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مقایسه روش های طبقه بندی، شبکه عصبی مصنوعی و رگرسیون چندمتغیره در برآورد بازیابی فلز از بلوک کانسنگ | ||
| نشریه مهندسی منابع معدنی | ||
| مقاله 2، دوره 5، شماره 2 - شماره پیاپی 16، تیر 1399، صفحه 21-41 اصل مقاله (1.44 M) | ||
| نوع مقاله: علمی-پژوهشی | ||
| شناسه دیجیتال (DOI): 10.30479/jmre.2019.10997.1284 | ||
| نویسندگان | ||
| جواد غلام نژاد* 1؛ رضا لطفیان2؛ یوسف میرزائیان لرد کیوان3 | ||
| 1دانشیار، دانشکده مهندسی معدن و متالورژی، دانشگاه یزد، یزد | ||
| 2دانشجوی دکتری، دانشکده مهندسی معدن و متالورژی، دانشگاه یزد، یزد | ||
| 3استادیار، دانشکده مهندسی معدن و متالورژی، دانشگاه یزد، یزد | ||
| تاریخ دریافت: 26 خرداد 1398، تاریخ بازنگری: 03 دی 1398، تاریخ پذیرش: 18 آذر 1398 | ||
| چکیده | ||
| با توجه به نقش بازیابی در محاسبه ارزش اقتصادی بلوک کانسنگ و تأثیر مقدار این ارزش بر محاسبات طراحی و برنامه‌ریزی تولید معدن، تعیین بازیابی فلز از بلوک کانسنگ ارسالی به کارخانه فرآوری، از اهمیت بالایی برخوردار است. هدف از این پژوهش، بررسی قابلیت برآورد بازیابی بلوک کانسنگ به‌صورت کیفی و با روشهای مبتنی بر طبقه‌بندی داده‌ها از مجموعه روشهای داده‌کاوی و به‌صورت کمّی، با دو روش رگرسیون چندمتغیره و مدل هوشمند شبکه عصبی، بر اساس داده‌های آنالیز خوراک ورودی کارخانه است. برای نیل به این هدف، معدن مس میدوک مورد مطالعه قرار گرفت و با استفاده از 58 نمونه آنالیزشده عیار خوراک کارخانه، شامل عیارهای Cu، CuOو CuS و میزان بازیابی عنصر Cu در محصول نهایی، فرآیند پیش‌بینی بازیابی کل ذخیره به‌صورت کیفی با روشهای طبقه‌بندی درخت تصمیم، قانون بیز و الگوریتم نزدیک‌ترین همسایه انجام شد. برای برآورد کمّی میزان بازیابی ذخیره، مدل رگرسیون چندمتغیره و شبکه عصبی مصنوعی برای شاخص‌های عیاری مذکور و میزان بازیابی بین 47 نمونه از 58 نمونه برقرار شد و توسط 11 نمونه آنالیزشده آزمایشی، مدل‌های به‌دست‌آمده اعتبارسنجی شدند. معیارهای میانگین خطا و جذر میانگین مربعات خطا در مدل رگرسیونی به ترتیب 021702/0 و 024972/0 و در مدل شبکه عصبی مصنوعی به ترتیب 015753/0 و 021404/0 محاسبه شدند. بنابراین مدل شبکه عصبی مصنوعی به‌عنوان ابزار دقیق‌تری در پیش‌بینی بازیابی نسبت به مدل رگرسیون چندمتغیره عمل می‌کند. نتایج آنالیز حساسیت این مدل نشان داد، عیار Cu مهم‌ترین عامل و عیار CuO و CuS نیز به ترتیب، دیگر عوامل تاثیرگذار بر تغییرات بازیابی هستند. | ||
| کلیدواژه‌ها | ||
| بازیابی؛ طبقه‌بندی؛ رگرسیون چندمتغیره؛ شبکه عصبی مصنوعی | ||
| عنوان مقاله [English] | ||
| Comparison of Artificial Neural Networks and Multivariate Linear Regression Classification Techniques in Metal Recovery Estimation | ||
| نویسندگان [English] | ||
| J. Gholamnejad1؛ R. Lotfian2؛ Y. Mirzaeian Lord Keivan3 | ||
| 1Associate Professor, Dept. of Mining and Metallurgical Engineering, Yazd university, Yazd, Iran | ||
| 2Ph.D. Student, Dept. of Mining and Metallurgical Engineering, Yazd university, Yazd, Iran | ||
| 3Assistant Professor, Dept. of Mining and Metallurgical Engineering, Yazd university, Yazd, Iran | ||
| چکیده [English] | ||
| Due to the role of recovery in calculating the economic value of ore blocks and the impact of the block's economic value on the design calculations of the final pit and production planning, determination of the amount of metal recovery from the ore material sent to the processing plant is very important. The aim of this study is to investigate the capability of estimating the recovery rate of ore in qualitative manner with three methods based on data classification from data mining techniques and quantitatively using multivariate regression and artificial neural networks. Hence, the Miduk copper mine was studied using 58 analyzed samples of the feed of the plant, including Cu, CuO and CuS grades, and the recovery rate of Cu in the final product of the plant. The process of predicting the total recovery of the reserve was made qualitatively by decision tree method, classification based on Bayes rule and k-nearest neighbor (kNN) classification algorithm. For quantitative estimation of recovery, multivariate regression and artificial neural network models were established between the mentioned grade parameters and recovery rates (For 47 samples of 58 samples) and with the 11 additional analyzed samples, the obtained models were validated. The coefficient of (R2), Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) in the regression model were 0.77, 0.027722 and 0.029722, respectively, and in the artificial neural network model, 0.82, 0.015753 and 0.024040, respectively. Therefore, the artificial neural networks model acts as a more accurate tool for predicting recovery versus the multivariable regression model. The results of sensitivity analysis of artificial neural network model showed that Cu grade is the most important factor and grade of CuO and CuS, respectively, as well as other factors influencing the changes in recovery rate. | ||
