A mixed integer programming based cut-off grade model for open-pit mining of complex poly-metallic resources

• Published in: Resources policy Vol. 72; p. 102076
• Main Authors: Khan, Asif, Asad, Mohammad Waqar Ali
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ltd 01.08.2021; Elsevier Science Ltd
• Subjects: Case studies; Cut off grades; Cut-off grade; Heuristic; Heuristic methods; Integer programming; Markets; Mathematical models; Metals; Mineral resources; Minerals; Mining; Mining industry; Mixed integer; Open pit mining; Optimization; OR in natural resources; Policy making; Present value; Refineries; Scheduling; Supply; Supply chains; Surface mines; OR in natural resources; Scheduling; Cut-off grade; Mineral resources; Open-pit mining; Optimization
• ISSN: 0301-4207; 1873-7641
• DOI: 10.1016/j.resourpol.2021.102076

The mineral supply chain in an ideal open-pit mining operation constitutes a set of sequential stages including a surface mine or an open-pit, a processing plant and a refinery or market. Cut-off grade defines the flow of materials from the mine to subsequent stages within this mineral supply chain. This underlines the significance of cut-off grades in establishing the economic viability of an operation. However, only heuristic methods are available for defining the cut-off grade policy for complex mineral resources (orebodies) that constitute multiple economic minerals. This article proposes a new mixed integer programming based mathematical model that generates an optimal cut-off grade policy for open-pit mining operations that mine, process and refine or market multiple metal products. An implementation of the new method in three separate case studies reflects better performance (i.e., 24%, 36% and 27% higher net present value) against available heuristics. •A model that defines the flow of materials in open-pit mining of poly-metallic resources.•The model considers multiple materials (metals) and destinations within the system.•Case studies confirm the validity and robustness of the proposed model.•The innovative model reflects better performance as compared to traditional methods.