Tailings Dams Failures: Updated Statistical Model for Discharge Volume and Runout

Abstract

This paper presents a statistical model to estimate the volume of released tailings (V F) and the maximum distance travelled by the tailings (D max) in the event of a tailings dam failure, based on physical parameters of the dams. The dataset of historical tailings dam failures is updated from the one used by Rico et al., (Floods from tailings dam failures, Journal of Hazardous Materials, 154 (1) (2008) 79-87) for their regression model. It includes events out of the range of the dams contained in the previous dataset. A new linear regression model for the calculation of D max , which considers the potential energy associated with the released volume is proposed. A reduction in the uncertainty in the estimation of D max when large tailings dam failures are evaluated, is demonstrated. Since site conditions vary significantly it is important to directly consider the uncertainty associated with such predictions, rather than directly using these types of regression equations. Here, we formally quantify the uncertainty distribution for the conditional estimation of V F and D max , given tailings dam attributes, and advocate its use to better represent the tailings dam failure data and to characterize the risk associated with a potential failure.

Three-dimensional numerical simulation of mud flow from a tailing dam failure across complex terrain

“…The free surface and velocity field of the simulation against different physical times t = 0.4 s and t = 0.56 s are shown in Fig. 4, which is similar to the results in Kleefsman et al (2005) and Lee et al (2010). The small pictures on the top right of the screenshots represent the fluid behind the gate.…”

“…While the Feijão tailing dam almost discharged all the tailings it had stored, tailing dam accidents will generally only release part of the tailings stored, according to many historical accidents. The volume of tailings released after the dam break at the A'xi gold mine tailing reservoir is estimated to be approximately 756 000 m 3 based on the regression equation proposed by Larrauri and Lall (2018):…”

“…These related dam-break analyses can be divided into empirical models and numerical models. The empirical model predicts the volume of released tailings (V F ) and the run-out distance (D max ) by establishing a regression equation based on historical cases of collapse, dam height and the impounded volume of tailings (Rico et al, 2008a;Larrauri and Lall, 2018). However, owing to the difference in the actual state of the tailing reservoir and the surrounding environment, and without considering the rheological properties of the tailings such as the viscosity coefficient and yield stress (Martin et al, 2015), the predicted results may deviate greatly from the actual dam-break situation.…”

“…However, the shallow water equations are two-dimensional simplifications of Navier-Stokes equations, neglecting the observed nonlinear behavior through the mud depth (Han et al, 2019). For more reliable results, the 3-D Navier-Stokes equations are recommended to control the flow dynamic (Wang et al, 2016;Lee et al, 2010).…”

The Application of the Seismic Cone Penetration Test (SCPTU) in Tailings Water Conditions Monitoring

“…The technological water cycle is monitored and managed both on a global scale related to the entire copper production process, and on a local scale covering the storage facility area. The monitoring of water conditions in an outflow, above-ground facility is an essential element determining the safety of the operated facility [3][4][5][6]. While safe accumulation of the controlled volume of technological water in the decant pond is reduced to maintaining a certain distance from the crown of the dams to the shoreline of the pond, a network of piezometers is used to identify the location of the depression line in the tailings massif [7].…”