Mining Process Research Articles

Multi-skill resource-constrained project scheduling problem: a case study in an open-pit mining process

In this article, a case study is analysed using an adaptation of a project scheduling problem with multitasking resource restrictions. The objective is to reduce the operational costs of an open-pit mining process that uses mobile surface equipment, by allocating resources to activities, providing one or more skills, and considering constraints related to sequence, precedence, resource capacity and use, and operator allocation. To solve the problem, optimization models are proposed, i.e. single-product models with/without operator assignment, and multiproduct models with/without operator assignment. These models contribute towards the body of knowledge on optimization models for resource-constrained project scheduling problems. They were tested with real historical data from a ceramics company, considering different lot sizes and types of products, among other characteristics. The results validate the performance of the proposed models in terms of the quality of the solutions, savings and computational time for agile decision making.

MantaRay-ProM: An efficient process model discovery algorithm

Discovering the business process model from an organisation’s records of its operational processes is an active area of research in process mining. The discovered model may be used either during a new system rollout or to improve an existing system. In this paper, we present a process model discovery approach based on the recently proposed bio-inspired Manta Ray Foraging Optimization algorithm (MRFO). Since MRFO is designed to solve real-valued optimization problems, we adapted a binary version of MRFO to suit the domain of process mining. The proposed approach is compared with state-of-the-art process discovery algorithms on several synthetic and real-life event logs. The results show that compared to other algorithms, the proposed approach exhibits faster convergence and yields superior quality process models.

Microseismic Monitoring and Disaster Warning via Mining and Filling Processes of Residual Hazardous Ore Bodies

The thick ore bodies in the Xianglushan tungsten mine have been irregularly mined, forming a super large, connected irregular goaf group and tall, isolated irregular pillars inside. At the same time, there is a production capacity task of recovering residual and dangerous ore bodies. This poses the potential for serious ground-pressure disasters, such as roof caving, pillar collapse, and large-scale goaf collapse during mining. Based on the actual needs of the site, we established a microseismic monitoring system. After analyzing the mining and filling processes and their relationships, and, combined with the distribution characteristics of microseismic multiple parameters, we constructed a ground-pressure disaster warning mode and mechanism. We analyzed the stability of the goaf, further formed a warning system, and achieved disaster warning. In response to the current situation of the difficulty of early warning of ground pressure in the Xianglushan tungsten mine, continuous on-site monitoring of existing goaves, point pillars, and strip pillars, as well as analysis of stress changes during dynamic mining and filling processes, we explored scientific and reasonable early warning mechanisms and models, understanding the relationship between the changes in microseismic parameters during dynamic mining and filling processes and ground pressure, studying and improving the reliability of underground microseismic monitoring and early warning, and achieved the internal connection between building early warning systems and the prevention of ground-pressure disasters. The results indicate that the mining and filling process of the ore body is the main factor in maintaining a stable and balanced distribution of underground ground pressure in mining engineering. Microseismic monitoring can invert the evolution of ground pressure and form a feedback system with ground-pressure warning, achieving mine safety management.

Constructing a decision-making tool for HRM process by predicting organizational citizenship behavior

Purpose The complexity of citizenship behavior in organizations has long been a focus of research. Traditional methodologies have been predominantly used to address this complexity. This paper aims to tackle the problem using a cutting-edge technological tool: business process mining. The objective is to enhance citizenship behaviors by leveraging primary data collected from 326 white-collar employees in the Indian service industry. Design/methodology/approach The study focuses on two main processes: training and creativity, with the ultimate goal of fostering organizational citizenship behavior (OCB), both in its overall manifestation (OCB-O) and its individual components (OCB-I). Seven different machine learning algorithms were used: artificial neural, behavior, prediction network, linear discriminant classifier, K-nearest neighbor, support vector machine, extreme gradient boosting (XGBoost), random forest and naive Bayes. The approach involved mining the most effective path for predicting the outcome and automating the entire process to enhance efficiency and sustainability. Findings The study successfully predicted the OCB-O construct, demonstrating the effectiveness of the approach. An optimized path for prediction was identified, highlighting the potential for automation to streamline the process and improve accuracy. These findings suggest that leveraging automation can facilitate the prediction of behavioral constructs, enabling the customization of policies for future employees. Research limitations/implications The findings have significant implications for organizations aiming to enhance citizenship behaviors among their employees. By leveraging advanced technological tools such as business process mining and machine learning algorithms, companies can develop more effective strategies for fostering desirable behaviors. Furthermore, the automation of these processes offers the potential to streamline operations, reduce manual effort and improve predictive accuracy. Originality/value This study contributes to the existing literature by offering a novel approach to addressing the complexity of citizenship behavior in organizations. By combining business process mining with machine learning techniques, a unique perspective is provided on how technological advancements can be leveraged to enhance organizational outcomes. Moreover, the findings underscore the value of automation in refining existing processes and developing models applicable to future employees, thus improving overall organizational efficiency and effectiveness.

