Research graph
References from Collaborative Optimization of Ladle Furnace Operating Parameters Using Prediction Models and Case-Guided Genetic–Tabu Search. Local targets link to admitted publications; unresolved targets remain external evidence.
State of the art in applications of machine learning in steelmaking process modeling
10.1007/s12613-023-2646-1 · 2023 · External reference
10.3390/en18081981
10.3390/en18081981 · External reference
Numerical simulation of electric arc heating process in the refining ladle
10.1002/srin.202300195 · 2023 · External reference
A mathematical model for the thermal state of a steel ladle
10.1080/03019233.2023.2201544 · 2023 · External reference
Optimizing power-saving process for a 120-t ladle furnace
10.1007/s42243-024-01320-4 · 2025 · External reference
An online BOF terminal temperature control model based on big data learning
10.1007/s42243-023-00952-2 · 2023 · External reference
10.3390/app132312778
10.3390/app132312778 · External reference
10.3390/met14070773
10.3390/met14070773 · External reference
10.3390/su15086393
10.3390/su15086393 · External reference
End-point temperature preset of molten steel in the final refining unit based on an integration of deep neural network and multi-process operation simulation
10.2355/isijinternational.isijint-2020-540 · 2021 · External reference
Tree-structure ensemble general regression neural networks applied to predict the molten steel temperature in ladle furnace
10.1016/j.aei.2016.05.001 · 2016 · External reference
Molten steel temperature prediction model based on bootstrap feature subsets ensemble regression trees
10.1016/j.knosys.2016.02.018 · 2016 · External reference
Ladle furnace temperature prediction model based on large-scale data with random forest
10.1109/jas.2016.7510247 · 2017 · External reference
End temperature prediction of molten steel in LF based on CBR-BBN
10.1002/srin.201400512 · 2016 · External reference
The soft sensor of the molten steel temperature using the modified maximum entropy based pruned bootstrap feature subsets ensemble method
10.1016/j.ces.2018.05.037 · 2018 · External reference
Predicting molten steel endpoint temperature using a feature-weighted model optimized by mutual learning cuckoo search
10.1016/j.asoc.2019.105675 · 2019 · External reference
Development of an improved CBR model for predicting steel temperature in ladle furnace refining
10.1007/s12613-020-2234-6 · 2021 · External reference
Molten steel temperature prediction in ladle furnace using a dynamic ensemble for regression
10.1109/access.2021.3053357 · 2021 · External reference
Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search
10.1007/s00521-020-05413-5 · 2021 · External reference
A hybrid modeling method based on expert control and deep neural network for temperature prediction of molten steel in LF
10.2355/isijinternational.isijint-2021-251 · 2022 · External reference
Boosting the prediction of molten steel temperature in ladle furnace with a dynamic outlier ensemble
10.1016/j.engappai.2022.105359 · 2022 · External reference
A framework based on heterogeneous ensemble models for liquid steel temperature prediction in LF refining process
10.1016/j.asoc.2022.109724 · 2022 · External reference
Predicting temperature of molten steel in LF-refining process using IF-ZCA-DNN model
10.1007/s11663-023-02753-0 · 2023 · External reference
Temperature prediction model for ladle furnace based on mathematical mechanisms and the GA-BP algorithm
10.1177/03019233241240246 · 2024 · External reference
An error correction method based on CBR for end temperature prediction of molten steel in ladle furnace
10.2355/isijinternational.isijint-2024-058 · 2024 · External reference
Explainable machine learning model for predicting molten steel temperature in the LF refining process
10.1007/s12613-024-2950-4 · 2024 · External reference
Prediction of ladle furnace refining endpoint temperature based on particle swarm optimization algorithm and long short-term memory neural network
10.1007/s11837-024-06983-8 · 2025 · External reference
Improvement in stability and generalization ability of end-point temperature prediction model in ladle furnace
10.1007/s40831-025-01228-7 · 2025 · External reference
Enhanced temperature prediction in ladle furnace steel refining: Hybrid process modeling based on computational thermodynamics and statistical learning methods
10.1007/s11663-025-03715-4 · 2025 · External reference
10.3390/pr10030434
10.3390/pr10030434 · External reference
Prediction of alloy addition in ladle furnace (LF) based on LWOA-SCN
2023 · External reference
Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network
10.1007/s12613-021-2409-9 · 2023 · External reference
A real-time ferroalloy model for the optimum ladle furnace treatment during the secondary steelmaking
10.1080/03019233.2017.1368952 · 2019 · External reference
10.3390/pr12081761
10.3390/pr12081761 · External reference
10.3390/met11101587
10.3390/met11101587 · External reference
A new AdaBoost.IR soft sensor method for robust operation optimization of ladle furnace refining
10.2355/isijinternational.isijint-2016-371 · 2017 · External reference
10.3390/pr12122877
10.3390/pr12122877 · External reference
Improving retrieval performance of case based reasoning systems by fuzzy clustering
2024 · External reference
10.3390/su152014821
10.3390/su152014821 · External reference
Hybrid selection based multi/many-objective evolutionary algorithm
10.1038/s41598-022-10997-0 · 2022 · External reference
An efficient tabu search algorithm for the linear ordering problem
10.1299/jamdsm.2022jamdsm0041 · 2022 · External reference
An efficient optimization model and tabu search-based global optimization approach for the continuous p-dispersion problem
