目錄
Chapter 1 Overview of Research on the LF Refining Process
1.1 Overview of LF Refining
1.1.1 Development of LF Refining Technology
1.1.2 Metallurgical Functions of LF
1.1.3 Operational Workflow of LF Refining Process
1.2 Machine Learning Algorithms and Their Application in the Metallurgical Industry
1.2.1 Concept and Development of Machine Learning
1.2.2 Machine Learning Algorithm Classification
1.2.3 Application of Machine Learning in Metallurgical Industry
1.3 Research on LF Refining Technology
1.3.1 Prediction Model of Molten Steel Temperature
1.3.2 Slagmaking Model
1.3.3 Alloying Model of Molten Steel
1.3.4 Argon Blowing Model
References
Chapter 2 Research on Prediction Model of Molten Steel Temperature
2.1 Analysis of LF Refining Process
2.1.1 Description of LF Refining Process
2.1.2 Analysis Conservation of Energy
2.1.3 Analysis of Main Factors
2.2 Establishment of Prediction Model for Molten Steel Temperature
2.2.1 Modeling with ML Models
2.2.2 Data Processing Methods
2.2.3 Optimization Algorithms
2.2.4 ML Algorithms
2.2.5 SHAP
2.2.6 Model Evaluation
2.3 Evaluation of Prediction Model for Molten Steel Temperature
2.3.1 High Dimensional Data Visualization and Processing
2.3.2 Hyperparameter Optimization of XGBoost and LCBM
2.3.3 Outcomes of XCBoost, LGBM, MLP, KNN, and MLR
2.4 Model Explainability Analysis
2.4.1 Tree Structure Visualization
2.4.2 Global Explanation
2.4.3 Local Explanation
2.5 Conclusions
References
Chapter 3 Research on Slagmaking Desulfurization Model
3.1 Desulphurization Fundamental
3.1.1 Thermodynamic Fundamentals
3.1.2 Kinetic Fundamentals
3.2 C Calculation Using RELM Model
3.2.1 Analyzing of Database and Data for Cs Calculation
3.2.2 Modeling in RELM
3.2.3 Model Evaluation
3.3 Evaluation of C, Prediction Models
3.3.1 Effect of MgO and Activation Function on Cs
3.3.2 Comparison of the RELM Model with Other Models
3.4 Establishment of Slagmaking Model
3.4.1 Analysis of Factors for Ls
3.4.2 Refining Process and Modeling Hypothesis
3.4.3 . Modeling Based on Metallurgical Mechanism
3.4.4 Mathematical Modeling Based on Historical Production Data
3.5 Evaluation of Slagmaking Model
3.5.1 Testing of Slagmaking Model
3.5.2 .Software Development of Slagmaking Model
3.5.3 Plant Trial
3.6 Conclusions
References
Chapter 4 Research on Alloying Model
4.1 Analysis of the LF Refining Process
4.1.1 Description of the LF Refining Process
4.1.2 Data Collection and Normalization
4.2 Predicting Alloying Element Yield Using the PCA-DNN Model
4.2.1 Theories and Methods
4.2.2 Establishment of PCA-DNN Model
4.2.3 Model Evaluation
4.3 Evaluation of Alloying Element Yield Prediction Model Correlation Analysis
4.3.2 PCA
4.3.3 Structure Optimization of the PCA-DNN Model
4.3.4 Comparison of the PCA-DNN Model with Other Models
4.4 Calculation Model for Amount of Alloy Addition
4.4.1 Alloy Addition Principle
4.4.2 Evaluation of Calculation Model for Alloy Addition
4.5 Conclusions
References
Chapter 5 Research on Argon Bottom Blowing Model
5.1 Experimental Principles and Methods
5.1.1 Experimental Principles
5.1.2 Experimental Method
5.2 Experimental Schemes
5.2.1 Single Factor Analysis Experiment Scheme of Argon Bottom Blowing of LF
5.2.2 Experimental Scheme and Results of Argon Bottom Blowing of LF Based on RSM
5.3 Experimental Results of Single Factor Analysis
5.3.1 Effect of Porous Plug Radial Position on MT
5.3.2 Effect of Porous Plug Separation Angle on MT
5.3.3 Effects of Different Factors on MT and SEA
5.4 Experimental Results of Argon Bottom Blowing Based on RSM
5.4.1 Establishment of Prediction Models
5.4.2 Analysis of Variance and Model Evaluation
5.4.3 Visual Analysis of Response Surface
5.4.4 Multiobjective Optimization and Experimental Verification
5.4.5 Analysis of Slag Entrainment
5.5 Conclusions
References
Chapter 6 Development of LF Refining Model Set and Prospects for Intelligent
6.1 Architecture of LF Refining Model Set
6.2 Establishment of Model Set
6.2.1 Molten Steel Temperature Prediction Model Based on the Law of Energy Conservation, LCBM, and SHAP Analysis
6.2.2 Desulfurization Model Based on the Law of Mass Conservation and PMLR
6.2.3 Alloying Model Based on the Law of Mass Conservation uva and PCA-DNN
6.2.4 Argon Bottom Blowing Model Based on the Similarity Theory and RSM
6.3 System Structure Design
6.4 Technical Framework of Future for LF Intelligent Refining
References
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