Mining Process Research Articles

200 years of coupled relief disturbance in the heart of a former industrial city

Landscapes in industrialized areas undergo distinct stress due to numerous and rapid changes, including geomorphic disturbances. We investigated a distinct abandoned sloped area above the river channel in the heart of an industrial city with a population of 300,000 inhabitants. Traces of numerous past changes enabled us to reconstruct a narrative of coupled and chained disturbances spanning over 200 years. Ten different landscape-forming processes of natural, mining, urban, and war origins were identified and dated through field mapping, dendrogeomorphic and geophysical surveys, speleological exploration, and analysis of historical maps and reports. The most impactful processes included slope retreat, structural landslides, stone quarrying, and ground subsidence induced by coal mining, while rockfalls, urbanization, and war disruptions had lesser effects. The collapse of an air-raid shelter, combined with other processes, created a remarkable cave exposing coal seams. These identified processes interact within a complex environment, either immediately or with time gaps of tens or even hundreds of years. The responses of relief and landscape to these events were captured in discernible relief generations. The derelict landscape, composed of numerous spatial units resulting from many temporal events, is unique—a landscape devastated and yet valuable. We discuss two potential scenarios for the site's future development: gradual erasure of past relief generations leading to landscape homogenization, or the occurrence of further disturbances increasing the complexity of the palimpsest.

Modelling and optimization of trajectory deviation for compound directional drilling in coal mines

During the sliding mode of compound directional drilling process in coal mines, the inclined angle and azimuth angle need to be changed to minimize the trajectory deviation between the actual trajectory and the designed trajectory. The inclined angle and azimuth angle can be changed by adjusting the tool face angle. In this article, a modelling and optimization method is proposed to obtain optimal tool face angle for sliding mode. Firstly, the gaussian process regression method is employed to build calculation model of trajectory deviation. The optimization model of trajectory deviation consists of the established calculation model of trajectory deviation and the set constraints. Then, the optimal inclination angle and azimuth angle are obtained by solving the constitutive optimization model using an improved particle swarm optimization algorithm. Next, the calculation model of tool face angle is built to represent the relationship between inclination angle, azimuth angle and tool face angle. The optimal tool face angle is calculated based on optimal inclined angle and azimuth angle. Finally, an industrial case study based on the actual drilling data verifies that the proposed method is beneficial to track designed trajectory during drilling process. The proposed method meets the requirements of on-site construction accuracy and ensures the safety and stability of drilling.

Enhance health evidence quality in classification tasks: A triangulation approach utilizing case-based reasoning and process features.

Machine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. Unlike clinical trials, real-world data used in ML are often gathered for multiple purposes, leading to bias and missing information for a specific classification task. This challenge is especially pronounced in healthcare because of stringent ethical considerations and resource constraints.This study proposed an integrated approach to enhance the quality of health evidence from a classification task for predicting Medicare's Diagnosis-Related Groups of ischemic heart disease (IHD) patients. Eligible participants were identified from the Medical Information Mart for Intensive Care IV (MIMIC IV), a publicly available hospital database. Six ML models were selected for model triangulation. Sequential triangulation was employed via Local Process Mining (LPM) and Qualitative Comparative Analysis (QCA). A total of 1545 IHD hospitalizations from 916 patients were identified from the MIMIC IV. Eight health process features were identified through LPM aligned with clinical knowledge. The correlation coefficients for process features, ranging from 0.24 to 0.42, are higher than those for non-process features ranged from 0.02 to 0.36. A total of 56 unique combinations were identified from the QCA, with 28 configurations having raw coverage lower than 1.0%. The overall model performance (i.e. weighted F1 and area under the curve scores) increased after adopting this integrated approach. The proportion of cases misclassified by any of the six models decreased by 47% after incorporating process features (from 5.29% to 2.91%) and further decreased to 0.0% after applying the QCA solutions. The integrated approach demonstrates its ability to enhance quality of a classification task through its clinical relevance, improved model performance, and reduced case-level error rates. However, more scalable QCA methods are needed for larger datasets. Developing health process feature engineering for broader applications can be a future direction.

