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E. Process Control

E1 Multivariate statistical process control

A Reliable Multi-Objective Control Strategy for Batch Processes based on Bootstrap Aggregated Neural Network Models
Mukherjee A and Zhang J
Journal of Process Control, 2008, 18, 720-734
Batch-to-Batch Optimal Control of a Batch Polymerisation Process based on Stacked Neural Network Models
Zhang J
Chemical Engineering Science, 2008, 63, 1273-1281
Batch-to-Batch Optimal Control of Batch Processes Based on Recursively Updated Nonlinear Partial Least Squares Models
Chunfu L, Zhang J and Wang G
Chemical Engineering Communications, 2007, 194, 261-297
Fault detection in dynamic processes using a simplified monitoring-specific CVA state space modelling approach
Stubbs S, Zhang J, Morris J.
Computers & Chemical Engineering, 2012, Vol 41, 77-87
Fault localization in batch processes through progressive principal component analysis modeling
Hong JJ, Zhang J, Morris J
Ind Eng Chem Res, 2011, Vol 50 (13), 8153-8162
Integrated tracking control strategy for batch processes using a batch-wise linear time-varying perturbation model
Xiong Z, Zhang J, Xu Y and Wang X
IET Control Theory & Applications, 2007, 1, 179-188
Multiscale Multivariate Statistical Process Control
Morris AJ,
Encyclopedia of Systems and Control (editors Tariq Samad and John Baillieul), 2014
Multiway interval partial least squares for batch process performance
Stubbs S, Zhang J, Morris J.
Ind Eng Chem Res, 2013, Vol 52 (35), 12399-12407
On-line multivariate statistical monitoring of batch processes using Gaussian mixture model
Chen T, Zhang J.
Computers & Chemical Engineering, 2010, Vol 34, 500-507
Penalized reconstruction-based multivariate contribution analysis for fault isolation
He B, Zhang J, Chen T and Yang X
Ind Eng Chem Res, 2013, Vol 52 (23), 7784-7794
Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach
He B, Ynag X, Chen T, Zhang J
Journal of Process Control, 2012, Vol 22, 1228-1236

E2 Process control

A Reliable Multi-Objective Control Strategy for Batch Processes based on Bootstrap Aggregated Neural Network Models
Mukherjee A and Zhang J
Journal of Process Control, 2008, 18, 720-734
Adaptive Statistic Tracking Control Based on Two-Step Neural Networks With Time Delays
Yi Y, Guo L and Wang H
IEEE T. Neural Netw., 2009, 20(3), 420-429
An ILC-Based Adaptive Control for General Stochastic Systems With Strictly Decreasing Entropy
Afshar P, Wang H and Chai TY
IEEE T. Neural Netw., 2009, 20(3), 471-482
Artifical intelligence techniques applied as estimator in chemical process systems - A literature survey
Ali J M, Hussain M A, Tade M O and Zhang J.
Expert Systems with Applications, 2015, Vol 42 No 14, 5915-5913
Batch to batch iterative learning control using updated models based on a moving window of historical data
Jewaratnam J, Zhang J, Hussain A and Morris J
Procedia Engineering, 2012, Vol 42, 232-240
Batch-to-batch control of fed-batch processes using control-affine feedforward neural network
Xiong Z, Xu Y, Zhang J and Dong J
Neural Computing & Applications, 2008, 17, 425-432
Batch-to-Batch Optimal Control of a Batch Polymerisation Process based on Stacked Neural Network Models
Zhang J
Chemical Engineering Science, 2008, 63, 1273-1281
Batch-to-Batch Optimal Control of Batch Processes Based on Recursively Updated Nonlinear Partial Least Squares Models
Chunfu L, Zhang J and Wang G
Chemical Engineering Communications, 2007, 194, 261-297
Compressor Surge Control Using a Variable Area Throttle and Fuzzy Logic Control
Al-Mawali s, Zhang J.
Transactions of the Institute of Measurement and Control, 2010, Vol 32 No 4, 347-375
Constrained PI tracking control for output probability distributions based on two-step neural networks
Yi Y, Guo L and Wang H
IEEE T. Circuits Syst., 2009, 56, 1416-1426
Distillation control structure selection for energy efficient operations
Osuolale F, Zhang J.
Chemical Engineering and Technology, 2015, Vol 38, No 5, 907-916
Distribution function tracking filter design using hybrid characteristic functions
Zhou J, Zhou D, Wang H, Guo L and Chai TY
Automatica, 2010, 46(1), 101-109
Energy efficiency optimisation for distillation column using artificial neural network models
Osuolale F, Zhang J.
Energy, 2016, Vol 106, 562-578
ILC-based fixed-structure controller design for output PDF shaping in stochastic systems using LMI techniques
Wang H and Afshar P
IEEE T. Automat. Cont., 2009, 54, 760-773
Inferential estimation of kerosene dry point in refineries with varying crudes
Zhou C, Liu Q, Huang D X, Zhang J.
Journal of Process Control, 2012, Vol 22 No 6, 1122-1126
Integrated tracking control strategy for batch processes using a batch-wise linear time-varying perturbation model
Xiong Z, Zhang J, Xu Y and Wang X
IET Control Theory & Applications, 2007, 1, 179-188
Intelligent optimal-setting control for grinding circuits of mineral processing process
Zhou P, Chai T and Wang H
IEEE T. Automat. Sci. Eng., 2009, 6, 730-743
Iterative learning control of a crystallisation process using batch wise updated linearised models identified using PLS
Zhang J, Nguyan J and Morris AJ
Computer Aided Chemical Engineering 26, Proceedings of the 19th European Symposium on Computer Aided Process Engineering, 2009, 387-392
Modelling and control of reactive polymer composite moulding using bootstrap aggregated neural network models
Zhang J, Pantelelis N G.
Chemical Product and Process Modeling, 2011, Vol 6 (2), Article 5
Modelling of a post combustion CO2 capture process using neural networks
Li F, Zhang J, Oko E and Wang M
Fuel, 2015, 151, 156-163
Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant
Oko E, Wang M and Zhang J.
Fuel, 2015, 151, 139-145
Noniterative N-infinity Based Model Order Reduction of LTI Systems Using LMIs
Nobakhti A and Wang H
IEEE T. Cont. Syst. Tec., 2009, 17, 494-501
Nonlinear wave modeling and dynamic analysis of internal thermally coupled distillation columns
Liu X, Zhou Y, Cong L, Zhang J.
AIChE Journal, 2012, Vol 58 No 4, 1146-1156
Optimal control of fed-batch processess using particle swarm optimisation with staked neural network models
Herrara F, Zhang J
Computers & Chemical Engineering, 2009, Vol 33, No 10, 1593-1601
Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model
Xiong Z, Zhang J and Dong J
Chinese Journal of Chemical Engineering, 2008, 16, 235-240
Process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control
Chen Z and Morris J
Biotechnology J., 2009, 4(5), 610-619
Reliable optimal control of a fed-batch fermentation process using ant colony optimisation and bootstrap aggregated neural network models
Zhang J, Feng M
Appl. Metaheuristics Process. Eng., 2014, 183-200
Reliable optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks
Mohammed K R, Zhang J.
Neural computing & Applications, 2013, Vol 23, 1891-1898
Robust output feedback stabilization for discrete-time systems with time-varying input delay
Hao S, Liu T, Zhang J, Sun X and Zhong C
Syst. Sci. Cont. Eng. An Open Access J., 2015, 3, 300-306