Professor, North China university of Science and Technology, China
Professor Xiaojie Liu is an associate professor at North China University of Science and Technology, specializing in the optimization of blast furnace operations and intelligent ironmaking technologies. With a focus on integrating big data, machine learning, and advanced process optimization, Liu has significantly contributed to the development of sustainable and efficient metallurgical processes. He obtained his PhD from Northeastern University in 2016, and has since been a key figure in advancing industrial applications of new technologies in ironmaking. As an active reviewer and editorial board member for renowned journals, such as Chinese Journal of Iron and Steel Research and Metallurgical Automation, Liu’s research not only drives academic excellence but also impacts real-world industrial practices. His innovations in hydrogen-rich smelting and data-driven optimization techniques are helping shape the future of the steel industry. He continues to mentor students and lead projects that push the boundaries of intelligent manufacturing.
Profile
Scopus
Strengths for the Award
- Innovative Research in Ironmaking and Big Data:
Professor Xiaojie Liu’s research focuses on cutting-edge areas of ironmaking, particularly integrating advanced technologies like machine learning, big data mining, and intelligent systems into blast furnace operations. His work on intelligent ironmaking and hydrogen-rich smelting could have substantial industrial and environmental implications.
- Diverse and High-impact Publications:
With a wide range of peer-reviewed articles in prominent journals like Metals, ISIJ International, and Journal of Cleaner Production, Professor Liu’s research is well-regarded internationally. His work on topics such as blast furnace optimization, molten iron quality prediction, and viscosity modeling in metallurgical processes is both timely and impactful.
- Patents and Technological Innovations:
Liu’s research includes the development of new methodologies, such as the VMD-PSO-BP model for predicting blast furnace permeability and deep learning techniques for raw material granularity recognition, reflecting both scientific rigor and practical applicability.
- Leadership in Academic and Editorial Roles:
As a peer reviewer and young editorial board member of multiple respected journals, Professor Liu plays an influential role in advancing academic discourse. His contributions to the Chinese Journal of Iron and Steel Research and Metallurgical Automation demonstrate his leadership and commitment to fostering innovation within his field.
- Collaboration with Industry:
Liu’s active involvement in industry projects related to ironmaking technologies and process optimization reflects his ability to bridge the gap between academia and industrial application. His work has the potential to drive significant improvements in metallurgical processes.
- Mentorship and Teaching:
As an associate professor and master’s tutor, Liu has contributed to the education and development of young engineers and researchers, enhancing the talent pipeline in his field.
Areas for Improvement
- Broader Interdisciplinary Collaborations:
While Professor Liu has made impressive contributions within the field of metallurgical engineering, exploring broader interdisciplinary collaborations with fields such as materials science, artificial intelligence, and environmental engineering could further enrich his research and lead to more transformative innovations.
- Greater Public Engagement:
While his academic achievements are notable, increasing public engagement through popular science outreach, media, or public lectures would help make his research more accessible to a wider audience and demonstrate the real-world impact of his work.
- Diversifying Research Topics:
While his focus on blast furnace and ironmaking technologies is commendable, expanding his research into complementary areas such as renewable energy integration into smelting processes or circular economy practices in metallurgy could further enhance his contribution to sustainable industry practices.
Education
Professor Xiaojie Liu completed his doctoral studies in Metallurgical Engineering at Northeastern University in 2016, where he focused on applying machine learning techniques to optimize blast furnace operations. His research during this time centered on the use of big data to improve efficiency and sustainability in ironmaking. Before pursuing his PhD, Liu completed both his undergraduate and master’s degrees in the same field, establishing a strong foundation in both the theoretical and practical aspects of metallurgy. His educational background has been instrumental in his career, allowing him to integrate advanced computational models with traditional ironmaking processes. As an academic, Liu has continued to evolve his expertise by staying at the forefront of industry innovations. He currently holds the position of associate professor at North China University of Science and Technology, where he also acts as a master’s tutor, guiding future generations of engineers in the metallurgical industry.
