The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Xuexin Li - One of the best experts on this subject based on the ideXlab platform.
-
remote sensing assessment of safety risk of iron Tailings pond based on runoff coefficient
Sensors, 2018Co-Authors: Aiman Liang, Xuexin LiAbstract:: Iron Tailings ponds are engineered Dam and dyke systems used to capture iron Tailings. They are high-risk hazards with high potential energy. If the Tailings Dam broke, it would pose a serious threat to the surrounding ecological environment, residents' lives, and property. Rainfall is one of the most important influencing factors causing the Tailings Dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of Tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the Tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of Tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk Tailings ponds, 16 moderate-risk Tailings ponds, and 4 high-risk Tailings ponds in the study area. This method could be useful for selecting targeted Tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk Tailings ponds in rainy season.
-
remote sensing assessment of safety risk of iron Tailings pond based on runoff coefficient
Sensors, 2018Co-Authors: Aiman Liang, Xuexin LiAbstract:: Iron Tailings ponds are engineered Dam and dyke systems used to capture iron Tailings. They are high-risk hazards with high potential energy. If the Tailings Dam broke, it would pose a serious threat to the surrounding ecological environment, residents' lives, and property. Rainfall is one of the most important influencing factors causing the Tailings Dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of Tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the Tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of Tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk Tailings ponds, 16 moderate-risk Tailings ponds, and 4 high-risk Tailings ponds in the study area. This method could be useful for selecting targeted Tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk Tailings ponds in rainy season.
Longjun Dong - One of the best experts on this subject based on the ideXlab platform.
-
some developments and new insights for environmental sustainability and disaster control of Tailings Dam
Journal of Cleaner Production, 2020Co-Authors: Longjun Dong, Sijia Deng, Fei Yue WangAbstract:Abstract Tailings Dam is an indispensable part of mining safety production and environmental sustainability. The stability, monitoring, pre-alarming, and sustainability of Tailings Dam are crucial for mining disaster control, environment, and human beings. The paper introduces the Damage of Tailings Dam failure by 44 typical accidents (2138 deaths in total), and then presents some developments and new insights for environmental sustainability and disaster control of Tailings Dam. Firstly, three kinds of methods for stability analysis of Tailings Dam were summarized and compared. Limit equilibrium methods (LEMs) were focused on Sweden methods, Janbu method, Bishop method, and Morgenstern-Price method. Numerical simulation methods were discussed about finite element method (FED), discrete element method (DEM), boundary element method (BEM), and finite difference method (FDM). Uncertainty methods were compared, including gray system evaluation method, reliability method, and fuzzy evaluation method. Secondly, the research progress in the field of safety monitoring and pre-alarming of Tailings Dam was reviewed, covering the aspects of traditional monitoring methods and coupled monitoring systems. Finally, the conception map of disaster prevention, disaster control, and environmental sustainability for safety management and environment to enhance cleaner production work in Tailings Dam was presented. The map for safety work and environmental sustainability achieves the goals from “lagging” to “real time”, from “point” to “solid”, from “surface” to “internal”, and from “passive” to “coupled passive and active”.
-
some developments and new insights of environmental problems and deep mining strategy for cleaner production in mines
Journal of Cleaner Production, 2019Co-Authors: Longjun Dong, Xiaojie Tong, Jian Zhou, Shaofeng Wang, Bing LiuAbstract:Abstract Cleaner production is the continuous application of an integrated preventive environmental strategy which stressed the importance of environment and human beings. Although the application of cleaner production is becoming more and more mature in different industries, since the complexity of the mining operation itself and its extensive and complex impacts on the ecological environment, the application of cleaner production in the mining industry encounters great challenges. For this purpose, the paper presents some developments and new insights of environmental problems and deep mining strategy for cleaner production in mines. Firstly, the general impacts on the ecological environment of mining industry and the current corresponding solutions as well as future prospects are presented. Secondly, the ecological environment pollution induced by Tailings Dam and its elimination approaches are reviewed. For the accelerating volume of Tailings Dam waiting to be processed, the exploration and research of the comprehensive utilization and treatment of Tailings is expected to be more effective with larger consumption and wider range of application. The development direction is the establishment of mine without Tailings. With the development of modern technology, some intelligent monitoring and warning technologies have helped the mining engineers to keep a vigilant eye on Tailings Dam continually. Finally, to convert the “harm” of four highs and one disturbance induced by the complex mechanical environment in deep mines into “benefit”, various specific measures with relatively high novelty and sustainability are recommended. Moreover, the conception map of safer and more efficient exploitation of resources in deep mines is depicted for industrial best practice and future research directions to enhance cleaner production work in mining.
