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A. Process Analytical Techniques B. Process Monitoring (on-line, in-line, non-invasive) C. Multivariate Data Analysis D. Process Modelling E. Process Control

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E1 Multivariate statistical process control E2 Process control

title year authors journal volume pages Categories Actions
A Reliable Multi-Objective Control Strategy for Batch Processes based on Bootstrap Aggregated Neural Network Models 2008 Mukherjee A and Zhang J Journal of Process Control 18 720-734 E1 Multivariate statistical process control, E2 Process control DOI
Adaptive Statistic Tracking Control Based on Two-Step Neural Networks With Time Delays 2009 Yi Y, Guo L and Wang H IEEE T. Neural Netw. 20(3) 420-429 E2 Process control DOI
An ILC-Based Adaptive Control for General Stochastic Systems With Strictly Decreasing Entropy 2009 Afshar P, Wang H and Chai TY IEEE T. Neural Netw. 20(3) 471-482 E2 Process control DOI
Artifical intelligence techniques applied as estimator in chemical process systems - A literature survey 2015 Ali J M, Hussain M A, Tade M O and Zhang J. Expert Systems with Applications Vol 42 No 14 5915-5913 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Batch to batch iterative learning control using updated models based on a moving window of historical data 2012 Jewaratnam J, Zhang J, Hussain A and Morris J Procedia Engineering Vol 42 232-240 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Batch-to-batch control of fed-batch processes using control-affine feedforward neural network 2008 Xiong Z, Xu Y, Zhang J and Dong J Neural Computing & Applications 17 425-432 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Batch-to-Batch Optimal Control of a Batch Polymerisation Process based on Stacked Neural Network Models 2008 Zhang J Chemical Engineering Science 63 1273-1281 E1 Multivariate statistical process control, E2 Process control DOI
Batch-to-Batch Optimal Control of Batch Processes Based on Recursively Updated Nonlinear Partial Least Squares Models 2007 Chunfu L, Zhang J and Wang G Chemical Engineering Communications 194 261-297 E1 Multivariate statistical process control, E2 Process control DOI
Compressor Surge Control Using a Variable Area Throttle and Fuzzy Logic Control 2010 Al-Mawali s, Zhang J. Transactions of the Institute of Measurement and Control Vol 32 No 4 347-375 E2 Process control
Constrained model-free reinforcement learning for process optimization 2021 Pan Elton, Petsagkourakis Panagiotis, Mowbray Max, Zhang Dongda, Rio-Chanona Ehecatl Antonio del Computers & Chemical Engineering 154 107462 E2 Process control DOI
Constrained PI tracking control for output probability distributions based on two-step neural networks 2009 Yi Y, Guo L and Wang H IEEE T. Circuits Syst. 56 1416-1426 E2 Process control DOI
Data-driven model predictive control for continuous pharmaceutical manufacturing 2025 Vega-Zambrano Consuelo, Diangelakis Nikolaos A., Charitopoulos Vassilis M. International Journal of Pharmaceutics 672 125322 E2 Process control DOI
Distillation control structure selection for energy efficient operations 2015 Osuolale F, Zhang J. Chemical Engineering and Technology Vol 38, No 5 907-916 E2 Process control DOI
Distribution function tracking filter design using hybrid characteristic functions 2010 Zhou J, Zhou D, Wang H, Guo L and Chai TY Automatica 46(1) 101-109 E2 Process control DOI
Energy efficiency optimisation for distillation column using artificial neural network models 2016 Osuolale F, Zhang J. Energy Vol 106 562-578 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
ILC-based fixed-structure controller design for output PDF shaping in stochastic systems using LMI techniques 2009 Wang H and Afshar P IEEE T. Automat. Cont. 54 760-773 E2 Process control DOI
Inferential estimation of kerosene dry point in refineries with varying crudes 2012 Zhou C, Liu Q, Huang D X, Zhang J. Journal of Process Control Vol 22 No 6 1122-1126 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking 2020 Qiu W, Xiong Z, Zhang J, Hong Y, Li W Journal of Process Control 85 41-51 E2 Process control DOI
Integrated tracking control strategy for batch processes using a batch-wise linear time-varying perturbation model 2007 Xiong Z, Zhang J, Xu Y and Wang X IET Control Theory & Applications 1 179-188 E1 Multivariate statistical process control, E2 Process control DOI
