advanced

Editorial for special issue on intelligent ferrous metallurgy
Min Jiang, Qing Liu, Mansheng Chu, Yanping Bao
2026,vol. 33,no.10pp. 3159-3162. https://doi.org/10.1007/s12613-026-3594-3
[FullText HTML] [PDF] SpringerLink
Paradigm evolution of intelligent discrete processes in specialty metallurgy: From experience-driven to AI4Science
Xi Chen, Zhouhua Jiang, Yanwu Dong, Yibin Zhu, Yuxiao Liu
2026,vol. 33,no.10pp. 3163-3182. https://doi.org/10.1007/s12613-026-3536-0
[FullText HTML] [PDF] SpringerLink
Artificial intelligence for smarter steelmaking processes: Toward enhanced efficiency, quality and sustainability
Nanfu Zong, Tao Jing, Jean-Christophe Gebelin
2026,vol. 33,no.10pp. 3183-3193. https://doi.org/10.1007/s12613-026-3445-2
[FullText HTML] [PDF] SpringerLink
Prediction of blast furnace gas utilization rate based on mechanism and data-driven approaches
Zhifeng Zhang, Jue Tang, Mansheng Chu, Xinjing Shi, Quan Shi
2026,vol. 33,no.10pp. 3194-3206. https://doi.org/10.1007/s12613-026-3466-x
[FullText HTML] [PDF] SpringerLink
Multistep time-series prediction model for blast furnace permeability index based on deep learning
Lingru Meng, Nan Wang, Xiaohui Zhang, Lei Fang, Yonghui Liu
2026,vol. 33,no.10pp. 3207-3224. https://doi.org/10.1007/s12613-026-3471-0
[FullText HTML] [PDF] SpringerLink
Inferring operation–response lag times for hot-metal silicon in blast furnaces using an interpretable machine learning–augmented stochastic differential equation framework
Zenghao Liu, Qingyun Huang, Yang You, Yuxiao Xue, Peng Hu, Haixi He, Jiawei Liao, Xuewei Lü
2026,vol. 33,no.10pp. 3225-3248. https://doi.org/10.1007/s12613-026-3444-3
[FullText HTML] [PDF] SpringerLink
Prediction of BOF endpoint P content by Optuna-RF with K-medoids clustering based on metallurgical parameter constraint
Maohui Zou, Jian Yang, Xiaofeng Qiu, Wei Deng, Han Sun, Yuntong Li
2026,vol. 33,no.10pp. 3249-3262. https://doi.org/10.1007/s12613-025-3359-4
[FullText HTML] [PDF] SpringerLink
Integrated mechanism–data driven model for endpoint carbon prediction in converter processes
Zhengbiao Hu, Changhe Li, Xingrun Chen, Lili Jiang, Xingmin Guo, Jianfa Jing, Xiaodong Chen, Jun Zhu, Tingting Lu
2026,vol. 33,no.10pp. 3263-3276. https://doi.org/10.1007/s12613-026-3392-y
[FullText HTML] [PDF] SpringerLink
Feature attention model attention for basic oxygen furnace endpoint temperature prediction
Peng Li, Dongping Zhan, Bingqian Huang, Sheng Chang, Zhouhua Jiang, Huishu Zhang, Pengchao Li
2026,vol. 33,no.10pp. 3277-3293. https://doi.org/10.1007/s12613-026-3487-5
[FullText HTML] [PDF] SpringerLink
Decision support system for optimizing BOF tapping cut-off in low-P steel production using machine learning and constraint programming
Gibeom Kim, Chi-Seong Kim, Woong-Hee Han, Chang-Hee Yim, Dae-Geun Hong
2026,vol. 33,no.10pp. 3294-3317. https://doi.org/10.1007/s12613-026-3526-2
[FullText HTML] [PDF] SpringerLink
Hybrid model for predicting and optimizing energy consumption in converter processes based on feature selection and interpretability
Feixiang Dai, Lu Zhang, Xiangjun Bao, Xiaojing Yang, Guang Chen
