|تعداد مشاهده مقاله||111,534,283|
|تعداد دریافت فایل اصل مقاله||86,166,970|
Model development for prediction of autogenous mill power consumption in Sangan iron ore processing plant
|International Journal of Mining and Geo-Engineering|
|مقاله 1، دوره 56، شماره 4، اسفند 2022، صفحه 301-307 اصل مقاله (565.35 K)|
|نوع مقاله: Research Paper|
|شناسه دیجیتال (DOI): 10.22059/ijmge.2022.320882.594899|
|Davood Namaei Roudi؛ Ali Behnamfard*|
|Faculty of Engineering, University of Birjand, Birjand, Iran|
|The variables including ore hardness based on the SAG power index (SPI), particle size of mill product (P80), trunnion pressure of the mill free head (p) and working time period of mill liner (H) were considered as variables for development of an adequate model for the prediction of autogenous (AG) mill power consumption in Sangan iron ore processing plant. The one-parameter models (SPI as variable) showed no adequate precision for the prediction of Sangan AG mill power consumption. Two-parameter models (SPI and P80 as variables), proposed by Starkey and Dobby, showed no adequate precision for the Sangan AG mill power consumption. Nonetheless, by exerting an adjustment factor in the model (0.604513 which obtained by what-if analysis using Solver Add-Ins program), the model precision increased significantly (an error of 7.11%). Finally, a four-parameter model in which the Sangan AG mill power consumption is predicated as a function of SPI, P80, p, and H was developed. Hence, initially the relationship between the mill power consumption and each of the variables was obtained and then the four-parameter model was developed by summation of these four equations and applying a similar coefficient of 0.25 for all of them. This model was modified through finding the best coefficients by what-if analysis using solver Add-Ins program through minimizing the ARE error function. The error function for the training and testing data sets was determined to be 2.93% and 2.39%, respectively.|
|Autogenous mill؛ Power consumption؛ Modelling؛ What-if analysis|
 Wei, D., and Craig, I. K. (2009). Grinding mill circuits—a survey of control and economic concerns, International Journal of Mineral Processing, 90(1-4), 56-66.
 Jeswiet, J., and Szekeres, A. (2016). Energy consumption in mining comminution, Procedia CIRP, 48, 140-145.
 Palacios, J. L., Fernandes, I., Abadias, A., Valero, A., Valero, A., Reuter, M. A. (2019). Avoided energy cost of producing minerals: The case of iron ore, Energy Reports, 5, 364-374.
 Curry, J. A., Ismay, M. J., Jameson, G. J. (2014). Mine operating costs and the potential impacts of energy and grinding, Minerals Engineering, 56, 70-80.
 Morrell, S. (2004). Predicting the specific energy of autogenous and semi-autogenous mills from small diameter drill core samples, Minerals Engineering, 17(3), 447-451.
 Silva, M., and Casali, A. (2015). Modeling SAG milling power and specific energy consumption including the feed percentage of intermediate size particles, Minerals Engineering, 70, 156-161.
 Behnamfard, A., Nemaei Roudi, D., Veglio, F. (2020). The performance improvement of a full-scale autogenous mill by setting the feed ore properties, Journal of Cleaner Production, 271, 122554.
 Morrell, S. (2004). A new autogenous and semi-autogenous mill model for scale-up, design and optimization, Minerals Engineering, 17(3), 437-445.
 Starkey, J., and Dobby, G. (1996). Application of the Minnovex SAG power index at five Canadian SAG plants, Proceeding Autogenous and Semi-Autogenous Grinding, 345-360.
 Musa, F., and Morrison, R. (2009). A more sustainable approach to assessing comminution efficiency, Minerals Engineering, 22(7-8), 593-601.
 Starkey, J., Dobby, G. and Kosick, G. (1994). A new tool for SAG hardness testing, Proc. Canadian Mineral Processor’s Conference, Ottawa.
 Bueno, M. P., Kojovic, T., Powell, M. S., Shi, F. (2013). Multi-component AG/SAG mill model, Minerals Engineering, 43, 12-21.
 Jahani, M., Noaparast, M., Farzanegan, A., Langarizadeh, G. (2011). Application of SPI for Modeling energy consumption in Sarcheshmeh SAG and ball mills, Journal of Mining and the Environment, 2(1), 27-40.
 Jahani, M., Noaparast, M., Farzanegan, A., Moghaddam, M. Y., Langarizadeh, G. (2013). Introducing an Empirical New Model to Predict SAG Mill Power Consumption, 23rd international mining congress & Exhibition of Turkey, 16-19 April 2013, Antalya, Turkey.
 Djordjevic, N., Shi, F. N., Morrison, R. (2004). Determination of lifter design, speed and filling effects in AG mills by 3D DEM, Minerals Engineering, 17(11-12), 1135-1142.
 Delaney, G. W., Cleary, P. W., Morrison, R. D., Cummins, S., & Loveday, B. (2013). Predicting breakage and the evolution of rock size and shape distributions in Ag and SAG mills using DEM. Minerals Engineering, 50, 132-139.
 Cleary, P. W., & Morrison, R. D. (2016). Comminution mechanisms, particle shape evolution and collision energy partitioning in tumbling mills. Minerals Engineering, 86, 75-95.
 Cleary, P. W. (1998). Predicting charge motion, power draw, segregation, and wear in ball mills using discrete element methods. Minerals Engineering, 11(11), 1061-1080.
 Akbari Nasab, A., Sam, A., Banisi, S. (2005). The effect of feed ore hardness on the power consumption of autogenous mills in the grinding circuit of Gol Gohar iron ore processing plant, Iranian Mining Engineering Conference, Tarbiat Modares University, Tehran, Iran (In Persian).
 Dobby, G, Bennett, C., Kosick, G. (2001). Advances in SAG circuit design and simulation applied to the mine block model. In International Autogenous and Semiautogenous Grinding Technology SAG 2001, (Barratt, D., Allan, M., Mular, A., Eds.), Vancouver, Canada.
 Azimi, E. (2006). Study of the efficiency of the grinding circuit of the new processing plant of Sarcheshmeh Copper Complex, M.Sc. thesis, Shahid Bahonar University of Kerman, Iran (In Persian).
 Amelunxen, P., Berrios, P., Rodriguez, E. (2014). The SAG grindability index.
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