IEEE ICPS 2023 will host two tutorials during the conference, addressing 1) Advanced Technologies for Industrial Systems: Cyber-Security Protection, Intelligent Control, and Data Analytics, and 2) Advanced Technologies for Industrial Systems: Intelligent Alarm Monitoring and Applications. Tutorials in IES conferences can provide an opportunity for attendees to learn about a specific topic or technology in a structured and focused manner. They can help to supplement the knowledge gained from attending talks and sessions, and provide attendees with a deeper understanding of a particular area.

Wenkai Hu
China University of Geosciences

Jiandong Wang
Shandong University of Science
and Technology

Chuan-Ke Zhang
China University of Geosciences

Tutorial 1: Advanced Technologies for Industrial Systems: Cyber-Security Protection, Intelligent Control, and Data Analytics


• Chunjie Zhou (Huazhong University of Science and Technology)
• Chunjie Yang (Zhejiang University)
• Fan Yang (Tsinghua University)

Title: Cyber Security Protection Technology for Control Systems in the Context of Industrial Internet

Abstract:Industrial control systems are typical examples of complex cyber-physical systems that play a vital role in the national economy and people's daily lives. As critical national infrastructure, they have widespread applications across various industries. With the increasing adoption of industrial internet technologies and the deep integration of informatization and industrialization, the issue of information security in industrial control systems has become increasingly prominent. However, the operational modes, working characteristics, and structural features of industrial control systems make their information security issues different from those of traditional IT systems. This report first introduces industrial internet technologies and analyzes the information security protection framework of the industrial internet. Then, based on the characteristics of industrial control systems, it provides a detailed analysis of the unique information security issues and challenges faced by control systems in the industrial internet environment. Finally, the report discusses possible solutions to information security protection in this context and introduces the key technologies involved.

Chunjie Zhou is a distinguished professor, doctoral supervisor, and Special Class I professor of the Huazhong Scholars Program at Huazhong University of Science and Technology. He has received the Baosteel Outstanding Teacher Award and serves as a member of the Teaching Steering Committee for Automation in Higher Education Institutions under the Ministry of Education. He is also the Associate Dean of the School of Artificial Intelligence and Automation at Huazhong University of Science and Technology and the Chairman of the Wuhan Automation Association. His research focuses on the security of the industrial internet and industrial cyber-physical systems. As a principal investigator, he has led projects funded by the National Natural Science Foundation, national key research and development plans, and other national programs. He has published over 40 academic papers in significant domestic and international journals and conferences and has been invited to contribute articles to prestigious journals like Proceedings of the IEEE. He has also published more than 20 papers in top-tier international journals such as IEEE Transactions. He has participated in the development of over 10 national standards related to the security of industrial control systems and held more than 20 authorized national invention patents.

Title: Key technology and application of high-performance intelligent operation control of large blast furnace ironmaking system

Abstract:High-performance operation control of large blast furnace iron-making system is a major demand for "safe, high-quality, efficient and low-carbon" operation of the iron and steel industry. This report addresses the challenges of high-performance operation control brought by the high smelting temperature, high spatial and temporal dynamics, and high complexity of the large blast furnace ironmaking system, describes the key technologies of intelligent sensing, intelligent diagnosis and safe operation, and intelligent optimized cooperative control of the system, introduces the system development and implementation results, and discusses the future research direction and challenges.

Chunjie Yang is a distinguished professor of Zhejiang University, doctoral supervisor, deputy director of the National Engineering Research Center for Industrial Automation, an expert enjoying the special government allowance of the State Council, and a winner of the first prize of the National Science and Technology Progress Award. He is a standing member of Technical Committee on Process Control, member of Metallurgical Automation Branch of Chinese Society of Metals. He is mainly engaged in research on Industrial Internet, digital twins, optimal control and fault diagnosis of ironmaking system, etc. He has presided over a number of important scientific research work such as the Industrial Internet Innovation and Development Project of the Ministry of Industry and Information Technology of the People's Republic of China and the key projects of the National Natural Science Foundation of China. He authorized over 50 invention patents, published over 100 academic papers, won one first prize and two second prizes of the National Science and Technology Progress Award, and six provincial and ministerial level science and technology awards.

Title: Causality and Root Cause Analysis Based on Data Analytics

Abstract:This presentation will introduce advanced alarm strategy and abnormal situation monitoring based on process data analytics and, in particular, correlation/causality analysis based on mining of process and alarm data in combination with process connectivity knowledge, with applications to root cause analysis of propagated or even plant-wide abnormalities. The methods of Granger causality and transfer entropy will be demonstrated.

