Mining System Research Articles

Two-dimensional identification of time-varying batch nonlinear processes with input dead-zone nonlinearity

ABSTRACT In the time dimension, the batch process with input dead-zone nonlinearity often has the characteristics of short operation time and time-varying process parameters. A two-dimensional recursive least squares (2D-RLS) identification method for time-varying nonlinear systems with input dead zone is proposed. By mining system dynamic information from the time direction of the same batch and different batch directions, the identification parameters are updated. The proposed two-dimensional identification strategy converts the time-varying parameter estimation problem of time dimension into the equivalent time-invariant parameter estimation problem of batch dimension. Sufficient production data in batch dimension ensure that the proposed identification algorithm can eliminate the influence of random measurement noise. The identification strategy with unequal length of batch data is also given. The convergence of the proposed algorithm is analysed. Finally, numerical simulation and experimental tests are used to verify the effectiveness and superiority of the proposed algorithm.

VLC channel model in underground mining scenarios with the extinction effect and shadowing effect

In this work, a novel, to our knowledge, visible light communication (VLC) channel model is proposed for underground mining scenarios taking into account the impact of coal dust particles and obstacles. Specifically, the extinction effect of the coal dust particles is analyzed on the basis of the Mie theory, and the quantitative formula of the influence on channel direct current (DC) gain is derived. Meanwhile, the effect of a random shadowing phenomenon is investigated and quantified with the geometric and statistical model considering the position, size, and shape of the obstacles. The channel impulse response, path loss, root mean square delay spread, and bit error rate (BER) are further investigated in two different underground mining scenarios, namely, a mining roadway and coal mine working face. Simulation results show that the shadowing effect plays a major role in the influence of DC gain attenuation. Furthermore, the BER performance is noticeably degraded due to the presence of coal dust particles and obstacles, especially when the receiver is located far from the transmitter. This work will benefit the design of the VLC systems in underground mines.

A Collaborative Study of English Teaching Based on Optimized Apriori Algorithm under the Integration of Curriculum Civics

The new period introduces new intellectual and political demands for college-level English courses and fosters favourable conditions as well as new intellectual and political opportunities. The knowledge and politics courses currently taught in our higher education institutions combine English instruction with cooperative research and knowledge and politics courses, producing some excellent results in China. However, there are still significant challenges to implementing the relatively new ideas for education reform. In order to increase the effectiveness of the mine, this paper will enhance the apriori calculation method in accordance with the features of a large amount of data, and it requests the English Education Graduate School's participation for a demonstration. To address the issue of inaccurate existing metrics, this document integrates the proposed measures with conventional measures. In the past, the new measuring technique and the enhanced algorithm were used to create a model of the mining system based on the performance of the pupils. In the end, we used the student successes to build the mine system model, integrate the new measurement technique with the improved algorithm, and draw significant data-driven conclusions. According on experimental findings, the proposed algorithm performs better than Mr. Apriori. The mixed metre tonnes serve as the foundation for the most precise selection as compared to the general metre tonnes. The system offers a range of data extraction services in accordance with the university's role, which serves to enhance college English education's collaboration with curriculum and governmental thinking.

Analysis productivity of the mine facility department using the objective matrix method (OMAX): case study in coal mining company

PT Kideco Jaya Agung is one of the coal mining companies in Paser Regency, East Kalimantan, using an open pit mining system which at the end of 2012 succeeded in realizing the production, transportation, shipping target of 34 million tons. Coal mining is carried out using excavating equipment and hauling equipment. Activities begin with land clearing using bulldozers and / or small excavators, blasting overburden and / or removal of overburden, cleaning coal, loading and transporting coal to the ROM Stockpile. Throughout 2021 there has been a decrease in production results, it is necessary to analyze the productivity of the company's performance, which in this case leads to the Mine Facility department because it is directly related to production. The need to analyze productivity measurements so that it can be seen whether the Mine Facility department in this company is productive or not. The productivity measurement method that can be used to solve the problem is Objective Matrix (OMAX), because the OMAX productivity measurement model overcomes the problems of complexity and difficulty of productivity measurement by combining all important productivity criteria into an integrated form and interrelated with each other and easy to combine.

Research on the method of determining the block size for an open-pit mine integrating mining parameters and shovel-truck’s operation efficiency

The production plan of an open-pit mine depends on the block model, so it's crucial to determine the appropriate method and size for partitioning it. This study proposes a new method based on a closed shell three-dimensional geological model for determining block model size in open-pit mines. Instead of using regular block models, the shell model is directly cut, and the discrete geological body is referred to as the "mining model." Mining parameters and the shovel-truck's performance are integrated into the method. Bench height determines the Z-axis size, bench slope angle determines the inclination angle, and shovel width determines the X-axis size of the block model. The operation efficiency of the shovel-truck considers the probability distribution of simultaneous operations, allowing the determination of the Y-axis size of block models for different types of shovels. The developed "Mining Model" module in the software "Life Cycle Mining System" is used for practical implementation. By comparing the results with traditional block models, the superiority of the proposed method is demonstrated. This study provides a more accurate model for optimizing the production plan of open-pit mines throughout their life cycle.

BAGGING ENSEMBLE MINING TECHNIQUE WITH DEEP BELIEF NETWORK (DBN) ALGORITHM-BASED HEART DISEASE PREDICTION

Cardiovascular disease is the most important disease of the heart, and the stage of the disease is diagnosed, the disease can be diagnosed anytime. The following method is used to find out its status. The heart disease prediction is based on Bagging Ensemble Technique with Deep Belief Network (DBN) algorithms. The problem for heart disease prediction from the collection of the dataset. Feature extraction using the Bag of Words methods to the correct data and discontinuities collect the heart disease-based matching data's extraction from the dataset and the various combination of the data set is available in Kaggle. It is mostly used for text classification methods. The proposed system of data mining is the most important technique for data aggregation. Data mining has various methods available, and one of the techniques is the bagging Ensemble Technique. In this method using for homogeneous data are quickly collected and parallel processing for the data collections. The first process for collecting data using the bagging Ensemble Technique is based on collected preprocessing. The Cardiovascular Disease prediction using Deep Belief Network (DBN) algorithm is compared with the existing system heart disease prediction, to prediction for display the data past and present movement in classification time and prediction accuracy, sensitivity and specificity. The proposed system for classification is compared with various techniques, and the proposed methods are DBN algorithms compared to compare to 5000 data'. The performance of CNN is 89%, RNN is 90%, LSTMs is 92%, and DBM is 95.6%. Finally, the heart disease prediction or classifications are given by the DBN algorithm.

Enhancing the adaptability of a mining complex in a dynamic environment by utilizing the technology for the development of a man-made deposit with a mobile ore preparation complex

The paper deals with the issue of the development of man-made deposits of the bulk type, especially that formed during the development of iron ore deposits. An analysis of the actual state of man-made deposits was performed. Based on analysis the technology of man-made deposits mining was proposed. It involves the extraction of technogenic raw materials from the bulk and their separation according to quality characteristics using a mobile ore preparation complex. According to the proposed technology, the conditioned raw materials are sent to the processing place, and the empty rock is laid in layers in the created space. The parameters of the mining system were studied in the paper: the width of the excavator’s cut step is limited on the one hand by the unloading radius of the dragline, and on the other by the turning radius of the dump truck. A significant role is played by the raw material utilization ratio: the larger it is, the narrower the excavator cut step will be. The results of the research can be useful for design organizations and mining enterprises which implement resource-saving technologies.

Innovative technology for mining parallel superimposed inclined tabular deposits

Ore deposits are often parallel superimposed inclined tabular ones with waste rock inclusions between ore beds. The present research aims to develop an innovative safe and efficient technology to reduce the cost of waste rock transportation outside the stoping block and provide a rational flowsheet for mining superimposed beds with original structural elements. The paper proposes a new technological flowsheet for mining superimposed deposits with disposal of waste rocks occurring between beds in previously prepared rooms and establishes new dependencies of room dimensions on the volume of rocks to be disposed. The research results enable expansion of the scope of employment of room mining systems and significant reduction in costs in the working area. The efficiency of ore mining is achieved by creating room crowns from unmined interbedded rocks, disposing broken waste rocks in worked-out spaces and rationally mining the deposits. Implementation of the proposed technology enables reduction of the cost of mining ore from parallel superimposed inclined tabular deposits.

A Framework Design for Centralised Monitoring of Patient Disease Diagnosis for Better Improvement

Healthcare recommendation systems have garnered significant attention in recent times due to their capacity to improve patient outcomes and treatment. This literature review intends to assess the current state of patient healthcare referral systems by examining relevant studies, techniques, and findings. The report focuses on key research areas, challenges, and viable strategies for the future in the field of patient-centered health recommendation systems. Currently, healthcare administration is in high demand due to its significant advantages in managing hospitals or medical practices. Health management systems are increasingly affecting the entire world on a daily basis. The rising demand for healthcare is attributed to various factors, including the availability of healthcare solutions. The health prediction system is an online initiative designed to provide user support and advice. This study proposes a technology that allows consumers to receive immediate online health guidance from an intelligent healthcare system. The system encompasses a multitude of disorders and symptoms associated with different bodily systems. Data mining technologies can be utilized to identify the most probable disease associated with a patient's symptoms. By logging into the system, a doctor can retrieve and review their patient's information and reports within the doctor's module. Physicians have the ability to analyze the patient's browsing history and the specific information they are seeking, taking into account their medical prognosis. The doctor has access to his data. The database administrator has the ability to incorporate additional disease information, such as the type of disease and its symptoms. The data mining system runs based on the condition's name and symptoms. The administrator has access to the database including information on diseases and symptoms. Recommender systems employ diverse machine learning techniques in many domains, such as the healthcare recommendation system (HRS), to advise and promote services or entities to users. Due to the vast array of algorithms documented in the literature, the science of artificial intelligence is now widely employing machine learning techniques in various application domains, including the HRS. Nevertheless, the process of selecting an appropriate machine learning algorithm for a health recommender system seems to be time-consuming.

Genetic Algorithm-Based Multitier Ensemble Classifier for Diagnosis of Heart Disease

Designing a hybrid or ensemble data mining system appropriate to the application is a research challenge. Heart disease is a life threatening disease that need to be recognized correctly in the starting stage before it becomes more complex. Using artificial intelligence techniques in a hybrid and ensemble architecture can support the prediction of heart disease more effectively based on the given sample cases. This paper proposes a classification system called genetic algorithm-based ensemble classification system (GA-ECS) for the identification of heart disease. As feature selection is the crucial step before applying the data mining techniques, the genetic algorithm is used in GA-ECS to identify the best features in a given dataset. The Cleveland heart disease dataset is used for testing GA-ECS. The performance of GA-ECS is compared with different machine learning classifiers for the prediction of heart disease. GA-ECS showed a promising outcome with an accuracy of 90% for the diagnosis of heart disease.

A Walking Trajectory Tracking Control Based on Uncertainties Estimation for a Drilling Robot for Rockburst Prevention

A walking trajectory tracking control approach for a walking electrohydraulic control system is developed to reduce the walking trajectory tracking deviation and enhance robustness. The model uncertainties are estimated by a designed state observer. A saturation function is used to attenuate sliding mode chattering in the designed sliding mode controller. Additionally, a walking trajectory tracking control strategy is proposed to improve the walking trajectory tracking performance in terms of response time, tracking precision, and robustness, including walking longitudinal and lateral trajectory tracking controllers. Finally, simulation and experimental results are employed to verify the trajectory tracking performance and observability of the model uncertainties. The results testify that the proposed approach is better than other comparative methods, and the longitudinal and lateral trajectory tracking average absolute errors are controlled in 10.23 mm and 22.34 mm, respectively, thereby improving the walking trajectory tracking performance of the walking electrohydraulic control system for the coal mine drilling robot for rockburst prevention.

Anthropogenic processes drive spatiotemporal variability of sulfate in groundwater from a multi-aquifer system: Dilution caused by mine drainage

The water quality evolution of surface and groundwater caused by mining activities and mine drainage is a grave public concern worldwide. To explore the effect of mine drainage on sulfate evolution, a multi-aquifer system in a typical coal mine in Northwest China was investigated using multi-isotopes (δ34SSO4, δ18OSO4, δD, and δ18Owater) and Positive Matrix Factorization (PMF) model. Before mining, the Jurassic aquifer was dominated by gypsum dissolution, accompanied by cation exchange and bacterial sulfate reduction, and the phreatic aquifers and surface water were dominated by carbonate dissolution. Significant increase in sulfate in phreatic aquifers due to mine drainage during the early stages of coal mining. However, in contrast to common mining activities that result in sulfate contamination from pyrite oxidation, mine drainage in this mining area resulted in accelerated groundwater flow and enhanced hydraulic connections between the phreatic and confined aquifers. Dilution caused by the altered groundwater flow system controlled the evolution of sulphate, leading to different degrees of sulfate decrease in all aquifers and surface water. As the hydrogeochemical characteristic of Jurassic aquifer evolved toward phreatic aquifer, this factor should be considered to avoid misjudgment in determining the source of mine water intrusion. The study reveals the hydrogeochemical evolution induced by mine drainage, which could benefit to the management of groundwater resources in mining areas.

