Search

One of the most prevalent and destructive types of cyber-attacks on power systems is the false data injection (FDI) attack. In a false data injection attack, the attacker inflicts large damages on the network by manipulating the measurements. The pivotal solution to opposing this type of cyber-attack is to use phasor measurement units (PMUs). In this paper, a new method is presented to confront the FDI attack by using the optimal placement of PMU instruments. In the proposed algorithm at the beginning, all PMUs placements that achieve network observability are determined using the tabu search (TS) algorithm. Then, from the observable placement vectors, the placements that minimize the possibility of a cyber-attack on the network is identified. For this purpose, a new attack criterion is presented, which is obtained from the adversary strategy in the attack scheme. Since the measurements obtained from the PMUs must be transferred to a phasor data concentrator (PDC) center, the PDC placement also must be determined. In this paper, the optimal placement of PDC is presented by considering the cost of communication infrastructure, because the cost of communication infrastructure between PMUs and PDC is significant. For this purpose, we have used the Kruskal algorithm. The simulations performed on the IEEE 30-bus and 118-bus test system confirm the effectiveness of the proposed method for opposing cyber-attacks and reducing the cost of communication infrastructure. [ABSTRACT FROM AUTHOR]

The underground coal mine is the largest source of methane generation in the mining sector. Methane is produced from underground and surface mines as well as during post-mining activities, including coal processing, storage, and transportation. Emission, accumulation, and methane gas explosion are the most important risk factors in underground coal mines. Reducing methane emission in coal mines enhances factors of safety, production, and revenue. Nowadays, a market is created for the gas produced by implementing various projects for reducing methane emissions. Numerical methods are powerful tools for studying the gas flow inside a coal block. It is possible to consider a number of boreholes inside the coal seam using high-precision simulations and numerical methods. The values of methane concentration, methane properties, coal porosity, and its mechanical properties can be changed in the model; the effects of different parameters can be estimated for different types of coals. The purpose of this study is to estimate the gas drainage of coal seams using COMSOL software to determine the appropriate pattern for drilling drainage holes. It is inferred from the model that the methane outlet pressure emitted from the coal block in the modeled coal sample decreases over time. The gas outlet pressure from the boreholes on the first days decreases. The amount may vary based on the porosity of the coal, the number of boreholes in the coal, and the diameter and height of the boreholes. The decrease in gas pressure is more than two-borehole mode due to the greater number of boreholes and, therefore, more suction pressure. Diagrams show that the larger the diameter of the boreholes, the faster the pressure drop occurs. That is to conclude that the pressure drop occurs over a shorter period when compared to that of a shorter diameter. This method can be used for all mines, and gas flow can be easily checked. [ABSTRACT FROM AUTHOR]

Today, with the advancement of technology and the use of advanced systems in the field of mining, one of the issues that can seriously affect the economic and operational conditions of a mine is the occurrence of system breakdowns. Given the importance of the loading system in mines and the high cost of downtime due to failure, one of the proposed approaches to deal with this issue is to use the concept of resilience analysis. Applying the notion of resilience to the field of mine engineering has a significant benefit for the safety and improves system performance after failure. Accordingly, in this paper, the resilience of the loading system of Jajarm bauxite mine in a period of 2 years is evaluated. By using statistical analysis, a suitable function is fitted to the TBF (time between failures) and TTR (time to recovery) subsystems, the reliability and maintainability values of the system are calculated, finally, using impact indicators such as supportability, organizational resilience, and PHM technology; total resilience of the loading system is calculated. The results show that the loading system resilience is at its highest level when PHM technology and organizational resilience are at 95% so that the loading system resilience is 92% after 100 h of operation. This means that the loading system is 92% more likely to recover after a breakdown and returns to pre-breakdown conditions. [ABSTRACT FROM AUTHOR]

Backbreak (BB) is a detrimental outcome of blasting activities in mineral extraction processes within mines. It involves the development of fractures and cracks at considerable distances behind the last row of blast pits, leading to reduced bench safety and increased operational costs. Given the multitude of factors influencing BB, various techniques have been developed to predict and optimize its occurrence. This particular study focused on analyzing 48 blasts in the tailings section of Gol Gohar Mine No. 1 to forecast BB using the whale optimization algorithm (WOA), multiverse optimizer (MVO), sine cosine algorithm (SCA), ant lion optimizer (ALO), and multivariate linear regression (MLR). Comparative analysis of the four BB prediction models revealed that the MVO algorithm yielded the most favorable outcomes, with the train data exhibiting parameter values of 0.9901, 0.2161, 0.1127, 98.8472, and 0.0180 for R2, RMSE, MSE, VAF, and MAPE, respectively, while the test data displayed values of 0.6357, 1.4955, 1.2003, 63.5472, and 0.1951 for the same parameters. In addition, the analysis specifically emphasized the substantial influence of spacing, burden, and GSI as the primary determinants of the backbreak phenomenon. In stark contrast, however, powder factor, delay time, and joint condition are identified as having negligible effects on backbreak. [ABSTRACT FROM AUTHOR]

Resilience is about the ability of the system to resist, adapt to, and expeditiously recover from a disruptive event. The first and maybe the crucial step of resilience management is known as resilience analysis. However, there are many obstacles in front of the analyzers to analyze the resilience of systems. One of these obstacles is precise resilience data accessibility. Conventional resilience analysis methods frequently only consider historical data (e.g., time to repair and time to failure). However, to analyze the system resilience more precisely, the effect of the risk factors, which are known as observed and unobserved covariates, should be considered in the collected resilience database. These covariates will lead to the observed and unobserved heterogeneities among the collected database. Ignoring the effect of covariate may lead to erroneous conclusion about the resilience level of the system. Since it is hard to find a homogeneous operating condition, in this study, a formulation is proposed to model the effect of these covariates on complex system resilience. Finally, it is applied to a transportation system of a surface mine. [ABSTRACT FROM AUTHOR]

Caving process is a complex dynamic phenomenon influences safety and productivity of coal longwall mining. It improves safety due to reduction of load on support, face convergence and abutment stresses. Proper caving with respect to the quality and time of occurrence ensures continuity of operation and subsequently, the productivity of coal extraction. Therefore, a reliable prediction of strata behaviour and its caving potential are imperative in design of longwall projects. This paper presents a hybrid probabilistically qualitative–quantitative model to evaluate cavability of immediate roof and to estimate main caving span in longwall mining by combining empirical model and numerical solution. For this purpose, numerical simulation was incorporated to Roof Strata Cavability index (RSCi) as summation of ratings for nine significant parameters. Distinct element code was used to simulate numerically main caving span corresponding to various RSCi classes probabilistically. The newly proposed model was verified against actual field data collected from different longwall panels around the world. The results of proposed model agreed well with those of collected data. [ABSTRACT FROM AUTHOR]

Heat transfer enhancement in thermal systems has great importance in achieving better performances at decreased spaces. In the present work, by combining two passive enhancement methods of using miniaturized channels and utilizing nanofluid, the heat transfer from a heated surface is investigated. In this regard, the thermal performance of an Aluminum minichannel heat sink with rectangular cross-sections was experimentally investigated. The hydraulic diameter of the minichannels is 2 mm. Distilled water, TiO2–water nanofluid, Al2O3-water nanofluid and hybrid Al2O3/TiO2–water nanofluid were used with a volume fraction of 0.5% as coolants. Constant heat flux boundary condition was considered and maintained by a heater with the power of 36 W at the bottom of the heat sink. The effect of different Reynolds numbers ranged from 400 to 1000 on the thermal performance of the heat sink was evaluated. The experimental results indicated significant improvements in thermal characteristics by using nanofluids as a coolant instead of pure water. The convective heat transfer coefficient enhanced up to 16.97% compared to pure water. The wall temperature reduced up to 5 °C with using Al2O3/TiO2–water hybrid nanofluid instead of pure water. [ABSTRACT FROM AUTHOR]

The increase of productivity and reduction of expenses are the most important incentive factors for investors in the dimension stone's industry. The consumed electrical current is one of the most influential factors in the productivity and expenses. Therefore, study of the factors influencing the reduction of the maximum consumed electrical current and evaluation of the rock cutting process have a significant role in the planning, increase of productivity and reduction of expenses. In the present study, three granite stone samples were selected as hard rocks and prepared from Iranian quarries, and the laboratory experiments were done to measure the physical and mechanical properties of the stones. In addition, three coolant and lubricant fluids including water and two types of soap water with ratios of 1 to 40 and 1 to 20 were selected for determining their effects on the maximum electrical current consumed by the machine. Cutting tests were done based on three different cutting depths and totally 27 tests were done for three fluids and the maximum electrical current consumed in each test was measured and tested. In this study, the investigations and analyses of the laboratory results were done using the statistical analysis. The results obtained from the research showed that soap water with ratios of 1 to 40 had the maximum effect and productivity on the process of reducing the consumed electrical current compared to other fluids under study. Also, the range of consumed electrical current's change was reduced with the reduction of physical and mechanical properties. [ABSTRACT FROM AUTHOR]

