ANC–BNC titrations and geochemical modeling for characterizing calcareous and siliceous mining waste
- Authors: Drapeau, Clémentine , Delolme, Cécile , Vézin, Clément , Blanc, Denise , Baumgartl, Thomas
- Date: 2021
- Type: Text , Journal article
- Relation: Minerals Vol. 11, no. 3 (2021), p. 1-16
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- Description: Pyrite and calcite are mineral phases that play a major role in acid and neutral mine drainage processes. However, the prediction of acid mine drainage (AMD) or contaminated neutral drainage (CND) requires knowledge of the mineral composition of mining waste and the related potential for element release. This paper studies the combination of acid–base neutralizing capacity (ANC–BNC) with geochemical modeling for the characterization of mining waste and prediction of AMD and CND. The proposed approach is validated with three synthetic mineral assemblages: (1) siliceous sand with pyrite only, representing mining waste responsible for AMD, (2) siliceous sand with calcite and pyrite, representing calcareous waste responsible for CND, and (3) siliceous sand with calcite only, simulating calcareous matrices without any pyrite. The geochemical modeling approach using PHREEQC software was used to model pH evolution and main element release as a function of the added amount of acid or base over the entire pH range: 1 < pH < 13. For calcareous matrices (sand with calcite), the results are typical of a carbonated environment, the geochemistry of which is well known. For matrices containing pyrite, the results identify different pH values favoring the dissolution of pyrite: pH = 2 in a pyrite-only environment and pH = 6 where pyrite coexists with calcite. The neutral conditions can be explained by the buffering capacity of calcite, which allows iron oxyhy-droxide precipitation. Major element release is then related to the dissolution and precipitation of the mineral assemblages. The geochemical modeling allows the prediction of element speciation in the solid and liquid phases. Our findings clearly prove the potential of combined ANC–BNC experiments along with geochemical modeling for the characterization of mining waste and the assessment of risk of AMD and CND. © 2020 by the authors. *Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Thomas Baumgartl” is provided in this record**
Lead mobilization and speciation in mining waste : experiments and modeling
- Authors: Drapeau, Clementine , Argane, Rabei , Delolme, Cecile , Blanc, Denise , Baumgartl, Thomas
- Date: 2021
- Type: Text , Journal article
- Relation: Minerals Vol. 11, no. 6 (2021), p.
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- Description: Mining produces significant amounts of solid mineral waste. Mine waste storage facilities are often challenging to manage and may cause environmental problems. Mining waste is often linked to contaminated mine drainage, including acidic waters with more or less elevated concentrations of trace metals such as lead. This work presents a study on the mobilization of lead from waste from two typical mining sites: Zeida and Mibladen, two now-closed former Pb–Zn mines in the Moulouya region of Morocco. Our research investigates the mobilization potential of Pb from the waste of these mines. The study involved acid–base neutralization capacity tests (ANC–BNC) combined with geochemical modeling. Experimental data allowed for the quantification of the buffering capacity of the samples and the mobilization rates of lead as a function of pH. The geochemical model was fitted to experimental results with thermodynamic considerations. The geochemical model allowed for the identification of the mineral phases involved in providing the buffering capacity of carbonated mining waste (Mibladen) and the meager buffering capacity of the silicate mining waste (Zeida). These cases are representative of contaminated neutral drainage (CND) and acid mine drainage (AMD), respectively. The results highlight the consistency between the ANC–BNC experimental data and the associated modeling in terms of geochemical behavior, validating the approach and identifying the main mechanisms involved. The modeling approach identifies the dissolution of the main solid phases, which impact the pH and the speciation of lead as a function of the pH. This innovative approach, combining ANC–BNC experiments and geochemical modeling, allowed for the accurate identification of mineral phases and surface complexation phenomena, which control the release of lead and its speciation in drainage solutions, as well as within solid phases, as a function of pH. © 2021 by the authors. Licensee MDPI, Basel, Switzerland. **Please note that there are multiple authors for this article therefore only the name of the first 5 including Federation University Australia affiliate “Thomas Baumgartl” is provided in this record**
Evaluation and assessment of blast-induced ground vibrations in an underground gold mine : a case study
- Authors: Tribe, Jarryd , Koroznikova, Larissa , Khandelwal, Manoj , Giri, Jason
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4673-4694
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- Description: Ground vibrations induced during rock fragmentation by blasting remain a potential source of hazard for the stability of nearby structures. In this paper, to forecast the effect of blast-induced ground vibrations, dimensional analysis (DA) is proposed to predict peak particle velocity (PPV). In conventional predictor equations, the major and critical parameter for the estimation of PPV is square root scaled distance. The new formula based on DA was obtained considering various blast design parameters in order to improve the capability of PPV prediction. After obtaining the new DA equation for the prediction of PPV, 360 data sets were used to determine the unknown coefficients of the new equation as well as site constants of different conventional predictor equations. Then, ten additional randomly selected data sets were used to compare the capability of the new model with conventional predictor equations. The results were compared based on coefficient of determination (R2) and mean absolute error (MAE) between measured and predicted values of PPV. The proposed formula with the greatest R2 and the lowest MAE was the better option for predicting the PPV of induced vibrations for the measured field data. © 2021, International Association for Mathematical Geosciences.