Occupational Safety Assessment for Surface Mine Systems: The Case in Jordan

Surface mining is one of the hazardous industries that have several risky operations, including transportation, treatment, and mineral extraction. To avoid the risk of disaster, it is important to evaluate safety procedures and determine expected hazards. The aim of this study is to develop a thorough safety evaluation model for the surface mining industry based on the analytic hierarchy process (AHP), one important multi-criteria decision-making approach. A total of 11 criteria and 36 sub-criteria that are both independent and homogeneous were involved in the decision problem. Further, a deep sensitivity analysis was conducted to assess the stability of the ranking preference. The findings indicate that four out of the eleven criteria are particularly significant. To test the model’s applicability and effectiveness, a case study was conducted involving three surface mining companies located in the north of Jordan. The results demonstrate that the model is reliable, applicable, and effective in addressing real-world problems.

On the Relative Kinematics and Control of Dual-Arm Cutting Robots for a Coal Mine

There is an unbalanced problem in the traditional laneway excavation process for coal mining because the laneway excavation and support are at the same position in space but they are separated in time, consequently leading to problems of low efficiency in laneway excavation. To overcome these problems, an advanced dual-arm tunneling robotic system for a coal mine is developed that can achieve the synchronous operation of excavation and the permanent support of laneways to efficiently complete excavation tasks for large-sized cross-section laneways. A dual-arm cutting robot (DACR) has an important influence on the forming quality and excavation efficiency of large-sized cross-section laneways. As a result, the relative kinematics, workspace, and control of dual-arm cutting robots are investigated in this research. First, a relative kinematic model of the DACR is established, and a closed-loop control strategy for the robot is proposed based on the relative kinematics. Second, an associated workspace (AW) for the DACR is presented and generated, which can provide a reference for the cutting trajectory planning of a DACR. Finally, the relative kinematics, closed-loop kinematic controller, and associated workspace generation algorithm are verified through simulation results.

An Ensemble Feature Selection Approach-Based Machine Learning Classifiers for Prediction of COVID-19 Disease.

The respiratory disease of coronavirus disease 2019 (COVID-19) has wreaked havoc on the economy of every nation by infecting and killing millions of people. This deadly disease has taken a toll on the life of the entire human race, and an exact cure for it is still not developed. Thus, the control and cure of this disease mainly depend on restricting its transmission rate through early detection. The detection of coronavirus infection facilitates the isolation and exclusive care of infected patients. This research paper proposes a novel data mining system that combines the ensemble feature selection method and machine learning classifier for the effective identification of COVID-19 infection. Different feature selection approaches including chi-square test, recursive feature elimination (RFE), genetic algorithm (GA), particle swarm optimization (PSO), and random forest are evaluated for their effectiveness in enhancing the classification accuracy of the machine learning classifiers. The classifiers that are considered in this research work are decision tree, naïve Bayes, K-nearest neighbor (KNN), multilayer perceptron (MLP), and support vector machine (SVM). Two COVID-19 datasets were used for testing from which the best features supporting the dataset were extracted by the proposed system. The performance of the machine learning classifiers based on the ensemble feature selection methods is analyzed.

Analysis of the truck haulage system in an underground mine using long-term log data

ABSTRACT This paper addresses the collection, analysis, and optimization of log data related to the truck haulage system in a mine. After selecting an underground mine in South Korea as the research area, the Mine Production Management System (MPMS) using Bluetooth beacons and tablet PCs was utilized to collect log data related to the haulage system over 18 months. Through the analysis of the collected log data, strategies to enhance the productivity and efficiency of the haulage system were derived. Weekly analysis of log data files was performed, identifying periods with abnormal truck cycle times and conducting an analysis of the underlying causes. It was revealed that the truck cycle time is influenced by the haulage system, equipment operation, and the log data collection system. To shorten truck cycle times and improve the productivity and efficiency of transportation systems, continuous collection and regular analysis of log data and dynamic system and equipment operation based on this are important. Analyzing log data collected over an extended period can help predict future trends in the haulage system and formulate measures and strategies in response. Therefore, it can serve as a valuable decision-making tool for establishing short-term, medium-term, and long-term production and operation plans.

