Exploitation
Javad Lotfi Godarzi; Ahmad Reza Sayadi; Amin Mousavi; Micah Nehring
Abstract
The production rate and cut-off grade are two critical variables in the design and planning of open-pit mines. Generally, the production rate depends on the reserve amount, which is influenced by the cut-off grade. Additionally, the cut-off grade is affected by the production cost, which is influenced ...
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The production rate and cut-off grade are two critical variables in the design and planning of open-pit mines. Generally, the production rate depends on the reserve amount, which is influenced by the cut-off grade. Additionally, the cut-off grade is affected by the production cost, which is influenced by the production rate and product price. A conventional approach optimizes each variable individually, and neglects the trade-off between production rate and cut-off grade, leading to a sub-optimal solution. This work aimed to address the simultaneous optimization of the production rate and cut-off grade and provided a novel solution for this problem. In this context, a non-linear mathematical model was developed. The Particle Swarm Optimization (PSO) algorithm was used due to the model's non-linear nature and the continuous decision variables. Implementing the model for a typical copper mine showed that the suggested model resulted in a concurrent optimization of production rate and cut-off grade. The maximum NPV of 1.153 billion dollars occurred at a production rate of 15.66 Mt/y, and a cut-off grade of 0.64%. Additionally, a sensitivity analysis was conducted for key factors such as product price, discount rate, and maximum capital cost.
Zohreh Nabavi; Mohammad Mirzehi; Hesam Dehghani; Pedram Ashtari
Abstract
Back-break is one of the adverse effects of blasting, which results in unstable mine walls, high duration, falling machinery, and inappropriate fragmentation. Thus, the economic benefits of the mine are reduced, and safety is severely affected. Back-break can be influenced by various parameters such ...
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Back-break is one of the adverse effects of blasting, which results in unstable mine walls, high duration, falling machinery, and inappropriate fragmentation. Thus, the economic benefits of the mine are reduced, and safety is severely affected. Back-break can be influenced by various parameters such as rock mass properties, blast geometry, and explosive properties. Therefore, during the blasting process, back-break must be accurately predicted, and other production activities must be done to prevent and reduce its adverse effects. In this regard, a hybrid model of extreme gradient boosting (XGB) is proposed for predicting back-break using gray wolf optimization (GWO) and particle swarm optimization (PSO). Additionally, validation of the hybrid model is conducted using XGBoost, gene expression programming (GEP), random forest (RF), linear multiple regression (LMR), and non-linear multiple regression (NLMR) methods. For this purpose, the data obtained from 90 blasting operations in the Chadormalu iron ore mine are collected by considering the parameters of the blast pattern design. According to the results obtained, the performance and accuracy level of hybrid models including GWO-XGB (R2 = 99, RMSE = 0.01, MAE = 0.001, VAF = 0.99, a-20 = 0.98), and PSO-XGB (99, 0.01, 0.001, 0.99, 0.98) are better than the XGBoost (97, 0.185, 0.132, 0.98, 95), GEP (96, 0.233, 0.186, 0.967, 0.935), RF (97, 0.210, 0.156, 0.97, 0.94), LMR (96, 0.235, 0.181, 0.964, 0.92), and NLMR (96, 0.229, 0.177, 0.968, 0.93) models. Notably, the GWO-XGB hybrid model has superior overall performance as compared to the PSO-XGB model. Based on the sensitivity analysis results, hole depth and stemming are the essential effective parameters for back-break.
