one of the most important environmental hazards of mining.Further research in this area of mining is required to help improve on safety of the working environment.Review of previous studies has shown that many empirical and artificial intelligence(AI)methods have been proposed as a forecasting model.As an alternative to the previous methods,this study proposes a new class of advanced artificial neural network known as brain inspired emotional neural network(BIENN)to predict AOp.The proposed BI-ENN approach is compared with two classical AOp predictors(generalised predictor and McKenzie formula)and three established AI methods of backpropagation neural network(BPNN),group method of data handling(GMDH),and support vector machine(SVM).From the analysis of the results,BI-ENN is the best by achieving the least RMSE,MAPE,NRMSE and highest R,VAF and PI values of 1.0941,0.8339%,0.1243%,0.8249,68.0512%and 1.2367 respectively and thus can be used for monitoring and controlling AOp.展开更多
In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of...In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.展开更多
The neural network with multi hierarchic structure is provided in this paper to evaluate sustainable development of the coal mines based on analyzing its effect factors. The whole evaluating system is composed of 5 ne...The neural network with multi hierarchic structure is provided in this paper to evaluate sustainable development of the coal mines based on analyzing its effect factors. The whole evaluating system is composed of 5 neural networks.The feasibility of this method has been proved by case study. This study will provide a scientfic and theoretic foundation for evaluating the sustainable development of coal mines.展开更多
Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used ...Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.展开更多
Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has chal...Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.展开更多
Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challe...Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.展开更多
The major factors influencing subsidence factor were comprehensively analyzed. Then the artificial neural network model for calculating subsidence factor was set up with the theory of artificial neural network （ANN）...The major factors influencing subsidence factor were comprehensively analyzed. Then the artificial neural network model for calculating subsidence factor was set up with the theory of artificial neural network （ANN）. A large amount of data from observation stations in China was collected and used as learning and training samples to train and test the artificial neural network model. The calculated results of the ANN model and the observed values were compared and analyzed in this paper. The results demonstrate that many factors can be considered in this model and the result is more precise and closer to observed values to calculate the subsidence factor by the ANN model. It can satisfy the need of engineering.展开更多
A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improv...A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.展开更多
Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in th...Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted.The implementation process included calculating the well log parameters,stratum contrasting the seismic data and the well logs,and extracting,studying and predicting seismic attributes.Seismic inversion parameters,including the layer velocity and wave impedance,were calculated and effectively used for prediction and analysis.Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure.This enhanced the stability of the inversion method.Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the water-bearing/resisting layer in the Quaternary system.Interpretation of the 1～2 m thin coal seams,and also of the water-bearing/resisting layer in the Quaternary system,is provided.The upper mining limit can be lifted from 60 m to 45 m.The predictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit,which is one of the principles of green mining.展开更多
文摘Artificial neural network procedures were used to predict the combustible value （i.e. 100-Ash） and combustible recovery of coal flotation concentrate in different operational conditions. The pulp density,pH,rotation rate,coal particle size,dosage of collector,frother and conditioner were used as inputs to the network. Feed-forward artificial neural networks with 5-30-2-1 and 7-10-3-1 arrangements were capable to estimate the combustible value and combustible recovery of coal flotation concentrate respectively as the outputs. Quite satisfactory correlations of 1 and 0.91 in training and testing stages for combustible value and of 1 and 0.95 in training and testing stages for combustible recovery prediction were achieved. The proposed neural network models can be used to determine the most advantageous operational conditions for the expected concentrate assay and recovery in the coal flotation process.
