Mining Safety and Sustainability I

Safety and sustainability are becoming ever bigger challenges for the mining industry with the increasing depth of mining. It is of great significance to reduce the disaster risk of mining accidents, enhance the safety of mining operations, and improve the efficiency and sustainability of development of mineral resource. This book provides a platform to present new research and recent advances in the safety and sustainability of mining. More specifically, Mining Safety and Sustainability presents recent theoretical and experimental studies with a focus on safety mining, green mining, intelligent mining and mines, sustainable development, risk management of mines, ecological restoration of mines, mining methods and technologies, and damage monitoring and prediction. It will be further helpful to provide theoretical support and technical support for guiding the normative, green, safe, and sustainable development of the mining industry

Standardization of a method for studying susceptibility of Indian coals to self-heating

This paper establishes the standardization of an electro-chemical method called wet oxidation potential (WOP) technique for determining the susceptibility of coal to spontaneous combustion. A total of 78 coal samples collected from 13 different mining companies, spread over most of the Indian Coalfields, have been used for this investigation. Experiments were carried out at different concentrations of KMnO4, viz., 0.05, 0.1, 0.15, and 0.2 N in 1 N KOH and at 27, 40, and 45 °C.With a combination of different concentrations ofKMnO4 and temperature, 12 experiments were carried out for each coal sample. Altogether, 936 experiments have been carried out by adopting different experimental conditions to standardize WOP method for wider applications in mining industries. Physical, chemical, and petrographical compositions of coal samples were studied by proximate, ultimate, and petrographic analyses. In order to determine the best combinations of experimental conditions to achieve optimum results in wet oxidation potential method, results were first analyzed by principal component analysis and then by artificial neural network analysis. These analyses clearly reveal that susceptibility index Brate of reduction of potential difference^ (RPD12), keeping experimental condition with 0.2 N KMnO4 in 1 N KOH solution at 45 °C, produces optimal results in finding out the susceptibility of coal to spontaneous heating. Further, coals are classified according to their proneness to spontaneous heating with multilayer perceptron (MLP) classifier. A correct classification with accuracy of 94.29%on test data has been achieved with this classifier. The results have been further validated by tenfold cross validation method to show its consistent performance over the chosen feature

Development of Mathematical Models for the Assessment of Fire Risk of Some Indian Coals using Soft Computing Techniques

Coal is the dominant energy source in India and meets 56% of the country’s primary commercial energy supply. In the light of the realization of the supremacy of coal to meet the future energy demands, rapid mechanization of mines is taking place to augment the Indian coal production from 643.75 million tons (MT) per annum in 2014-15 to an expected level of 1086 MT per annum by 2024-25. Most of the coals in India are obtained from low-rank coal seams. Fires have been raging in several coal mines in Indian coalfields. Spontaneous heating of coal is a major problem in the global mining industry. Different researchers have reported that a majority (75%) of these fires owe their origin to spontaneous combustion of coal. Fires, whether surface or underground, pose serious and environmental problems are causing huge loss of coal due to burning and loss of lives, sterilization of coal reserves and environmental pollution on a massive scale. Over the years, the number of active mine fires in India has increased to an alarming 70 locations covering a cumulative area of 17 km2. In Indian coalfield, the fire has engulfed more than 50 million tons of prime coking coal, and about 200 million tons of coals are locked up due to fires. The seriousness of the problem has been realized by the Ministry of Coal, the Ministry of Labour, various statutory agencies and mining companies. The recommendations made in the 10th Conference on Safety in Mine held at New Delhi in 2007 as well as in the Indian Chamber of Commerce (ICC)-2006, New Delhi, it was stated that all the coal mining companies should rank their coal mines on a uniform scale according to their fire risk on scientific basis. This will help the mine planners/engineers to adopt precautionary measures/steps in advance against the occurrence and spread of coal mine fire. Most of the research work carried out in India focused on the assessment of spontaneous combustion liabilities of coals based on limited conventional experimental techniques. The investigators have proposed/established statistical models to establish correlation between various coal parameters, but limited work was done on the development of soft computing techniques to predict the propensity of coal to self-heating that is yet to get due attention. Also, the classifications that have been made earlier are based on limited works which were empirical in nature, without adequate and sound mathematical base. Keeping this in view, an attempt was made in this research work to study forty-nine coal samples of various ranks covering the majority of the Indian coalfields. The experimental/analytical methods that were used to assess the tendencies of coals to spontaneous heating were: proximate analysis, ultimate analysis, petrographic analysis, crossing point temperature, Olpinski index, flammability temperature, wet oxidation potential analysis and differential thermal analysis (DTA). The statistical regression analysis was carried out between the parameters of intrinsic properties and the susceptibility indices and the best-correlated parameters were used as inputs to the soft computing models. Further different ANN models such as Multilayer Perceptron Network (MLP), Functional Link Artificial Neural Network (FLANN) and Radial Basis Function (RBF) were applied for the assessment of fire risk potential of Indian coals. The proposed appropriate ANN fire risk prediction models were designed based on the best-correlated parameters (ultimate analysis) selected as inputs after rigorous statistical analysis. After the successful application of all the proposed ANN models, comparative studies were made based on Mean Magnitude of Relative Error (MMRE) as the performance parameter, model performance curves and Pearson residual boxplots. From the proposed ANN techniques, it was observed that Szb provided better fire risk prediction with RBF model vis-à-vis MLP and FLANN. The results of the proposed RBF network model was closely matching with the field records of the investigated Indian coals and can help the mine management to adopt appropriate strategies and effective action plans in advance to prevent occurrence and spread of fire