Experimental Investigation on collection efficiency of Deep-Sea hydraulic collection parameters

Hydraulic suction collection has been extensively utilized in deep-sea mining due to its high flow rate and efficiency. This paper introduces a novel hydraulic collection model to investigate the impact of various collection parameters on collection efficiency. We analyze the effect of different ore collection parameters on collection efficiency using a radial basis function network and make predictions. The optimal parameter range was obtained by performing combined parameter experiments. The two-parameter combination give better results compared to a single parameter. A theoretical model of collection efficiency is established based on experimental observations and principles of water jet theory. The accuracy of the model is confirmed through experimental validation, and the research findings can provide valuable insights for the ore collection process in deep-sea mining.

The interface between research funding and environmental policies in an emergent economy using neural topic modeling: Proposals for a research agenda

AbstractResearchers have been calling for further analysis of the relationships between academia and policy making. Since several studies are conducted with resources from public funding agencies, it is essential to assess whether governments have been supporting research that can contribute to future environmental policies and whether research has been supporting the elaboration of public policies. To fill these gaps, the objective of this study was to evaluate the similarities and gaps between research and environmental policies. A bibliometric analysis of studies funded by Chilean agencies and text analytics techniques were employed to analyze research and environmental policies. We used BERTopic, a topic modeling technique that generates coherent topic representations. Then, topics obtained from the two sets of documents (research and policies) were compared. This study found that sustainability, life‐cycle assessment, climate change, and ecosystem services are central research themes in the country. The topic modeling analysis showed that research has been concentrated on the areas of “Sustainable Fisheries and Aquaculture Management” and “Sustainable Bioenergy Production and Chemical Processes.” The topics most frequently present in the environmental policies were “Environmental Education for Children: Awareness and Care,” “Bird Conservation and Habitat Importance in Chile,” and “National Biodiversity Conservation and Sustainable Use Strategy.” The greatest gaps are observed in the clusters of “Sustainable Bioenergy Production and Chemical Processes” and “Copper Mining and Mineral Processing Techniques” (prevalent in research) and in “Anatomy and Physiology of Aquatic and Terrestrial Organisms” and “Biogeography and Taxonomy of Southern Hemisphere Insects” (prevalent in policy).

Sequence analysis and process mining perspectives to goal setting: What distinguishes business students with high and low self-efficacy beliefs?

This study investigates the relationship between students' self-efficacy beliefs, goal-setting, and learning tactics in an online business course. Using sequence analysis and process mining techniques, we analyzed log data from 209 students to identify distinct learning tactics and their association with self-efficacy beliefs, inferred from initial goal-setting and final grades. Four learning tactics were identified, with significant differences observed between students with high and low self-efficacy beliefs. High self-efficacy students demonstrated more varied and adaptive tactics, including greater use of quizzes and project-focused activities. In contrast, low self-efficacy students and those who didn't set goals showed less engagement and fewer monitoring activities. The project-focused tactic had the strongest correlation with final grades and goal achievement. Goal-setting at the course's start was linked to more effective learning behaviors and better outcomes. The study reveals how students' online learning behavior changes based on initial goal-setting and subsequent reflection. These findings contribute to research on self-regulated learning in online environments and offer practical implications for designing online courses and learning analytics interventions. Understanding these differences enables the development of targeted interventions to improve learning tactics and self-efficacy beliefs among students, ultimately enhancing their ability to achieve learning goals in online educational settings.

Analysis and discovery of procrastination patterns in a language learning MOOC

Online learning has been gaining a broad notoriety in society in the last years. The ease with which users from all over the world can learn is one of its main attractions. MOOCs are one of these technologies, which have enabled users to learn almost any subject of their interest. The use of MOOCs generates a massive amount of data that has been used by researchers with different goals: to predict dropout, predict grades and determine learning styles. Users of MOOCs face several challenges, some of which have been extensively studied. However, there are some of them, such as procrastination, whose study as a determining factor in the failure of such courses has not been addressed in sufficient depth. Through this paper, the influence of procrastination on the failure rates of users in a MOOC has been uncovered. Furthermore, by using process mining, this research has revealed the existence of a pattern of procrastination and the type of material used during study sessions by the users who fail the course. Speclifically, diverse forms of procrastination have been identified, resulting in differing effects on the educational outcomes attained by the user. These findings possess considerable implications, as they underscore the potential worth of detecting specific procrastination patterns to ascertain users who necessitate supplementary support during their educational pursuits within MOOCs.

