Mine Economic and Management
mahdi sanei; Mohammadreza Ameri
Abstract
The risks associated with mining activities constitute a critical area of inquiry within Islamic jurisprudence, particularly because mineral resources serve as strategic assets that significantly influence global economic stability. A rigorous examination of these hazards through a jurisprudential framework ...
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The risks associated with mining activities constitute a critical area of inquiry within Islamic jurisprudence, particularly because mineral resources serve as strategic assets that significantly influence global economic stability. A rigorous examination of these hazards through a jurisprudential framework underscores the necessity of formulating effective, ethically grounded strategies for their mitigation to ensure the responsible and equitable exploitation of mineral reserves. In modern industrial contexts, mining operations are increasingly confronted with a wide spectrum of hazards, ranging from physical and chemical risks to environmental, social, and health-related challenges, each of which poses substantial threats to human welfare, ecological integrity, and the sustainability of natural resources. Employing an analytical–descriptive methodology, this study systematically investigates these hazards and contextualizes them within established jurisprudential principles. Through this alignment, the article proposes comprehensive strategies—including targeted education, heightened awareness, expert consultation, continuous evaluation, and robust monitoring mechanisms —to reduce or eliminate mining-related risks throughout the processes of policy formulation, legislative development, and operational implementation. Adherence to these jurisprudentially informed measures not only minimizes potential harm to individuals and the environment but also ensures the provision of appropriate remedies and compensation in cases involving negligence or procedural lapses. Consequently, the study emphasizes that employers, mine proprietors, technical supervisors, mining personnel, and governmental authorities each bear distinct and critical responsibilities in the collective effort to mitigate and ultimately eliminate mining hazards.
Mine Economic and Management
Mohamad Reza ameri; Mohammad Mehdi Rajaei; Abuzar Faraji
Abstract
AbstractThis study provides a systematic bibliometric and thematic review of research on risk assessment in the mining industry. The focus is on fuzzy inference systems (FIS), artificial intelligence (AI), and hybrid FIS–AI approaches. A dataset of 1,607 articles from Scopus was analyzed to identify ...
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AbstractThis study provides a systematic bibliometric and thematic review of research on risk assessment in the mining industry. The focus is on fuzzy inference systems (FIS), artificial intelligence (AI), and hybrid FIS–AI approaches. A dataset of 1,607 articles from Scopus was analyzed to identify publication trends, geographic distribution, citation patterns, and key themes. Using the PRISMA protocol, titles and abstracts were screened, and relevant studies were selected for detailed review The results indicate a steady growth in research output over the past decade, reflecting the increasing importance of intelligent systems in addressing uncertainty and complexity in mining operations. Developed countries tend to prioritize AI-driven methods such as machine learning, neural networks, and hybrid systems. In contrast, developing countries place greater reliance on fuzzy logic approaches, particularly in contexts where reliable data are limited. This methodological divergence underscores uneven technological development and highlights the existing knowledge gap across regions. Three main research pillars are identified: safety (39%), operational efficiency (45%), and environmental sustainability (16%). Methodologically, fuzzy approaches dominate (48%), followed by AI (34%) and hybrid methods (18%). These findings confirm the global relevance of AI and FIS in mining risk assessment and emphasize the need for collaboration to close existing gaps.
Mine Economic and Management
Sarina Akbari; Reza Ghezelbash; Hamidreza Ramazi; Abbas Maghsoudi
Abstract
Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies ...
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Natural hazards, particularly landslides, have long posed significant threats to people, buildings, and the surrounding environment. Therefore, comprehensive planning for urban and rural development necessitates the development and implementation of landslide risk zoning models. Numerous methodologies have been proposed for generating landslide hazard maps, which can potentially aid in predicting future landslide-prone areas. This study employed an integrated approach that combines statistical and multi-criteria decision-making (MCDM) methodologies. The Frequency Ratio (FR) and Analytical Hierarchy Process (AHP) were utilized as knowledge-driven approaches, while the Support Vector Machine (SVM) using an RBF kernel, a widely recognized machine learning algorithm, was applied as a data-driven method. Ten factors influencing landslides were considered, including slope angle, aspect, altitude, geology, land use, climate, erosion, and distances from rivers, faults, and roads. The results revealed that landslides are more predictable in the southern, southwestern, and central regions of the studied area. A quantitative assessment of the different methods using prediction-rate curves indicated that the SVM method outperformed the FR and AHP-FR approaches in identifying susceptible areas. The findings of this work could be effectively employed to mitigate potential future hazards and associated damages.
