The construction of waste rock dumps on existing tailing ponds has been put into practice in China to save precious land resources. This work focuses on the safety assessment of the Daheishan molybdenum mine waste roc...The construction of waste rock dumps on existing tailing ponds has been put into practice in China to save precious land resources. This work focuses on the safety assessment of the Daheishan molybdenum mine waste rock dump under construction on two adjoining tailings ponds. The consolidation of the tailings foundation and the filling quality of the waste rock are investigated by the transient electromagnetic method through detecting water-rich areas and loose packing areas, from which, the depth of phreatic line is also estimated. With such information and the material parameters, the numerical method based on shear strength reduction is applied to analyzing the overall stability of the waste rock dump and the tailings ponds over a number of typical cross sections under both current and designed conditions, where the complex geological profiles exposed by site investigation are considered. Through numerical experiments, the influence of soft lenses in the tailings and possible loose packing areas in the waste rock is examined. Although large displacements may develop due to the soft tailings foundation, the results show that the waste rock dump satisfies the safety requirements under both present and designed conditions.展开更多
电力系统是一个时变的复杂系统。近年来,基于数据驱动的机器学习方法在电力系统暂态稳定评估领域得到了广泛应用。然而,当电力系统运行受到较大扰动发生工况变化时,机器学习模型需要根据新的运行数据进行训练,故其难以及时应对新拓扑结...电力系统是一个时变的复杂系统。近年来,基于数据驱动的机器学习方法在电力系统暂态稳定评估领域得到了广泛应用。然而,当电力系统运行受到较大扰动发生工况变化时,机器学习模型需要根据新的运行数据进行训练,故其难以及时应对新拓扑结构下系统的暂态稳定情况评估。为解决该问题,首先,提出了一种模型更新机制,按照不同条件对模型进行更新;其次,引入了基于多面近端支持向量机(multisurface proximal support vector machine,MPSVM)的斜双随机森林(oblique double random forest with MPSVM,MPDRF)模型,并将其作为分类器对电力系统的稳定状态进行评估;最后,在新英格兰10机39节点系统上的进行仿真测试,验证该方法的有效性。研究结果表明,所提的结合更新机制的电力系统暂态稳定评估方法的评估性能优于普通方法的。展开更多
基金Projects(51209118,71373245)supported by the National Natural Science Foundation of ChinaProject(2014JBKY01)supported by the Fundamental Research Funds for CASST,China
文摘The construction of waste rock dumps on existing tailing ponds has been put into practice in China to save precious land resources. This work focuses on the safety assessment of the Daheishan molybdenum mine waste rock dump under construction on two adjoining tailings ponds. The consolidation of the tailings foundation and the filling quality of the waste rock are investigated by the transient electromagnetic method through detecting water-rich areas and loose packing areas, from which, the depth of phreatic line is also estimated. With such information and the material parameters, the numerical method based on shear strength reduction is applied to analyzing the overall stability of the waste rock dump and the tailings ponds over a number of typical cross sections under both current and designed conditions, where the complex geological profiles exposed by site investigation are considered. Through numerical experiments, the influence of soft lenses in the tailings and possible loose packing areas in the waste rock is examined. Although large displacements may develop due to the soft tailings foundation, the results show that the waste rock dump satisfies the safety requirements under both present and designed conditions.
文摘电力系统是一个时变的复杂系统。近年来,基于数据驱动的机器学习方法在电力系统暂态稳定评估领域得到了广泛应用。然而,当电力系统运行受到较大扰动发生工况变化时,机器学习模型需要根据新的运行数据进行训练,故其难以及时应对新拓扑结构下系统的暂态稳定情况评估。为解决该问题,首先,提出了一种模型更新机制,按照不同条件对模型进行更新;其次,引入了基于多面近端支持向量机(multisurface proximal support vector machine,MPSVM)的斜双随机森林(oblique double random forest with MPSVM,MPDRF)模型,并将其作为分类器对电力系统的稳定状态进行评估;最后,在新英格兰10机39节点系统上的进行仿真测试,验证该方法的有效性。研究结果表明,所提的结合更新机制的电力系统暂态稳定评估方法的评估性能优于普通方法的。