This paper presents a new index to assess caving potential of roof strata above longwall panels. Nine inherent parameters were chosen as significant affecting factors in three categories, including roof strata characteristics, roof discontinuities properties and local features. Fuzzy hybrid multi criteria decision making was used by combining fuzzy analytic network process technique and fuzzy decision making trial and evaluation laboratory method to develop a new rating system. Subsequently, roof strata cavability index (RSCi) was defined as a simple summation of ratings for all parameters, indicating the cavability level qualitatively. The RSCi was applied to determine the cavability of various actual cases of worldwide longwall panels. In addition, the correlation between RSCi and span of main caving was examined where reliable performance of the new index was noted. It was concluded that RSCi is a simple and efficient tool to assess the cavability of immediate roof and evaluation of caving intervals in longwall mining. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Central South University is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Environmental concern has become one of the major global issues that affect all nations individually and/or collectively. One of the most important methods for environmental impact assessment (EIA) is Folchi method. An improvement for Folchi method in EIA is done in this paper by using fuzzy method. A fuzzy expert semi-quantitative methodology is designed to assess the EIA within the Folchi framework. The proposed fuzzy method has the advantage of allowing consideration of uncertainties in the Folchi method in comparison with classic expert semi-quantitative method that only unique codes are used to quantify the effect of each impacting factor on each designed environmental component. Since the fuzzy system is used to minimize subjectivity of the weights which are computed in the Folchi method, the "Fuzzy Delphi" and "Folchi" methods were merged. Due to the high potential of cement plants in the creation of environmental impact, environment impact assessment of the development of cement plants is very important. To test the proposed method, the Shahrood cement plant was selected as a case study. According to the results, the contamination generated by the plant, especially for environmental components such as air quality, soil of the area, area landscape, area usage, and ecology, is more significant than other environmental components. The newly proposed approach is an efficient tool in EIA and hence can be operational in humanistic decision-making systems with existence of uncertainty, vagueness, and imprecision. [ABSTRACT FROM AUTHOR]

Many real-world dynamics can be modeled as nonlinear time-delay systems. In order to capture a more realistic model for system dynamics, the exact values of time-delay should be taken into account. For nonlinear time-delay systems, the estimation of delays in both state and output equations is discussed. A cost function is defined based on least-square error between actual and estimated values of the output measurement. The value of time-delays in the nonlinear system are then derived using a gradient-based optimization method. Because of the implicit description of the cost function with respect to the delay value, its gradients cannot be obtained by standard analytical differentiation rules. In this case, the optimal computational methods are utilized to derive two formulas for computing the gradient. An optimization scheme is then formulated to estimate both state and output delays. The effectiveness of the proposed estimation method is finally demonstrated using the simulation results on a benchmark chemical process. [ABSTRACT FROM AUTHOR]

This paper was assesses the cavability of roof strata in mechanized longwall coal mining in terms of their principle parameters. For this purpose, the fuzzy decision-making trial and evaluation laboratory (DEMATEL) method was employed to study and analysis the influencing parameters and interrelationships among them. Since this method is based on the survey of experts to indicate the influences and involves uncertainty, therefore, the fuzzy coding was used. Given that the behavior of roof strata in the caving process is controlled by many factors, seventeen effective parameters in five categories were recognized. As a result of analysis, the parameters with the highest exerted and received influence were introduced. Besides, the impact and relation of factors in the cavability system were determined. After that, an approach was proposed for weighting the parameters in the fuzzy DEMATEL framework and then the importance of significant parameters were assessed. The conducted method provides a systematic approach for holistic analysis the principles parameters affecting the cavability of immediate roof strata in mechanized longwall coal mining and hence, is worthwhile for studying this complex and dynamic problem under uncertainty. [ABSTRACT FROM AUTHOR]

The purpose of this study is the application of meta-heuristic algorithms and fuzzy logic in the optimization and clustering to predict the sawability of dimension stone. Survey and classification of dimension stones based on their physical and mechanical properties can be so impressive in the optimization of machine applications that are in this industry such as circular diamond saw block cutting machine. In this paper, physical and mechanical properties were obtained from laboratory testing on dimension stone block samples collected from 12 quarries located in Iran and their results were optimized and classified by one of the strongest meta-heuristic algorithms and fuzzy clustering technique. The clustering of dimension stone was determined by Lloyd’s algorithm (k-means clustering) based on imperialist competitive algorithm and fuzzy C-mean by MATLAB software. The hourly production rate of each studied dimension stones was considered as a criterion to evaluate the clustering efficacy. The results of this study showed that the Imperialist Competitive algorithm and fuzzy C-mean are very suitable for clustering with respect to the physical and mechanical properties of the dimension stone, and the results obtained showed the superiority of the ICA. [ABSTRACT FROM AUTHOR]