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come

Data Mining of the Thermal Performance of Cool-Pipes in Massive Concrete via In Situ Monitoring

Embedded cool-pipes are very important for massive concrete because their cooling effect can effectively avoid thermal cracks. In this study, a data mining approach to analyzing the thermal performance of cool-pipes via in situ monitoring is proposed. Delicate monitoring program is applied in a high arch dam project that provides a good and mass data source. The factors and relations related to the thermal performance of cool-pipes are obtained in a built theory thermal model. The supporting vector machine (SVM) technology is applied to mine the data. The thermal performances of iron pipes and high-density polyethylene (HDPE) pipes are compared. The data mining result shows that iron pipe has a better heat removal performance when flow rate is lower than 50 L/min. It has revealed that a turning flow rate exists for iron pipe which is 80 L/min. The prediction and classification results obtained from the data mining model agree well with the monitored data, which illustrates the validness of the approach

Volume II: Mining Innovation

Contemporary exploitation of natural raw materials by borehole, opencast, underground, seabed, and anthropogenic deposits is closely related to, among others, geomechanics, automation, computer science, and numerical methods. More and more often, individual fields of science coexist and complement each other, contributing to lowering exploitation costs, increasing production, and reduction of the time needed to prepare and exploit the deposit. The continuous development of national economies is related to the increasing demand for energy, metal, rock, and chemical resources. Very often, exploitation is carried out in complex geological and mining conditions, which are accompanied by natural hazards such as rock bursts, methane, coal dust explosion, spontaneous combustion, water, gas, and temperature. In order to conduct a safe and economically justified operation, modern construction materials are being used more and more often in mining to support excavations, both under static and dynamic loads. The individual production stages are supported by specialized computer programs for cutting the deposit as well as for modeling the behavior of the rock mass after excavation in it. Currently, the automation and monitoring of the mining works play a very important role, which will significantly contribute to the improvement of safety conditions. In this Special Issue of Energies, we focus on innovative laboratory, numerical, and industrial research that has a positive impact on the development of safety and exploitation in mining

Advanced Mobile Robotics: Volume 3

Mobile robotics is a challenging field with great potential. It covers disciplines including electrical engineering, mechanical engineering, computer science, cognitive science, and social science. It is essential to the design of automated robots, in combination with artificial intelligence, vision, and sensor technologies. Mobile robots are widely used for surveillance, guidance, transportation and entertainment tasks, as well as medical applications. This Special Issue intends to concentrate on recent developments concerning mobile robots and the research surrounding them to enhance studies on the fundamental problems observed in the robots. Various multidisciplinary approaches and integrative contributions including navigation, learning and adaptation, networked system, biologically inspired robots and cognitive methods are welcome contributions to this Special Issue, both from a research and an application perspective

