Semi-autogenous grinding mills play a critical role in the processing stage of many mining operations. They are also one of the most intensive energy consumers of the entire process. Current forecasting techniques of energy consumption base their inferences on feeding ore mineralogical features, SAG dimensions, and operational variables. Experts recognize their capabilities to provide adequate guidelines but also their lack of accuracy when real-time forecasting is desired. As an alternative, we propose the use of real-time operational variables (feed tonnage, bearing pressure, and spindle speed) to forecast the upcoming energy consumption via machine learning and deep learning techniques. Several predictive methods were studied: polynomial regression, k-nearest neighbor, support vector machine, multilayer perceptron, long short-term memory, and gated recurrent units. A step-by-step workflow on how to deal with real datasets, and how to find optimum models and final model selection is presented. In particular, recurrent neural networks achieved the best forecasting metrics in the energy consumption prediction task. The workflow has the potential of being extended to any other temporal and multivariate mineral processing datasets.
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van den Boogaart K, Tolosana-Delgado R (2018) Predictive Geometallurgy: An Interdisciplinary Key Challenge for Mathematical Geosciences. In: Handbook of Mathematical Geosciences pages 673–686 Springer
Cherkassky V, Ma Y (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural networks 17(1):113–126
Cho K, Van Merriënboer B, Bahdanau D, Bengio Y (2014) On the properties of neural machine translation: Encoder-decoder approaches. arXiv:1409.1259
Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555
Cochilco (2013) Actualización de Información sobre el Consumo de Energía asociado a la Minería del Cobre al año 2012.Tech. rep.. COCHILCO
Cortes C, Vapnik V (1995) Support-vector networks. Machine learning 20(3):273–297
Curilem M, Acuña G, Cubillos F, Vyhmeister E (2011) Neural networks and support vector machine models applied to energy consumption optimization in semiautogeneous grinding. Chemical Engineering Transactions 25:761–766
Dey R, Salemt FM (2017) Gate-variants of Gated Recurrent Unit GRU neural networks. In: 2017 IEEE 60th international midwest symposium on circuits and systems (MWSCAS). IEEE, pp 1597–1600
Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning, volume 1. MIT press, Cambridge
Hearst MA, Dumais ST, Osuna E, Platt J, Scholkopf B (1998) Support vector machines. IEEE Intelligent Systems and their applications 13(4):18–28
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural computation 9(8):1735–1780
Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural networks 2(5):359–366
Hoseinian FS, Abdollahzadeh A, Rezai B (2018) Semi-autogenous mill power prediction by a hybrid neural genetic algorithm. Journal of Central South University 25(1):151–158
Hoseinian F , Faradonbeh RS, Abdollahzadeh A, Rezai B, Soltani-Mohammadi S (2017) Semi-autogenous mill power model development using gene expression programming. Powder Technology 308:61–69
Inapakurthi RK, Miriyala SS, Mitra K (2020) Recurrent Neural Networks based Modelling of Industrial Grinding Operation. Chemical Engineering Science, 115585
Izenman AJ (2008) Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning Springer, 1st edition
Jnr WV, Morrell S (1995) The development of a dynamic model for autogenous and semi-autogenous grinding. Minerals Engineering 8(11):1285–1297
Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. arXiv:1412.6980
Morrell S (2004a) A new autogenous and semi-autogenous mill model for scale-up, design and optimisation. Minerals Engineering 17(3):437–445
Morrell S (2004b) Predicting the specific energy of autogenous and semi-autogenous mills from small diameter drill core samples. Minerals Engineering 17(3):447–451
Navot A, Shpigelman L, Tishby N, Vaadia E (2006) Nearest neighbor based feature selection for regression and its application to neural activity. In: Advances in neural information processing systems, pages 996–1002
Ortiz J, Kracht W, Townley B, Lois P, Cardenas E, Miranda R, Alvarez M (2015) Workflows in geometallurgical prediction: challenges and outlook. In: 17th Annual Conference of the International Association for Mathematical Geosciences IAMG
Pamparana G, Kracht W, Haas J, Díaz-Ferrán G, Palma-Behnke R, Román R (2017) Integrating photovoltaic solar energy and a battery energy storage system to operate a semi-autogenous grinding mill. Journal of Cleaner Production 165:273–280
Ramchoun H, Idrissi MAJ, Ghanou Y, Ettaouil M (2016) Multilayer Perceptron: Architecture Optimization and Training. IJIMAI 4(1):26–30
Román-Collado R, Ordoñez M, Mundaca L (2018) Has electricity turned green or black in Chile? A structural decomposition analysis of energy consumption. Energy 162:282–298
Rosenblatt F (1961) Principles of neurodynamics, perceptrons and the theory of brain mechanisms (No. VG-1196-G-8). Cornell Aeronautical Lab Inc, Buffalo, NY
Rumelhart DE, Hinton GE, Williams RJ (1986) Learning representations by back-propagating errors. Nature 323(6088):533–536
Salazar J-L, Valdés-González H, Vyhmesiter E, Cubillos F (2014) Model predictive control of semiautogenous mills sag. Minerals Engineering 64:92–96
Silva M, Casali A (2015) Modelling SAG milling power and specific energy consumption including the feed percentage of intermediate size particles. Minerals Engineering 70:156–161
Smola AJ, Schö̈lkopf B (2004) A tutorial on support vector regression. Statistics and computing 14 (3):199–222
Van Ooyen A, Nienhuis B (1992) Improving the convergence of the back-propagation algorithm. Neural networks 5(3):465–471
Vapnik V (1995) The nature of statistical learning theory. Springer-Verlag, New York
Warner B, Misra M (1996) Understanding neural networks as statistical tools. The american statistician 50(4):284–293
Werbos PJ (1990) Backpropagation through time: what it does and how to do it. Proceedings of the IEEE 78(10):1550–1560
Wu Z, King S (2016) Investigating gated recurrent neural networks for speech synthesis. arXiv:1601.02539
The authors received funding provided by the Natural Sciences and Engineering Council of Canada (NSERC), funding reference number RGPIN-2017-04200 and RGPAS-2017-507956, and the Chilean National Commission for Scientific and Technological Research (CONICYT), through CONICYT/PIA Project AFB180004, and the CONICYT/FONDAP Project 15110019.
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Avalos, S., Kracht, W. & Ortiz, J.M. Machine Learning and Deep Learning Methods in Mining Operations: a Data-Driven SAG Mill Energy Consumption Prediction Application. Mining, Metallurgy & Exploration 37, 1197–1212 (2020). https://doi.org/10.1007/s42461-020-00238-1
- Energy consumption
- Semi-autogenous grinding mill
- Machine learning
- Deep learning