Recently, there has been a great interest in the application of Lyapunov exponents for calculation of chaos levels in dynamical systems. Accordingly, this study aims at presenting two new methods for utilizing Lyapunov exponents to evaluate the spatiotemporal chaos in various images. Further, early detection of cancerous tumors could be obtained by measuring the chaotic indices in biomedical images. Unlike the available systems described by partial differential equations, the proposed method employs a number of interactive dynamic variables for image modeling. Since the Lyapunov exponents cannot be applied to such systems, the image model should be modified. The mean Lyapunov exponent is defined as a chaotic index for measuring the contour borders irregularities in images to detect benign or malignant tumors. Moreover, a two-dimensional mean Lyapunov exponent is incorporated to identify irregularities existing in each axis of the targeted images. Experiments on a set of region of interest in breast mammogram images yielded a sensitivity of 95 % and a specificity of 97.3 % and verified the remarkable precision of the proposed methods in classifying of breast lesions obtained from breast mammogram images. [ABSTRACT FROM AUTHOR]

According to the human requirement, such as mineral extraction, transportation, etc., tunnel construction is an evident need. Mining and tunneling activities are full of problems with uncertainty, which make these problems complex and difficult. Overbreak is one of these problems that we are encountering in the tunneling process, particularly in the drill and blast method. Overbreak causes to decreasing the safety and increasing the operational costs. Therefore, it must be minimized. Overbreak prediction is the first step to decreasing the damaging effects of this phenomenon. Overbreak influencing factors are classified into two groups of uncontrollable factors and controllable factors. In this study, 267 sets of causing factors and overbreak data were used to overbreak prediction model's development using the multiple linear and nonlinear regression analysis, artificial neural network, fuzzy logic, adaptive neuro-fuzzy inference system, and support vector machine. The determination coefficient values of these models have been obtained as 0.84, 0.87, 0.93, 96, 0.97, and 0.87, respectively. The results illustrated that the fuzzy and adaptive neuro-fuzzy inference system models have done more appropriate prediction than other prediction models. [ABSTRACT FROM AUTHOR]

The appropriate maintenance strategy for a specific system requires a detailed analysis of the system's reliability. Reliability analysis relies greatly on the historical failure and repair data. Lack of data, poor quality data, data censoring, and combining data from different systems working in different operating conditions are some of the challenges for maintenance analysts. Hence, based on the nature of historical failure and repair data (independence of data, constant failure rates, identically distributed variables, etc.), an appropriate statistical model should be selected to predict the reliability of the system. Thereafter, based on the defined reliability characteristics of the system, an appropriate maintenance strategy needs to be established. In this study, a systematic methodology is developed, based on the available guidelines for maintenance strategy selection, founded on the reliability analysis of historical failure data. The application of the methodology is shown by a case study from the crushing and mixing bed hall unit at a cement factory. [ABSTRACT FROM AUTHOR]

A robust nonlinear observer for a class of nonlinear time-delay systems is introduced. The generalized sector constraint is employed to deal with many commonly encountered nonlinearity in dynamic equation. It is assumed that time-varying delay exists in both state and output equations, and further it directly appears in nonlinear function. By utilizing two correction terms in nonlinear observer related to the dynamic equation as well as system's nonlinearity, the stability analysis of observer error dynamic is presented. For uncertain systems, a synthesis method to obtain robust nonlinear observer is then given in terms of some LMI's. The effectiveness of proposed observer and related synthesis method are finally demonstrated through simulation examples. [ABSTRACT FROM AUTHOR]

The phenomenon of coal spontaneous combustion is one of the common hazards in coal mines and also one of the important reasons for the loss of coal in piles and mines. Based on previous researches, different types of coals have different spontaneous combustion characteristics. For coal loss prevention, a measure is necessary for prediction of coal spontaneous combustion. In this study, a new engineering classification system called ''Coal Spontaneous Combustion Potential Index (CSCPI)'' is presented based on the Fuzzy Delphi Analytic Hierarchy Process (FDAHP) approach. CSCPI classifies coals based on their spontaneous combustion capability. After recognition of the roles of the effective parameters influencing the initiation of a spontaneous combustion, a series of intrinsic, geological, and mining characteristics of coal seams are investigated. Then, the main stages of the implementation of the FDAHP method are studied and the weight of each parameter involved is calculated. A classification list of each parameter is formed, the CSCPI system is described, and the engineering classifying system is subsequently presented. In the CSCPI system, each coal seam can be rated by a number from 0 to 100; a higher number implies a greater ease for the coal spontaneous combustion capability. Based on the CSCPI system, the propensity of spontaneous combustion of coal can be classified into three potential levels: low, medium, and high. Finally, using the events of coal spontaneous combustion occurring in one of the Iranian coal mines, Eastern Alborz Coal Mines, an initial validation of the mentioned systematic approach is conducted. Comparison of the results obtained in this study illustrate a relatively good agreement. [ABSTRACT FROM AUTHOR]