Classification of coal-bearing strata abnormal structure based on POA–ELM

In order to identify and classify the abnormal structures in coal-bearing strata more accurately, a POA−ELM model based on the pelican optimization algorithm (POA) and the extreme learning machine (ELM) is proposed. The performance of extreme learning machine is unstable because the input weights and hidden layer bias are generated randomly. The POA can be used to optimize the input weights and hidden layer bias of extreme learning machine, so as to improve the performance of extreme learning machine model. The POA−ELM model is applied to identify and classify the abnormal structures in coal-bearing strata. Firstly, three coal-bearing strata simulation models of small fault, scour zone and collapse column are established with the COMSOL Multiphysics5.5. The Ricker wave is the source signal. The in-seam wave signals are collected by wave transmission method, and the in-seam wave data set is established. Then the z-score method is used to standardize the in-seam wave data and the principal component analysis (PCA) is used to reduce the dimension. Secondly, the POA is used to optimize the extreme learning machine, and the POA−ELM classification model is constructed with MATLAB. The POA−ELM model is used to classify small fault, scour zone and collapse column. The classification performance of ELM and POA−ELM is evaluated and compared by cross-validation method and evaluation indices such as accuracy, precision and recall rate. The results show that the POA can effectively optimize the ELM, and the POA−ELM model has higher classification accuracy and better stability. The classification accuracy of POA−ELM for abnormal structures can reach more than 99%. Thirdly, in order to verify the classification effect of POA−ELM in practical applications, after wavelet de-noising, z-score standardization and PCA dimensionality reduction, the real fault in-seam wave data are used as the test set and imported into the POA−ELM model for classification. The results show that the identification accuracy of POA−ELM model for real fault can reach more than 97%. Finally, based on the same data set, the classification effects of POA−ELM, ELM, support vector machine (SVM) and BP neural network are compared. The results show that the identification and classification accuracy of POA−ELM model is the highest. Through research and analysis, the POA can effectively optimize the ELM, and the POA−ELM model can accurately classify different geological structures and effectively identify real faults, which is better than other methods

Data Mining

The availability of big data due to computerization and automation has generated an urgent need for new techniques to analyze and convert big data into useful information and knowledge. Data mining is a promising and leading-edge technology for mining large volumes of data, looking for hidden information, and aiding knowledge discovery. It can be used for characterization, classification, discrimination, anomaly detection, association, clustering, trend or evolution prediction, and much more in fields such as science, medicine, economics, engineering, computers, and even business analytics. This book presents basic concepts, ideas, and research in data mining

Numerical modeling study of a neutron depth profiling (NDP) system for the Missouri S&T reactor

”For decades, Neutron Depth Profiling has been used for the non-destructive analysis and quantification of boron in electronic materials and lithium in lithium ion batteries. NDP is one of the few non-destructive analytical techniques capable of measuring the depth profiles of light elements to depths of several microns with nanometer spatial resolution. The technique, however, is applicable only to a handful of light elements with large neutron absorption cross sections. This work discusses the possibility of coupling Particle Induced X-ray Emission spectroscopy with Neutron Depth Profiling to yield additional information about the depth profiles of other elements within a material. The technical feasibility of developing such a system at the Missouri University of Science and Technology Reactor (MSTR) beam port is discussed. This work uses a combination of experimental neutron flux measurements with Monte Carlo radiation transport calculations to simulate a proposed NDP-PIXE apparatus at MSTR. In addition, the possibility of implementing an Artificial Neural Network to perform automated data analysis of NDP is presented. It was found that the performance of the Artificial Neural Network is at least as accurate as traditional processing approaches using stopping tables but with the added advantage that the Artificial Neural Network method requires fewer geometric approximations and accounts for all charged particle transport physics implicitly”--Abstract, page iii