Publications

Book

Shang, C. (2018). Dynamic Modeling of Complex Industrial Processes: Data-Driven Methods and Application Research. Springer, 2018. ISBN 978-981-10-6676-4. (143 pages)

Journal Articles

[J26] Han, B., Shang, C., & Huang, D. (2021) Multiple kernel learning-aided robust optimization: Learning procedure, computational tractability, and usage in multi-stage decision-making. European Journal of Operational Research. doi: 10.1016/j.ejor.2020.11.027.
[J25] Shang, C., Ding, Steven. X., & Ye, H. (2021). Distributionally robust fault detection design and assessment for dynamical systems. Automatica. 125, 109434. (Regular Paper).
[J24] Liu, Q., Shang, C., & Huang, D. (2021). Efficient low-order system identification from low-quality step response data with rank-constrained optimization. Control Engineering Practice. 107, 104671. (Featured Paper for Emerging Leaders in Control Engineering Practice Series).
[J23] Shang, C., & You, F. (2020). A posteriori probabilistic bounds of convex scenario programs with validation tests. IEEE Transactions on Automatic Control. doi: 10.1109/TAC.2020.3024273. (Regular Paper) [code]
[J22] Scott, D., Shang, C., Huang, B., & Huang, D. (2020) A holistic probabilistic framework for monitoring non-stationary dynamic industrial processes. IEEE Transactions on Control Systems Technology. doi: 10.1109/TCST.2020.3025610. [code]
[J21] Shang, C., Chen, W. H., Stroock, A. D., & You, F. (2020). Robust model predictive control of irrigation systems with active uncertainty learning and data analytics. IEEE Transactions on Control Systems Technology. 28(4), 1493-1504. (Regular Paper)
[J20] Shang, C., Ji, H., Huang, X., Yang, F., & Huang, D. (2019). Generalized grouped contributions for hierarchical fault diagnosis with group Lasso. Control Engineering Practice, 93, 104193. [code]
[J19] Shang, C., & You, F. (2019). Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era. Engineering, 5(6), 1010-1016.
[J18] Shang, C., & You, F. (2019). A data-driven robust optimization approach to scenario-based stochastic model predictive control. Journal of Process Control, 75, 24-39.
[J17] Wang, K., Shang, C., Liu, L., Jiang, Y., Huang, D., & Yang, F. (2019). Dynamic soft sensor development based on convolutional neural networks. Industrial & Engineering Chemistry Research, 58(26), 11521-11531.
[J16] Shang, C., & You, F. (2018). Distributionally robust optimization for planning and scheduling under uncertainty. Computers & Chemical Engineering, 110, 53-68.
[J15] Shang, C., Yang, F., Huang, B., & Huang, D. (2018). Recursive slow feature analysis for adaptive monitoring of industrial processes. IEEE Transactions on Industrial Electronics, 65(11), 8895-8905.
[J14] Li, F., Zhang, J., Shang, C., Huang, D., Oko, E., & Wang, M. (2018). Modelling of a post-combustion CO2 capture process using deep belief network. Applied Thermal Engineering, 130, 997-1003.
[J13] Shang, C., Huang, X., & You, F. (2017). Data-driven robust optimization based on kernel learning. Computers & Chemical Engineering, 106, 464-479. [code]
[J12] Gao, X., Shang, C., Huang, D., & Yang, F. (2017). A novel approach to monitoring and maintenance of industrial PID controllers. Control Engineering Practice, 64, 111-126.
[J11] Gao, X., Yang, F., Shang, C., & Huang, D. (2017). A novel data-driven method for simultaneous performance assessment and retuning of PID controllers. Industrial & Engineering Chemistry Research, 56(8), 2127-2139.
[J10] Gao, X., Zhang, J., Yang, F., Shang, C., & Huang, D. (2017). Robust Proportional–Integral–Derivative (PID) design for parameter uncertain second-order plus time delay (SOPTD) processes based on reference model approximation. Industrial & Engineering Chemistry Research, 56(41), 11903-11918.
[J9] Shang, C., Huang, B., Yang, F., & Huang, D. (2016). Slow feature analysis for monitoring and diagnosis of control performance. Journal of Process Control, 39, 21-34.
[J8] Guo, F., Shang, C., Huang, B., Wang, K., Yang, F., & Huang, D. (2016). Monitoring of operating point and process dynamics via probabilistic slow feature analysis. Chemometrics and Intelligent Laboratory Systems, 151, 115-125. [code]
[J7] Gao, X., Yang, F., Shang, C., & Huang, D. (2016). A review of control loop monitoring and diagnosis: Prospects of controller maintenance in big data era. Chinese Journal of Chemical Engineering, 24(8), 952-962.
[J6] Shang, C., Huang, B., Yang, F., & Huang, D. (2015). Probabilistic slow feature analysis-based representation learning from massive process data for soft sensor modeling. AIChE Journal, 2015, 61(12), 4126-4139. [code]
[J5] Shang, C., Yang, F., Gao, X., Huang, X., Suykens, J. A. K., & Huang, D. (2015). Concurrent monitoring of operating condition deviations and process dynamics anomalies with slow feature analysis. AIChE Journal, 2015, 61(11), 3666-3682. [code]
[J4] Shang, C., Huang, X., Suykens, J. A. K., & Huang, D. (2015) Enhancing dynamic soft sensors based on DPLS: A temporal smoothness regularization approach. Journal of Process Control, 28, 17-26.
[J3] Gao, X., Shang, C., Jiang, Y., Huang, D., & Chen, T. (2014). Refinery scheduling with varying crude: A deep belief network classification and multimodel approach. AIChE Journal, 60(7), 2525-2532.
[J2] Shang, C., Yang, F., Huang, D., & Lyu, W. (2014). Data-driven soft sensor development based on deep learning technique. Journal of Process Control, 24(3), 223-233. (ESI Highly Cited Paper)
[J1] Shang, C., Gao, X., Yang, F., & Huang, D. (2014). Novel Bayesian framework for dynamic soft sensor based on support vector machine with finite impulse response. IEEE Transactions on Control Systems Technology, 22(4), 1550-1557.