| کلیدواژه‌ها [English] | ||
| Recovery, Classification, Multivariate regression, Artificial neural network | ||
| مراجع | ||
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[1] Alonso, S. G., De La Torre-Díez, I., Hamrioui, S., López-Coronado, M., Barreno, D. C., Nozaleda, L. M., and Franco, M. (2018). “Data mining algorithms and techniques in mental health: a systematic review”. Journal of Medical Systems, 42(9): 161. [2] Injadat, M., Salo, F., and Nassif, A. B. (2016). “Data mining techniques in social media: A survey”. Neurocomputing, 214: 654-670. [3] Gandhi, N., and Armstrong, L. J. (2016). “A review of the application of data mining techniques for decision making in agriculture”. In 2016 2nd International Conference on Contemporary Computing and Informatics (IC3I), IEEE, 1-6. [4] Almonacid, F., Fernandez, E. F., Mellit, A., and Kalogirou, S. (2017). “Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology”. Renewable and Sustainable Energy Reviews, 75: 938-953. [5] Li, H., Zhang, Z., and Liu, Z. (2017). “Application of artificial neural networks for catalysis: A review”. Catalysts, 7(10): 306. [6] Ghaedi, A. M., and Vafaei, A. (2017). “Applications of artificial neural networks for adsorption removal of dyes from aqueous solution: a review”. Advances in Colloid and Interface Science, 245: 20-39. [7] Mellit, A., and Kalogirou, S. A. (2018). “A Survey on the Application of Artificial Intelligence Techniques for Photovoltaic Systems”. In McEvoy’s Handbook of Photovoltaics, Academic Press, 735-761. [8] Tkáč, M., and Verner, R. (2016). “Artificial neural networks in business: Two decades of research”. Applied Soft Computing, 38: 788-804. [9] Ebrahimi, E., Monjezi, M., Khalesi, M. R., and Armaghani, D. J. (2016). “Prediction and optimization of back-break and rock fragmentation using an artificial neural network and a bee colony algorithm”. Bulletin of Engineering Geology and the Environment, 75(1): 27-36. [10] Faradonbeh, R. S., Monjezi, M., and Armaghani, D. J. (2016). “Genetic programing and non-linear multiple regression techniques to predict backbreak in blasting operation”. Engineering with Computers, 32(1): 123-133. [11] Khandelwal, M., and Singh, T. N. (2006). “Prediction of blast induced ground vibrations and frequency in opencast mine: a neural network approach”. Journal of Sound and Vibration, 289(4-5): 711-725. [12] Meulenkamp, F., and Grima, M. A. (1999). “Application of neural networks for the prediction of the unconfined compressive strength (UCS) from Equotip hardness”. International Journal of Rock Mechanics and Mining Sciences, 36(1): 29-39. [13] Khandelwal, M., Kumar, D. L., and Yellishetty, M. (2011). “Application of soft computing to predict blast-induced ground vibration”. Engineering with Computers, 27(2): 117-125. [14] Mohamed, M. T. (2011). “Performance of fuzzy logic and artificial neural network in prediction of ground and air vibrations”. International Journal of Rock Mechanics and Mining Sciences, 48(5): 845. [15] Vasović, D., Kostić, S., Ravilić, M., and Trajković, S. (2014). “Environmental impact of blasting at Drenovac limestone quarry (Serbia)”. Environmental Earth Sciences, 72(10): 3915-3928. [16] Singh, T. N. (2004). “Artificial neural network approach for prediction and control of ground vibrations in mines”. Mining Technology, 113(4): 251-256. [17] Singh, T. N., and Singh, V. (2005). “An intelligent approach to prediction and control ground vibration in mines”. Geotechnical & Geological Engineering, 23(3): 249-262. [18] Yagiz, S., Gokceoglu, C., Sezer, E., and Iplikci, S. (2009). “Application of two non-linear prediction tools to the estimation of tunnel boring machine performance”. Engineering Applications of Artificial Intelligence, 