Browsing by Author "Marais, Niel"
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- ItemBreach volume estimation of tailings storage facility Failures(Stellenbosch : Stellenbosch University, 2021-03) Marais, Niel; MacRobert, Charles ; Stellenbosch University. Faculty of Engineering. Dept. of Civil Engineering.ENGLISH ABSTRACT: Estimatingthe potentialtailingsreleasevolume(VF)and run-out distances(Dmax)ofTailings Storage Facilities(TSFs)form an integral part of the Tailings Dam Breach Assessment (TDBA) process.These estimationslargely relyon empirical relationships such as those suggested by Rico et al (2008), Concha & Lall (2018) and Quelopana (2019). These empirical relationships arefunctions of the TSFsgeometric characteristics(height of the dam and total volume of the dam).Rourke &Luppnow (2015) assessed the effects of the supernatant pool present on the TSF prior to failure on the recorded outflow volume, a strong linear relationship was identified between the magnitude of failure and the pool ratio based on five failure cases which provided pool ratio data. The aim of thisthesis wasto compile a database of recorded TSF failures that provided the TSFgeometric characteristics mentionedabove. Thedatabase of 56 failures was compiled from various literature sources,one suchsource isthe World Mine Tailings Failure Database (WMTF) compiled by Bowker & Newman (2019).The main limitation encountered when compiling the failure database for analysis was the availability of recordeddata, this was attributed to inaccurate or incomplete reporting of TSF failure data. The WMTF database contains more than 300 recorded failures dating back to 1915.The information contained in the databasewas then used to examine the relationships between the recorded TSFfailures’ geometric characteristics, recorded outflow volumes and run-out distanceson a larger database with more failure cases. The relationships observed during the regressionanalysis phase of the thesis were then used to define four prediction models: two for estimatingVFand two for estimatingDmaxusing Eureqa modelling software. The four models were defined as follows: Model VF.1: The first model defined for the estimationof VFwas modelled to be a function of the impoundment volume and height of a dam and utilized the full database of 56 failure cases. The aim was to develop a model that is comparable to the existing models. The resulting model performed better than the three existing models, achieving an R2value of0.72with aRoot Mean Square Error(RMSE)of 1.207 Mm3.Model VF.2: The second model defined for the estimationof VFwas modelled to be a function of the recorded pool ratio before failure, using 7 cases from the database which provided pool ratio data. The aim of developing this model was to improve the current model developed by Rourke & Luppnow (2015). The resulting model performed near identical to the existing one,achieving an R2value of 0.98and aRMSEof 0.037Mm3. It is recommended that a study is completed looking specifically at the relationship between the pool ratio and saturation levels of the tailing material on the potential release volume. Model Dmax.1: The first model defined for the estimationof Dmaxwas modelled using 37 cases from the database which presented recorded Dmaxvalues for the failures. The function was defined to incorporate the impoundment volume, release volume and height of the dam as the predictor variable Hf. The aim was to develop a more accurate model than existing models. The model performedrelatively well compared to the existing models of Rico et al (2008) and Concha Larrauri & Lall (2019), achieving aR2value of 0.81with a RMSEof 47.72 km.This was attributed to the variance between values for both Dmaxand Hf. Additionally, Dmax variessubstantially between failures and is dependent on various external factors such as site topography, TSF proximity to a water course and possible natural or manmadebarriers. Model Dmax.2: The second model defined for the estimationof Dmaxwas modelled using the same 7 cases used for model VF.2. In addition to the pool ratio, the gradient of the flow path was introduced as a variable. The gradient was taken from the center of the tailings dam to the lowest point along the flow path of the breached tailings material. The model performed relatively well, achieving a R2value of 0.77 and an RMSE of 3. The model, however, is very limited, again attributed to the small dataset available for analysis. Overall, the models performed as expected, model VF.1 performed the best and may be applicable as a first approximation for predicting potential downstream impacts of a TSF failure given its stability and accuracy over a larger dataset. The models developed to incorporate pool ratio data performed well but it is necessary to expand on the size of the dataset to provide a more accurate representation. They do,however, show a strong relationship between the size of the supernatant pond and the expected tailings release volume. When looking at the models predicting the run-out distance it is important to note the complexity of variables influencing the distance that the tailings may travel. Site specific investigations and modeling should be conducted to identify the most probable flow path that consider the presence and volume of vegetation, natural barriers,and buildings.