Intelligent optimal-setting control for grinding circuits of mineral processing process 2009 Zhou P, Chai T and Wang H IEEE T. Automat. Sci. Eng. 6 730-743 E2 Process control DOI
Iterative learning control of a crystallisation process using batch wise updated linearised models identified using PLS 2009 Zhang J, Nguyan J and Morris AJ Computer Aided Chemical Engineering 26, Proceedings of the 19th European Symposium on Computer Aided Process Engineering 387-392 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Model-based real-time optimisation of a fed-batch cyanobacterial hydrogen production process using economic model predictive control strategy 2016 del Rio-Chanona Ehecatl Antonio, Zhang Dongda, Vassiliadis Vassilios S. Chemical Engineering Science 142 289-298 E2 Process control DOI
Modelling and control of reactive polymer composite moulding using bootstrap aggregated neural network models 2011 Zhang J, Pantelelis N G. Chemical Product and Process Modeling Vol 6 (2) Article 5 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Modelling of a post combustion CO2 capture process using neural networks 2015 Li F, Zhang J, Oko E and Wang M Fuel 151 156-163 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Modern day monitoring and control challenges outlined on an industrial-scale benchmark fermentation process 2019 Goldrick Stephen, Duran-Villalobos Carlos A., Jankauskas Karolis, Lovett David, Farid Suzanne S., Lennox Barry Computers & Chemical Engineering 130 106471 E2 Process control DOI
Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant 2015 Oko E, Wang M and Zhang J. Fuel 151 139-145 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Noniterative N-infinity Based Model Order Reduction of LTI Systems Using LMIs 2009 Nobakhti A and Wang H IEEE T. Cont. Syst. Tec. 17 494-501 E2 Process control DOI
Nonlinear wave modeling and dynamic analysis of internal thermally coupled distillation columns 2012 Liu X, Zhou Y, Cong L, Zhang J. AIChE Journal Vol 58 No 4 1146-1156 D1 Kinetic modelling, E2 Process control DOI
Optimal control of fed-batch processess using particle swarm optimisation with staked neural network models 2009 Herrara F, Zhang J Computers & Chemical Engineering Vol 33, No 10 1593-1601 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model 2008 Xiong Z, Zhang J and Dong J Chinese Journal of Chemical Engineering 16 235-240 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Process analytical technology and compensating for nonlinear effects in process spectroscopic data for improved process monitoring and control 2009 Chen Z and Morris J Biotechnology J. 4(5) 610-619 C1 Multivariate data analysis, E2 Process control DOI
Reinforcement learning for batch bioprocess optimization 2020 Petsagkourakis P., Sandoval I.O., Bradford E., Zhang D., del Rio-Chanona E.A. Computers & Chemical Engineering 133 106649 E2 Process control DOI
Reliable optimal control of a fed-batch fermentation process using ant colony optimisation and bootstrap aggregated neural network models 2014 Zhang J, Feng M Appl. Metaheuristics Process. Eng. 183-200 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Reliable optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks 2013 Mohammed K R, Zhang J. Neural computing & Applications Vol 23 1891-1898 D2 Multi-block, predictive and multi-scale modelling methods, E2 Process control DOI
Robust output feedback stabilization for discrete-time systems with time-varying input delay 2015 Hao S, Liu T, Zhang J, Sun X and Zhong C Syst. Sci. Cont. Eng. An Open Access J. 3 300-306 E2 Process control DOI
Safe chance constrained reinforcement learning for batch process control 2022 Mowbray M., Petsagkourakis P., del Rio-Chanona E.A., Zhang D. Computers & Chemical Engineering 157 107630 E2 Process control DOI
Stochastic data-driven model predictive control using gaussian processes 2020 Bradford Eric, Imsland Lars, Zhang Dongda, del Rio Chanona Ehecatl Antonio Computers & Chemical Engineering 139 106844 E2 Process control DOI
Using process data to generate an optimal control policy via apprenticeship and reinforcement learning 2021 Mowbray Max, Smith Robin, Del Rio-Chanona Ehecatl A., Zhang Dongda AIChE Journal 67 E2 Process control DOI