Data-driven classification of the chemical composition of calcine in a ferronickel furnace oven using machine learning techniques
暂无分享,去创建一个
[1] Junwoo Park,et al. A Short Review of the Effect of Iron Ore Selection on Mineral Phases of Iron Ore Sinter , 2021, Minerals.
[2] Shikun Chen,et al. Development of Data-Driven Machine Learning Models for the Prediction of Casting Surface Defects , 2021, Metals.
[3] Jersson X. Leon-Medina,et al. Monitoring of the refractory lining in a shielded electric arc furnace: An online multitarget regression trees approach , 2021, Structural Control and Health Monitoring.
[4] Zhimin Lv,et al. A Novel Multi-Objective Process Parameter Interval Optimization Method for Steel Production , 2021, Metals.
[5] Cesar Pedraza,et al. Temperature Prediction Using Multivariate Time Series Deep Learning in the Lining of an Electric Arc Furnace for Ferronickel Production , 2021, Sensors.
[6] Mohammad Habibi Parsa,et al. A data augmentation approach to XGboost-based mineral potential mapping: An example of carbonate-hosted ZnPb mineral systems of Western Iran , 2021 .
[7] A. Nesterov,et al. Prediction of Composition and Properties of Final Smelting Products Based on Integral Indices of the Blast Furnace Burden and Temperature Blasting Mode , 2021, Steel in Translation.
[8] Ana López,et al. Machine Learning Methods for the Prediction of the Inclusion Content of Clean Steel Fabricated by Electric Arc Furnace and Rolling , 2021, Metals.
[9] Diego Alexander Tibaduiza Burgos,et al. A Data Cleaning Approach for a Structural Health Monitoring System in a 75 MW Electric Arc Ferronickel Furnace , 2020, Proceedings of 7th International Electronic Conference on Sensors and Applications.
[10] Kai Song,et al. A steel property optimization model based on the XGBoost algorithm and improved PSO , 2020 .
[11] Mustafa Kumral,et al. Optimization of Mining–Mineral Processing Integration Using Unsupervised Machine Learning Algorithms , 2020, Natural Resources Research.
[12] Mustafa Kumral,et al. Dimensioning a stockpile operation using principal component analysis , 2019, International Journal of Minerals, Metallurgy and Materials.
[13] Yuwen Zhang,et al. A general method for predicting the bank thickness of a smelting furnace with phase change , 2019, Applied Thermal Engineering.
[14] E. Keskinkilic. Nickel Laterite Smelting Processes and Some Examples of Recent Possible Modifications to the Conventional Route , 2019, Metals.
[15] Przemysław Grzegorzewski,et al. Data Preprocessing in Industrial Manufacturing , 2018, Studies in Systems, Decision and Control.
[16] Fang Dong,et al. Ensemble Machine Learning Systems for the Estimation of Steel Quality Control , 2018, 2018 IEEE International Conference on Big Data (Big Data).
[17] Douglas Alleman,et al. Ore Composition’s Impact on Smelting Profitability: An Optimum Pricing Index Model for Long-Term Nickel Ore Feedstock Purchasing Agreements , 2018, Applied Sciences.
[18] Kishalay Mitra,et al. TRANSFORM-ANN for online optimization of complex industrial processes: Casting process as case study , 2018, Eur. J. Oper. Res..
[19] Marcel Lacroix,et al. Inverse heat transfer prediction of the state of the brick wall of a melting furnace , 2017 .
[20] Junkai Wang,et al. A Data-Driven Model for Energy Consumption in the Sintering Process , 2016 .
[21] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[22] Jiří David,et al. Usage of clustering methods for sequence plan optimization in steel production , 2016 .
[23] Ponnuthurai N. Suganthan,et al. Modeling of steelmaking process with effective machine learning techniques , 2015, Expert Syst. Appl..
[24] Fionn Murtagh,et al. Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion? , 2011, Journal of Classification.
[25] Lee Vinagre Monteiro,et al. APPLICATION OF NEURAL NETWORKS FOR MODELING STEELMAKING PROCESS , 2012 .
[26] C. Pardo,et al. Combinación de métodos factoriales y de análisis de conglomerados en R: el paquete FactoClass , 2007 .
[27] J. Carriazo,et al. Characterization by XRD and electron paramagnetic resonance (EPR) of waste materials from Cerro Matoso Mine (Colombia) , 2006 .
[28] Jan Kusiak,et al. Artificial Neural Network Predictive System for Oxygen Steelmaking Converter , 2003 .
[29] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .