Correlation and Prediction of Molten Steel Temperature in Steel Melting Shop Using Reliable Machine Learning (RML) Approach

[1]  X. Ai,et al.  Hybrid End-Point Static Control Model for 80 Tons BOF Steelmaking , 2022, Transactions of the Indian Institute of Metals.

[2]  Iqbal H. Sarker Machine Learning: Algorithms, Real-World Applications and Research Directions , 2021, SN Computer Science.

[3]  Subhash Chander Sharma,et al.  Stacking Regression Algorithms to Predict PM2.5 in the Smart City Using Internet of Things , 2020 .

[4]  Noora Shrestha Detecting Multicollinearity in Regression Analysis , 2020, American Journal of Applied Mathematics and Statistics.

[5]  G. Murali,et al.  Thermo-kinetics, mass and heat balance in an energy optimizing furnace for primary steel making , 2020 .

[6]  S. A. Botnikov,et al.  Development of the Metal Temperature Prediction Model for Steel-pouring and Tundish Ladles Used at the Casting and Rolling Complex , 2019, Metallurgist.

[7]  Xiaoping Liu,et al.  End-point Prediction of BOF Steelmaking Based on KNNWTSVR and LWOA , 2018, Transactions of the Indian Institute of Metals.

[8]  Zhizhong Mao,et al.  Tree-Structure Ensemble General Regression Neural Networks applied to predict the molten steel temperature in Ladle Furnace , 2016, Adv. Eng. Informatics.

[9]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[10]  Sanjay Chandra,et al.  Temperature Prediction Model for Controlling Casting Superheat Temperature , 2004 .

[11]  C. Kumar,et al.  Static and Dynamic Control Model of BOF Steelmaking Process and Its Validation With Steel Plant Data , 2019, AISTech2019 Proceedings of the Iron and Steel Technology Conference.