Mine Economic and Management
Saira Sherin; Salim Raza
Abstract
Despite a decline in mining accidents and improvements in safety performance, the proportion of accidents in mines remains high in developing countries. Although underground mining is one of the most hazardous occupations, surface mining also carries multiple risks that receive comparatively less attention. ...
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Despite a decline in mining accidents and improvements in safety performance, the proportion of accidents in mines remains high in developing countries. Although underground mining is one of the most hazardous occupations, surface mining also carries multiple risks that receive comparatively less attention. In developing countries like Pakistan, research is focused mainly on fatal and serious accidents, often overlooking minor and near-miss accidents. This study assesses the risks of fatalities and injuries faced by occupational groups engaged in surface mining. For this purpose, an analytical hierarchy process is used to analyze fatalities data and Fuzzy TOPSIS for injuries data. It can be concluded that all occupational groups are exposed to fatalities and injuries risks due to various hazards. However, some activities are more prone to fatalities while others are to injuries. Laborers are most frequently involved in such accidents. Common risks such as falling rocks and slippage from the top affect all occupational groups equally. Incidents involving slippages from the tops result in more fatalities, whereas machinery-related risks lead to more injuries than fatalities. Hazards causing minor injuries are frequently overlooked in terms of prevention and control efforts until they lead to serious injuries/fatalities. It is suggested that every accident, regardless of severity, be reported and thoroughly analyzed regularly to minimize the recurrence of incidents. The essential measures for creating a safer mining environment include implementing appropriate mechanization, providing regular training to workers, enforcing the use of personal protective equipment, and strict adherence to mining laws.
Mine Economic and Management
Mahdi Pouresmaieli; Mohammad Ataei; Ali Nouri Qarahasanlou; Abbas Barabadi
Abstract
The mining industry operates in a complex and dynamic environment and faces many challenges that can negatively affect sustainable development goals. To avoid these effects, mining needs to adopt strategic decisions. Therefore, it requires effective decision-making processes for resource optimization, ...
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The mining industry operates in a complex and dynamic environment and faces many challenges that can negatively affect sustainable development goals. To avoid these effects, mining needs to adopt strategic decisions. Therefore, it requires effective decision-making processes for resource optimization, operational efficiency, and sustainability. Multicriteria decision-making methods (MCDM) have been considered valuable decision-support tools in the mining industry. This article comprehensively examines MCDM methods and their applications in the mining industry. This article discusses the basic principles and concepts of MCDM methods, including the ability to prioritize and weigh conflicting, multiple criteria and support decision-makers in evaluating diverse options. According to the results, 1579 MCDM articles in mining have been published from the beginning to April 15, 2023, and a scientometric analysis was done on these articles. In another part of this article, 19 MCDM methods, among the most important MCDM methods in this field, have been examined. The process of doing work in 17 cases of the reviewed methods is presented visually. Overall, this paper is a valuable resource for researchers, mining industry professionals, policymakers, and decision-makers that can lead to a deeper understanding of the application of MCDM methods in mining. By facilitating informed decision-making processes, MCDM methods can potentially increase operational efficiency, resource optimization, and sustainable development in various mining sectors, ultimately contributing to mining projects' long-term success and sustainability.
Mine Economic and Management
Aditi Nag; Smriti Mishra
Abstract
Integrating Artificial Intelligence (AI) into heritage tourism has opened new avenues for transforming visitors’ engagement with historical sites. This research paper delves into a novel paradigm, focusing on AI integration in inter- and intra-regional mining heritage site planning and design. ...
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Integrating Artificial Intelligence (AI) into heritage tourism has opened new avenues for transforming visitors’ engagement with historical sites. This research paper delves into a novel paradigm, focusing on AI integration in inter- and intra-regional mining heritage site planning and design. Recognizing this context's unique challenges and opportunities, the study aims to uncover critical ideas and theories on how AI enhances visitor experience, promotes cultural preservation, sustainability, and stakeholder collaboration. Acknowledging the distinctive challenges and opportunities presented by inter- and intra-regional mining heritage contexts, this research work underscores the critical importance of striking a harmonious equilibrium between technological advancements and preserving historical and cultural legacies. Drawing from a cross-disciplinary approach, the study examines the profound implications of integrating AI into mining heritage sites' planning and design strategies. The study reviews 199 articles on AI-driven planning and design benefits, examining potential advantages. Ethical considerations, algorithmic biases, and the role of interdisciplinary research are also explored. The study highlights the intricate interplay between AI-enhanced engagement, responsible tourism practices, and the meaningful representation of local cultures. By shedding light on this uncharted territory, the research contributes to developing informed strategies that harness AI's potential to shape inter- and intra-regional mining heritage site planning and design, fostering responsible and impactful tourism experiences. By delving into this paradigm, it hopes to arm the researchers, policy-makers, practitioners, and other stakeholders with information and understanding that will help them forge a progressive and morally upright future, in which technology co-exists peacefully with practices for cultural preservation and sustainable tourism.
Mine Economic and Management
Hadi Fattahi; Hossein Ghaedi
Abstract
The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach ...
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The maximum energy consumption of stone cutting machines is one of the important cost factors during the process of cutting construction stones. Accurately predicting and estimating the maximum energy consumption performance of the cutting machine, along with estimating the cutting costs, can help approach the optimal cutting operating conditions to reduce energy consumption and minimize machine depreciation. However, due to the uncertainty and complexity of building stone textures and properties, determining the maximum energy consumption of the device is a difficult and challenging task. Therefore, this paper employs the rock engineering system method to solve the aforementioned problem. To this end, 120 test samples were collected from a marble factory in the Mahalat region of Iran, representing 12 types of carbonate rocks. The input parameters considered for the analysis were the Mohs hardness, uniaxial compressive strength, Young's modulus, production rate, and Schimazek’s F-abrasiveness factors. In the study, 80% of the collected data, equivalent to 96 data points, were utilized to construct the model using the rock engineering system-based method. The obtained results were then compared with other regression methods including linear, power, exponential, polynomial, and multiple logarithmic regression methods. Finally, the remaining 20 percent of the data, comprising 24 data points, were used to evaluate the accuracy of the models. Based on the statistical indicators, namely root mean square error, mean square error, and coefficient of determination, it was found that the rock engineering system-based method outperformed other regression methods in terms of accuracy and efficiency when estimating the maximum energy consumption.
Mine Economic and Management
K. Shah; S. Ur Rehman
Abstract
Truck and shovel are the most common raw material transportation system used in the cement quarry operations. One of the major challenges associated with the cement quarry operations is the efficient allocation of truck and shovel to the mining faces. In order to minimize the truck and shovel operating ...
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Truck and shovel are the most common raw material transportation system used in the cement quarry operations. One of the major challenges associated with the cement quarry operations is the efficient allocation of truck and shovel to the mining faces. In order to minimize the truck and shovel operating cost, subject to quantity and quality constraints, the mixed integer linear programing (MILP) model for truck and shovel allocation to mining faces for cement quarry is presented. This model is implemented using the optimization IDE tool GUSEK (GLPK under SciTE Extended Kit) and the GLPK (GNU Linear Programming Kit) standalone solver. The MILP model is applied to an existing cement quarry operation, the Kohat cement quarry located at Kohat (Pakistan) as a case study. The analysis of the results of the relating case study reveals that significant gains are achievable through employing the MILP model. The results obtained not only show a significant cost reduction but also help in achieving a better coordination among the quarry and quality department.
Mine Economic and Management
R. Bastami; A. Aghajani Bazzazi; H. Hamidian Shoormasti; K. Ahangari
Abstract
The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone ...
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The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (GEP), linear multivariate regression (LMR), and non-linear multivariate regression (NLMR) models. In all models, the ANFO value, number of detonators, Emolite value, hole number, hole length, hole diameter, burden, spacing, stemming, sub-drilling, specific gravity of rock, hardness, and uniaxial compressive strength are used as the input parameters. The ANN model results in the test stage indicating a higher correlation coefficient (0.954) and a lower root mean square error (973) compared to the other models. In addition, it has a better conformity with the real blasting costs in comparison with the other models. Although the ANNs method is regarded as one of the intelligent and powerful techniques in parameter prediction, its most important fault is its inability to provide mathematical equations for engineering operations. In contrast, the GEP model exhibits a reliable output by presenting a mathematical equation for BC prediction with a correlation coefficient of 0.933 and a root mean square error of 1088. Based on the sensitivity analysis, the spacing and ANFO values have the maximum and minimum effects on the BC function, respectively. The number of detonators, Emolite value, hole number, specific gravity, hardness, and rock uniaxial compressive strength have a positive correlation with BC, while the ANFO value, hole length, hole diameter, burden, spacing, stemming, and sub-drilling have a negative correlation with BC.
Mine Economic and Management
H. Dehghani
Abstract
Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent ...
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Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to solve this problem, it is necessary to use the artificial algorithms, which have a good ability to predict the volatility of various phenomena. In the present work, the gene expression programming (GEP) method was used to predict the copper price volatility. In order to understand the ability of this method, the results obtained were compared with the other classical prediction methods. The results indicated that the GEP method was much better than the time series and multivariate regression methods in terms of the prediction accuracy.
