IJE TRANSACTIONS B: Applications Vol. 32, No. 2 (February 2019) 346-353    Article in Press

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H. Nourali and M. Osanloo
( Received: January 01, 2019 – Accepted in Revised Form: January 28, 2019 )

Abstract    One of the most important issues in all stages of mining study is capital cost estimation. Determination of capital expenditure is a challenging issue for mine designers. In recent decade, quite a few number of studies have focused on proposing estimation models to predict mining capital cost. However, these efforts have not achieved to a predictor model with reliable range of error. Both of overestimation and underestimation of capital expenditure are causing huge problems. The former leads to estimating the value of projects less than the real value, and the latter causes to fail or postpone the project. In this paper, in order to achieve a reliable cost model, the technical and economic data of 15 open pit porphyry copper mines have been collected. The proposed cost model is developed based on stepwise multi variate regression . The R square of the presented model was 97.53% and indicated a proper fit on the data set. In addition, the mean absolute error with respect to the average capital cost of data set used in the modelling procedure was obtained . The results showed that this model is capable of estimating open pit porphyry copper mine capital expenditure in an acceptable range of error.


Keywords    CAPEX; Capital cost estimation; Mine investment; Stepwise Multi Varaite Regression (SMVR).



یکی از مهمترین بخشهای مطالعات معدنی، تخمین هزینه سرمایه گذاری اولیه این قبیل پروژهها میباشد. تعیین میزان هزینه سرمایهای همواره یکی از مسائل چالش برانگیز برای مهندسین معدن به شمار میرود. در دهه اخیر مطالعات بسیاری در زمینه ارائه مدلهای تخمینگر هزینه سرمایهای انجام شده است. اما این مطالعات منجر به ارائه مدلی با یک محدوده خطای قابل قبول نشده است. تخیمن هزینه سرمایهای بیش از مقدار واقعی و یا کمتر از آن، منجر به ایجاد مشکلات عدیدهای در یک پروژه معدنی میگردد. تخمین بیش از حد موجب کاهش ارزش پروژه در مطالعات و تخمین کمتر از میزان موجب شکست و یا به تعویق افتادن روند اجرای پروژه خواهد گردید. لذا در تحقیق حاضر، به منظور دستیابی به یک مدل تخمینگر قابل اعتماد، دادههای فنی و اقتصادی 15 معدن روباز مس پورفیری جمع آوری گردید. بر این اساس مدل تخمینگری بر مبنای رگرسیون چند متغیره به روش گام به گام توسعه داده شد. مقدار ضریب همبستگی بدست آمده از فرآیند مدلسازی نشان میدهد مدل مذکور به خوبی بر دادهها برازش یافته است. به علاوه نسبت میانگین خطای مطلق به میانگین هزینه سرمایهای دادههای اولیه که در فرآیند مدلسازی مورد استفاده قرار گرفتهاند معادل بدست آمد. نتایج نشان میدهد، مدل ارائه شده توانایی تخمین هزینه سرمایهگذاری اولیه معادن روباز مس پورفیری در یک بازه خطای قابل قبول را داراست.