10.1287/ijoc.2023.0089 · 2025 · External reference
End temperature prediction of molten steel in LF based on CBR-BBN
10.1002/srin.201400512 · ExternalCitation · doi-reference
Numerical simulation of electric arc heating process in the refining ladle
10.1002/srin.202300195 · ExternalCitation · doi-reference
Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search
10.1007/s00521-020-05413-5 · ExternalCitation · doi-reference
Predicting temperature of molten steel in LF-refining process using IF-ZCA-DNN model
10.1007/s11663-023-02753-0 · ExternalCitation · doi-reference
Enhanced temperature prediction in ladle furnace steel refining: Hybrid process modeling based on computational thermodynamics and statistical learning methods
10.1007/s11663-025-03715-4 · ExternalCitation · doi-reference
Prediction of ladle furnace refining endpoint temperature based on particle swarm optimization algorithm and long short-term memory neural network
10.1007/s11837-024-06983-8 · ExternalCitation · doi-reference
Development of an improved CBR model for predicting steel temperature in ladle furnace refining
10.1007/s12613-020-2234-6 · ExternalCitation · doi-reference
Predicting the alloying element yield in a ladle furnace using principal component analysis and deep neural network
10.1007/s12613-021-2409-9 · 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
Explainable machine learning model for predicting molten steel temperature in the LF refining process
10.1007/s12613-024-2950-4 · ExternalCitation · doi-reference
Improvement in stability and generalization ability of end-point temperature prediction model in ladle furnace
10.1007/s40831-025-01228-7 · ExternalCitation · doi-reference
An online BOF terminal temperature control model based on big data learning
10.1007/s42243-023-00952-2 · ExternalCitation · doi-reference
Optimizing power-saving process for a 120-t ladle furnace
10.1007/s42243-024-01320-4 · ExternalCitation · doi-reference
Tree-structure ensemble general regression neural networks applied to predict the molten steel temperature in ladle furnace
10.1016/j.aei.2016.05.001 · ExternalCitation · doi-reference
Predicting molten steel endpoint temperature using a feature-weighted model optimized by mutual learning cuckoo search
10.1016/j.asoc.2019.105675 · ExternalCitation · doi-reference
A framework based on heterogeneous ensemble models for liquid steel temperature prediction in LF refining process
10.1016/j.asoc.2022.109724 · ExternalCitation · doi-reference
The soft sensor of the molten steel temperature using the modified maximum entropy based pruned bootstrap feature subsets ensemble method
10.1016/j.ces.2018.05.037 · ExternalCitation · doi-reference
Boosting the prediction of molten steel temperature in ladle furnace with a dynamic outlier ensemble
10.1016/j.engappai.2022.105359 · ExternalCitation · doi-reference
Molten steel temperature prediction model based on bootstrap feature subsets ensemble regression trees
10.1016/j.knosys.2016.02.018 · ExternalCitation · doi-reference
Hybrid selection based multi/many-objective evolutionary algorithm
10.1038/s41598-022-10997-0 · ExternalCitation · doi-reference
A real-time ferroalloy model for the optimum ladle furnace treatment during the secondary steelmaking
10.1080/03019233.2017.1368952 · ExternalCitation · doi-reference
A mathematical model for the thermal state of a steel ladle
10.1080/03019233.2023.2201544 · ExternalCitation · doi-reference
Molten steel temperature prediction in ladle furnace using a dynamic ensemble for regression
10.1109/access.2021.3053357 · ExternalCitation · doi-reference
Ladle furnace temperature prediction model based on large-scale data with random forest
10.1109/jas.2016.7510247 · ExternalCitation · doi-reference
Temperature prediction model for ladle furnace based on mathematical mechanisms and the GA-BP algorithm
10.1177/03019233241240246 · ExternalCitation · doi-reference
An efficient optimization model and tabu search-based global optimization approach for the continuous p-dispersion problem
10.1287/ijoc.2023.0089 · ExternalCitation · doi-reference
An efficient tabu search algorithm for the linear ordering problem
10.1299/jamdsm.2022jamdsm0041 · ExternalCitation · doi-reference
A new AdaBoost.IR soft sensor method for robust operation optimization of ladle furnace refining
10.2355/isijinternational.isijint-2016-371 · ExternalCitation · doi-reference
End-point temperature preset of molten steel in the final refining unit based on an integration of deep neural network and multi-process operation simulation
10.2355/isijinternational.isijint-2020-540 · ExternalCitation · doi-reference
A hybrid modeling method based on expert control and deep neural network for temperature prediction of molten steel in LF
10.2355/isijinternational.isijint-2021-251 · ExternalCitation · doi-reference
An error correction method based on CBR for end temperature prediction of molten steel in ladle furnace
10.2355/isijinternational.isijint-2024-058 · ExternalCitation · doi-reference
10.3390/app132312778
10.3390/app132312778 · ExternalCitation · doi-reference
10.3390/en18081981
10.3390/en18081981 · ExternalCitation · doi-reference
10.3390/met11101587
10.3390/met11101587 · ExternalCitation · doi-reference
10.3390/met14070773
10.3390/met14070773 · ExternalCitation · doi-reference
10.3390/pr10030434
10.3390/pr10030434 · ExternalCitation · doi-reference
10.3390/pr12081761
10.3390/pr12081761 · ExternalCitation · doi-reference
10.3390/pr12122877
10.3390/pr12122877 · ExternalCitation · doi-reference
10.3390/su15086393
10.3390/su15086393 · ExternalCitation · doi-reference
10.3390/su152014821
10.3390/su152014821 · ExternalCitation · doi-reference