A technology-specific process mining maturity grid for manufacturing and logistics

Process mining applies data mining to the data produced in the slipstream of the executing process instances. Hence, it enables organisations gain transparency about how exactly the processes are executed. Organisations can use this transparency to find opportunities to improve their business processes, such as manufacturing and logistics. Previous research provides methodological guidance on how process mining projects can be conducted and how process mining capabilities can be assessed from an organisational perspective. However, organisations tend to lack experience with novel technologies, which is why they often miss out on opportunities to improve their operations using the insights gained through process mining. This study is the first to contribute to the existing literature by examining process mining capabilities from a technological perspective, focussing on the manufacturing and logistics domain. We conducted a systematic case study review and 12 semi-structured interviews with different manufacturing organisations to develop and validate our technology-specific process mining maturity grid for manufacturing and logistics. The resulting maturity grid is a tool for demonstrating development paths for the further application of process mining in organisations. The grid details maturity using three focus areas (i.e. data input, process mining utilisation, and integration) structured into five maturity levels.

Digital Health Transformation: Leveraging a Knowledge Graph Reasoning Framework and Conversational Agents for Enhanced Knowledge Management

The research focuses on the limitations of traditional systems in optimizing information flow in the healthcare domain. It focuses on integrating knowledge graphs (KGs) and utilizing AI-powered applications, specifically conversational agents (CAs), particularly during peak operational hours in emergency departments (EDs). Leveraging the Cross Industry Standard Process for Data Mining (CRISP-DM) framework, the authors tailored a customized methodology, CRISP-knowledge graph (CRISP-KG), designed to harness KGs for constructing an intelligent knowledge base (KB) for CAs. This KG augmentation empowers CAs with advanced reasoning, knowledge management, and context awareness abilities. We utilized a hybrid method integrating a participatory design collaborative methodology (CM) and Methontology to construct a domain-centric robust formal ontological model depicting and mapping information flow during peak hours in EDs. The ultimate objective is to empower CAs with intelligent KBs, enabling seamless interaction with end users and enhancing the quality of care within EDs. The authors leveraged semantic web rule language (SWRL) to enhance inferencing capabilities within the KG framework further, facilitating efficient information management for assisting healthcare practitioners and patients. This innovative assistive solution helps efficiently manage information flow and information provision during peak hours. It also leads to better care outcomes and streamlined workflows within EDs.

Probabilistic Temporal Reasoning using Superposition Semantics

Temporal logics over finite traces have recently seen wide application in a number of areas, from business process modelling, monitoring, and mining to planning and decision making. However, real-life dynamic systems contain a degree of uncertainty which cannot be handled with classical logics. We thus propose a new probabilistic temporal logic over finite traces using superposition semantics, where all possible evolutions are possible, until observed. We study the properties of the logic and provide automata-based mechanisms for deriving probabilistic inferences from its formulas. We then study a fragment of the logic with better computational properties. Notably, formulas in this fragment can be discovered from event log data using off-the-shelf existing declarative process discovery techniques.

Coal Gangue Utilization: Applications, Challenges, and Sustainable Development Strategies

Coal gangue is a kind of typical by-product emitted during the coal mining and washing process. With the increase in coal resource utilization, a large amount of coal gangue was not reasonably utilized, causing environmental pollution and resource waste. The main purpose of this article is to introduce the surface structural features and compositional characteristics of coal gangue and to summarize the utilization of coal gangue in the fields of building materials, energy production, agricultural utilization, and high-value-added areas such as catalysts and adsorbents. Secondly, this review discussed the environmental challenges and technical difficulties derived from the process of coal gangue utilization and how to solve these problems through innovative methods and technological improvements. Finally, the article proposed the development direction and strategies for the future resource utilization of coal gangue, emphasizing the importance of coal gangue as a sustainable resource and its significant role in achieving a circular economy for reducing environmental pollution. By analyzing the potentiality of coal gangue for resource utilization systematically, this article aims to provide valuable references and insights for researchers and decision-makers in related fields.

Getting the Data in Shape for Your Process Mining Analysis: An In-Depth Analysis of the Pre-Analysis Stage

Getting the Data in Shape for Your Process Mining Analysis: An In-Depth Analysis of the Pre-Analysis Stage

Mining and industrial processing wastes of phosphate rocks in Egypt: potentiality of rare earth elements