Fan Yang received the B.Eng. degree in Automation and the Ph.D. degree in Control Science and Engineering from Tsinghua University, Beijing, China, in 2002 and 2008, respectively. After working as a Postdoctoral Fellow with Tsinghua University and the University of Alberta, he joined the Department of Automation, Tsinghua University in 2011, where he is currently a Professor. His research interests include topology modeling of large-scale processes, abnormal events monitoring, process hazard analysis, and smart alarm management. He was a recipient of the Young Research Paper Award from the IEEE Control Systems Society Beijing Chapter in 2006, the Science and Technology Progress Award from the Chinese Association of Automation in 2018, the Zhang Zhongjun Excellent Paper Award in 2019, and the Teaching Achievement Awards from Tsinghua University in 2012, 2014, 2016, and 2019 and from the Chinese Association of Automation in 2016.

Tutorial 2: Advanced Technologies for Industrial Systems: Intelligent Alarm Monitoring and Applications


• Chunli Wang (SINOPEC Research Institute of Safety Engineering Co., Ltd)
• Jiandong Wang (Shandong University of Science and Technology)
• Jun Shang (Tongji University)

Title: Introduction to Alarm Management Standards, Technologies, and Applications in the Petrochemical Industry

Abstract:This report will mainly introduce domestic and international alarm management standards and discuss in detail the history of alarm management standards in the petrochemical industry. It will also systematically introduce alarm system performance evaluation and optimization technologies, advanced alarm management technologies, and the application of these alarm management and technologies in actual chemical industries.

Chunli Wang is an Expert and a Professorial Senior Engineer in the SINOPEC Research Institute of Safety Engineering Co., Ltd. He has been consistently engaged in the research and development of intelligent monitoring and early warning technology for abnormal conditions, process control and optimization technology, and process safety management in petrochemical industry, and participated in writing the first national standard for alarm management in China, namely, “Process Industry Alarm System Management”.

Title: Optimal Design of Multivariate Alarm Systems Based on Normal Operating Zones

Abstract:This talk will focus on the optimal design of multivariate alarm systems for multiple-correlated process variables. The geometric space formulated by allowable variational ranges of process variables is referred to as the normal operating zone (NOZ). If an operating point is inside the NOZ, then the operating condition is regarded as being normal; otherwise, an alarm arises to indicate the deviation of an operating point from the NOZ. The NOZ model is built and dynamic alarm thresholds are designed to implement such a multivariate alarm system. Numerical and industrial examples will be provided to illustrate the design methods.

Jiandong Wang is a Professor in the College of Electrical Engineering and Automation at the Shandong University of Science and Technology, Qingdao, Shandong Province, China. He received the B.E. in Automatic Control from Beijing University of Chemical Technology, Beijing, China, in 1997, and the M.Sc. and Ph.D. in Electrical and Computer Engineering from the University of Alberta, Canada, in 2003 and 2007, respectively. From 1997 to 2001, he was a Control Engineer with the Beijing Tsinghua Energy Simulation Company, Beijing, China. From December 2006 to October 2016, he was an Assistant/Associate/Full Professor with the College of Engineering, Peking University, China. His research interests include process control, industrial alarm systems, optimal scheduling and their applications to industrial problems. Dr. Wang has served as an Associate Editor/Guest Editor for Journal of Franklin Institute, Systems and Control Letters, and Control Engineering Practice.

Title: Feature Vectors in the Early Classification of Alarm Floods

Abstract:Early classification of ongoing alarm floods in industrial monitoring systems is crucial for safe and efficient operations. It provides online decision support for plant operators to take timely action without waiting for the end of an alarm flood. This presentation discusses feature vectors in the problem of early classification of alarm floods. We will analyze the properties of different feature vectors in different approaches. We will also discuss the advantages and disadvantages of different feature vectors in the sense of classification accuracy, computational complexity, and generalization ability.

Jun Shang received the B.Eng. degree in control science and engineering from Harbin Institute of Technology, Harbin, China, in 2013, and the Ph.D. degree in control science and engineering from Tsinghua University, Beijing, China, in 2018. From September 2018 to January 2023, he was a Postdoctoral Fellow with the Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada. He is currently a Professor with the Department of Control Science and Engineering, Tongji University, Shanghai, China. His research interests include cyber-physical security, alarm management, fault diagnosis, and networked control.

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