基金Dr. Steve Jones, Scientific Advisor of the Canon Foundation for Scientific Research (7200 The Quorum, Oxford Business Park, Oxford OX4 2JZ, England). Canon Foundation for Scientific Research funded the UPC 2013 tuition fees of the corresponding author during her writing this article
文摘In computational physics proton transfer phenomena could be viewed as pattern classification problems based on a set of input features allowing classification of the proton motion into two categories： transfer ＇occurred＇ and transfer ＇not occurred＇. The goal of this paper is to evaluate the use of artificial neural networks in the classification of proton transfer events, based on the feed-forward back propagation neural network, used as a classifier to distinguish between the two transfer cases. In this paper, we use a new developed data mining and pattern recognition tool for automating, controlling, and drawing charts of the output data of an Empirical Valence Bond existing code. The study analyzes the need for pattern recognition in aqueous proton transfer processes and how the learning approach in error back propagation （multilayer perceptron algorithms） could be satisfactorily employed in the present case. We present a tool for pattern recognition and validate the code including a real physical case study. The results of applying the artificial neural networks methodology to crowd patterns based upon selected physical properties （e.g., temperature, density） show the abilities of the network to learn proton transfer patterns corresponding to properties of the aqueous environments, which is in turn proved to be fully compatible with previous proton transfer studies.
文摘According to the nonlinear and time dependent features of the ventilation systems for coal mines, a neural network method is applied to control the ventilator for coal mines in real time. The technical processes of coal mine ventilation system are introduced, and the principle of controlling a ventilation fan is also explained in detail. The artificial neutral network method is used to calculate the wind quantity needed by work spots in coal mine on the basis of the data collected by the system, including ventilation conditions, environmental temperatures, humidity, coal dust and the contents of all kinds of poisonous and harmful gases. Then the speed of ventilation fan is controlled according to the required wind which is determined by an overall integration of data. A neural network method is presented for overall optimized solution or the genetic algorithm of simulated annealing.
文摘Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.
基金Supported by the National Natural Science Foundation of China （10972178）
文摘Forecast is very important for preventing and controlling the disaster of spontaneous combustion （sponcom）. Gaseous products of coal, such as carbon monoxide, ethylene, propane and hydrogen, are commonly used as indicators to reflect its status quo of sponcom in coal mines. Nevertheless, since the corresponding relationship between the temperature and the indicators is non-linear and can＇t be depicted with simple mathematical formula, it is very difficult to diagnose and forecast coal sponcom by monitoring indicator gases＇ distribution. A forward feeding 3-layer artificial neural network （ANN） model is employed to express the corresponding relation between temperature and index gases of coal sponcom more accurately. A large amount of data from programmed temperature oxidation experiments were employed to train the network to gain the connection strength between nerve cells and to accomplish the model. It proved in real coal productions that the ANN model can forecast coal sponcom accurately.
文摘It always adopts the direct hierarchy analysis to value the exploitation conditions of surface mining areas. This way has some unavoidable shortcomings because it is mainly under the aid of experts and it is affected by the subjective thinking of the experts. This paper puts forwards a new approach that divides the whole exploitation conditions into sixteen subsidiary systems and each subsidiary system forms a neural network system. The whole decision system of exploitation conditions of surface mining areas is composed of sixteen subsidiary neural network systems. Each neural network is practiced with the data of the worksite, which is reasonable and scientific. This way will be a new decision approach for exploiting the surface mining areas.