2026,vol. 33,no.10pp. 3318-3331. https://doi.org/10.1007/s12613-026-3459-9
[FullText HTML] [PDF] SpringerLink
Consistency-filtered latent-enhanced deep learning for robust chromium yield prediction in electric arc furnace steelmaking
Yuchi Zou, Hang Hu, Nanlv Liu, Guangsheng Wei, Yufeng Guo, Lingzhi Yang
2026,vol. 33,no.10pp. 3332-3350. https://doi.org/10.1007/s12613-026-3518-2
[FullText HTML] [PDF] SpringerLink
Numerical simulation of slag-foaming dynamics in CaO–SiO2–Al2O3–FeO steelmaking slags
Huan Liu, Kejiang Li, Heng Zhou, Jianliang Zhang, Marco A. Ramírez-Argáez, Alberto N. Conejo
2026,vol. 33,no.10pp. 3351-3362. https://doi.org/10.1007/s12613-025-3348-7
[FullText HTML] [PDF] SpringerLink
Molten steel quality control in LF refining furnace based on hybrid neuro-symbolic dynamic adjustment
Xiangtuo Zhang, Shenyang Song, Jinyao Wang, Jing Li, Changcheng Wang, Yuhong Huang, Xiao Han
2026,vol. 33,no.10pp. 3363-3378. https://doi.org/10.1007/s12613-026-3427-4
[FullText HTML] [PDF] SpringerLink
Knowledge model for response to ladle furnace refining time disturbance based on endpoint molten-steel temperature prediction
Hongzhi Chen, Mo Lan, Zicheng Xin, Bailin Wang, Zhibin Gao, Hai Chang, Jiangshan Zhang, Shuaipeng Yuan, Xijie Yang, Qing Liu
2026,vol. 33,no.10pp. 3379-3393. https://doi.org/10.1007/s12613-026-3532-4
[FullText HTML] [PDF] SpringerLink
A structure-informed coupled method for precision deoxidation thermodynamics in Fe-based melts
Zhigang Yu, Lijie Guo, Yijie Feng, Zelu Wu, Qianjin Guo, Xingli Zou, Jun Luan, Xionggang Lu
2026,vol. 33,no.10pp. 3394-3406. https://doi.org/10.1007/s12613-026-3545-z
[FullText HTML] [PDF] SpringerLink
Adaptive multivariate data fusion-based instability-state prediction framework for continuous casting process
Qikai Yang, Shuo Ma, Chunzheng Yang, Ming Li, Yuhong Liu, Qing Liu, Liang Ma
2026,vol. 33,no.10pp. 3407-3424. https://doi.org/10.1007/s12613-026-3423-8
[FullText HTML] [PDF] SpringerLink
Development of a GCN-LSTM based prediction model for sticking breakout in continuous casting
Rongrong Wang, Chao Gu, Zhongliang Wang, Yanping Bao
2026,vol. 33,no.10pp. 3425-3436. https://doi.org/10.1007/s12613-026-3551-1
[FullText HTML] [PDF] SpringerLink
Management and technical challenges of laminar flow-dedicated line production in steel manufacturing
Lu Lin, Chaoping Xiao, Menglong Feng, Jiaqing Zeng, Qing He, Yuxiang Dai, Huaizhou Cui, Shuguang Liu
2026,vol. 33,no.10pp. 3437-3447. https://doi.org/10.1007/s12613-026-3550-2
[FullText HTML] [PDF] SpringerLink
Hierarchical decomposition based optimization method for production and inventory planning in steel galvanizing operations
Yuming Zhao, Yang Yang, Lijie Su, Gongshu Wang, Lixin Tang
2026,vol. 33,no.10pp. 3448-3461. https://doi.org/10.1007/s12613-026-3506-6
[FullText HTML] [PDF] SpringerLink
Modeling of martensite start temperature in low-carbon steels using artificial neural networks
Muhammad Ishtiaq, Xiaosong Wang, Aqil Inam, Jallu Krishnaiah, Sung Gyu Kang, Nagireddy Gari Subba Reddy
2026,vol. 33,no.10pp. 3462-3473. https://doi.org/10.1007/s12613-026-3446-1
[FullText HTML] [PDF] SpringerLink