Abstract Globally, Egypt ranked the eighth position in the production of phosphorus with about 5 million tons annually. Phosphate ore is considered one of the commonest sources of rare earth elements (REEs). Mining and industrial processes (beneficiation) of phosphate ore produce huge amounts of wastes in different sizes. The present study aims to evaluate the potentiality of the phosphate ore wastes as a source of rare earth elements (REEs). The study targeted some phosphate production sites in Egypt, including: Abu Tartur, West and East El-Sebaiya phosphate mines, in addition to the fertilizers factory in Assiut. The collected waste samples, during 2023, were analyzed in terms of mineralogical composition using XRD (X-Ray Diffraction) and chemical composition using XRF (X-Ray Fluorescence). The results of chemical analysis recorded the highest concentrations of Y, Ce, La, Nd, Sc, Sm, U, and Th in Abu Tartur waste samples, while the lowest concentrations were recorded in the fertilizers factory waste samples. These wastes contain higher concentrations of REEs than the Algerian and USA wastes. Furthermore, an enrichment of the mostly environmental hazardous elements As, Cr, Cd, Pb, and Se are detected in Abu Tartur and West and East El-Sebaiya. Thus, the wastes from the industry and mining processing of phosphate represented a vital economic source for the production of REEs. The use of wastes for production of trace and rare earth elements represents an economic and environmental add value for phosphate industry.

Stability Analysis of Surrounding Rock in Mining Tunnels Based on Microseismic Monitoring and Numerical Simulation

In response to the safety hazards and environmental impacts caused by the decrease in the stability of the surrounding rock of the roadway and the frequent occurrence of microseismic activities during coal mining, the 4331 fully mechanized mining face of Nanpingdong Coal Mine was selected as a case study. Microseismic monitoring technology was used to analyze the spatial distribution of microseismic events in the surrounding rock during mining, and by establishing a FLAC3D numerical model, the displacement of surrounding rock and the evolution law of plastic zone during mining process are studied. The results confirmed that elastic strain energy in the rock is the primary source of microseismic energy. Using FISH language, a distribution cloud map of elastic strain energy was generated and compared with the microseismic event distribution and energy results. The findings indicate that as mining advances, the frequency and energy of microseismic events increase, particularly near faults, with roadway roof rupture exacerbating the events. The distribution of microseismic events correlates strongly with the depth of mining face advancement, highlighting the significant impact of mining activities on surrounding rock stability. The numerical simulation results closely align with on-site microseismic monitoring data, validating the simulation’s accuracy. This study proposes a method for dynamic monitoring and control of roadway surrounding rock stability through real-time microseismic monitoring and numerical simulation, aiming to mitigate surface environmental damage from underground mining.

Analysis of the Correlation Between Factors Affecting Green Mining in Open-Pit Mines

Green mining is of great significance for the sustainable development of mines. However, the current green mine evaluation index system proposed in China is complex, and the intrinsic logical relationships between the indicators are not yet clear. To address this issue, this article conducts an in-depth analysis and study of the green mine evaluation index system. First, 16 indicators directly related to mining activities were selected. Then, using data mining technology based on the Apriori algorithm, the actual data of 30 open-pit mines were analyzed, and 7 effective association rules among the green mining indicators of open-pit mines were extracted. The results show that the association rule composed of ecological function requirements, mining technology, and advanced technology and equipment has the highest confidence level, with a confidence of 100% and a support of 68%. Advanced technology and equipment are identified as a key indicator for achieving green mining, playing a role in all 7 association rules. The remaining six effective association rules further emphasize the importance of the compatibility between different mining processes in achieving green mining goals, indicating that green mining is a complex system engineering requiring comprehensive decision making. The effective association rules proposed in this article provide a theoretical basis for mining enterprises to formulate more scientific work plans and thereby achieve green development goals.

Study on deformation failure and permeability resistance of large faults under mining

The deformation and failure of large faults caused by coal seam mining are easy to form channels connected with aquifers, which is an important threat to the safe mining of coal seams. This study takes Beigongcun No.1 fault in No.7 mining area of Dongtan Coal Mine as a case, aiming to explore the deformation and permeability resistance of large faults. Through the establishment of engineering geological model, the FLAC 3D software is used to simulate the fluid-solid coupling of the mining process. The results show that as the working face advances, the compressive stress within 40 m from the fault decreases significantly, and it is most affected by mining at 20 m below the coal seam after mining to the fault. The calculated fault permeability strength is lower than the Ordovician limestone water pressure. Further simulation of fault mining with different dip angles shows that the failure modes are basically the same, but the failure depth is different. In the dip angle of 50° to 80°, the fault failure depth of 65° is the smallest, and the fault resistance strength of 60° is the largest. This shows that the depth of fault failure is the largest under a special dip angle.