Relating behaviour of data-aware process models

Data Petri nets (DPNs) have gained traction as a model for data-aware processes, thanks to their ability to balance simplicity with expressiveness, and because they can be automatically discovered from event logs. While model checking techniques for DPNs have been studied, more complex analysis tasks that are highly relevant for BPM are beyond methods known in the literature. We focus here on equivalence and inclusion of process behaviour with respect to language and configuration spaces, optionally taking data into account. Such comparisons are important in the context of key process mining tasks, namely process repair and discovery, and related to conformance checking. To solve these tasks, we propose approaches for bounded DPNs based on constraint graphs, which are faithful abstractions of the reachable state space. Though the considered verification tasks are undecidable in general, we show that our method is a decision procedure DPNs that admit a finite history set. This property guarantees that constraint graphs are finite and computable, and was shown to hold for large classes of DPNs that are mined automatically, and DPNs presented in the literature. The new techniques are implemented in the tool ada, and an evaluation proving feasibility is provided.

Surface damage reduction effect of ultra-high working face in Shangwan coal mine

Green mining is the basic background of high-quality mining, and source reduction is the most important part, among which the optimization of working face height and length is the most active. How to control the working face parameters, reasonably evaluate the surface subsidence characteristics of the super-long working face, and identify the limit working face length and the surface discontinuous deformation threshold is particularly important. In order to solve the problem of irreversible damage to the surface caused by the mining process of the working face, this paper discusses the whole process of surface movement in the 8.8 m super-large mining height working face of Shangwan Coal Mine through field investigation, the maximum subsidence is 6.20 m, with a subsidence coefficient of 0.72. Combined with the inflection point trajectory, advancing degree and subsidence coefficient, the 0.9 coefficient of the working face subsidence basin boundary and the loss reduction strategy far from the limit working face threshold are proposed. The results show that the subsidence basin of 12,401 working face accounts for 34%, the continuous deformation area of surface accounts for 64%, and the influence area of discontinuous deformation area is within 10 times of mining height. With the help of borehole detection verification, the caving zone height within the coal seam overburden to be between 33.2 and 33.25 m and the fracture zone height between 118.08 and 132.83 m. Therefore, the caving ratio is about 3.8, and the fracture ratio is about 13–15, which provides a strong basis for the optimization design of working face and the timing of surface ecological management.

Applications of satellite platforms and machine learning for mapping and monitoring grasslands and pastures: A systematic and comprehensive review

Grasslands and pastures are critical ecosystems globally, essential for their agricultural and environmental roles. Extensive research from 2000 to 2022, comprising 504 articles, has explored the integration of remote sensing and statistical modelling for mapping and monitoring grassland and pasture ecosystems. These articles were sourced from the SCOPUS database and analysed using text mining and natural language processing techniques to investigate the evolution of publication trends over the past twenty-two years. The number of publications per year on this topic has grown consistently in the considered period, from 3 in 2000 to 93 in 2022, doubling their weight compared to the total number of Scopus publications. The quantitative analysis of satellite platform utilisation revealed the increasing importance of Sentinel-2, even though MODIS remained the most utilised satellite platform throughout the study period. The increasing availability of big data has helped spread the utilisation of machine learning algorithms, mostly in the last ten years. Among these, random forest appeared to be the most widespread for grassland and pasture studies. Researchers' primary interest in this field centres on the technologies and their applications. This is evidenced by cluster analysis, which reveals a dominant focus on terms related to the 'Instruments' (25.8 %) and 'Parameters' (24.9 %) categories. This analysis aims to outline the progression of research, offering insights that could be useful in forecasting future trends and facilitating stakeholder engagement in this sector.

Understanding Patterns of Care for Older Adults: Data to Action

ObjectiveOlder adult population is growing rapidly, the current system is not adequately designed to meet the needs of individuals to live safely and independently in the place of their choice. To understand the variations in care and identify where to intervene, we examined the transitions in care of community-dwelling older adults (COA) leading up to 24-hour assisted living at special care homes (SCH). ApproachWe conducted a retrospective-cohort study using health administrative data from a Canadian province with a long history of comprehensive, longitudinal data. Using process mining techniques, health service utilization of COA, 65 and older, was mapped as a journey rather than independent care events. We examined the common patterns experienced leading up to SCH admission by sequencing transitions between care settings. Consideration was given to geography that the individual resides in and their health status to understand variations. ResultsOverall health utilization patterns revealed predominant reliance on acute care and SCH admissions. However, geographic variations were identified (regions and urban/rural). More than 2000 transition patterns between emergency department, hospital, community, and SCH were observed. ConclusionThe ability for COA to age in place is dependent on the individual, where they live, and the availability of and access to services to meet their needs. To minimize undesirable and early admissions to SCH, a multifaceted, collaborative approach to align community-based supports for COA is required. The results of these analyses will help inform areas in which to focus interventions and serve as a baseline while evaluating impact.

