Machine Learning Models for Predicting and Controlling the Pressure Difference of Blast Furnace
- 20 September 2023
- journal article
- research article
- Published by Springer Nature in JOM
- Vol. 75 (11) , 4550-4561
Abstract
No abstract availableKeywords
Funding Information
- National Natural Science Foundation of China (51904026)
- Postdoctoral Research Foundation of China (BX20200045)
This publication has 38 references indexed in Scilit:
- Modeling and simulation of hydrogen injection into a blast furnace to reduce carbon dioxide emissionsJournal of Cleaner Production, 2017
- Performance assessment of permeability index prediction in an ironmaking process via soft computing techniquesProceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, 2016
- An improved ELM algorithm for the measurement of hot metal temperature in blast furnaceNeurocomputing, 2016
- Adaptive Least Squares Support Vector Machine Predictor for Blast Furnace Ironmaking ProcessISIJ International, 2015
- Multi-model control of blast furnace burden surface based on fuzzy SVMNeurocomputing, 2015
- A bottom-up analysis of China’s iron and steel industrial energy consumption and CO2 emissionsApplied Energy, 2014
- Optional SVM for Fault Diagnosis of Blast Furnace with Imbalanced DataISIJ International, 2011
- A Novel Fault Diagnosis System for Blast Furnace Based on Support Vector Machine EnsembleISIJ International, 2010
- Application of Least Squares Support Vector Machines to Predict the Silicon Content in Blast Furnace Hot MetalISIJ International, 2008
- An introduction to decision tree modelingJournal of Chemometrics, 2004