Underground Mining Critical Article Review and Annotated Bibliography
CRITICAL ARTICLE REVIEW
“Mining is a capital intensive business, so a small increase in mining productivity will often result in savings of millions of dollars” (Topal & Ramazan, 2010). For this reason, many researchers are focused on developing and improving mathematical models to optimize different mining process. Aiming to increase the Net Present Value of underground mines Nehring, Topal, Kizil and Knights in Integrated short- and medium-term underground mine production scheduling present a new mathematical formulation, based on mixed integer programming, for mine planning scheduling that shows to able not only to increase NPV but also to add feasibility for mining production when compared with current used models, which are already considered optimal; despite presenting strong arguments, a clear formulation and organized information, it is possible to note some limitations, such as: a trial case study instead of real data to demonstrate model’s efficiency and fixed mining parameters.
The current mining planning scheduling issue is to find a way to conciliate both short- and medium- term objective, minimize deviation from target mill feed grade and maximize NPV, respectively, since both are really important to a mining plant as a whole . Nowadays, most mining companies use a model to find an optimal medium- or long- term schedule and then use this data to schedule short- term production (Martinez & Newman, 2011). The authors argue that in the current way it is only possible to achieve a local optimum that results in a short- term schedule with large variation in mill feed supply, which is prejudicial to mining processing system. So, they proposed a model formulation based in mixed integer programming that considers the two phases (short- and medium- term) simultaneously, making it possible to achieve a global optimum: an optimized schedule (high NPV) that allows a consistent mill feed grade (low deviation).
The paper is divided in 2 main parts: model
“Mining is a capital intensive business, so a small increase in mining productivity will often result in savings of millions of dollars” (Topal & Ramazan, 2010). For this reason, many researchers are focused on developing and improving mathematical models to optimize different mining process. Aiming to increase the Net Present Value of underground mines Nehring, Topal, Kizil and Knights in Integrated short- and medium-term underground mine production scheduling present a new mathematical formulation, based on mixed integer programming, for mine planning scheduling that shows to able not only to increase NPV but also to add feasibility for mining production when compared with current used models, which are already considered optimal; despite presenting strong arguments, a clear formulation and organized information, it is possible to note some limitations, such as: a trial case study instead of real data to demonstrate model’s efficiency and fixed mining parameters.
The current mining planning scheduling issue is to find a way to conciliate both short- and medium- term objective, minimize deviation from target mill feed grade and maximize NPV, respectively, since both are really important to a mining plant as a whole . Nowadays, most mining companies use a model to find an optimal medium- or long- term schedule and then use this data to schedule short- term production (Martinez & Newman, 2011). The authors argue that in the current way it is only possible to achieve a local optimum that results in a short- term schedule with large variation in mill feed supply, which is prejudicial to mining processing system. So, they proposed a model formulation based in mixed integer programming that considers the two phases (short- and medium- term) simultaneously, making it possible to achieve a global optimum: an optimized schedule (high NPV) that allows a consistent mill feed grade (low deviation).
The paper is divided in 2 main parts: model
Bibliography: 3. Camus, J., Knights, P., & Bosman, S. T. (2009). Value Generation in Mining: A New Model, in: Knights, P. & Lever, P. J.A., Technology solutions for challenging financial times. Australian mining technology conference, Brisbane, (189-200). 4. Camus, J. P., & Jarpa, S. G. (1996). Long range planning at Chuquicamata mine. In R. V. Ramani (Ed.), 26th Proceedings of the Applications of Computers and Operations Research in the Mineral Industry (pp. 237-241). 7. Nehring, M., & Topal, E. (2007). Production schedule optimisation in underground hard rock mining using mixed integer programming Project Evaluation Conference 2007, Australasian Institute of Mining and Metallurgy (Vol. 2007, pp. 169-175).