Cemented paste backfill for mineral tailings management: Review and future perspectives

Qi, Chongchong; Fourie, Andy

doi:10.1016/j.mineng.2019.106025

Cited by 403 publications

(132 citation statements)

References 171 publications

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“…e preparation of the specimen is shown in Table 3. rough a large number of experiments in the early stage, it is concluded that the cement content, paste concentration, and tailings-waste ratio are important factors affecting the paste strength, and other parameters can be derived from these basic parameters or be idealized as secondary factors [11,27]. e proportion is based on previous exploratory experiments and slump tests with slump values ranging from 23 to 26, which are of good fluidity and stability.…”

Section: Experiments Preparationmentioning

confidence: 99%

“…Qi and Fourie [11] summarized recent progress in CPB design, with particular emphasis on flocculation and sedimentation, CPB mix design and CPB pipe transport, and envisaged a future in which the CPB design is optimized in an integrated CPB design system, accelerated by artificial intelligence and interpreted using atomic simulation. Qi [12][13][14] proposed a strength prediction model integrating boosted regression trees (BRT) and particle swarm optimization (PSO) and thought that more efficient reuse of waste tailings as CPB can be achieved by reducing the required number of mechanical experiments during engineering applications.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

Zhang

2019

Advances in Materials Science and Engineering

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Paste backfilling is an important support for the development of green mines and deep mining. It can effectively reduce a series of risks of underground goaf and surface tailings ponds. Reasonable strength of backfill is an effective guarantee for controlling ground stress and realizing safe mining function. Under the combination of complex materials and local conditions, ensuring the optimal design and effective proportion for paste backfill strength is the bottleneck problem that restricts the safety, economy, and efficiency of filling mining. The strength developing trend of paste backfilling prepared from waste rock and unclassified tailings has been studied. Different levels of cement contents, tailings-waste ratios, and slurry concentrations were investigated through orthogonal design to obtain the relationship between the UCS and the multi-influential factors. Combined with the experimental results and the previous strength prediction models, the waste rock-unclassified tailings paste strength prediction model was proposed. Introducing the water-cement ratio, the cement-tailings ratio, the amount of cement, and the packing density that characterizing the overall gradation of unclassified tailings and waste rock, as well as the curing time, a strength prediction model of multifactors was developed. Moreover, the microscopic structure of the paste prepared from waste-unclassified tailings was analyzed with an Environment Scanning Electron Microscope (ESEM), and the influence mechanism was ascertained. The weight coefficient of strength development is carded in this paper, and the strength model of unclassified tailings-waste paste considering five factors is obtained, which is of great significance to guide the mining engineering.

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Section: Experiments Preparationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

Zhang

2019

Advances in Materials Science and Engineering

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“…ACC is the ratio of the rate number of correct predictions and the total number of predictions [88]. RMSE represents the difference between data observations and data estimates [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103]. Equations for the different measures are given below:…”

Section: Validation Methodsmentioning

confidence: 99%

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Pham

Phong

Nguyen

et al. 2020

Water

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106

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Risk of flash floods is currently an important problem in many parts of Vietnam. In this study, we used four machine-learning methods, namely Kernel Logistic Regression (KLR), Radial Basis Function Classifier (RBFC), Multinomial Naïve Bayes (NBM), and Logistic Model Tree (LMT) to generate flash flood susceptibility maps at the minor part of Nghe An province of the Center region (Vietnam) where recurrent flood problems are being experienced. Performance of these four methods was evaluated to select the best method for flash flood susceptibility mapping. In the model studies, ten flash flood conditioning factors, namely soil, slope, curvature, river density, flow direction, distance from rivers, elevation, aspect, land use, and geology, were chosen based on topography and geo-environmental conditions of the site. For the validation of models, the area under Receiver Operating Characteristic (ROC), Area Under Curve (AUC), and various statistical indices were used. The results indicated that performance of all the models is good for generating flash flood susceptibility maps (AUC = 0.983–0.988). However, performance of LMT model is the best among the four methods (LMT: AUC = 0.988; KLR: AUC = 0.985; RBFC: AUC = 0.984; and NBM: AUC = 0.983). The present study would be useful for the construction of accurate flash flood susceptibility maps with the objectives of identifying flood-susceptible areas/zones for proper flash flood risk management.

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“…To investigate the performance of the models, the root mean square error (RMSE), R 2 , Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account, which are shown in Equations (9)-(12) [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56]:…”

Section: Development Of Bbo-ann Pso-ann Mpmr and Elm To Predict Ppvmentioning

confidence: 99%

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

Kumar

Samui

et al. 2020

Applied Sciences

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Ground vibration induced by blasting operations is an important undesirable effect in surface mines and has significant environmental impacts on surrounding areas. Therefore, the precise prediction of blast-induced ground vibration is a challenging task for engineers and for managers. This study explores and evaluates the use of two stochastic metaheuristic algorithms, namely biogeography-based optimization (BBO) and particle swarm optimization (PSO), as well as one deterministic optimization algorithm, namely the DIRECT method, to improve the performance of an artificial neural network (ANN) for predicting the ground vibration. It is worth mentioning this is the first time that BBO-ANN and DIRECT-ANN models have been applied to predict ground vibration. To demonstrate model reliability and effectiveness, a minimax probability machine regression (MPMR), extreme learning machine (ELM), and three well-known empirical methods were also tested. To collect the required datasets, two quarry mines in the Shur river dam region, located in the southwest of Iran, were monitored, and the values of input and output parameters were measured. Five statistical indicators, namely the percentage root mean square error (%RMSE), coefficient of determination (R2), Ratio of RMSE to the standard deviation of the observations (RSR), mean absolute error (MAE), and degree of agreement (d) were taken into account for the model assessment. According to the results, BBO-ANN provided a better generalization capability than the other predictive models. As a conclusion, BBO, as a robust evolutionary algorithm, can be successfully linked to the ANN for better performance.

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Cemented paste backfill for mineral tailings management: Review and future perspectives

Cited by 403 publications

References 171 publications

A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

A Novel Prediction Model of Strength of Paste Backfill Prepared from Waste-Unclassified Tailings

A Comparative Study of Kernel Logistic Regression, Radial Basis Function Classifier, Multinomial Naïve Bayes, and Logistic Model Tree for Flash Flood Susceptibility Mapping

Developing a New Computational Intelligence Approach for Approximating the Blast-Induced Ground Vibration

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