Research graph
References from Tin-Smelting Parameter Optimization via an RBFN-Assisted Dynamic Multiobjective Approach. Local targets link to admitted publications; unresolved targets remain external evidence.
10.3390/met13101742
10.3390/met13101742 · External reference
10.3390/met10101393
10.3390/met10101393 · External reference
Interactive data-driven multiobjective optimization of metallurgical properties of microalloyed steels using the DESDEO framework
10.1016/j.engappai.2023.105918 · 2023 · External reference
Evolutionary data driven modeling and tri-objective optimization for noisy BOF steel making data
10.1016/j.dche.2023.100094 · 2023 · External reference
Explaining hardness modeling with XAI of C45 steel spur-gear induction hardening
10.1007/s12289-023-01780-1 · 2023 · External reference
Book review: Nirupam Chakraborti “Data-Driven Evolutionary Modeling in Materials Technology”
10.1007/s10710-023-09455-1 · 2023 · External reference
10.3390/recycling9040054
10.3390/recycling9040054 · External reference
A novel dynamic operation optimization method based on multiobjective deep reinforcement learning for steelmaking process
10.1109/tnnls.2023.3244945 · 2024 · External reference
10.3390/met14111273
10.3390/met14111273 · External reference
Unresolved reference
External reference
State of the art in applications of machine learning in steelmaking process modeling
10.1007/s12613-023-2646-1 · 2023 · External reference
Optimal data-driven control of manufacturing processes using reinforcement learning: An application to wire arc additive manufacturing
10.1007/s10845-023-02307-w · 2025 · External reference
Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators
10.1007/s12613-023-2693-7 · 2024 · External reference
Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data
10.1016/j.aej.2024.04.026 · 2024 · External reference
Deep transformers for analyzing BOF steelmaking data
10.1007/s11663-025-03615-7 · 2025 · External reference
Effect of ludwigite on pellet preparation and metallurgical properties
10.1007/s40831-024-00789-3 · 2024 · External reference
10.3390/pr12010032
10.3390/pr12010032 · External reference
Energy consumption prediction of tin smelting based on grey wolf optimized support vector machine regression and SHAP values
2024 · External reference
Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Expert Analysis and Process Metallurgy
10.1002/srin.202300385 · 2024 · External reference
Hot metal temperature prediction technique based on feature fusion and GSO-DF
10.2355/isijinternational.isijint-2024-127 · 2024 · External reference
A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness
10.1007/s10845-023-02204-2 · 2024 · External reference
An interpretable and reliable framework for alloy discovery in thermomechanical processing
2025 · External reference
Local machine learning model-based multi-objective optimization for managing system interdependencies in production: A case study from the ironmaking industry
2024 · External reference
Safe reinforcement learning for industrial optimal control: A case study from metallurgical industry
10.1016/j.ins.2023.119684 · 2023 · External reference
Unresolved reference
External reference
An environmentally sustainable optimization approach for blast furnace ironmaking process based on hybrid mechanism and expensive modelling
2026 · External reference
Research on Adaptive Regulation System and Energy Consumption Optimization Control of Steelmaking Process Parameters in the Steel Industry Based on Reinforcement Learning
2024 · External reference
Online control algorithm for thickener underflow concentration based on reinforcement learning
2023 · External reference
The Integrated Virtual Blast Furnace: Enabling Physics-Based Operational Guidance
2024 · External reference
Silicon content prediction of hot metal in blast furnace based on hybrid neural networks model
2024 · External reference
A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm
10.1109/tcyb.2018.2850350 · 2019 · External reference
Evaluation, Prediction, and Feedback of Blast Furnace Hearth Activity Based on Expert Analysis and Process Metallurgy
10.1002/srin.202300385 · ExternalCitation · doi-reference
Book review: Nirupam Chakraborti “Data-Driven Evolutionary Modeling in Materials Technology”
10.1007/s10710-023-09455-1 · ExternalCitation · doi-reference
A novel strategy based on machine learning of selective cooling control of work roll for improvement of cold rolled strip flatness
10.1007/s10845-023-02204-2 · ExternalCitation · doi-reference
Optimal data-driven control of manufacturing processes using reinforcement learning: An application to wire arc additive manufacturing
10.1007/s10845-023-02307-w · ExternalCitation · doi-reference
Deep transformers for analyzing BOF steelmaking data
10.1007/s11663-025-03615-7 · ExternalCitation · doi-reference
Explaining hardness modeling with XAI of C45 steel spur-gear induction hardening
10.1007/s12289-023-01780-1 · ExternalCitation · doi-reference
State of the art in applications of machine learning in steelmaking process modeling
10.1007/s12613-023-2646-1 · ExternalCitation · doi-reference
Process metallurgy and data-driven prediction and feedback of blast furnace heat indicators
10.1007/s12613-023-2693-7 · ExternalCitation · doi-reference
Effect of ludwigite on pellet preparation and metallurgical properties
10.1007/s40831-024-00789-3 · ExternalCitation · doi-reference
Optuna-DFNN: An Optuna framework driven deep fuzzy neural network for predicting sintering performance in big data
10.1016/j.aej.2024.04.026 · ExternalCitation · doi-reference
Evolutionary data driven modeling and tri-objective optimization for noisy BOF steel making data
10.1016/j.dche.2023.100094 · ExternalCitation · doi-reference
Interactive data-driven multiobjective optimization of metallurgical properties of microalloyed steels using the DESDEO framework
10.1016/j.engappai.2023.105918 · ExternalCitation · doi-reference
Safe reinforcement learning for industrial optimal control: A case study from metallurgical industry
10.1016/j.ins.2023.119684 · ExternalCitation · doi-reference
A Statistical Study on Parameter Selection of Operators in Continuous State Transition Algorithm
10.1109/tcyb.2018.2850350 · ExternalCitation · doi-reference
A novel dynamic operation optimization method based on multiobjective deep reinforcement learning for steelmaking process
10.1109/tnnls.2023.3244945 · ExternalCitation · doi-reference
Hot metal temperature prediction technique based on feature fusion and GSO-DF
10.2355/isijinternational.isijint-2024-127 · ExternalCitation · doi-reference
10.3390/met10101393
10.3390/met10101393 · ExternalCitation · doi-reference
10.3390/met13101742
10.3390/met13101742 · ExternalCitation · doi-reference
10.3390/met14111273
10.3390/met14111273 · ExternalCitation · doi-reference
10.3390/pr12010032
10.3390/pr12010032 · ExternalCitation · doi-reference
10.3390/recycling9040054
10.3390/recycling9040054 · ExternalCitation · doi-reference