文摘A pplication o f m echanical excavators is one o f th e m o st com m only used excavation m eth o d s because itcan bring th e p ro ject m ore productivity, accuracy and safety. A m ong th e m echanical excavators, roadhead ers are m echanical m iners w h ich have b een extensively u se d in tu n n elin g , m ining an d civil indu stries. Perform ance pred ictio n is an im p o rta n t issue for successful ro a d h e a d e r application andgenerally deals w ith m achine selection, p ro d u ctio n rate an d b it consu m p tio n . The m ain aim o f thisresearch is to investigate th e c u ttin g p erfo rm an ce （in stan tan eo u s c u ttin g rates （ICRs）） o f m ed iu m -d u tyro ad h ead ers by using artificial neural n etw o rk （ANN） approach. T here are d ifferent categories forANNs, b u t based o n train in g alg o rith m th e re are tw o m ain k in d s： supervised and u n su p erv ised . Them u lti-lay er p ercep tro n （MLP） an d K ohonen self-organizing feature m ap （KSOFM） are th e m o st w idelyused neu ral netw o rk s for supervised an d u n su p erv ised ones, respectively. For gaining this goal, ad atab ase w as prim arily provided from ro ad h e a d e rs＇ p erfo rm an ce an d geom echanical characteristics o frock form ations in tu n n els and d rift galleries in Tabas coal m ine, th e larg est an d th e only fullymech an ized coal m ine in Iran. T hen th e datab ase w as analyzed in o rd e r to yield th e m ost im p o rtan tfactor for ICR by using relatively im p o rta n t factor in w hich G arson eq u atio n w as utilized. The MLPn etw o rk w as train ed by 3 in p u t p ara m e te rs including rock m ass pro p erties, rock quality d esignation（RQD）, in tact rock p ro p erties such as uniaxial com pressive stre n g th （UCS） an d Brazilian ten sile stren g th（BTS）, and o n e o u tp u t p a ra m e te r （ICR）. In o rd e r to have m ore v alidation o n MLP o u tp u ts, KSOFM visualizationw as applied. The m ean square e rro r （MSE） an d regression coefficient （R ） o f MLP w e re found tobe 5.49 an d 0.97, respectively. M oreover, KSOFM n etw o rk has a m ap size o f 8 x 5 and final qu an tizatio nan d topographic erro rs w e re 0.383 an d 0.032, respectively. The results show th a t MLP neural n etw orkshave a strong capability to p red ict an d ev alu ate th e perfo rm an ce o f m ed iu m -d u ty ro ad h ead ers in coalm easu re rocks. Furtherm ore, it is concluded th a t KSOFM neural n etw o rk is an efficient w ay for u n d e rstand in g system beh av io r an d know ledge extraction. Finally, it is indicated th a t UCS has m ore influenceo n ICR b y applying th e b e st train ed MLP n etw o rk w eig h ts in G arson eq u atio n w h ich is also confirm ed byKSOFM.
基金Supported by the Science Foundation of the Liaoning Province(2004C011)
文摘In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limi-tations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advan-tages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With op-timization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
基金Projects 40771143 supported by the National Natural Science Foundation of China2007AA12Z162 by the Hi-tech Research and Development Program of China
文摘In this study,analyses are conducted on the information features of a construction site,a cornfield and subsidence seeper land in a coal mining area with a synthetic aperture radar (SAR) image of medium resolution. Based on features of land cover of the coal mining area,on texture feature extraction and a selection method of a gray-level co-occurrence matrix (GLCM) of the SAR image,we propose in this study that the optimum window size for computing the GLCM is an appropriate sized window that can effectively distinguish different types of land cover. Next,a band combination was carried out over the text feature images and the band-filtered SAR image to secure a new multi-band image. After the transformation of the new image with principal component analysis,a classification is conducted selectively on three principal component bands with the most information. Finally,through training and experimenting with the samples,a better three-layered BP neural network was established to classify the SAR image. The results show that,assisted by texture information,the neural network classification improved the accuracy of SAR image classification by 14.6%,compared with a classification by maximum likelihood estimation without texture information.
基金National Natural Science Foundation of China( 5 97340 90 )
文摘This paper mainly discusses the selection of the technical parameters of fully mechanized top coal caving mining using the neural network technique. The comparison between computing results and experiment data shows that the set up neural network model has high accuracy and decision making benefit.
文摘This paper is about the application of ANN (artificial neural networks) theory in evaluation of mine design schemes and a quantified evaluation method based on a three\|layer neural network is given. It studies the structure of the three\|layer neural network, its learning process, its operating algorithm to realize the evaluation of mine design schemes in a computer and a practical example is also involved in it.
基金This work was supported by the Ghana National Petroleum Corporation(GNPC)through the GNPC Professorial Chair in Mining Engineering at the University of Mines and Technology(UMaT),GhanaThe authors thank the Ghana National Petroleum Corporation(GNPC)for providing funding to support this work through the GNPC Professorial Chair in Mining Engineering at the University of Mines and Technology(UMaT),Ghana.
