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

Models and methods to make decisions while mining production scheduling

Purpose is to develop a new approach to the design of mining operations basing upon models and methods of decision making. Methods. The paper has applied a complex approach involving approaches of decision-making theory. Analysis of the pro-duction development scenarios is proposed for strategic activity planning; criteria to make decisions under the uncertainty conditions as well as decision-making trees for day-to-day management are proposed to determine balanced production level. Findings. It has been identified that mining production design is of the determined character demonstrating changes in “state of the nature” depending upon the made decisions. The idea of mining production is to reduce uncertainty gradually by means of analysis of production scenarios, and elimination of unfavourable alternatives. Operative management is implemented while constructing decision trees, and optimizing operation parameters. Representation of sets of rational equipment types as well as development scenarios, and their comparison in terms of decision-making parameters makes it possible to determine adequate capacity of a working area, and to reduce expenditures connected with the equipment purchase and maintenance. In this context, limiting factors, effecting anticipatory mining out-put, are taken into consideration. Successive comparison of the alternatives helps identify decision-making area for different scenarios of the production development. Originality. To manage mining production, approaches of decision-making theory have been proposed which involve the use of decision trees, decision-making criteria, and analysis of scenarios basing upon representation of operating procedures in the form of a network model within which the shortest route corresponds to optimum decision. Practical implications. Decision-making system has been developed making it possible to optimize operation parameters, to reduce prime cost of mining, and to select a structure of engineering connections with the specified production level. The described approaches may be applied at the stage of a stope design as well as in the process of a field development. Specific attention has been paid to a software development to implement the approaches.Мета. Розробити новий підхід до проектування гірничого виробництва, який базується на моделях та методах теорії прийняття рішень. Методика. В роботі застосовано комплексний метод, який включає підходи теорії прийняття рішень. Для стратегічного планування діяльності запропоновано досліджувати сценарії розвитку виробництва, для визначення раціонального рівня виробництва – критерії прийняття рішень в умовах невизначеності, а також дерева прийняття рішень для поточного управління. Результати. Виявлено, що процес проектування гірничого виробництва має детермінований характер, який демонструє зміну “станів природи” залежно від прийнятих рішень. Суть проектування гірничого виробництва зводиться до послідовного зменшення невизначеності шляхом дослідження сценаріїв виробництва та виключення несприятливих альтернатив. Оперативне управління здійснюється шляхом побудови дерев рішень та оптимізації параметрів експлуатації. Представлення множин раціональних типів обладнання, сценаріїв розвитку подій та порівняння їх за критеріями прийняття рішень дозволяє визначити раціональний рівень видобутку виймальної дільниці і знизити витрати на придбання та обслуговування обладнання, при цьому враховуються обмежувальні фактори, які впливають на величину очікуваного видобутку. Послідовне порівняння альтернатив дозволяє встановити поле прийнятних рішень для різних сценаріїв розвитку виробництва. Наукова новизна. Для управління гірничим виробництвом запропоновано підходи теорії прийняття рішень, які включають застосування дерев рішень, критеріїв прийняття рішень та аналіз сценаріїв, котрі базуються на представленні технологічного процесу у вигляді мережевої моделі, в якій найкоротший маршрут відповідає оптимальному рішенню. Практична значимість. Розроблена система прийняття рішень, дозволяє оптимізувати параметри експлуатації, знизити собівартість видобутку, вибрати структуру технологічних зв’язків з заданим рівнем продуктивності. Описані в роботі підходи можуть бути використані як на стадії проектування очисного забою так і в процесі експлуатації родовища корисних копалин. Особливу увагу приділено розробці програмного забезпечення для впровадження описаних підходів у виробництво.Цель. Разработать новый подход к проектированию горного производства, который базируется на моделях и методах теории принятия решений. Методика. В работе использован комплексный метод, который включает подходы теории принятия решений. Для стратегического планирования деятельности предложено исследовать сценарии развития производства, для определения рационального уровня производства – критерии принятия решений в условиях неопределенности, а также деревья принятия решений для текущего управления. Результаты. Установлено, что процесс проектирования горного производства носит детерминированный характер, который отражает изменение “состояний природы” в зависимости от принятых решений. Суть проектирования сводится к последовательному уменьшению неопределенности путем исследования сценариев производства и исключения неблагоприятных альтернатив. Оперативное управление осуществляется посредством построения деревьев решений и оптимизации параметров эксплуатации. Представление множества рациональных типов оборудования, сценариев развития событий та сравнение их по критериям принятия решений позволяет определить рациональный уровень добычи очистного участка и снизить затраты на приобретение и обслуживание оборудования, при этом учитываются ограничивающие факторы, которые влияют на величину ожидаемой прибыли. Последовательное сравнение альтернатив позволяет установить поле приемлемых решений для разных сценариев развития производства. Научная новизна. Для управления горным производством предложены подходы теории принятия решений, которые включают применения деревьев, критериев принятия решений и анализ сценариев, основанных на представлении технологического процесса в виде сетевой модели, где кратчайший маршрут соответствует оптимальному решению. Практическая значимость. Разработана система поддержки принятия решений, которая позволит оптимизировать параметры эксплуатации, снизить себестоимость добычи, выбрать структуру технологических взаимосвязей с заданным уровнем производительности. Описанные в работе подходы могут быть использованы как на стадии проектирования очистного забоя, так и в процессе эксплуатации месторождения полезных ископаемых. Особое внимание уделено разработке программного обеспечения для внедрения описанных подходов в горное дело.The study has been carried out within the framework of research project of the National Academy Sciences of Ukraine for young scientists “Resource-saving techniques to support mine workings under the complex hydrogeological conditions”; Agreement #29-04/06-2019; official registration #0119U102370