When a system's performance is inadequate, the concept of availability importance can be used to improve it. The availability of an item depends on the combined aspects of its reliability and maintainability. In a system consisting of many subsystems, the availability of some subsystems is more important to system performance than others. The availability measure determines the priority of availability across subsystems. Most researchers only consider operation time and ignore the influence of the operating environment; therefore, their estimations are not accurate enough. In contrast to previous research, we focus on the influence of the operating environment on the system/subsystem's characteristics with a view to prioritizing them based on the importance of availability. The paper considers part of the mining fleet system of Sungun copper mine, including the wagon drill, loader, bulldozer, and dump truck subsystems. We identify an ordered list of possibilities for availability improvement and suggest changes or remedial actions for each item to either reduce its failure rate or reduce the time required to repair it. [ABSTRACT FROM AUTHOR]

An improvement for rock engineering system (RES) coding method is done in this paper by using fuzzy systems. A fuzzy expert semi-quantitative coding methodology is designed to assess the cavability of rock mass within the RES framework. The proposed fuzzy method has the advantage of allowing consideration of uncertainties in the RES analysis by using membership functions in comparison with classic expert semi-quantitative coding method that only unique codes are used to quantify the interaction matrix. Since the cavability of the rock mass is one of the fundamental issues for the caving mining method, the presented improved coding method is creatively used to assess the influencing parameters on cavability of rock mass in block caving mines. Fifteen parameters are considered as the main factors modeling the cavability of the rock mass, and the interactions between these parameters are calculated by proposed fuzzy system. Finally in this paper, the parameters, which are dominant or subordinant, and also the parameters, which are interactive, are introduced. The proposed approach could be a simple but efficient tool in evaluation of the parameters affecting the cavability of rock mass in block caving mines and hence be useful in decision making under uncertainties. [ABSTRACT FROM AUTHOR]

This article investigates chaotic attitude maneuvers in a satellite for a range of parameters via Lyapunov exponents (LEs) and designs an appropriate robust nonlinear controller to ensure chaos suppression and achieve desired performance. Since the dynamic equations of satellite are described as a nonlinear non-autonomous system, an improved technique for calculating the LEs of such systems as a measure of chaos phenomenon is presented. Using the proposed algorithm, the chaotic behavior of satellite is proved within a range of its parameters. Then, by converting dynamic equations to canonical form, a robust nonlinear control is proposed using back-stepping sliding mode method. The stability of closed-loop system and its robustness against disturbances and uncertainties are guaranteed by proving a theorem. Moreover, by converting the system description into a compatible form with the conditions of the Melnikov theorem, an analytical approach is presented to ensure chaos suppression in the controlled system. Finally, the simulation results for different operational conditions of a three-axis stabilized satellite are provided to show the effectiveness of the proposed analyses and syntheses. [ABSTRACT FROM AUTHOR]

Mining methods such as block caving or sublevel caving rely on the characteristics of the rock mass to cave efficiently to fulfill an economical production. The identification of influencing parameters and cavability assessment are, thus, a prime geotechnical focus for all potential caving projects. In the caving operation, many factors, such as natural and induced factors, affect the caving performance. In this study, after discussing the caving process and identifying all effective parameters, the interaction matrix based on the rock engineering system (RES) is introduced to study the influencing parameters in rock mass cavability. The interaction matrix analyzes the interrelationship between the parameters affecting rock engineering activities. As the interaction matrix codes are not unique, probabilistic coding can be performed non-deterministically, allowing consideration of uncertainties in the RES analysis. As a result, the parameters with the highest probability of being dominant or subordinate, and also the parameters with the highest probability of being interactive, are introduced. The proposed approach could be a simple but efficient tool in the evaluation of the parameters affecting the cavability of rock mass in block caving mines and, hence, useful in decision-making under uncertainties. [ABSTRACT FROM AUTHOR]

