Staff Profile

Roles and Responsibilities

Degree Programme Director, MSc Applied Process Control 

Postgraduate Research Student Admission Tutor 

Progression Panel 

Qualifications

BSc, Hebei University of Technology, Tianjin, China, 1986.

PhD, City University, London, 1991. 

Memberships

Senior Member, IEEE

Jie Zhang's profile can be viewed on Google Scholar

Research Interest

Advanced Process Control, Neural Networks, Fuzzy Systems, Neuro-fuzzy Systems, Process Modelling, Process Fault Detection and Diagnosis, Process Monitoring, Batch Process Control, Optimisation, Optimal Control of Batch Processes, Genetic Algorithms, Intelligent Control Systems.

Research Projects 

1. European Commission, “ Research and Development in Coal-fired Supercritical Power Plant with Post-combustion Carbon Capture using Process Systems Engineering techniques”, 1 January 2014 – 31 December 2017, 121,800 Euros, PI.

2. European Commission, “intelligent Reactive polymer composites Moulding (iREMO)”, 1 September 2009 – 31 August 2012, 400,400 Euros, PI.

3. European Commission, “ECOCARB: Reduction of emissions and energy utilisation of coke oven underfiring heating systems through advanced diagnostic and control”, 1 July 2008 – 31 December 2011, GBP270,588 (392,837 Euros), CI.

4. Knowledge Transfer Partnership, with Sallefield Ltd and National Nuclear Laboratories, 27 July 2009 – 26 July 2012, GBP215,358, CI.

5. Syngenta Limited, studentship - Integration of Spectroscopic and Process Data for Enhanced Process Performance Monitoring, 1 October 2007 – 31 March 2011, GBP20,000, CI.

6. BP International Ltd, Dorothy Hodgkin Postgraduate Award, 1 November 2007 – 30 April 2011, GBP88,752, CI.

7. UK Department for Innovation, Universities & Skills, 2007/8 UK/China Fellowship for Excellence, 1 March – 30 September 2008, GBP8400. 

8. European Commission, “Bioproduction”, 1 September 2006 – 31 August 2010, GBP187,552, CI.

9. Associated Octel Company Limited, “Multivariate statistical techniques to the control of the Lilestralis Process”, 22 May – 21 October 2006, GBP5000,CI.

10. Teaching Company Directorate (TCS Programmes 2777), “Blast Furnace Application of Neural Networks”, GBP387,986, 27 September 1999 – 12 May 2004, CI.

11. EPSRC Grant (GR/S90461), “UK-China Network on Intelligent Automation, Computing and Manufacturing”, GBP63,784, 1 September 2004 – 31 August 2007, CI.

12. EPSRC Grant (GR/R10875), “Intelligent Computer Integrated Batch Manufacturing”, GBP27,714, 30 April 2001 – 29 September 2004, Sole Investigator, EPSRC Overall Assessment: Tending to Outstanding.

13. EPSRC Grant (GR/N13319), “Nonlinear Optimising Control in Agile Batch Manufacturing”, GBP137,147, 17 October 2000 – 16 October 2003, Sole Investigator, EPSRC Overall Assessment: Tending to Outstanding.            

Undergraduate Teaching

Particle Technology
Process Design

Postgraduate Teaching

Data Analysis and Reconciliation for Control
Process Data Modelling
Time Series and Systems Identification
Process Control
Introduction to Mathematics and Statistics