Construction and application of an intelligent prediction model for the coal pillar width of a fully mechanized caving face based on the fusion of multiple physical parameters

The scientific and reasonable width of coal pillars is of great significance to ensure safe and sustainable mining in the western mining area of China. To achieve a precise analysis of the reasonable width of coal pillars in fully mechanized caving face sections of gently inclined coal seams in western China, this paper analyzes and studies various factors that affect the retention of coal pillars in the section, and calculates the correlation coefficients between these influencing factors. We selected parameters with good universality and established a data set of gently inclined coal seams based on 106 collected engineering cases. We used the LSTM algorithm loaded with a simulated annealing algorithm for training, and constructed a coal pillar width prediction model. Compared with other prediction algorithms such as the original LSTM algorithm, the residual sum of squares and root mean square error were reduced by 27.2% and 24.2%, respectively, and the correlation coefficient was increased by 12.6%. An engineering case analysis was conducted using the W1123 working face of the Kuangou Coal Mine. The engineering verification showed that the SA-CNN-LSTM coal pillar width prediction model established in this paper has good stability and accuracy for multi-parameter nonlinear coupling prediction results. We have established an effective solution for achieving the accurate reservation of coal pillar widths in the fully mechanized caving faces of gently inclined coal seams

Support Vector Machines for the Estimation of Specific Charge in Tunnel Blasting

Mine tunnels, short transportation tunnels, and hydro-power plan underground spaces excavations are carried out based on Drilling and Blasting (D&B) method. Determination of specific charge in tunnel D&B, according to the involved parameters, is very significant to present an appropriate D&B design. Suitable explosive charge selection and distribution lead to reduced undesirable effects of D&B such as inappropriate pull rate, over-break, under-break, unauthorized ground vibration, air blast, and fly rock. So far, different models are presented to estimate specific charge in tunnel blasting. In this study, 332 data sets, including geomechanical characteristics, D&B, and specific charge are gathered from 33 tunnels. The data are related to three dams and hydropower plans in Iran (Gotvand, Masjed-Solayman, and Siah-Bishe). Specific charge is modeled in inclined hole cut drilling pattern. In this regard, Support Vector Machine (SVM) algorithm based on polynomial Kernel function is used as a tool for modeling. Rock Quality Designation (RQD) index, Uniaxial Compressive Strength (UCS), tunnel cross-section area, maximum depth of blast hole, and blast hole coupling ratio are considered as independent input variables and the specific charge is considered as a dependent output variable. The modeling results confirm the acceptable performance of SVM in specific charge estimation with minimum error

Recurrent neural networks and proper orthogonal decomposition with interval data for real-time predictions of mechanised tunnelling processes

A surrogate modelling strategy for predictions of interval settlement fields in real time during machine driven construction of tunnels, accounting for uncertain geotechnical parameters in terms of intervals, is presented in the paper. Artificial Neural Network and Proper Orthogonal Decomposition approaches are combined to approximate and predict tunnelling induced time variant surface settlement fields computed by a process-oriented finite element simulation model. The surrogate models are generated, trained and tested in the design (offline) stage of a tunnel project based on finite element analyses to compute the surface settlements for selected scenarios of the tunnelling process steering parameters taking uncertain geotechnical parameters by means of possible ranges (intervals) into account. The resulting mappings of time constant geotechnical interval parameters and time variant deterministic steering parameters onto the time variant interval settlement field are solved offline by optimisation and online by interval analyses approaches using the midpoint-radius representation of interval data. During the tunnel construction, the surrogate model is designed to be used in real-time to predict interval fields of the surface settlements in each stage of the advancement of the tunnel boring machine for selected realisations of the steering parameters to support the steering decisions of the machine driver

Multi-parameter comprehensive early warning of coal pillar rockburst risk based on DNN

A multi-parameter comprehensive early warning method for coal pillar-type rockburst risk based on the deep neural network (DNN) is proposed in this study. By utilizing preprocessed data from the surveillance of coal pillar impact hazards in Yangcheng Coal Mine, this study incorporates training samples derived from three distinct coal pillar-type impact hazard monitoring methodologies: microseismic monitoring, borehole cutting analysis, and real-time stress monitoring. The data characteristics of the monitoring data were extracted, evaluated, classified, and verified by monitoring the data of different working faces. This method was applied to develop the depth of multi-parameter neural network comprehensive early warning software in engineering practice. The results showed that the accuracy of the depth for burst monitoring data processing is improved by 6.89%–16.87% compared to the traditional monitoring methods. This method has a better early warning effect to avoid the occurrence of coal pillar rockburst hazard

Classification of skin disease using deep learning neural networks with mobilenet V2 and LSTM

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning-based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2x lesser computations than the conven-tional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity