Prof. / Dr. Jing Wang

Office Address

Room 507, Science and Technology Building.

Mailing Address

Mail Box 4, No. 15 Beisanhuan East Road, Chaoyang District, Beijing 100029, P.R. China

Email

jwang@mail.buct.edu.cn

Phone

86-10-64434930


Research Interests

1. Modeling, optimization and control for complex system
2. Process monitoring, fault detection and diagnosis
3. Advance control such as fault-tolerant control and iterative learning control
4. System analysis and control for biochemical process

Working/Education Experience

2014.1-2015.1 University of Delaware, Visiting Professor
2011.12-now Beijing University of Chemical Technology, Professor
2003.4-2011.11 Beijing University of Chemical Technology, Associate Professor
1999.4-2003.3 Beijing University of Chemical Technology, Lecturer.
1994.9-1998.10 Ph.D. Northeastern University, Major: Control Theory and Control Engineering
1990.9-1994.7 B.S. Northeastern University, Major: Industry Automation

Teaching Courses

1.EEE34400C: Automatic Control Theory (II)
2.EE513: Linear system theory
3.EE510: Nonlinear System Control
4.EE662: Modern Control Frontiers

Projects

1. National Science Foundation of China, 61573050, Fault diagnosis and control in the unified framework for the batch process, Principal Investigator
2. National Science Foundation of China, 61174128, Learning control of batch polymerization process based on the integration of data-driven and model-driven, Principal Investigator
3. National Science Foundation of China, 60704011, Modeling, optimization and control for Agile Responsive Manufacturing process with microscopic quality, Principal Investigator
4. Beijing Natural Science Foundation, 4132044, Status assessment based on time period segmentation in batch process, Principal Investigator
5. Fundamental Research Funds for the Central Universities, China, YS1404, State monitoring, optimization and safe operation for complex process, Principal Investigator
6. Fundamental Research Funds for the Central Universities, China, ZZ1223, Status assessment and fault diagnosis of batch polymerization process, Principal Investigator

Publications

[1] Jinglin Zhou, Yuwei Ren, and Jing Wang*, Quality-Relevant Fault Monitoring Based on Locally Linear Embedding Orthogonal Projection to Latent Structure, Industrial & Engineering Chemistry Research, 2019,?58?(3),?pp 1262–1272, DOI: 10.1021/acs.iecr.8b03849
[2] Jing Wang, Qilun Wang,Intelligent explicit model predictive control based on machine learning for microbial desalination cells, Proc IMechE Part I: J Systems and Control Engineering, 2018, Doi:10.1177/0959651818816845
[3] Xiaolu Chen, Jing Wang*, Jinglin Zhou, Probability density estimation and Bayesian causal analysis based fault detection and root identification, Industrial & Engineering Chemistry Research, 2018, ?57(43):?14656-14664 (SCI Top)
[4] Jing Wang*, Changfeng Shao, Yang-Quan Chen, Fractional order sliding mode control via disturbance observer for a class of fractional order systems with mismatched disturbance, Mechatronics, 2018, 53: 8-19
[5] Jing Wang*, Qilun Wang, Jinglin Zhou, Xiaohui Wang, Long Cheng, Operation space design of microbial fuel cells combined anaerobic-anoxic-oxic process based on support vector regression inverse model, Engineering Applications of Artificial intelligence, 2018,72, 340-349
[6] J. L. Zhou, S. L. Zhang, H. Zhang, J. Wang*, A quality-related statistical process monitoring method based on global plus local projection to latent structures, Industrial & Engineering Chemistry Research, 2018,?57?(15): 5323-5337
[7] Xiaolu Chen, Jing Wang*, Jinglin Zhou, Process Monitoring Based on Multivariate Causality Analysis and Probability Inference, IEEE ACCESS, 2018, 6: 6360-6369
[8] Jing Wang, Bin Zhong, Jinglin Zhou*, Quality-Relevant Fault Monitoring Based on Locality Preserving Partial Least Squares Statistical Models, Industrial & Engineering Chemistry Research, 2017, 56:7009–7020. DOI: 10.1021/acs.iecr.7b00248
[9] Jing Wang*, Jingjing Zhang, Bo Qu, Haiyan Wu, Jinglin Zhou, Unified Architecture of Active Fault Detection and Partial Active Fault Tolerant Control for Incipient Faults, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017, 47(4), 1688-1700. DOI: 10.1109/TSMC.2017.2667683
[10] Jing Wang*, Daiwei Yang, Wei Jiang, Jinglin Zhou, Semi-supervised incremental support vector machine learning based on neighborhood kernel estimation, IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2017,47(10): 2677-2687, DOI: 10.1109/TSMC.2017. 2667703
[11] Jing Wang*, Wenshuang Ge, Jinglin Zhou, Haiyan Wu, Qibing Jin, Fault isolation based on residual evaluation and contribution analysis and contribution analysis, Journal of the Franklin Institute, 2017, 354, 2591-2612
[12] Ruixuan Wang, Jing Wang*, Jinglin Zhou, Haiyan Wu, Fault diagnosis based on the integration of exponential discriminant analysis and Local Linear Embedding, The Canadian Journal of Chemical Engineering, 2018, 96: 463–483. DOI:10.1002/cjce.22921
[13] Bin Zhong, Jing Wang*, Jinglin Zhou, Haiyan Wu, Qibing Jin, Quality-Related Statistical Process Monitoring Method Based on Global and Local Partial Least-Squares Projection, Industrial & Engineering Chemistry Research, 2016, 55, 1609?1622
[14] Ge Wenshuang, Wang Jing*, Zhou Jinglin, Wu Haiyan, Jin Qibing, Incipient Fault Detection Based on Fault Extraction and Residual Evaluation, Industrial & Engineering Chemistry Research, 2015, 54(14): 3664-3677
[15] Jin Qibing,?Wang Zhu,?Yang Ruigeng,?Wang Jing. An effective direct closed loop identification method for linear multivariable systems with colored noise, Journal of Process Control, 2014, 24(5):485-492
[16] Jing Wang*, Huatong Wei, Liulin Cao, Qibing Jin. A soft-transition sub-PCA fault monitoring of batch processes, Industrial & Engineering Chemistry Research, 2013, 52 (29): 9879–9888.
[17] Haiyan Wu, Liulin Cao, Jing Wang*,Gray-box modeling and control of polymer molecular weight distribution using orthogonal polynomial neural networks,Journal of Process Control,2012,22(9), 1624–1636
[18] Wang Jing*, Cao Liulin, Wu Haiyan, Li Xiaoguang, Jin Qibing, Dynamic Modeling and Optimal Control of Batch Reactors, Based on Structure Approaching Hybrid Neural Networks, Industrial & Engineering Chemistry Research, 2011, 50 (10), 6174–6186
[19] 陈晓露, 王瑞璇, 王晶*, 周靖林, 基于混合型判别分析的工业过程监控及故障诊断, 自动化学报, 2018 已录用
[20] 谭程元,王晶*,基于扩张状态观测器的鲁棒迭代学习控制,控制理论与应用,2018, 35(11):1680-1686