Inorganic Precipitation of Calcite in Mine Tailings Using Trona

Mine Water and the Environment - Mine tailings pose environmental risks such as dust emission and acid mine drainage (AMD) and also present geotechnical risks, i.e. tailings dam failures. In-situ...     

Prediction of the Groundwater Rebound Process in a Backfilled Open Cut Mine Using an Artificial Neural Network

A neural network model was used to predict the groundwater rebound process after cessation of dewatering at a restored open cut coal site in the East Midlands area of the UK. Time (days after dewatering), water table levels in the aquifer and the backfilled site, hydraulic conductivity of the aquifer and backfilled site, and precipitation were used as input. The output of the network was the water table height, until the water table reached its equilibrium position. A feed-forward artificial neural network that uses batch gradient descent with a momentum-learning algorithm and 6-1-6-1 arrangement was found capable of predicting the groundwater rebound process. Predicted values were very close to the monitored results. The correlation coefficient values were 0.98221 for the training set, and 0.99329, 0.99499, 0.98667, 0.98289, and 0.97141 during the testing stage for the five monitoring points, showing that the model prediction was satisfactory.     

Mechanical properties of Michigan Basin's gypsum before and after saturation

The stability analysis of an abandoned underground gypsum mine requires the determination of the mine pillar's strength.This is especially important for flooded abandoned mines where the gypsum pillars become saturated and are subjected to dissolution after flooding.Further,mine pillars are subjected to blast vibrations that generate some level of macro-and micro-fracturing.Testing samples of gypsum must,therefore,simulate these conditions as close as possible.In this research,the strength of gypsum is investigated in an as-received saturated condition using uniaxial compressive strength(UCS),Brazilian tensile strength(BTS)and point load index(PLI)tests.The scale effect was investigated and new correlations were derived to describe the effect of sample size on both UCS and BTS under dry and saturated conditions.Effects of blasting on these parameters were observed and the importance of choosing the proper samples was discussed.Finally,correlations were derived for both compressive and tensile strengths under dry and saturated conditions from the PLI test results,which are commonly used as a simple substitute for the indirect determination of UCS and BTS.     

Analysis of drying and saturating natural gypsum samples for mechanical testing

The stability of underground abandoned gypsum mines is dependent on the gypsum pillar's strength,and most abandoned mines are in a fully saturated condition. Moisture affects the strength of gypsum and is therefore commonly measured when testing rock strength. For most rocks, this is a simple task of weighing the rock's mass before and after oven-heating at a specified temperature and duration. For natural gypsum, however, this is not a straightforward process. Heating natural gypsum can result in dehydration and transformation of gypsum to hemihydrate and anhydrite, thus changing the physical characteristics of the gypsum such as its particle density which in turn affects the moisture content and strength measurements. To prevent transformation when determining the moisture content of gypsum,the American Society for Testing Materials(ASTM) recommends lowering the drying temperature from 110℃ to 60℃ . To investigate the temperature at which gypsum transforms to hemihydrate, we used a helium pycnometer to measure the particle densities of gypsum, hemihydrate and anhydrite. In this research, we suggest that a higher drying temperature of 80℃ can be used for drying gypsum without transforming gypsum to hemihydrate. Further, preparing saturated samples for mechanical testing,which is required in stability analyses of abandoned mines, is challenging due to the dissolution of gypsum when placed in water. To address this problem, we investigated the following methods to saturate gypsum cores taking into account the solubility of gypsum:(1) water immersion,(2) vacuum saturation, and(3) improved vacuum saturation. The research indicates that all the three methods are acceptable but they should be conducted using a saturated gypsum-water solution to minimize dissolution. Further, the research found that the improved vacuum saturation method saturated the test samples within 24 h, while duration of 30 h was required for the other two methods.     

Prediction of Pyrite Oxidation in a Coal Washing Waste Pile Applying Artificial Neural Networks (ANNs) and Adaptive Neuro-fuzzy Inference Systems (ANFIS)

Two models were evaluated as alternative methods for predicting pyrite oxidation in the Alborz Sharghi coal washing waste pile (in northeastern Iran). The first model applies a ‘feed-forward artificial neural network (ANN) with 4-7-1 structure’. The model uses depth, initial remaining pyrite fraction, mole fraction of oxygen, and annual precipitation as input parameters and returns the remaining pyrite fraction in the related depth of the pile as its output. In the second model, an adaptive neuro-fuzzy inference system (ANFIS), which uses generalised bell membership functions and the Takagi–Sugeno-type fuzzy inference system, was applied with the same input–output parameters. The correlation coefficient, root mean squared error, and average absolute relative error for the training stage of the ANNs were 0.81, 0.169, and 0.12, respectively, while the values for ANFIS were 0.91, 0.091, and 0.078, respectively. Comparison of the correlation coefficients and the error parameters revealed that both models successfully predicted remaining pyrite fraction from various depths of the pile. However, ANFIS was found to be more reliable and more accurate.