文摘Blasting is the live wire of mining and its operations,with air overpressure(AOp)recognised as an end product of blasting.AOp is known to be one of the most important environmental hazards of mining.Further research in this area of mining is required to help improve on safety of the working environment.Review of previous studies has shown that many empirical and artificial intelligence(AI)methods have been proposed as a forecasting model.As an alternative to the previous methods,this study proposes a new class of advanced artificial neural network known as brain inspired emotional neural network(BIENN)to predict AOp.The proposed BI-ENN approach is compared with two classical AOp predictors(generalised predictor and McKenzie formula)and three established AI methods of backpropagation neural network(BPNN),group method of data handling(GMDH),and support vector machine(SVM).From the analysis of the results,BI-ENN is the best by achieving the least RMSE,MAPE,NRMSE and highest R,VAF and PI values of 1.0941,0.8339%,0.1243%,0.8249,68.0512%and 1.2367 respectively and thus can be used for monitoring and controlling AOp.
基金Project(107021) supported by the Key Foundation of Chinese Ministry of Education Project(2009643013) supported by China Scholarship Fund
文摘In order to accurately and quickly identify the safety status pattern of coalmines,a new safety status pattern recognition method based on the extension neural network (ENN) was proposed,and the design of structure of network,the rationale of recognition algorithm and the performance of proposed method were discussed in detail.The safety status pattern recognition problem of coalmines can be regard as a classification problem whose features are defined in a range,so using the ENN is most appropriate for this problem.The ENN-based recognition method can use a novel extension distance to measure the similarity between the object to be recognized and the class centers.To demonstrate the effectiveness of the proposed method,a real-world application on the geological safety status pattern recognition of coalmines was tested.Comparative experiments with existing method and other traditional ANN-based methods were conducted.The experimental results show that the proposed ENN-based recognition method can identify the safety status pattern of coalmines accurately with shorter learning time and simpler structure.The experimental results also confirm that the proposed method has a better performance in recognition accuracy,generalization ability and fault-tolerant ability,which are very useful in recognizing the safety status pattern in the process of coal production.
文摘The neural network with multi hierarchic structure is provided in this paper to evaluate sustainable development of the coal mines based on analyzing its effect factors. The whole evaluating system is composed of 5 neural networks.The feasibility of this method has been proved by case study. This study will provide a scientfic and theoretic foundation for evaluating the sustainable development of coal mines.
文摘Based on the neural network technique, this paper proposes a BP neural network model which integrates geological factors which affect top coal caving in a comprehensive index. The index of top coal caving may be used to forecast the mining cost of working faces, which shows the model’s potential prospect of applications.
文摘Frequency and scale of the blasting events are increasing to boost limestone production. Mines areapproaching close to inhabited areas due to growing population and limited availability of land resourceswhich has challenged the management to go for safe blasts with special reference to opencast mining.The study aims to predict the distance covered by the flyrock induced by blasting using artificial neuralnetwork （ANN） and multi-variate regression analysis （MVRA） for better assessment. Blast design andgeotechnical parameters, such as linear charge concentration, burden, stemming length, specific charge,unconfined compressive strength （UCS）, and rock quality designation （RQD）, have been selected as inputparameters and flyrock distance used as output parameter. ANN has been trained using 95 datasets ofexperimental blasts conducted in 4 opencast limestone mines in India. Thirty datasets have been used fortesting and validation of trained neural network. Flyrock distances have been predicted by ANN, MVRA,as well as further calculated using motion analysis of flyrock projectiles and compared with the observeddata. Back propagation neural network （BPNN） has been proven to be a superior predictive tool whencompared with MVRA. 2014 Institute of Rock and Soil Mechanics, Chinese Academy of Sciences. Production and hosting byElsevier B.V. All rights reserved.