The Impact of Linguistic Signals on Cognitive Change in Support Seekers in Online Mental Health Communities: Text Analysis and Empirical Study.

In online mental health communities, the interactions among members can significantly reduce their psychological distress and enhance their mental well-being. The overall quality of support from others varies due to differences in people's capacities to help others. This results in some support seekers' needs being met, while others remain unresolved. This study aimed to examine which characteristics of the comments posted to provide support can make support seekers feel better (ie, result in cognitive change). We used signaling theory to model the factors affecting cognitive change and used consulting strategies from the offline, face-to-face psychological counseling process to construct 6 characteristics: intimacy, emotional polarity, the use of first-person words, the use of future-tense words, specificity, and language style. Through text mining and natural language processing (NLP) technology, we identified linguistic features in online text and conducted an empirical analysis using 12,868 online mental health support reply data items from Zhihu to verify the effectiveness of those features. The findings showed that support comments are more likely to alter support seekers' cognitive processes if those comments have lower intimacy (βintimacy=-1.706, P<.001), higher positive emotional polarity (βemotional_polarity=.890, P<.001), lower specificity (βspecificity=-.018, P<.001), more first-person words (βfirst-person=.120, P<.001), more future- and present-tense words (βfuture-words=.301, P<.001), and fewer function words (βlinguistic_style=-.838, P<.001). The result is consistent with psychotherapists' psychotherapeutic strategy in offline counseling scenarios. Our research contributes to both theory and practice by proposing a model to reveal the factors that make support seekers feel better. The findings have significance for support providers. Additionally, our study offers pointers for managing and designing online communities for mental health.

Analysis of Causes and Consequences of Failures in Process of Andesite Crushing by Jaw Crusher

Mining and mineral processing are essential for the functioning of many economic sectors and for meeting human needs. Diagnostics and evaluations of faults are necessary to ensure the successful and responsible management of mining and processing processes for mineral raw materials. Fault-free operation contributes to increased efficiency, productivity, safety, and reliability, reduces the cost of the process under consideration, and reduces environmental impacts. This study aims to identify and analyze possible component failures associated with the jaw crusher used in the process of andesite crushing in an open-pit quarry and compare different approaches to their assessment. The benefit of this is using three different failure analysis methods to assess the criticality of individual jaw crusher components. This approach’s novelty lies in the synergy that occurs when assessing the failures’ impacts on safety, quality, and the environment.

Control technology of roadway floor heave based on a new grouting material

Abstract In the mining process of the working face, mining activities will inevitably damage the surrounding rock of the roadway near the working face and pose a threat to the safety of coal miners. The stability of the roadway is of great significance for the safe production of coal mines. In this paper, a new type of grouting material is used to treat the failure characteristics of the roadway with rock bolts and bottom plate unloading grooves, and the deformation of the roadway after treatment is monitored. The results show that stress concentration occurs after the mining of the working face, with a large influence range and broken surrounding rock. The new grouting material has a wide setting time range, which can meet the grouting needs of different broken surrounding rocks. The dry shrinkage and bleeding rate of the stone-forming body are small, and the slurry has good stability. The strength of consolidated coal after 8 hours can reach 13.1 MPa. After roadway treatment, the displacements of the roadway are 59 mm and 46 mm respectively. The deformation of the roadway is effectively controlled.

Three-Dimensional Deformation Prediction Based on the Improved Segmented Knothe–Dynamic Probabilistic Integral–Interferometric Synthetic Aperture Radar Model

Coal is the main mineral resource, but over-exploitation will cause a series of geological disasters. Interferometric synthetic aperture radar (InSAR) technology provides a superior monitoring method to compensate for the inadequacy of traditional measurements for mine surface deformation monitoring. In this study, the whole process of mining a working face in Huaibei Mining District, Anhui Province, is taken as the object of study. The ALOS PALSAR satellite radar image data and ground measurements were acquired, and the ISK-DPIM-InSAR deformation monitoring model with the dynamic probabilistic integral model (DPIM) was proposed by combining the probabilistic integral method (PIM) and the improved segmented Knothe time function (ISK). The ISK-DPIM-InSAR model constructs the inversion equations of InSAR line-of-sight deformation, north–south and east–west horizontal movement deformation, vertical deformation, inverts the optimal values of the predicted parameters of the workforce through the particle swarm algorithm, and substitutes it into the ISK-DPIM-InSAR model for predicting the three-dimensional dynamic deformation of a mining face. Simulated workface experiments determined the feasibility of the model, and by comparing the level observation results of the working face, it is confirmed that the ISK-DPIM-InSAR model can accurately monitor the three-dimensional deformation of the surface in the mining area.

Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost

Fully mechanized mining equipment is core to the coal mining process. The selection process for this type of equipment is complex and heavily relies on experts’ experience for determining equipment parameters. This paper proposes a fully mechanized mining equipment parameter prediction model based on Extreme Gradient Boosting Regression Trees (XGBoost), which is developed based on the mapping relationships among geological parameters, fully mechanized mining face conditions, and the parameters of fully mechanized mining equipment. Feature selection is performed based on the feature importance ranking obtained through the Random Forest (RF) method, thereby reducing the model complexity. Different optimization algorithms are used to optimize the hyperparameters of XGBoost, and the results show that the Whale Optimization Algorithm (WOA) outperforms other algorithms in terms of convergence speed and optimization effectiveness. By comparing different prediction algorithms, it is found that the WOA-XGBoost model achieves higher prediction accuracy on the test set, with an average absolute error of 0.0458, root mean square error of 0.1610, and a coefficient of determination (R2) of 0.9451. Finally, a RF-WOA-XGBoost-based parameter prediction model for fully mechanized mining equipment is established, which is suitable for lightly inclined mining faces. This model reduces input complexity, improves the selection speed, minimizes reliance on experts, and ensures prediction accuracy, providing an effective reference for the parameter selection of fully mechanized mining equipment.

The WHY in Business Processes: Discovery of Causal Execution Dependencies

Abstract Unraveling the causal relationships among the execution of process activities is a crucial element in predicting the consequences of process interventions and making informed decisions regarding process improvements. Process discovery algorithms exploit time precedence as their main source of model derivation. Hence, a causal view can supplement process discovery, being a new perspective in which relations reflect genuine cause-effect dependencies among the tasks. This calls for faithful new techniques to discover the causal execution dependencies among the tasks in the process. To this end, our work offers a systematic approach to the unveiling of the causal business process by leveraging an existing causal discovery algorithm over activity timing. In addition, this work delves into a set of conditions under which process mining discovery algorithms generate a model that is incongruent with the causal business process model, and shows how the latter model can be methodologically employed for a sound analysis of the process. Our methodology searches for such discrepancies between the two models in the context of three causal patterns, and derives a new view in which these inconsistencies are annotated over the mined process model. We demonstrate our methodology employing two open process mining algorithms, the IBM Process Mining tool, and the LiNGAM causal discovery technique. We apply it to a synthesized dataset and two open benchmark datasets.

Application of Transformer Models for Advanced Process Optimization and Process Mining

Application of Transformer Models for Advanced Process Optimization and Process Mining

Toxicity of nickel, copper, and selenium in medaka embryos (oryzias latipes): a comparative study.

The indispensability of biometals nickel, copper, and selenium in pharmaceutical, agricultural, and other industrial applications, coupled with their release from mining processes, has made them potent environmental contaminants, especially when present in aquatic ecosystems at levels above the essential range. The toxicity of these biometals in fish embryogenesis, including their toxicity levels, was studied using medaka embryos. Test solutions (0.001-10 ppm) of the biometals, along with an isotonic solution as a control, were introduced into the embryos using a nanosecond pulsed electric field application. The exposed embryos were cultured at 25 ± 1°C and microscopically observed daily for 14 days in an isotonic solution. Developmental abnormalities and toxicity were observed during the 14-day observation period. All biometals caused some abnormalities in developing embryos at all concentrations. Major abnormalities included delayed development; deformities such as curvature of bones or spines; abnormal formation of the hearts, eyes, and circulatory systems; and mortality. The toxicity of the biometals was significantly different (p < 0.05) from that of the control. Gene expression analysis revealed that 4747, 1961, and 1952 genes were affected by copper, nickel, and selenium, respectively. Copper affected the highest number of genes and caused the highest toxicity. These results indicate that nickel, copper, and selenium can cause toxicity in developing fish embryos at concentrations ranging from 0.01 ppb to 10 ppm. Therefore, there is a need to constantly monitor the levels of these biometals, particularly in aquatic ecosystems, to preserve aquatic life.