Experience
Professor Xiaojie Liu is an accomplished researcher and educator at North China University of Science and Technology, where he serves as an associate professor and master’s tutor. With extensive experience in both academic and industrial applications, Liu’s work is primarily focused on optimizing the operations of blast furnaces using intelligent systems, big data analytics, and process optimization. Liu has collaborated with various industrial partners to apply his research findings in real-world settings, improving both the efficiency and sustainability of ironmaking operations. His work has led to the development of advanced predictive models for molten iron quality, permeability, and blast furnace raw material optimization. He is also a reviewer for top-tier journals, such as Journal of Cleaner Production, MMTB, and China Metallurgy, and has held editorial positions on journals like Chinese Journal of Iron and Steel Research and Metallurgical Automation. His contributions are shaping the future of the steel industry, blending cutting-edge research with practical solutions.
Research Focus
Professor Xiaojie Liu’s primary research interests lie in the areas of intelligent ironmaking and process optimization for blast furnaces. His work integrates big data mining, machine learning, and advanced computational models to improve the efficiency and environmental sustainability of iron and steel production. Liu’s research explores hydrogen-rich smelting processes to reduce carbon emissions and the application of deep learning for optimizing blast furnace operations, such as raw material granularity recognition and viscosity prediction. His ongoing projects focus on improving the precision and reliability of ironmaking systems by developing predictive models for molten iron quality and other critical parameters. Liu’s work also delves into the optimization of blast furnace operating parameters, utilizing intelligent systems for real-time process control and decision-making. Through these efforts, Liu is addressing key challenges in the metallurgical industry, such as energy consumption, material waste, and environmental impact, with the goal of creating a more sustainable and efficient future for steelmaking.
Publication Top Notes
- A novel anomaly detection and classification algorithm for application in tuyere images of blast furnace
Engineering Applications of Artificial Intelligence, 2025, 139, 109558 🏭🤖
- Blast furnace raw material granularity recognition model based on deep learning and multimodal fusion of 3D point cloud
Visual Computer, 2024, 40(10), pp. 6939–6954 🧠🔍
- Research on Molten Iron Quality Prediction Based on Machine Learning
Metals, 2024, 14(8), 856 🔮💡
- Research on Blast Furnace Ingredient Optimization Based on Improved Grey Wolf Optimization Algorithm
Metals, 2024, 14(7), 798 🐺📈
- Collaborative Optimization Model of Blast Furnace Raw Materials and Operating Parameters Based on Intelligent Calculation
ISIJ International, 2024, 64(8), pp. 1229–1239 🔧⚙️
- Prediction Model for Viscosity of Titanium-Bearing Slag Based on the HIsmelt Process
Transactions of the Indian Institute of Metals, 2024, 77(6), pp. 1597–1606 🏗️🌋
- Analysis of key measures of vanadium extraction from molten iron based on process theory and data mining
Kang T’ieh/Iron and Steel, 2024, 59(3), pp. 58–78 💎🛠️
- Prediction for permeability index of blast furnace based on VMD–PSO–BP model
Journal of Iron and Steel Research International, 2024, 31(3), pp. 573–583 🏗️🧪
- Research on cascade intelligent sinter quality prediction system based on big data technology
Ironmaking and Steelmaking, 2024, 51(1), pp. 3–14 🔍📊
- Prediction model of Bum-through Point based on GA-BP
Proceedings – 2024 39th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2024, pp. 1286–1291 🤖📅
Conclusion
Professor Xiaojie Liu’s qualifications for the Best Researcher Award are compelling. His pioneering research in intelligent ironmaking and big data applications, combined with his strong academic leadership and collaboration with industry, positions him as a highly impactful figure in his field. With his track record of publications in top-tier journals, a deep commitment to advancing ironmaking technologies, and his contribution to the development of sustainable industrial practices, Liu demonstrates the traits of a transformative researcher. If awarded, his continued work could drive significant innovations in the iron and steel industry, especially in areas critical to sustainability and efficiency.