Dr. Beike ZHANG

Associate Professor

Editor:College of Information Science and Technology Time:2024-11-11

Dr. Beike ZHANG


Associate Professor

Email: zhangbk@mail.buct.edu.cn


Background                                                                  

Dr. Zhang Beike, Associate Professor, Master's Supervisor, Executive Director of the Chinese Society for System Simulation. I have been teaching at the school since 2000. I have participated in national and provincial ministry projects multiple times, and together with Professor Wu Zhongguang, I have designed and developed the first set of computer automatic HAZOP software, SDG fault diagnosis software, and multifunctional process control experimental system in China. These achievements have been adopted by many research institutions and universities in China. Has 5 national software registrations and 4 software products.

 Areas of Research of Expertise

  • Chemical process safety analysis

  • Chemical process simulation, and fault diagnosis

Teaching                                             

Undergraduate Teaching

  • Intelligent Manufacturing Engineering Training

Research                                                

Funded Research Projects

The National Natural Science Foundation of China:

  • Research of information standardization and analysis method of on-line safety assessment for petrochemical process.

Five Representative Publications                                           

  1. CHEN X H, ZHANG B K, GAO D. Bearing fault diagnosis base on multi-scale CNN and LSTM model [J]. Journal of Intelligent Manufacturing, 2021, 32(4): 971-987.

  2. WANG Z H, ZHANG B K, GAO D. A novel knowledge graph development for industry design: A case study on indirect coal liquefaction process[J]. Computers in Industry,2022,139,103647.

  3. ZHANG H Q, ZHANG B K, GAO D. A new approach of integrating industry prior knowledge for HAZOP interaction [J]. Journal of Loss Prevention in the Process Industries, 2023,82,105005.

  4. ZHAO Y C, ZHANG B K, GAO D. Construction of petrochemical knowledge graph based on deep learning[J]. Journal of Loss Prevention in the Process Industries, 2022,76,104736.

  5. GAO D, ZHANG B K, XU X, WU C G. Scenario object model based on-line safety analysis for chemical process[J]. Journal of Chemists and Chemical Engineers. 2017 ,66 (11-12): 601-610.