基金This work was supported by the Pilot Seed Grant(Grant No.RES0049944)the Collaborative Research Project(Grant No.RES0043251)from the University of Alberta.
文摘Ore production is usually affected by multiple influencing inputs at open-pit mines.Nevertheless,the complex nonlinear relationships between these inputs and ore production remain unclear.This becomes even more challenging when training data(e.g.truck haulage information and weather conditions)are massive.In machine learning(ML)algorithms,deep neural network(DNN)is a superior method for processing nonlinear and massive data by adjusting the amount of neurons and hidden layers.This study adopted DNN to forecast ore production using truck haulage information and weather conditions at open-pit mines as training data.Before the prediction models were built,principal component analysis(PCA)was employed to reduce the data dimensionality and eliminate the multicollinearity among highly correlated input variables.To verify the superiority of DNN,three ANNs containing only one hidden layer and six traditional ML models were established as benchmark models.The DNN model with multiple hidden layers performed better than the ANN models with a single hidden layer.The DNN model outperformed the extensively applied benchmark models in predicting ore production.This can provide engineers and researchers with an accurate method to forecast ore production,which helps make sound budgetary decisions and mine planning at open-pit mines.
基金Supported by the 0utstanding Youth Science Foundation of Henan Province（0612002100） Science and Technology Department of Henan Province （072102290004）
文摘The major factors influencing subsidence factor were comprehensively analyzed. Then the artificial neural network model for calculating subsidence factor was set up with the theory of artificial neural network （ANN）. A large amount of data from observation stations in China was collected and used as learning and training samples to train and test the artificial neural network model. The calculated results of the ANN model and the observed values were compared and analyzed in this paper. The results demonstrate that many factors can be considered in this model and the result is more precise and closer to observed values to calculate the subsidence factor by the ANN model. It can satisfy the need of engineering.
基金Projects 50774080 supported by the National Natural Science Foundation of China200348 by the Foundation for the National Excellent Doctoral Dis-sertation of China
文摘A new mathematical model to estimate the parameters of the probability-integral method for mining subsidence prediction is proposed.Based on least squares support vector machine(LS-SVM) theory, it is capable of improving the precision and reliability of mining subsidence prediction.Many of the geological and mining factors involved are related in a nonlinear way.The new model is based on statistical theory(SLT) and empirical risk minimization(ERM) principles.Typical data collected from observation stations were used for the learning and training samples.The calculated results from the LS-SVM model were compared with the prediction results of a back propagation neural network(BPNN) model.The results show that the parameters were more precisely predicted by the LS-SVM model than by the BPNN model.The LS-SVM model was faster in computation and had better generalized performance.It provides a highly effective method for calculating the predicting parameters of the probability-integral method.
基金Projects 40574057 and 40874054 supported by the National Natural Science Foundation of ChinaProjects 2007CB209400 by the National Basic Research Program of ChinaFoundation of China University of Mining and Technology (OF4471)
文摘Non-liner wave equation inversion,wavelet analysis and artificial neural networks were used to obtain stratum parameters and the distribution of thin coal seams.The lithology of the water-bearing/resisting layer in the Quaternary system was also predicted.The implementation process included calculating the well log parameters,stratum contrasting the seismic data and the well logs,and extracting,studying and predicting seismic attributes.Seismic inversion parameters,including the layer velocity and wave impedance,were calculated and effectively used for prediction and analysis.Prior knowledge and seismic interpretation were used to remedy a dearth of seismic data during the inversion procedure.This enhanced the stability of the inversion method.Non-linear seismic inversion and artificial neural networks were used to interpret coal seismic lithology and to study the water-bearing/resisting layer in the Quaternary system.Interpretation of the 1～2 m thin coal seams,and also of the water-bearing/resisting layer in the Quaternary system,is provided.The upper mining limit can be lifted from 60 m to 45 m.The predictions show that this method can provide reliable data useful for thin coal seam exploitation and for lifting the upper mining limit,which is one of the principles of green mining.
