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Ironmaking & Steelmaking
Processes, Products and Applications
Volume 34, 2007 - Issue 2
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Articles

A neural network based model of sinter quality and sinter plant performance indices

P. J. Laitinen
&
H. Saxén
Pages 109-114 | Published online: 18 Jul 2013

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Liu Song, Lyu Qing, Liu Xiaojie & Sun Yanqin. (2020) Synthetically predicting the quality index of sinter using machine learning model. Ironmaking & Steelmaking 47:7, pages 828-836.
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B.-X. Wang, Y.-Z. Zhang, W. Chen, Y. Chen & H.-J. Zhang. (2017) Charging composition and structure optimisation in the sintering process (Part I). Ironmaking & Steelmaking 44:1, pages 52-58.
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A. Sanz-García, J. Fernández-Ceniceros, R. Fernández-Martínez & F. J. Martínez-de-Pisón. (2014) Methodology based on genetic optimisation to develop overall parsimony models for predicting temperature settings on annealing furnace. Ironmaking & Steelmaking 41:2, pages 87-98.
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A. Sanz-Garcia, F. Antoñanzas-Torres, J. Fernández-Ceniceros & F. J. Martínez-de-Pisón. (2014) Overall models based on ensemble methods for predicting continuous annealing furnace temperature settings. Ironmaking & Steelmaking 41:1, pages 51-60.
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H. K. D. H. Bhadeshia, R. C. Dimitriu, S. Forsik, J. H. Pak & J. H. Ryu. (2009) Performance of neural networks in materials science. Materials Science and Technology 25:4, pages 504-510.
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Articles from other publishers (8)

Soumya Sahoo, Ashutosh Pare, Subhabrata Mishra, Shatrughan Soren & Surendra Kumar Biswal. (2023) Prediction of Sinter Properties Using a Hyper-Parameter-Tuned Artificial Neural Network. ACS Omega.
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Arpit Mallick, Subhra Dhara & Sushant Rath. (2021) Application of machine learning algorithms for prediction of sinter machine productivity. Machine Learning with Applications 6, pages 100186.
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Huijun Shao, Zhengming Yi, Zhuo Chen, Zheng Zhou & Zhidan Deng. (2019) Application of artificial neural networks for prediction of sinter quality based on process parameters control. Transactions of the Institute of Measurement and Control 42:3, pages 422-429.
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Sen-Hui Wang, Hai-Feng Li, Yong-Jie Zhang & Zong-Shu Zou. (2019) A Hybrid Ensemble Model Based on ELM and Improved AdaBoost.RT Algorithm for Predicting the Iron Ore Sintering Characters. Computational Intelligence and Neuroscience 2019, pages 1-11.
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Xiaoxian Huang, Xiaohui Fan, Xuling Chen, Min Gan & Xinze Zhao. (2018) Soft-measuring models of thermal state in iron ore sintering process. Measurement 130, pages 145-150.
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Daniel Fernández-González, Ramón Martín-Duarte, Íñigo Ruiz-Bustinza, Javier Mochón, Carmen González-Gasca & Luis Felipe Verdeja. (2016) Optimization of Sinter Plant Operating Conditions Using Advanced Multivariate Statistics: Intelligent Data Processing. JOM 68:8, pages 2089-2095.
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Kailu Lu, Fei Qiao & Yumin Ma. (2015) An integrated prediction model of sinter comprehensive performance. An integrated prediction model of sinter comprehensive performance.
Mingxia FENG, Qiang Ll & Zongshu Zou. (2008) An Outlier Identification and Judgment Method for an Improved Neural-Network BOF Forecasting Model. steel research international 79:5, pages 323-332.
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