-
Pre-alarm system based on real-time monitoring and numerical simulation using internet of things and cloud computing for Tailings Dam in mines
IEEE Access, 2017Co-Authors: Longjun Dong, Weiwei Shu, Daoyuan Sun, Xibing Li, Lingyun ZhangAbstract:The Tailings Dam, a necessary facility to maintain the normal operation of mining enterprises, is a hazard source of human-caused debris flow with high potential energy. The real-time pre-alarm for the instability of Tailings Dam is vital to ensure the normal mining and safety of human lives and properties. Based on the internet of things and 5G wireless networks, the multiple and key information system of Tailings Dam is constructed using the sensor data, which include the stability indexes like phreatic line, reservoir water level, internal and external deformation of the Tailings Dam. The cloud platform is applied to predict the future state of the phreatic line based on real-time monitoring data, where the equation of phreatic line can be obtained. The numerical simulation model is established by considering the predicted equation of phreatic line, limit equilibrium state parameters, reservoir water level, and rainfall. Then, the safety factor, random reliability, and interval non-probabilistic reliability can be solved out through the cloud platform. Combined with the trend of real-time monitoring deformation, as well as calculated dynamic safety factor, random reliability, and interval non-probabilistic reliability, the stable or dangerous warning signals of Tailings Dam can be obtained by the remote real-time pre-alarm system. The main solved method for the key parameters and pre-alarm process are presented through a case study. It is proved that the pre-alarm system is efficient and real-time for the Tailings Dam stability with the integration and mutual validation of plenty of key information.
Xiaofei Jing - One of the best experts on this subject based on the ideXlab platform.
-
the effect of grain size on the hydrodynamics of mudflow surge from a Tailings Dam break
Applied Sciences, 2019Co-Authors: Xiaofei Jing, Shangwei Wu, Yulong Chen, David J Williams, Wensong WangAbstract:Due to the differences in mineral processing techniques, the grain-size of Tailings used in the construction of a Tailings pond is not commensurate. It has been determined that the hydrodynamic characteristics of mudflow resulting from the failure of Tailings Dams are directly influenced by grain-size, solids concentration, and the surface roughness of gully and impoundment geometry. However, the behavior and influence of the grain size of mudflow resulting from a Tailings Dam failure have not been sufficiently examined. To investigate the effect of grain size on the hydrodynamic characteristics of mudflow surging from Tailings Dam failure, the law of mudflow evolution, the change of dynamics pressure, and the velocity distributions of mudflow have been obtained via a series of flume experiments utilizing three types of grain size Tailings (d = 0.72 mm; d = 0.26 mm; d = 0.08 mm, respectively). This study proves conclusively that with an increase in grain size, the peak value of mudflow depth notably decreases in the same section. Furthermore, it has been noted that both the velocity and the dynamic pressure raise significantly, wherein the velocity displays two distinct primary stages; namely a rapid reduction stage and a slow reduction stage. This research provides a framework for the exploration of the effect of grain size on the hydrodynamics of slurry surging from a Tailings Dam failure, and all presented results provide an indispensable tool in terms of the accurate assessment of potential Damage in the case of a prospective impoundment failure.
-
overtopping failure of a reinforced Tailings Dam laboratory investigation and forecasting model of Dam failure
Water, 2019Co-Authors: Xiaofei Jing, Yulong Chen, David J Williams, Marcelo Llano Serna, Hengwei ZhengAbstract:Overtopping failure of reinforced Tailings Dam may cause significant Damage to the environment and even loss of life. In order to investigate the feature of overtopping of the reinforced Tailings Dam, which has rarely appeared in the literature, the displacement, the phreatic level and the internal stress of Dam during overtopping were measured by a series of physical model tests. This study conclusively showed that, as the number of reinforcement layers increased, the anti-erosion capacity of Tailings Dam was notably improved. It could be supported by the change of the dimension of Dam breach, the reduction of stress loss rate, and the rise of phreatic level from the tests. Based on the erosion principle, a mathematical model was proposed to predict the width of the Tailings Dam breach, considering the number of reinforcement layers. This research provided a framework for the exploration of the overtopping erosion of reinforced Tailings Dam, and all presented expressions could be applied to predict the development of breach during overtopping.
-
integration of dsm and sph to model Tailings Dam failure run out slurry routing across 3d real terrain
Water, 2018Co-Authors: Kun Wang, Karen A Hudsonedwards, Peng Yang, Wensheng Lyu, Chao Yang, Xiaofei JingAbstract:Tailings Dam failure accidents occur frequently, causing substantial Damage and loss of human and animal life. The prediction of run-out Tailings slurry routing following Dam failures is of great significance for disaster prevention and mitigation. Using satellite remote sensing digital surface model (DSM) data, Tailings pond parameters and the advanced meshless smoothed particle hydrodynamics (SPH) method, a 3D real-scale numerical modelling method was adopted to study the run-out Tailings slurry routing across real downstream terrains that have and have not been affected by Dam failures. Three case studies, including a physical modelling experiment, the 2015 Brazil Fundao Tailings Dam failure accident and an operating high-risk Tailings pond in China, were carried out. The physical modelling experiment and the known consequences were successfully modeled and validated using the SPH method. This and the other experiments showed that the run-out Tailings slurry would be tremendously destructive in the early stages of Dam failure, and emergency response time would be extremely short if the Dam collapses at its full designed capacity. The results could provide evidence for disaster prevention and mitigation engineering, emergency management plan optimization, and the development of more responsible site plans and sustainable site designs. However, improvements such as rheological model selection, terrain data quality, computing efficiency and land surface roughness need to be made for future studies. SPH numerical modelling is a powerful and advanced technique that is recommended for hazard assessment and the sustainable design of Tailings Dam facilities globally.
-
stability analysis of a copper Tailings Dam via laboratory model tests a chinese case study
Minerals Engineering, 2011Co-Authors: Guangzhi Yin, Zuoan Wei, Ling Wan, Guohong Shui, Xiaofei JingAbstract:Abstract The upstream method is a popular method for raising Tailings Dams. Currently in China there are more than 12,000 Tailings impoundments and almost 95% of them use the upstream method for the construction of the Dam. Statistical data has shown that the Tailings impoundment is one of the main sources of risk in the mining industry. Failures of Tailings impoundments have resulted in the loss of many lives, considerable property Damage, and irreversible pollution in downstream areas. Therefore, the safety of Tailings management facilities has been of increasing concern to governments and local communities. The management of a conventional Tailings storage facility requires the maintenance of a high level of structural stability. Therefore, according to the relevant mine Acts, the mine operators are required to conduct stability analyses for all types of Tailings facilities, whether they are new, active, or decommissioned. For the stability analysis of Tailings Dams, the accurate profile of the Tailings Dam is very important. The profiles are easily obtained for both active and decommissioned Tailings facilities because their data can be collected through field investigations. However, collecting basic data from newly constructed Tailings facilities is difficult. In this paper, a laboratory physical model test has been performed. The construction process for new Tailings impoundment has been physically simulated in the laboratory, where the Tailings particle composition and distribution below a beach, the change of phreatic surface of the Dam, and the engineering properties of the Tailings of the Dam profiles have been measured. A new Tailings facility, Yangtianqin Tailings impoundment, owned by Tongchang copper mine of Yuxi Mine Co., was used as a case study to illustrate the physical modeling of the Tailings Dam. In the model test, the geometrical model of pond area was constructed according to the scale factor, λ L , of 1:200 (model:prototype), and the Tailings discharge system was also established, the Tailings slurry then being discharged based on the design data. Finally, on the basis of the model test results on profiles, the stability analysis of the Tailings Dam at different heights was conducted under different conditions. The model test results and stability analysis show that the height of the Tailings Dam should be less than that originally planned. The original design of Yangtianqing Tailings impoundment should therefore be revised for the safety of the Tailings impoundment.
Aiman Liang - One of the best experts on this subject based on the ideXlab platform.
-
remote sensing assessment of safety risk of iron Tailings pond based on runoff coefficient
Sensors, 2018Co-Authors: Aiman Liang, Xuexin LiAbstract:: Iron Tailings ponds are engineered Dam and dyke systems used to capture iron Tailings. They are high-risk hazards with high potential energy. If the Tailings Dam broke, it would pose a serious threat to the surrounding ecological environment, residents' lives, and property. Rainfall is one of the most important influencing factors causing the Tailings Dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of Tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the Tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of Tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk Tailings ponds, 16 moderate-risk Tailings ponds, and 4 high-risk Tailings ponds in the study area. This method could be useful for selecting targeted Tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk Tailings ponds in rainy season.
-
remote sensing assessment of safety risk of iron Tailings pond based on runoff coefficient
Sensors, 2018Co-Authors: Aiman Liang, Xuexin LiAbstract:: Iron Tailings ponds are engineered Dam and dyke systems used to capture iron Tailings. They are high-risk hazards with high potential energy. If the Tailings Dam broke, it would pose a serious threat to the surrounding ecological environment, residents' lives, and property. Rainfall is one of the most important influencing factors causing the Tailings Dam break. This paper took Chengde Area, a typical iron-producing area, as the study area, and proposed a remote sensing method to evaluate the safety risk of Tailings ponds under rainfall condition by using runoff coefficient and catchment area. Firstly, the vegetation coverage in the study area was estimated using the pixel dichotomy model, and the vegetation type was classified by the support vector machine (SVM) method from Landsat 8 OLI image. Based on DEM, the slope of the study area was extracted, and the catchment area of the Tailings pond was plotted. Then, taking slope, vegetation coverage, and vegetation type as three influencing factors, the runoff coefficient was constructed by weight assignment of each factor using analytic hierarchy process (AHP) model in both quantitative and qualitative way. Finally, the safety risk of Tailings ponds was assessed according to average runoff coefficient and catchment area in the study area. The results showed that there were 124 low-risk Tailings ponds, 16 moderate-risk Tailings ponds, and 4 high-risk Tailings ponds in the study area. This method could be useful for selecting targeted Tailings ponds for focused safety monitoring. Necessary monitoring measurements should be carried out for the high-risk and moderate-risk Tailings ponds in rainy season.
Lingyun Zhang - One of the best experts on this subject based on the ideXlab platform.
-
Pre-alarm system based on real-time monitoring and numerical simulation using internet of things and cloud computing for Tailings Dam in mines
IEEE Access, 2017Co-Authors: Longjun Dong, Weiwei Shu, Daoyuan Sun, Xibing Li, Lingyun ZhangAbstract:The Tailings Dam, a necessary facility to maintain the normal operation of mining enterprises, is a hazard source of human-caused debris flow with high potential energy. The real-time pre-alarm for the instability of Tailings Dam is vital to ensure the normal mining and safety of human lives and properties. Based on the internet of things and 5G wireless networks, the multiple and key information system of Tailings Dam is constructed using the sensor data, which include the stability indexes like phreatic line, reservoir water level, internal and external deformation of the Tailings Dam. The cloud platform is applied to predict the future state of the phreatic line based on real-time monitoring data, where the equation of phreatic line can be obtained. The numerical simulation model is established by considering the predicted equation of phreatic line, limit equilibrium state parameters, reservoir water level, and rainfall. Then, the safety factor, random reliability, and interval non-probabilistic reliability can be solved out through the cloud platform. Combined with the trend of real-time monitoring deformation, as well as calculated dynamic safety factor, random reliability, and interval non-probabilistic reliability, the stable or dangerous warning signals of Tailings Dam can be obtained by the remote real-time pre-alarm system. The main solved method for the key parameters and pre-alarm process are presented through a case study. It is proved that the pre-alarm system is efficient and real-time for the Tailings Dam stability with the integration and mutual validation of plenty of key information.