Global stability is a necessary prerequisite for safe retreat mining and one of the crucial and complex problems in room and pillar mining, so its prediction plays an important role in the safety of retreat mining and the reduction of pillar failure risk. In this study, we have tried to develop predictive models for anticipating global stability. For this purpose, two of the most popular techniques, logistic regression analysis and fuzzy logic, were taken into account and a predictive model was constructed based on each. For training and testing of these models, a database including 80 retreat mining case histories from 18 room and pillar coal mines, located in West Virginia State, USA, was used. The models predict global stability based on the major contributing parameters of pillar stability. It was found that both models can be used to predict the global stability, but the comparison of two models, in terms of statistical performance indices, shows that the fuzzy logic model provides better results than the logistic regression. These models can be applied to identify the susceptibility of pillar failure in panels of coal mines, and this may help to reduce the casualties resulting from pillar instability. Finally, the sensitivity analysis was performed on database to determine the most important parameters on global stability. The results revealed that the pillar width is the most important parameter, whereas the depth of cover is the least important one. [ABSTRACT FROM AUTHOR]

One of the major strengths of a GIS is the ability to integrate and combine multiple layers of geoscience data for producing mineral potential maps showing favorable areas for mineral exploration. Once the data is prepared properly, the GIS, jointly with other statistical and geostatistical software packages, can be used to manipulate and visualize the data in order to produce a mineral prospectivity map. Many spatial modeling techniques can be employed to produce mineral potential maps. This paper demonstrates a technique to define favorable areas for REE mineralization with AHP technique using geological, geochemical, geophysical, alteration and faults density spatial data in the Kerman-Kashmar Tectonic Zone of central Iran. The AHP is a powerful and flexible multi-criteria decision-making tool for dealing with complex problems where both qualitative and quantitative aspects need to be considered. This approach is knowledgedriven method and can be applied in other areas for conventional use in mineral exploration. [ABSTRACT FROM AUTHOR]

Backbreak is an undesirable side effect of bench blasting operations in open pit mines. A large number of parameters affect backbreak, including controllable parameters (such as blast design parameters and explosive characteristics) and uncontrollable parameters (such as rock and discontinuities properties). The complexity of the backbreak phenomenon and the uncertainty in terms of the impact of various parameters makes its prediction very difficult. The aim of this paper is to determine the suitability of the stochastic modeling approach for the prediction of backbreak and to assess the influence of controllable parameters on the phenomenon. To achieve this, a database containing actual measured backbreak occurrences and the major effective controllable parameters on backbreak (i.e., burden, spacing, stemming length, powder factor, and geometric stiffness ratio) was created from 175 blasting events in the Sungun copper mine, Iran. From this database, first, a new site-specific empirical equation for predicting backbreak was developed using multiple regression analysis. Then, the backbreak phenomenon was simulated by the Monte Carlo (MC) method. The results reveal that stochastic modeling is a good means of modeling and evaluating the effects of the variability of blasting parameters on backbreak. Thus, the developed model is suitable for practical use in the Sungun copper mine. Finally, a sensitivity analysis showed that stemming length is the most important parameter in controlling backbreak. [ABSTRACT FROM AUTHOR]

Rock mass classification systems are one of the most common ways of determining rock mass excavatability and related equipment assessment. However, the strength and weak points of such rating-based classifications have always been questionable. Such classification systems assign quantifiable values to predefined classified geotechnical parameters of rock mass. This causes particular ambiguities, leading to the misuse of such classifications in practical applications. Recently, intelligence system approaches such as artificial neural networks (ANNs) and neuro-fuzzy methods, along with multiple regression models, have been used successfully to overcome such uncertainties. The purpose of the present study is the construction of several models by using an adaptive neuro-fuzzy inference system (ANFIS) method with two data clustering approaches, including fuzzy c-means (FCM) clustering and subtractive clustering, an ANN and non-linear multiple regression to estimate the basic rock mass diggability index. A set of data from several case studies was used to obtain the real rock mass diggability index and compared to the predicted values by the constructed models. In conclusion, it was observed that ANFIS based on the FCM model shows higher accuracy and correlation with actual data compared to that of the ANN and multiple regression. As a result, one can use the assimilation of ANNs with fuzzy clustering-based models to construct such rigorous predictor tools. [ABSTRACT FROM AUTHOR]

