Postdoctor, at University of Science and Technology Beijing, China.
Dr. Zicheng Xin is a metallurgical engineer specializing in process optimization and intelligent modeling in steelmaking. He is currently affiliated with the University of Science and Technology Beijing (USTB) and serves as a visiting professor at the Korea Invention Academy. His research focuses on multiscale modeling, simulation, and collaborative manufacturing for metallurgical processes. Dr. Xin has received prestigious awards, including the Golden Scientist Grand Award and Science & Technology Grand Award from the International Federation of Inventors’ Associations. He holds multiple patents related to LF refining and metallurgical process control. His contributions to steelmaking innovation are widely recognized through journal publications, patents, and software developments in metallurgical engineering.
Professional Profile
Scopus
🎓 Education
Dr. Zicheng Xin earned his Ph.D. in Metallurgical Engineering (2018–2022) from the State Key Laboratory of Advanced Metallurgy at the University of Science and Technology Beijing (USTB). His doctoral research focused on advanced metallurgical process simulation and optimization, integrating machine learning techniques to improve steel refining efficiency. His academic journey equipped him with expertise in process modeling, intelligent manufacturing, and metallurgical thermodynamics. Throughout his Ph.D., he collaborated with leading researchers and contributed significantly to developing real-time metallurgical analysis software. His education laid a strong foundation for his pioneering research in data-driven steel manufacturing and process control.
🏆 Experience
Dr. Xin has extensive experience in metallurgical engineering, with a focus on steelmaking process optimization and digital twin technology. He has been actively involved in research projects on LF refining, desulfurization, and molten steel temperature prediction. As a visiting professor at the Korea Invention Academy, he contributes to metallurgical innovation and industry-oriented problem-solving. His role at USTB involves mentoring students, conducting high-impact research, and developing real-time software for steel refining. His expertise extends to industrial collaborations where he implements AI-driven solutions for metallurgical process control.
🔍 Research Interests
Dr. Xin’s research focuses on:
1️⃣ Metallurgical process engineering & intelligence – Developing smart manufacturing techniques for steel production.
2️⃣ Simulation & optimization of metallurgical processes – Implementing machine learning for process control.
3️⃣ Ladle Furnace (LF) refining – Predicting molten steel temperature and optimizing slagging methods.
4️⃣ Desulfurization & alloying efficiency – Enhancing steel quality through AI-assisted modeling.
5️⃣ Multiscale modeling & collaborative manufacturing – Integrating data-driven approaches for efficiency improvement.
His research bridges theoretical metallurgy and industrial applications, contributing to the advancement of steel manufacturing.
🏅 Awards
Dr. Xin has received several prestigious awards for his contributions to metallurgical innovation:
✅ Golden Scientist Grand Award (2023, Second Place) – International Federation of Inventors’ Associations for Multiscale modeling and collaborative manufacturing for steelmaking plants.
✅ Science & Technology Grand Award (2023, Second Place) – International Federation of Inventors’ Associations for Multiscale modeling and collaborative manufacturing for steelmaking plants.
✅ Gold Medal, World Invention Innovation Contest (2023) – For the method and system predicting slagging lime addition in LF refining.
His awards highlight his impact on industrial metallurgy and smart steelmaking technologies.
📚Top Noted Publications
Dr. Xin has authored multiple high-impact journal articles on metallurgical process modeling and AI-driven optimization. Here are some of his key publications:
1️⃣ Predicting CaO Activity in Multiple Slag Systems
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Journal: Scientific Reports
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Year: 2025
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Summary: This study develops a machine learning model to predict the activity of CaO in various slag systems. The model integrates thermodynamic data and experimental results to enhance accuracy in steelmaking process simulations.
2️⃣ Analysis of Multi-Zone Reaction Mechanisms in BOF Steelmaking
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Journal: Materials
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Year: 2025
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Summary: This paper investigates the reaction mechanisms in different zones of a Basic Oxygen Furnace (BOF) during steelmaking. It provides insights into decarburization, slag formation, and alloying element behavior using computational modeling and experimental validation.
3️⃣ Modeling of LF Refining Process: A Review
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Journal: Journal of Iron and Steel Research International
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Year: 2024
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Summary: A comprehensive review of various modeling approaches (empirical, thermodynamic, and ML-based) used in Ladle Furnace (LF) refining. The paper highlights recent advancements and challenges in temperature and composition prediction.
4️⃣ Explainable ML Model for Predicting Molten Steel Temperature in LF Refining
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Journal: International Journal of Minerals, Metallurgy & Materials
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Year: 2024
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Summary: This study presents an interpretable machine learning model for predicting molten steel temperature during LF refining, focusing on feature importance and model transparency.
5️⃣ Predicting Molten Steel Temperature Using IF-ZCA-DNN Model
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Journal: Metallurgical and Materials Transactions B
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Year: 2023
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Summary: A deep learning approach integrating Independent Feature Selection (IF), Zero Component Analysis (ZCA), and Deep Neural Networks (DNN) to enhance molten steel temperature prediction accuracy.
6️⃣ Predicting Alloying Element Yield in LF Using PCA & DNN
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Journal: International Journal of Minerals, Metallurgy & Materials
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Year: 2023
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Summary: This paper develops a hybrid model combining Principal Component Analysis (PCA) and Deep Neural Networks (DNN) to predict the yield of alloying elements in Ladle Furnace refining.
7️⃣ Hybrid Modeling for Molten Steel Temperature Prediction in LF
8️⃣ Optimization of Argon Bottom Blowing in LF Using RSM
Conclusion
Dr. Zicheng Xin is a strong contender for the Best Researcher Award due to his impactful contributions to metallurgical process optimization, machine learning applications, and collaborative manufacturing. His awards, patents, and software developments demonstrate both theoretical innovation and industrial applicability.