Develop a data-driven approach under the integration of 4D visualization and process mining to simulate, diagnose and predict real-world construction execution

Develop a data-driven approach under the integration of 4D visualization and process mining to simulate, diagnose and predict real-world construction execution

Optimization of Marketing Strategy for State-Owned Energy Products through Sentiment Analysis with VADER and LSTM on Social Media

Sentiment analysis is also known as opinion mining. It has an important role in natural language processing and data mining. It involves extracting and analyzing subjective information from textual data to determine the sentiment. With the advancement of technology, it is increasingly important to understand users' opinions and sentiments regarding a particular product, service or issue. This research aims to optimize the marketing strategy of energy products in SOE subsidiaries through sentiment analysis using the VADER and LSTM methods on social media.The researcher analyzed data from twitter to identify consumer sentiment towards energy products. The results of the analysis show that the combination of VADER and LSTM is effective in identifying the nuances of sentiment, with the accuracy of sentiment classification reaching 85%. Based on the analysis, the researchers developed recommendations for marketing strategies that are more focused and responsive to public perception. This study also highlights the importance of two-way interaction between companies and consumers as part of an effective marketing strategy. The findings of this study are expected to be used as a reference by state-owned companies in the energy sector to improve the effectiveness of their marketing strategies through a sentiment analysis approach on social media.

A framework for understanding event abstraction problem solving: Current states of event abstraction studies

Event abstraction is a crucial step in applying process mining in real-world scenarios. However, practitioners often face challenges in selecting relevant research for their specific needs. To address this, we present a comprehensive framework for understanding event abstraction, comprising four key components: event abstraction sub-problems, consideration of process properties, data types for event abstraction, and various approaches to event abstraction. By systematically examining these components, practitioners can efficiently identify research that aligns with their requirements. Additionally, we analyze existing studies using this framework to provide practitioners with a clearer view of current research and suggest expanded applications of existing methods.

Study of the development patterns of water-conducting fracture zones under karst aquifers and the mechanism of water inrush

The hydrogeological conditions of the Qianbei coalfield are complex, and karst water in the roof rock frequently disrupts mining operations, leading to frequent water inrush incidents. Taking the representative Longfeng Coal Mine as a case study, research was conducted on the development pattern of the water-conducting fracture zone and the water inrush mechanisms beneath karst aquifers. On the basis of key stratum theory and calculations of the stratum stretching rate, the karst aquifer in the Changxing Formation was identified as the primary key stratum. It was deduced that the water-conducting fracture zone would develop into the karst aquifer, indicating a risk of roof water inrush at the working face. Numerical simulations were used to study the stress field, displacement field, and plastic zone distribution patterns in the overlying roof strata. Combined with similar simulation tests and digital speckle experiments, the spatiotemporal evolution characteristics of the water-conducting fracture zone were investigated. During the coal mining process, the water-conducting fracture zone will exhibit a "step-type" development characteristic, with the fracture morphology evolving from vertical to horizontal. Near the goaf boundary, the strain gradually decreases, and the instability of the primary key stratum significantly impacts the mining space below, leading to the closure of interlayer voids or the redistribution of water-conducting fissure patterns. Field measurements of the water-conducting fracture zone reveal that postmining roof fractures can be classified into tensile-shear, throughgoing, and discrete types, with decreasing water-conducting capacity in that order, the measured development height of the water-conducting fracture zone (51 m) aligns closely with the theoretical height (51.37 m) and the numerical simulation height (49.17 m). Finally, from the perspective of key stratum instability, the disaster mechanisms of dynamic water inrush and hydrostatic pressure water inrush beneath the karst aquifers in the northern Guizhou coalfield were revealed. The findings provide valuable insights for water prevention and control efforts in the Qianbei coalfield mining area.