Predicting the penetration rate of a tunnel boring machine (TBM) plays an important role in the economic and time planning of tunneling projects. In the past years, various empirical methods have been developed for the prediction of TBM penetration rates using traditional statistical analysis techniques. Soft computing techniques are now being used as an alternative statistical tool. In this study, a fuzzy logic model was developed to predict the penetration rate based on collected data from one hard rock TBM tunnel (the Queens Water Tunnel # 3, Stage 2) in New York City, USA. The model predicts the penetration rate of the TBM using rock properties such as uniaxial compressive strength, rock brittleness, distance between planes of weakness and the orientation of discontinuities in the rock mass. The results indicated that the fuzzy model can be used as a reliable predictor of TBM penetration rate for the studied tunneling project. The determination coefficient ( R), the variance account for and the root mean square error indices of the proposed fuzzy model are 0.8930, 89.06 and 0.13, respectively. [ABSTRACT FROM AUTHOR]

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations. [ABSTRACT FROM AUTHOR]

Knowledge of drillability of rock masses in engineering projects is very important in determining drilling costs. In drilling operations, so many parameters such as the properties of rock and the drilling equipment affect the drilling performance. In this study, after discussing the rock mass drillability process and identifying all the effective parameters, interaction matrixes based on the rock engineering systems, that analyze the interrelationship between the parameters affecting rock engineering activities, is introduced to study the rock mass drillability tribosystem. Given that interaction matrix codes are not unique numbers, and then possible interactive intensities are calculated for each matrix and a group decision-making method, Fuzzy-Delphi-AHP technique has been used to obtain appropriate weights. As a result, rock mass drillability index (RMDI) is presented to classify the rock mass drillability. The results indicate the ability of this method to analyze rock mass drillability procedure. Drilling data along with laboratory rock properties from Sungun copper mine were collected and were ranked according to the new classification system. Fifteen zones at the mine site were ranked based upon the new index RMDI and a reasonable correlation was obtained between measured drilling rate at the zones and RMDI data. [ABSTRACT FROM AUTHOR]

Roof fall is one of the serious hazards associated with underground coal mining. Roof fall can cause fatal and non-fatal injuries on miners, stoppages in mining operations and equipment breakdowns. Therefore, accurate prediction of roof fall rate is very important in controlling and eliminating of related problems. In this study, the fuzzy logic was applied to predict roof fall rate in coal mines. The predictive fuzzy model was implemented on fuzzy logic toolbox of MATLAB using Mamdani algorithm and was developed based on experts' knowledge and also a database including 109 datasets of roof performance from US coal mines. 22 datasets of this database were used to assess the performance of this fuzzy model. The comparison between obtained results from model and actual roof fall rate showed that the fuzzy model can predict roof fall rate very well. [ABSTRACT FROM AUTHOR]

The Sonajil intrusive is located about 60 km southeast of Ahar, in Azarbaijan province in northwestern Iran, and is intruded into by andesitic trachytic Eocene pyroclastics and agglomerate. To the center, the stock is hydrothermally altered, that contains sub-economic copper mineralization. The 1,248 cells with 100 × 100 m grid were sampled, and measurement of elements by ICP-MS method and major oxides included by X-ray fluorescence method was performed. After statistical interpretations, geochemical anomalies and indices alteration (sericitization, alkali, Spitz-Darling, chloritization, Hashimoto, Hashiguchi) were calculated, and their map distribution was prepared. Based on these maps and other interpretations of data, our study indicated that despite the anomaly of copper and other elements such as gold, molybdenum, lead, arsenic and antimony of not being quite consistent, almost all of them are within the alteration zones. [ABSTRACT FROM AUTHOR]

Iran has very high potential for production and export of dimension stone (rock quarried to size and shape specifications); nevertheless, in spite of growing mine production during the past decade, in many instances this potential has been overlooked. Construction, decorative, and facing stones of Iran are among the highest-quality products in the world. In this paper, Iran's dimensional stone mines are analyzed using strengths, weaknesses, opportunities and threats (SWOT) analysis in combination with analytic hierarchy process (AHP). SWOT analysis is a tool commonly used for analyzing both the internal and external environments in order to attain a systematic approach and support for a decision situation. Conventionally, SWOT analysis is qualitative and does not quantify the effect of the factors. Consequently, it is not effective in priority setting. To overcome this limitation, SWOT analysis has been used in combination with other quantitative multi-criteria decision making methods especially the AHP. In this study, first an environment analysis was performed by an expert team familiar with these mines. In this way, the SWOT factors were identified and the subfactors which had very significant effects on the success of these mines were determined. Using the SWOT subfactors, the SWOT matrix and alternative strategies based on these subfactors were developed. Subsequently, the strategies were prioritized and the best strategies for Iran's dimensional stone mines were determined. [ABSTRACT FROM AUTHOR]