22(4-5): 808-814. [19] Sharma, L. K., Singh, R., Umrao, R. K., Sharma, K. M., and Singh, T. N. (2017). “Evaluating the modulus of elasticity of soil using soft computing system”. Engineering with Computers, 33(3): 497-507. [20] Armaghani, D.J., Mohamad, E.T., Hajihassani, M., Yagiz, S., and Motaghedi, H. (2016). “Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances”. Engineering with Computers, 32(2): 189-206. [21] Saghatforoush, A., Monjezi, M., Faradonbeh, R. S., and Armaghani, D. J. (2016). “Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting”. Engineering with Computers, 32(2): 255-266. [22] Torabi-Kaveh, M., Naseri, F., Saneie, S., and Sarshari, B. (2015). “Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones”. Arabian journal of Geosciences, 8(5): 2889-2897. [23] Taheri, K., Hasanipanah, M., Golzar, S. B., and Majid, M. Z. A. (2017). “A hybrid artificial bee colony algorithm-artificial neural network for forecasting the blast-produced ground vibration”. Engineering with Computers, 33(3): 689-700. [24] Hasanipanah, M., Faradonbeh, R. S., Amnieh, H. B., Armaghani, D. J., and Monjezi, M. (2017). “Forecasting blast-induced ground vibration developing a CART model”. Engineering with Computers, 33(2): 307-316. [25] Amiri, M., Amnieh, H. B., Hasanipanah, M., and Khanli, L. M. (2016). “A new combination of artificial neural network and K-nearest neighbors models to predict blast-induced ground vibration and air-overpressure”. Engineering with Computers, 32(4): 631-644. [26] Siami-Irdemoosa, E., and Dindarloo, S. R. (2015). “Prediction of fuel consumption of mining dump trucks: A neural networks approach”. Applied Energy, 151: 77-84. [27] Han, J., Pei, J., and Kamber, M. (2011). “Data mining: concepts and techniques”. Morgan Kaufmann Publishers, pp. 744. [28] Friedl, M. A., and Brodley, C. E. (1997). “Decision tree classification of land cover from remotely sensed data”. Remote Sensing of Environment, 61(3): 399-409. [29] Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J. (1984). “Classification and regression trees”. Belmont, CA: Wadsworth, International Group, pp. 432. [30] Ye, N. (2003). “The handbook of Data Mining”. Lawrence Erlbaum Associates Publishers, pp. 689. [31] Theodoridis, S., Pikrakis, A., Koutroumbas, K., and Cavouras, D. (2010). “Introduction to pattern recognition: a matlab approach”. 1st Edition, Academic Press, pp. 231. [32] Duda, R. O., Hart, P. E., and Stork, D. G. (2001). “Pattern classification”. 2nd Edition, John Wiley & Sons, pp. 688. [33] McCulloch, W. S., and Pitts, W. (1943). “A logical calculus of the ideas immanent in nervous activity”. The Bulletin of Mathematical Biophysics, 5(4): 115-133. [34] Haykin, S. (1994). “Neural networks : A compherehensive foundation”. 2nd Edition, Publisher: Prentice Hall, pp. 842. [35] Rumelhart, D. E. (1986). “Parallel distributed processing: Explorations in the microstructure of cognition”. Learning Internal Representations by Error Propagation, 1: 318-362. [36] MathWorks Inc., MATLAB 7.4 R2016a (2016). Software, MathWorks, Inc. [37] Hasanipanah, M., Armaghani, D. J., Monjezi, M., and Shams, S. (2016). “Risk assessment and prediction of rock fragmentation produced by blasting operation: a rock engineering system”. Environmental Earth Sciences, 75(9): 808. [38] Hasanipanah, M., Naderi, R., Kashir, J., Noorani, S. A., and Qaleh, A. Z. A. (2017). “Prediction of blast-produced ground vibration using particle swarm optimization”. Engineering with Computers, 33(2): 173-179. [39] Hasanipanah, M., Armaghani, D. J., Amnieh, H. B., Majid, M. Z. A., and Tahir, M. M. (2017). “Application of PSO to develop a powerful equation for prediction of flyrock due to blasting”. Neural Computing and Applications, 28(1): 1043-1050. [40] Myers, R. H., and Myers, R. H. (1990). “Classical and modern regression with applications”. Duxbury Press, 2nd Edition, pp. 488. [41] Pilevar, S. A., Ayoubi, S. H., and Khademi, H. (2011). “Comparison of artificial neural network (ANN) and multivariate linear regression (MLR) models to predict soil organic carbon using digital terrain analysis (Case Study: Zargham Abad Semirom, Isfahan Proviance)”. | ||
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