Research and Simulation on the Development of a Hydraulic Prop Support System of Powered Roof Support to Increase Work Safety.

The underground mining environment is currently based on technology that uses mainly analogue sensors in machine and equipment control systems. The primary machine performing the most important functions in a mining system is the powered roof support. In order for it to work properly, it is important that it achieves the required power. To ensure this, it is necessary to continuously and precisely monitor the pressure in the under-piston space of the prop. Due to the extreme environmental conditions, pressure sensors should have high sensitivity, large transmission capacity, small size and light weight. To achieve these requirements, the authors of the article propose to implement a monitoring system based on photonics technology. To achieve this goal, several studies were carried out. The range of these studies included simulations, bench tests and tests under real conditions. The obtained test results showed the possibility of developing the control system for the powered roof support, the additional function to supercharge power. Based on the analysis of the obtained test results, assumptions were developed for the development of a power charging system with monitoring sensors. Based on the guidelines obtained from the research results, thedevelopment of the above prototype based on photonics technology is proposed.

Research on the Intelligent System Architecture and Control Strategy of Mining Robot Crowds

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China’s coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China’s and even the world’s coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as “humans, machines, the environment, and management” in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified.

Integrated Hydrological and Hydrochemical Analysis of Arsenic and Iron Behavior in Waters of a Decommissioned Tin Mine in Ehrenfriedersdorf, Germany

Arsenic contamination poses significant challenges to environmental and public health, with mining activities contributing to its wider distribution. This study investigates the fate of arsenic and iron in mine waters at a decommissioned tin mine, now a visitor mine, located in Ehrenfriedersdorf, Germany, situated in the Free State of Saxony. Despite the general shortage of data, which is common for old mining sites, we explored the complex interplay of climatic conditions, hydrological processes, and arsenic and iron behavior in the mine waters through a comprehensive approach encompassing general site characterization, mine water monitoring, and analysis of local weather data. Over a period of three years, we conducted 14 sampling campaigns, collecting a total of 95 water samples, each consisting of three filtration subsamples, resulting in the analysis of 285 water samples. These samples were collected both aboveground and underground. Aboveground samples included mine outflows, a tailing outflow, and an adjacent creek, while underground sampling points were scattered throughout the mine initially and later focused on the identified “main” mine water system. The chemical data from the analyses were correlated with local climatic water balances to reveal distinctive patterns in arsenic and iron concentrations at various locations within the mine system. Our findings shed light on the hydrological behavior of the mine, helping to elucidate the impact of precipitation and potential evapotranspiration on arsenic and iron concentrations in a tailing outflow, in the flooded mine body, and at the portal of the main dewatering gallery. Our findings emphasize the importance of sustained monitoring and the utilization of local weather data to comprehend metalloid and metal contamination risks in similar mining environments.

Distantly Supervised Explainable Stance Detection via Chain-of-Thought Supervision

Investigating public attitudes on social media is crucial for opinion mining systems. Stance detection aims to predict the attitude towards a specific target expressed in a text. However, effective neural stance detectors require substantial training data, which are challenging to curate due to the dynamic nature of social media. Moreover, deep neural networks (DNNs) lack explainability, rendering them unsuitable for scenarios requiring explanations. We propose a distantly supervised explainable stance detection framework (DS-ESD), comprising an instruction-based chain-of-thought (CoT) method, a generative network, and a transformer-based stance predictor. The CoT method employs prompt templates to extract stance detection explanations from a very large language model (VLLM). The generative network learns the input-explanation mapping, and a transformer-based stance classifier is trained with VLLM-annotated stance labels, implementing distant supervision. We propose a label rectification strategy to mitigate the impact of erroneous labels. Experiments on three benchmark datasets showed that our model outperformed the compared methods, validating its efficacy in stance detection tasks. This research contributes to the advancement of explainable stance detection frameworks, leveraging distant supervision and label rectification strategies to enhance performance and interpretability.