The prediction of production rate in ornamental stones sawing is very important in cost estimation and process planning of the rock sawing plants. The main aim of this paper is finding a mathematical correlation between production rate and rock brittleness indexes. The utilized data have been collected from several stone factories in Iran. Seventeen different granite and carbonate rocks have been experienced sawing conditions with large-diameter circular saws. The laboratory tests such as uniaxial compressive strength and tensile strength, were carried out on the rock samples which were collected from these factories. The ratio of compressive strength to tensile strength ( B), the ratio of compressive strength minus tensile strength to compressive strength plus tensile strength ( B), and the area under the line of compressive strength and the line of tensile strength ( B) were used as brittleness indexes in this study. Then, the relationships between these brittleness indexes and production rate were investigated by using a regression analysis. The results show that, there is not a good relationship between the production rate and B and B. However, the production rate correlates with B very well. Therefore, there is a reliable prediction for ornamental stones production based on B as a brittleness indexes. [ABSTRACT FROM AUTHOR]

Grade estimation is very important in designing open pits. In the process of grade estimation, underestimation can result in loss of economic ore, whereas overestimation would unnecessarily increase stripping ratio. Normally, kriging method, which suffers from underestimation and/or overestimation due to smoothing effect, is used for grade estimation. To overcome drawbacks of the kriging method, more efficient techniques such as conditional simulation can be applied. In this paper, utilizing sequential Gaussian conditional simulation, grade models were constructed for Sungun copper deposit situated in the North West of Iran. According to the obtained results, it was observed that conditional simulation can effectively cope with the weakness of kriging method. Also, it was observed that as compared to the kriging method, grade distribution, resulted from the conditional simulation, is almost identical to that of the real exploration data. Accordingly, using conditional simulation, the amount of mineable ore was significantly increased, and also, average net present value as the mines' most important economic indicator was improved by 40%. [ABSTRACT FROM AUTHOR]

Copyright of Arabian Journal of Geosciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Fuzzy model as a knowledge-driven approach relies on the geologist's input to weight the importance of each data layer as it relates to the particular exploration model being used. In this paper we described a fuzzy model that uses a linear membership function for deriving fuzzy membership values of input evidential maps. The five multiclass evidential maps described were fuzzified using the linear membership function. The evidential maps and classes were ranked on the basis of our knowledge and information about REE mineralization in the study area. The model is demonstrated for mapping REE mineralization potential in an area in the Kerman-Kashmar Tectonic Zone (KKTZ), which separates the Yazd block to the west and the Tabas block to the east in Central Iran. At the fuzzy model described in this paper, we used four γ values to decrease the noise, and we applied selected optimum γ to predict potentially mineralized zones, which occupy less than 6 % of the study area. [ABSTRACT FROM AUTHOR]

Copyright of Arabian Journal of Geosciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

These days, the application of electronic power transformers (EPTs) is expanding in place of ordinary power transformers. These transformers can transmit power via three or four wire converters. Their dynamic performance is extremely important, due to their complex structure. In this paper, a new method is proposed for improving the dynamic performance of distribution electronic power transformers (DEPT) by using sliding mode control (SMC). Hence, to express the dynamic characteristics of a system, different factors such as the voltage unbalance, voltage sag, voltage harmonics and voltage flicker in the system primary side are considered. The four controlling aims of the improvement in dynaniic performance include: 1) maintaining the input currents so that they are in sinusoidal form and in phase with the input voltages so they have a unity power factor, 2) keeping the dc-link voltage within the reference amount, 3) keeping the output voltages at a fixed amount and 4) keeping the output voltages in sinusoidal and symmetrical forms. Simulation results indicate the potential and capability of the proposed method in improving DEPT behavior. [ABSTRACT FROM AUTHOR]

Using the analytic hierarchy process (AHP) method for multi-index evaluation has special advantages, while the use of geographic information systems (GIS) is suitable for spatial analysis. Combining AHP with GIS provides an effective approach for studies of mineral potential mapping evaluation. Selection of potential areas for exploration is a complex process in which many diverse criteria are to be considered. In this article, AHP and GIS are used for providing potential maps for Cu porphyry mineralization on the basis of criteria derived from geologic, geochemical, and geophysical, and remote sensing data including alteration and faults. Each criterion was evaluated with the aid of AHP and the result mapped by GIS. This approach allows the use of a mixture of quantitative and qualitative information for decision-making. The results of application in this article provide acceptable outcomes for copper porphyry exploration. [ABSTRACT FROM AUTHOR]