Selected Conference Publications

[C14] Yin, X., Wang, H., Shang, C., & Huang, D. A novel semi-supervised probabilistic model of Fisher Discriminant Analysis for data-driven fault classification and detection. 2nd International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China, Oct. 23-25, 2020.
[C13] Wang, C., Shang, C., Huang, D., & Yu, B. A novel probabilistic fault detection scheme with adjustable reliability estimates. 1st Virtual IFAC World Congress, Jul. 11-17, 2020.
[C12] Wang, C., Shang, C., Huang, D., & Yu, B. Robust interval prediction model identification with an a posteriori reliability guarantee. 1st Virtual IFAC World Congress, Jul. 11-17, 2020.
[C11] Chen, W.H., Shang, C., Zhu S., Haldeman, K., Santiago, M., Stroock, A.D., & You, F. Theoretical exploration of irrigation control for stem water potential through model predictive control. American Control Conference, Denver, US, Jul. 1-3, 2020.
[C10] Wang, K., Shang, C., Yang, F., Jiang, Y., & Huang, D. Reaction temperature estimation in Shell coal gasification process. IEEE 15th International Conference on Automation Science and Engineering (CASE), Vancouver, Canada, Aug. 22-26, 2019.
[C9] Shang, C., Huang, X., Yang, F., & Huang, D. Sparse slow feature analysis for enhanced control monitoring and fault isolation. 1st International Conference on Industrial Artificial Intelligence (IAI), Shenyang, China, Jul. 23-27, 2019. (Best Paper Award)
[C8] Wang, Z., You, K., Song, S., & Shang, C. Data-driven distributionally robust shortest path problem using the Wasserstein ambiguity set. IEEE 15th International Conference on Control and Automation (ICCA), Edinburgh, UK, Jul. 16-19, 2019.
[C7] Shang, C., & You, F. Robust optimization in high-dimensional data space with support vector clustering. 10th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Shenyang, China, Jul. 25–27, 2018.
[C6] Shang, C., Yang, F., & Huang, D. A systematic approach to dynamic monitoring of industrial processes based on second-order slow feature analysis. 10th IFAC Symposium on Advanced Control of Chemical Processes (AdChem), Shenyang, China, Jul. 25–27, 2018.
[C5] Wang, K., Shang, C., Yang, F., Jiang, Y., & Huang, D. Automatic hyper-parameter tuning for soft sensor modeling based on dynamic deep neural network. IEEE International Conference on Systems, Man, and Cybernetics (SMC), Banff, Canada, Oct. 5-8, 2017.
[C4] Shang, C., Huang, B., Lu, Y., Yang, F., & Huang, D. Dynamic modeling of gross errors via probabilistic slow feature analysis applied to a mining slurry preparation process. 17th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing, Vienna, Austria, Aug. 31-Sep. 2, 2016.
[C3] Shang, C., Gao, X., Yang, F., Lyu, W., & Huang, D. A comparative study on improved DPLS soft sensor models applied to a crude distillation unit. 9th IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Whistler, Canada, Jun. 7-10, 2015.
[C2] Shang, C., Yang, F., Gao, X., & Huang, D. Extracting latent dynamics from process data for quality prediction and performance assessment via slow feature regression. American Control Conference, Chicago, US, Jul. 1-3, 2015.
[C1] Gao, X., Shang, C., Yang, F., & Huang, D. Detecting and isolating plant-wide oscillations via slow feature analysis. American Control Conference, Chicago, US, Jul. 1-3, 2015.