[1] F. Habashi, Handbook of extractive metallurgy. Wiley-Vch, 1997.
[2] Saeedi and N. Sotoudeh, Iron Production. Isfahan University of Technology, Jihad Daneshgahi Press, 2006. [In Persian].
[3] M. Alizadeh, M. Alizadeh, and H. Jeylan. “Creating optimal conditions for producing suitable green pellets from iron ore concentrates with low specific surface area using organic and mineral additives,” Journal of Metallurgical Engineering, vol. 21, no. 3, pp. 216–224, 2018. [In Persian].
[4] K. Meier, Y. Nabialahi, and A. Mansouri Aliabadi. Iron Ore Pelletizing. Shamloo Publishing, 2012. [In Persian].
[5] B. Abazarpoor, R. Hejazi, M. Saghaeian, and V. Sheikhzadeh, “Effect of iron ore particle size and shape on green pellet quality,” in International Mineral Processing Congress, Moscow, 2018.
[6] L. Lu, Iron ore: mineralogy, processing and environmental sustainability, Elsevier, 2015.
[7] K. Meyer, Pelletizing of iron ores, Springer-Verlag, 1980.
[8] S. Sadrnezhaad, A. Ferdowsi, and H. Payab, “Mathematical model for a straight grate iron ore pellet induration process of industrial scale,” Computational Materials Science, vol. 44, no. 2, pp. 296-302, 2008.
[9] M. Barati, “Dynamic simulation of pellet induration process in straight-grate system,” International journal of mineral processing, vol. 89, no. 1-4, pp. 30-39, 2008.
[10] H. Nezamabadipoor, M. Rezaei, and A. Atighi. Statistical and Intelligent Modeling in Engineering and Industry. Shahid Bahonar University of Kerman Press, 2024. [In Persian].
[11] T. Umadevi, P. P. Kumar, P. Kumar, N. F. Lobo, and M. Ranjan, “Investigation of factors affecting pellet strength in straight grate induration machine,” Ironmaking & steelmaking, vol. 35, no. 5, pp. 321-326, 2008.
[12] H. Zhou, Y. He, B. Li, D. Song, Q. Zhu, and Y. Li, “Ironmaking process under artificial intelligence technology: A review,” Ironmaking & Steelmaking, 2024.
[13] A. J. Deo, S. K. Behera, and D. P. Das, “Online monitoring of iron ore pellet size distribution using lightweight convolutional neural network,” IEEE Transactions on Automation Science and Engineering, vol. 21, no. 2, pp. 1974-1985, 2023.
[14] F. Honpeng and G. Runlong, “Practice of Artificial Intelligence for Iron Ore Pelletizing System,” in 2024 5th IEEE Global Conference for Advancement in Technology (GCAT), 2024, pp. 1-4.
[15] Y.-h. Gong, C.-h. Wang, J. Li, M. N. Mahyuddin, and M. T. A. Seman, “Application of deep learning in iron ore sintering process: a review,” Journal of Iron and Steel Research International, vol. 31, no. 5, pp. 1033-1049, 2024.
[16] Q. Cao, C. Chi, and J. Shan, “Can artificial intelligence technology reduce carbon emissions? A global perspective,” Energy Economics, vol. 143, p. 108285, 2025.
[17] M. M. Carvalho, D. G. Faria, M. G. Pérez, M. Cardoso, and E. K. Vakkilainen, “Review on mathematical models for travelling-grate iron oxide pellet induration furnaces,” Energy Procedia, vol. 120, pp. 588-595, 2017.
[18] C. Cavalcanti, G. Wanderley, D. Braga, R. Brito, L. Vasconcelos, and K. D. Brito, “Rigorous modeling of the Traveling Grate stage in the iron ore pellet induration process,” Journal of Materials Research and Technology, vol. 31, pp. 3410-3424, 2024.
[19] Y. Wang, Y. Xu, X. Song, Q. Sun, J. Zhang, and Z. Liu, “Novel method for temperature prediction in rotary kiln process through machine learning and CFD,” Powder Technology, vol. 439, p. 119649, 2024.
[20] S. Mun and J. Yoo, “Operating Key Factor Analysis of a Rotary Kiln Using a Predictive Model and Shapley Additive Explanations,” Electronics, vol. 13, no. 22, p. 4413, 2024.
[21] J. Mourão, M. Huerta, U. de Medeiros, I. Cameron, K. O’Leary, and C. Howey, “Guidelines for selecting pellet plant technology,” in Proceedings of the 6th International Congress on the Science and Technology of Ironmaking—ICSTI, 2012.
[22] S. Ding, “Feature selection based F-score and ACO algorithm in support vector machine,” in Second International Symposium on Knowledge Acquisition and Modeling, 2009, pp. 19-23.
[23] C.-K. Zhang and H. Hu, “Feature selection using the hybrid of ant colony optimization and mutual information for the forecaster,” in International Conference on Machine Learning and Cybernetics, 2005, vol. 3, pp. 1728-1732.
[24] A. Sheikhi. Statistical Analysis and Modeling. Hamrah Elm Publishing, 2018. [In Persian].
[25] L. Breiman, “Random forests,” Machine learning, vol. 45, pp. 5-32, 2001.
[26] M. Rezaei and H. Nezamabadi-pour, “A Hybridization Method of Prototype Generation and Prototype Selection for K-NN Rule Based on GSA,” Journal of AI and Data Mining, vol. 10, no. 2, pp. 257-268, 2022.
[27] F. Rosenblatt, “The perceptron, a perceiving and recognizing automaton: (Project Para),” Cornell Aeronautical Laboratory, 1957.
[28] M. T. Mitchell, Machine learning, New York: McGraw-hill, 2007.
[29] Y. Bengio, I. Goodfellow, and A. Courville, Deep learning, MIT press Cambridge, MA, USA, 2017.
[30] M. Vazan. Deep Learning: Principles, Concepts, and Approaches. Miad Andisheh Publishing, 2020. [In Persian].
[31] Z. Liu, “A method of SVM with normalization in intrusion detection,” Procedia Environmental Sciences, vol. 11, pp. 256-262, 2011.
[32] K. Zhong, P. Jain, and I. S. Dhillon, “Mixed linear regression with multiple components,” Advances in neural information processing systems, vol. 29, 2016.
[33] P. Cunningham and S. J. Delany, “K-nearest neighbour classifiers-a tutorial,” ACM computing surveys (CSUR), vol. 54, no. 6, pp. 1-25, 2021.
[34] G. Louppe, Understanding random forests: From theory to practice. Universite de Liege (Belgium), 2014.
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