Extended Ultimate-Pit-Limit Methodology for Optimizing Surface-to-Underground Mining Transition in Metal Mines

The transition from surface mining to underground is a critical issue for metal mines. The commonly cited procedure cored by ultimate-pit-limit (UPL) methodology is restricted to maximize the profit from both surface and underground mining, due to the absence of the integration of the profit from either of them. Under the target for such maximization, this study proposes a new optimization approach, which directly relates the design of open-pit limit and underground stopes, by equalizing the marginal profit from either surface or underground mining. The variation of the crown pillar size is involved in this approach. The proposed approach is applied to the Dagushan iron mine, and results show the total profit increased from 3.79 billion CNYs (original design by conventional UPL methodology) to 4.17 billion CNYs (optimal design by the proposed approach), by 9.91%. Moreover, the marginal profit from surface and underground mining, as well as total profit, of all possible designs of surface-to-underground mining transition in Dagushan iron mine is calculated to validate the proposed approach. When the marginal profits satisfy the criterion of the proposed approach, the maximum value of the total profit appears, and this demonstrates the proposed approach is robust to maximize the total profit in surface-to-underground mining transition. This work contributes to existing literature studies primarily from practical aspect, by providing a unified approach to optimize the transition from surface to underground mining.

The Early Development of Passive Treatment Systems for Mining-Influenced Water: A North American Perspective

This paper reviews the early history (first 20 years) of passive treatment of mine water, from its beginnings, when it was viewed as a possible way to treat small flows of circumneutral and mildly acidic coal mine drainage, to its use for much larger flows and more contaminated mine water from metal mines. The original concepts of passive treatment have since been modified and used successfully to treat a wide range of mine water quality and quantities, far more than we would have believed possible.

Investigating the Working Efficiency of Typical Work in High-Altitude Alpine Metal Mining Areas Based on a SeqGAN-GABP Mixed Algorithm

Man-machine efficacy evaluations of typical work in the safe mining of high-altitude alpine metal mines are associated with fuzziness, multiple indexes, and large subjective components. This results in difficulties in the prediction of the typical work efficiency in high-altitude alpine metal mining areas. In this study, ergonomic theory was applied to establish the evaluation index system of typical work efficiency in high-altitude alpine metal mining areas by studying the cooperative relationship between operators, working machines, working environment, and design variables. First, we investigated the collaborative relationship between workers, operating machinery, operating environment, and design variables in order to establish the evaluation index system of typical work efficiency in high-altitude alpine metal mining areas. Second, principal component analysis (PCA) was integrated with the fusion entropy weight method to (i) analyze the coupling correlation and overlapping effects between the factors influencing efficiency at different altitudes and (ii) to determine the key influencing factors. Third, a model based on the sequence generative adversarial network genetic algorithm backpropagation (SeqGAN-GABP) hybrid algorithm was established to predict the trends in the operating efficiency of typical work types in high-altitude alpine metal mining areas. Finally, three high-altitude alpine metal mines in Xinjiang were selected as representative examples to verify the proposed framework by comparing it with other state-of the art models (multiple linear regression prediction model, backpropagation (BP) neural network model, and genetic algorithm back propagation (GA-BP) neural network model). Results determine the average relative error of each model as 2.74%, 1.97%, 1.29%, and 1.02%, respectively, indicating the greater accuracy of our proposed method in predicting the efficiency of typical work types in high-altitude alpine mining areas. This study can provide a scientific basis for the establishment of mining safety judgment standards in high-altitude alpine areas.