References    1.     Mohutsiwa, M. and Musingwini, C., "Parametric estimation of capital costs for establishing a coal mine: South africa case study", Journal of the Southern African Institute of Mining and Metallurgy,  Vol. 115, No. 8, (2015), 789-797. 2.     Shafiee, S. and Topal, E., "New approach for estimating total mining costs in surface coal mines", Mining Technology,  Vol. 121, No. 3, (2013), 109-116. 3.     Nourali, H., Osanloo, M.J.I.J.o.M., Reclamation and Environment, "A regression-tree-based model for mining capital cost estimation",  Vol., No., (2018), 1-13. 4.     Nourali, H. and Osanloo, M.J.R.P., "Mining capital cost estimation using support vector regression (svr)",  Vol., No., (2018). 5.     Huang, X.X., Newnes, L.B. and Parry, G.C., "The adaptation of product cost estimation techniques to estimate the cost of service", International Journal of Computer Integrated Manufacturing,  Vol. 25, No. 4-5, (2012), 417-431. 6.     Niazi, A., Dai, J.S., Balabani, S. and Seneviratne, L., "Product cost estimation: Technique classification and methodology review", Journal of Manufacturing Science and Engineering,  Vol. 128, No. 2, (2006), 563. 7.     Smith, A.E. and Mason, A.K., "Cost estimation predictive modeling: Regression versus neural network", The Engineering Economist,  Vol. 42, No. 2, (1996), 137-161. 8.     Daud, B.H., "A model for preliminary evaluation of underground coal mines, computer methods for the 80’s in the mineral industry, mine development and valuation", (Book) SME,  Vol. Chapter 3, No. Section 3, (1979). 9.     Petrick, A. and Dewey, R., Micro computer cost models for mining and milling, in Mineral resource management by personal computer. 1987. 10.   Prasad, L., "Mineral processing plant design and cost estimation", Processors Division of Canadian Institute of Mining, Metallurgy and Petroleum, Montreal,  Vol., No., (1969). 11.   Redpath, J.S., Estimation of preproduction and operating costs of small underground deposits, M.o.S.a. Services, Editor. 1986: Canada.252. 12.   Stebbins, S.A., Cost estimation handbook for small placer mines. 1987, US Bureau of Mines Information Circular 9170. 13.   Sayadi, A.R., Khalesi, M.R. and Khoshfarman Borji, M., "A parametric cost model for mineral grinding mills", Minerals Engineering,  Vol. 55, No., (2014), 96-102. 14.   Arfania, S., Sayadi, A.R. and Khalesi, M.R., "Cost modelling for flotation machines", Journal of the Southern African Institute of Mining and Metallurgy,  Vol. 117, No. 1, (2017), 89-96. 15.   Oraee, B., Lashgari, A. and Sayadi, A.R., "Estimation of capital and operating costs of backhoe loader", in Society of mininm engineers (SME), Denver, Canada. Vol., No. Issue, (2011, of Conference). 16.   Sayadi, A.R., Lashgari, A., Fouladgar, M.M. and Skibniewski, M.J., "Estimating capital and operational costs of backhoe shovels", Journal of Civil Engineering and Management,  Vol. 18, No. 3, (2012), 378-385. 17.   O Hara, T.A., "A parametric cost estimation method for open pit mines", in Society of mining engineers (SME), New York. Vol., No. Issue, (1980 of Conference). 18.   O’Hara, T.A., "Quick guides to the evaluation of ore-bodies, Bull Can Inst Min Metall,  Vol. 73,  (1980),  87-99. 19.   Bertisen, J. and Davis, G.A., "Bias and error in mine project capital cost estimation", The Engineering Economist,  Vol. 53, No. 2, (2008), 118-139. 20.   Pohl, G. and Mihaljek, D., "Project evaluation and uncertainty in practice: A statistical analysis of rate-of return divergences of 1,015 world bank projects", World Bank Economic Review,  Vol. 6, No. 2, (2002), 235-277. 21.   Mular, A.L., "The estimation of preliminary capital costs, in mineral processing plant design", in Society of mining engineers (SME), New York. Vol., No. Issue, (1978 of Conference). 22.   Noakes, M. and Lanz, T., "Cost estimation handbook for the australian mining industry, Australasian Institute of Mining and Metallurgy,  Vol. 20,  (1993),  412. 23.   Wellmer, F.W., Dalheimer, M. and Wagner, M., "Economic evaluations in exploration, Springer,  (2008),  35- 40 60-63. 24.   Camm, T.W., "The development of cost models using regression analysis", in SME Annual Meeting, Phoenix, Arizona. Vol., No. Issue, (1992 of Conference). 25.   Long, K.R., "Statistical methods of estimating mining costs", in SME annual meeting, Denver, Canada. Vol., No. Issue, (2011 of Conference). 26.   Adalier, O., Uğur, A., Korukoğlu, S. and Ertaş, K., "A new regression based software cost estimation model using power values", in International Conference on Intelligent Data Engineering and Automated Learning, Springer. Vol., No. Issue, (2007), 326-334. 27.   Bontempi, G. and Kruijtzer, W.J.S.C., "The use of intelligent data analysis techniques for system-level design: A software estimation example",  Vol. 8, No. 7, (2004), 477-490. 28.   Duckworth, D. and John, P.S., Copper mine project profiles - 2016 edition. 2016, CRU: London, United Kingdom.220.

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