A combination of expert-based system and advanced decision-tree algorithms to predict air-overpressure resulting from quarry blasting
- Authors: He, Ziguang , Armaghani, Danial , Masoumnezhad, Mojtaba , Khandelwal, Manoj , Zhou, Jian , Murlidhar, Bhatawdekar
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 2 (2021), p. 1889-1903
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- Description: This study combined a fuzzy Delphi method (FDM) and two advanced decision-tree algorithms to predict air-overpressure (AOp) caused by mine blasting. The FDM was used for input selection. Thus, the panel of experts selected four inputs, including powder factor, max charge per delay, stemming length, and distance from the blast face. Once the input selection was completed, two decision-tree algorithms, namely extreme gradient boosting tree (XGBoost-tree) and random forest (RF), were applied using the inputs selected by the experts. The models are evaluated with the following criteria: correlation coefficient, mean absolute error, gains chart, and Taylor diagram. The applied models were compared with the XGBoost-tree and RF models using the full set of data without input selection results. The results of hybridization showed that the XGBoost-tree model outperformed the RF model. Concerning the gains, the XGBoost-tree again outperformed the RF model. In comparison with the single decision-tree models, the single models had slightly better correlation coefficients; however, the hybridized models were simpler and easier to understand, analyze and implement. In addition, the Taylor diagram showed that the models applied outperformed some other conventional machine learning models, including support vector machine, k-nearest neighbors, and artificial neural network. Overall, the findings of this study suggest that combining expert opinion and advanced decision-tree algorithms can result in accurate and easy to understand predictions of AOp resulting from blasting in quarry sites. © 2020, International Association for Mathematical Geosciences.
Performance of hybrid SCA-RF and HHO-RF models for predicting backbreak in open-pit mine blasting operations
- Authors: Zhou, Jian , Dai, Yong , Khandelwal, Manoj , Monjezi, Masoud , Yu, Zhi , Qiu, Yingui
- Date: 2021
- Type: Text , Journal article
- Relation: Natural Resources Research Vol. 30, no. 6 (2021), p. 4753-4771
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- Description: Backbreak is an adverse phenomenon in blasting operation, which can cause, among others, mine walls instability, falling down of machinery, drilling efficiency reduction and stripping ratio enhancement. Therefore, this research aimed to develop two-hybrid RF (Random Forest) prediction models of random forest, which are optimized by Harris hawks optimizer (HHO) and sine cosine algorithm (SCA), for estimation of the backbreak distance. The HHO and SCA algorithms were adopted to determine two hyper-parameters (mtry and ntree) in the RF models, in which root mean square error (RMSE) was utilized as a fitness function. A database with 234 samples was established, in which six variables [i.e., hole length (L), burden (B), spacing (S), stemming (T), special drilling (SD) and powder factor (PF)] were used as input variables, and backbreak was defined as output variable. Additionally, three classical regression models (i.e., extreme learning machine, radial basis function network and general regression neural network) were adopted to verify the superiority of the hybrid RF prediction models. The predictive reliability of the proposed models was assessed by the combination of mean absolute error (MAE), RMSE, variance accounted for (VAF) and Pearson correlation coefficient (R2). The results revealed that the SCA-RF model outperformed all the other prediction models with MAE of (0.0444 and 0.0470), RMSE of (0.0816 and 0.0996), VAF of (96.82 and 95.88) and R2 of (0.9876 and 0.9829) in training and testing stages, respectively. A Gini index generated internally in the RF model showed that backbreak was significantly more sensitive to L and T than to SD. © 2021, International Association for Mathematical Geosciences.