Theory and method of genetic-neural optimizing cut-off grade and grade of crude ore

He, Yong; Zhu, Kunpeng; Gao, Siwei; Liu, Ting; Li, Yue

doi:10.1016/j.eswa.2008.09.018

Cited by 28 publications

(9 citation statements)

References 2 publications

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“…In fact; this issue is remarked as one of the specifications of the new algorithm in comparison to the previous algorithms. The recovery amount is always considered fixed in most of these algorithms (Bascetin and Nieto, 2007;He et al, 2009;Johnson et al, 2011). This is also true in capital costs (Fan et al, 2013).…”

Section: Introductionmentioning

confidence: 97%

A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects

Rahimi

Ghasemzadeh

2015

Resources Policy

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Section: Introductionmentioning

confidence: 97%

A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects

Rahimi

Ghasemzadeh

2015

Resources Policy

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“…As a result, Azimi and Osanloo (2011) utilised a mathematical-based programme to represent the optimal COG that can be calculated by non-linear, non-convex optimisation and Lagrangian genetic algorithm. Additionally, a genetic-neural method was proposed for optimisation of COG in crude ore by employing self-adaptive neural network using genetic algorithm to globally search the optimal COG (He et al 2009). …”

Section: Introductionmentioning

confidence: 99%

Determination of optimised cut-off grade utilising non-linear programming

et al. 2015

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One of the most fundamental problems in a mining operation is how to recognise an optimum cut-off grade, which defines the grade for discriminating between ore and waste in an ore body, including ore that is extracted at different periods over a mine life period. Therefore, the identification of an optimised cut-off grade (COG) is a crucial function which has to be monitored during the mine life. The main aim of this study is to propose a modified optimum COG model in order to maximise the profit value (PV) for mining projects. Maximising the PV of a mining operation, which is a nonlinear programming, is subject to different constraints involving a general grade distribution within a deposit and three stages of production namely mining, concentrating and refining. The proposed computer-based model is more effective in long-term planning of the open pit mines. To provide a better understanding of the algorithm efficiency, a numerical example is given and subsequently solved based on the Lane algorithm. In order to achieve this, the LINGO software was employed.

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“…Therefore, technical indicators of all units should be optimized jointly to achieve the global optimization of the production process [12][13][14]. The second is the optimization of the production process of metal mines, in which the objective is to maximize economic benefits while ignoring the resource efficiency [15][16][17][18][19]. These works emphasized the optimization targeting at maximizing economic benefits.…”

Section: Introductionmentioning

confidence: 99%

Production Process Optimization of Metal Mines Considering Economic Benefit and Resource Efficiency Using an NSGA-II Model

Xun-hong¹,

Gu²,

Liu³

et al. 2018

Preprint

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The optimization of the production process of metal mines has been traditionally driven only by economic benefits while ignoring resource efficiency. However, it has become increasingly aware of the importance of resource efficiency since mineral resource reserves continue to decrease while the demand continues to grow. To better utilize the mineral resources for sustainable development, this paper proposes a multi-objective optimization model of the production process of metal mines considering both economic benefits and resource efficiency. Specifically, the goals of the proposed model are to maximize the profit and resource utilization rate. Then, the fast and elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) is used to optimize the multi-objective optimization model. The proposed model has been applied to the optimization of the production process of a stage in the Huogeqi Copper Mine. The optimization results provide a set of Pareto-optimal solutions that can meet varying needs of decision makers. Moreover, compared with those of the current production indicators, the profit and resource utilization rate of some points in the optimization results can increase respectively by 2.99% and 2.64%. Additionally, the effects of the decision variables (geological cut-off grade, minimum industrial grade and loss ratio) on objective functions (profit and resource utilization rate) were discussed using variance analysis. The sensitivities of the Pareto-optimal solutions to the unit copper concentrate price were studied. The results show that the Pareto-optimal solutions at higher profits (with lower resource utilization rates) are more sensitive to the unit copper concentrate prices than those obtained in regions with lower profits.

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Theory and method of genetic-neural optimizing cut-off grade and grade of crude ore

Cited by 28 publications

References 2 publications

A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects

A new algorithm to determine optimum cut-off grades considering technical, economical, environmental and social aspects

Determination of optimised cut-off grade utilising non-linear programming

Production Process Optimization of Metal Mines Considering Economic Benefit and Resource Efficiency Using an NSGA-II Model

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