Classification of Logging Data Using Machine Learning Algorithms

A log data analysis plays an important role in the uranium mining process. Automating this analysis using machine learning methods improves the results and reduces the influence of the human factor. In particular, the identification of reservoir oxidation zones (ROZs) using machine learning allows a more accurate determination of ore reserves, and correct lithological classification allows the optimization of the mining process. However, training and tuning machine learning models requires labeled datasets, which are hardly available for uranium deposits. In addition, in problems of interpreting logging data using machine learning, data preprocessing is of great importance, in other words, a transformation of the original dataset that allows improving the classification or prediction result. This paper describes a uranium well log (UWL) dataset generated with the employment of floating data windows and designed to solve the problems of identifying ROZ and lithological classification (LC) on sandstone-type uranium deposits. Comparative results of the ways of solving these problems using classical machine learning methods and ensembles of machine learning algorithms are presented. It has been shown that an increase in the size of the floating data window can improve the quality of ROZ classification by 7–9% and LC by 6–12%. As a result, the best-quality indicators for solving these problems were obtained, f1_score_macro = 0.744 (ROZ) and accuracy = 0.694 (LC), using the light gradient boosting machine and extreme gradient boosting, respectively.

Data mining for the Analysis of Content Interaction in web-based Learning and Training Systems

The process of data mining is exploring databases for useful information and extracting it. Analysis how people interact with websites is the main goal of web usage mining, a subfield of data mining. The main, unobtrusive, and objective way to evaluate Web-based training and learning systems, namely how users engage with course materials, is web use mining. Many mining methods were developed with the classroom in mind, and we will showcase and explain them all. In order to prove effectiveness and improve instructional design as required, it is crucial to analyse and evaluate how learners behave in learning and training technology systems. This is particularly true when there are several interactive learning and training components accessible. We take a look at methods for figuring out what we want to learn, how we're going to learn it, and how our habits change as we go.

Prediction of Ground Subsidence Induced by Groundwater Mining Using Three-Dimensional Variable-Parameter Fully Coupled Simulation

In order to predict the ground settlement in a scientific, intuitive, and simple way, based on the theory of Bio-consolidation, a three-dimensional fluid-solid coupled numerical calculation programme FGS-3D for ground settlement was compiled by using the Fortran 95 language, and a front-end operation platform was developed by using Microsoft VisualBasic, so that a three-dimensional variable-parameter fully coupled viscoelastic-plastic model of ground settlement was constructed using the city of Yancheng as an example, and the development of ground settlement and horizontal displacement changes from 2021 to 2030 were predicted. The results show that the three-dimensional fully coupled finite-element numerical model of building load, groundwater seepage, and soil deformation established by the above computer development program can directly create a hydrogeological conceptual model of groundwater mining and predict ground settlement, so as to achieve the visualisation of the three-dimensional seepage of groundwater and the fully coupled simulation of ground subsidence in the whole process of groundwater mining. Under the joint action of construction load and groundwater mining, the water level of the aquifer in Yancheng City rises by 1.26 m on average in the main groundwater mining area of the group III pressurised aquifer, forming two smaller landing funnels, and the lowest water level of the two landing funnels is −15 m.

Temporal Protein Complex Identification Based on Dynamic Heterogeneous Protein Information Network Representation Learning.

Protein complexes, as the fundamental units of cellular function and regulation, play a crucial role in understanding the normal physiological functions of cells. Existing methods for protein complex identification attempt to introduce other biological information on top of the protein-protein interaction (PPI) network to assist in evaluating the degree of association between proteins. However, these methods usually treat protein interaction networks as flat homogeneous static networks. They cannot distinguish the roles and importance of different types of biological information, nor can they reflect the dynamic changes of protein complexes. In recent years, heterogeneous network representation learning has achieved great success in processing complex heterogeneous information and mining deep semantics. We thus propose a temporal protein complex identification method based on Dynamic Heterogeneous Protein information network Representation Learning, DHPRL. DHPRL naturally integrates multiple types of heterogeneous biological information in the cellular temporal dimension. It simultaneously models the temporal dynamic properties of proteins and the heterogeneity of biological information to improve the understanding of protein interactions and the accuracy of complex prediction. Firstly, we construct Dynamic Heterogeneous Protein Information Network (DHPIN) by integrating temporal gene expression information and GO attribute information. Then we design a dual-view collaborative contrast mechanism. Specifically, proposing to learn protein representations from two views of DHPIN (1-hop relation view and meta-path view) to model the consistency and specificity between nearest-neighbour bio information and deeper biological semantics. The dynamic PPI network is thereafter re-weighted based on the learned protein representations. Finally, we perform protein identification on the re-weighted dynamic PPI network. Extensive experimental results demonstrate that DHPRL can effectively model complicated biological information and achieve state-of-the-art performance in most cases.