| ArticleName |
Error in calculation of profits in mining complex-structure deposits |
| ArticleAuthorData |
Chersky Institute of Mining of the North, Siberian Branch. Russian Academy of Sciences, Yakutsk, Russia:
N. S. Batugina, Leading Researcher, Doctor of Economic Sciences, batuginan@mail.ru
V. L. Gavrilov, Leading Researcher, Candidate of Engineering Sciences
S. M. Tkach, Chief Researcher, Doctor of Engineering Sciences |
| Abstract |
Variation in the anticipated (calculated, target) profit Pr of mining in a deposit or a deposit area is caused by spatial or temporal variability in the base values such as output Q, average cut-off grade GA, recovery factor J, mineral loss L, dilution D, mining system failure probability W, end product price P and aggregate cost C. The authors show that profit is a function of these random variables conditioned by random (and often systematic) error committed during mining. This article proposes the model to calculate relative error in anticipated profit of mining a deposit or a deposit area. The degree and nature of the effect exerted by random errors in source data on the accuracy and reliability of economic–geological evaluation are analyzed. One of the highest uncertainties is the estimate of the root-mean-square error in the price variance, σp. The price variance brings the highest uncertainty both in evaluation of efficiency of an investment project and a mine. The error in the estimate of the aggregate cost per unit end product, σс, large depends on the geological parameters. it is shown that under error of 10–20 % in any argument, the error in the anticipated profit will reach 30 % and, in some cases, even 200–300 %. Permissible error in the forecast of profit is always much higher than the errors in separate factors, which is connected with the inaccuracy of appraisal of mineral reserves. This results in the complication of mining operations in terms of amount of excavation and economic damage. A more precise profit evaluation requires greater information on a deposit by the beginning of mining and processing project planning or reconstruction, which is only possible in case of effective geologic survey of mineral deposits scheduled for mining or under development. |
| keywords |
Complex-structure deposit, mining efficiency, geometrization of mineral reserves, reliability, uncertainty, calculation accuracy, error, profit |
| References |
1. Orlov V. P. The problems of subsurface use (2000–2006). Moscow : Geoinformmark, 2007. 464 p.
2. Kelchevskaya N. R., Altushkin I. A., Korol Yu. A., Bondarenko N. S. Peculiarities and importance of price formation in non-ferrous metallurgy (on example of copper concentrate production). Tsvetnye Metally. 2016. No. 8. pp. 13–19. DOI: 10.17580/tsm.2016.08.01
3. Kozin V. Z. Product recovery and product balance correction. Obogashchenie Rud. 2014. No. 2. pp. 32–35.
4. Mathee U. Stable processes and successful solutions for profit forming based on use of data about materials. Chernye Metally. 2018. No. 6. pp. 68–71.
5. Banshchikova Т. S., Leonenko N. А., Alekseev V. S. Physical-and-chemical methods of finelydispersed gold recovery from legacy placer gold in the Amur river region. Obogashchenie Rud. 2017. No. 6. pp. 32–37. DOI: 10.17580/or.2017.06.06
6. Batugin S. A., Chernyy E. D. Theoretical basis of testing and assessment of deposit reserves. Novosibirsk : Nauka, 1998. 344 p.
7. Ovchinnikov V. V. Reliability of regional exploration by the data of logical-and-probabilistic simulation modeling and applications. Regionalnaya geologiya i metallogeniya. 2014. No. 57. pp. 102–111.
8. Benndorf J. Adding project value by managing geological risk in mining. 2009. Available at: https://repository.tudelft.nl/islandora/object/uuid:85d0f9d6-b246-4bde-a5a7-fd58cad80ef8/datastream/OBJ (accessed: 19.04.2018).
9. Eggert R. G. Mineral exploration and development: risk and reward. 2010. Available at: http://www.miningnorth.com/_rsc/site-content/library/education/Mineral_Exploration_&_Development_Roderick_Eggert_Eng.pdf (accessed: 19.04.2018).
10. Kendrick T. Identifying and Managing Project Risk: Essential Tools for Failure-Proofing Your Project. 3rd ed. New York : AMACOM, 2015. 698 p.
11. Erkan B., Ertan G., Yeo J., Comfort L. K. Risk, profit, or safety: Sociotechnical systems under stress. Safety Science. 2016. Vol. 88. pp. 199–210.
12. Mineral Resource and Ore Reserve Estimation : The AusIMM Guide to Good Practice. 2nd ed. Carlton Victoria : The Australasian Institute of Mining and Metallurgy, 2014. 928 p.
13. Goodfellow R., Dimitrakopoulos R. Global optimization of open pit mining complexes with uncertainty. Applied Soft Computing. 2015. Vol. 40. pp. 292–304.
14. Navarra A., Montiel L., Dimitrakopoulos R. Stochastic strategic planning of open-pit mines with ore selectivity recourse. International Journal of Mining Reclamation and Environment. 2018. Vol. 32, Iss. 1. pp. 1–17.
15. Samis M., Davis G. A. Using Monte Carlo simulation with DCF and real options risk pricing techniques to analyse a mine financing proposal. International Journal of Financial Engineering and Risk Management. 2014. Vol. 1, No. 3. pp. 264–281.
16. Batugina N. S., Gavrilov V. L., Tkach S. M. Modeling profit margin in coal mining. Gornyi Zhurnal. 2017. No. 12. pp. 41–45. DOI: 10.17580/gzh.2017.12.08
17. Batugin S. A., Chernyi E. D. Problems of Bloc-by-Block Evaluation of Cut-Off Grades and Ecological Risk in Mineral Mining. Yakutsk : YaNTs SO RAN, 1993. 28 p. |