• Articles

  • Kargbo HO, Zhang J, Phan AN. Robust modelling development for optimisation of hydrogen production from biomass gasification process using bootstrap aggregated neural network. International Journal of Hydrogen Energy 2023, epub ahead of print.
  • Tahraoui H, Belhadj A-E, Triki Z, Boudellal NR, Seder S, Amrane A, Zhang J, Moula N, Tifoura A, Ferhat R, Bousselma A, Mihoubi N. Mixed coagulant-flocculant optimization for pharmaceutical effluent pretreatment using response surface methodology and Gaussian process regression. Process Safety and Environmental Protection 2023, 169, 909-927.
  • Bouchelkia N, Tahraoui H, Amrane A, Belkacemi H, Bollinger J-C, Bouzaza A, Zoukel A, Zhang J, Mouni L. Jujube stones based highly efficient activated carbon for methylene blue adsorption: Kinetics and isotherms modeling, thermodynamics and mechanism study, optimization via response surface methodology and machine learning approaches. Process Safety and Environmental Protection 2023, 170, 513-535.
  • Al Kalbani F, Zhang J. Inferential Composition Control of a Distillation Column Using Active Disturbance Rejection Control with Soft Sensors. Sensors 2023, 23(2), 1019.
  • Farch S, Yahoum MM, Toumi S, Tahraoui H, Lefnaoui S, Kebir M, Zamouche M, Amrane A, Zhang J, Hadadi A, Mouni L. Application of Walnut Shell Biowaste as an Inexpensive Adsorbent for Methylene Blue Dye: Isotherms, Kinetics, Thermodynamics, and Modeling. Separations 2023, 10(1), 60.
  • Qi R, Zhang J, Spencer K. A review on data-driven condition monitoring of industrial equipment. Algorithms 2023, 16(1), 9.
  • Yang Q, Huang H, Zhang J, Gao H, Liu P. A collaborative cuckoo search algorithm with modified operation mode. Engineering Applications of Artificial Intelligence 2023, 121, 106006.
  • Wang K, Zhang J, Huang D. Online temperature estimation of Shell coal gasification process based on extended Kalman filter. Chinese Journal of Chemical Engineering 2022, 47, 134-144.
  • Muhsin W, Zhang J. Multi-Objective Optimization of a Crude Oil Hydrotreating Process with a Crude Distillation Unit Based on Bootstrap Aggregated Neural Network Models. Processes 2022, 10, 1438.
  • Tahraoui H, Amrane A, Belhadj AE, Zhang J. Modeling the organic matter of water using the decision tree coupled with bootstrap aggregated and least-squares boosting. Environmental Technology & Innovation 2022, 27, 102419.
  • Tian Y, Zhang J, Chen Q, Hou S, Xiao L. Group decision making-based fusion for human activity recognition in body sensor networks. Sensors 2022, 22(21), 8225.
  • Yang Q, Liu P, Zhang J, Dong N. Combined heat and power economic dispatch using an adaptive cuckoo search with differential evolution mutation. Applied Energy 2022, 307, 118057.
  • Wang S, Zhang J. An Intelligent Process Fault Diagnosis System based on Andrews Plot and Convolutional Neural Network. Journal of Dynamics, Monitoring and Diagnostics 2022, 1(3), 127-138.
  • Tian Y, Zhang J, Chen Q, Liu Z. A novel selective ensemble learning method for smartphone sensor-based human activity recognition based on hybrid diversity enhancement and improved binary glowworm swarm optimization. IEEE Access 2022, 10, 125027-125041.
  • Joynes L, Zhang J. A Feedback Control Strategy for a Fed-Batch Monoclonal Antibody Production Process Utilising Infrequent and Irregular Sampled Measurements. Processes 2022, 10(8), 1448.
  • Fleming L, Emerson J, Stitt H, Zhang J, Coleman S. A Bayesian Data Modelling Framework for Chemical Processes using Adaptive Sequential Design with Gaussian Process Regression. Applied Stochastic Models in Business and Industry 2022, 38(5), 787-805.
  • Chen L, Liu X, Xuan B, Zhang J, Liu Z, Zhang Y. Selection of EMG sensors based on motion coordinated analysis. Sensors 2021, 21(4), 1-18.
  • Kargbo HO, Zhang J, Phan AN. Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network. Applied Energy 2021, 302, 117567.
  • Wang K, Zhang J, Shang C, Huang D. Operation optimization of Shell coal gasification process based on convolutional neural network models. Applied Energy 2021, 292, 116847.
  • Yang Q, Fu Y, Zhang J. Molten steel temperature prediction using a hybrid model based on information interaction-enhanced cuckoo search. Neural Computing and Applications 2021, 33, 6487-6509.
  • Corrigan J, Zhang J. Developing Accurate Data-driven Soft-sensors through Integrating Dynamic Kernel Slow Feature Analysis with Neural Networks. Journal of Process Control 2021, 106, 208-220.
  • Wang S, Zhang J. An Intelligent Process Fault Diagnosis System based on Neural Networks and Andrews Plot. Processes 2021, 9(9), 1659.
  • Liu S, Liu Z, Zhang J, Hu D. An Experience Transfer Approach for the Initial Data of Iterative Learning Control. Applied Sciences 2021, 11(4), 1631.
  • Yang Q, Dong N, Zhang J. An enhanced adaptive bat algorithm for microgrid energy scheduling. Energy 2021, 232, 121014.
  • Tian Y, Zhang J, Li L, Liu Z. A novel sensor-based human activity recognition method based on hybrid feature selection and combinational optimization. IEEE Access 2021, 9, 107235-107249.
  • Tian Y, Zhang J, Wang J, Geng Y, Wang X. Robust human activity recognition using single accelerometer via wavelet energy spectrum features and ensemble feature selection. Systems Science & Control Engineering 2020, 8(1), 83-96.
  • Tian Y, Zhang J. Optimizing sensor deployment for multi-sensor based HAR system with improved glowworm swarm optimization algorithm. Sensors 2020, 20(24), 7161.
  • Liu K, Zhang J. Nonlinear Process Modelling Using Echo State Networks Optimised by Covariance Matrix Adaption Evolutionary Strategy. Computers & Chemical Engineering 2020, 135, 106730.
  • Mu F, Gu Y, Zhang J, Zhang L. Milk Source Identification and Milk Quality Estimation Using an Electronic Nose and Machine Learning Techniques. Sensors 2020, 20(15), 4238.
  • Corrigan J, Zhang J. Integrating Dynamic Slow Feature Analysis with Neural Networks for Enhancing Soft Sensor Performance. Computers and Chemical Engineering 2020, 139, 106842.
  • Qiu W, Xiong Z, Zhang J, Hong Y, Li W. Integrated predictive iterative learning control based on updating reference trajectory for point-to-point tracking. Journal of Process Control 2020, 85, 41-51.
  • Zhu C, Zhang J. Developing Soft Sensors for Polymer Melt Index in an Industrial Polymerization Process using Deep Belief Networks. International Journal of Automation and Computing 2020, 17, 44-54.
  • Zhu C, Zhang J. Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks. AIMS Electronic and Electrical Engineering 2020, 4(3), 287–302.
  • Li X, Liu Z, Gao X, Zhang J. Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization. Sensors 2020, 20(22), 6533.
  • Tian Y, Zhang J, Chen L, Geng Y, Wang X. Single wearable accelerometer-based human activity recognition via kernel discriminant analysis and QPSO-KELM classifier. IEEE Access 2019, 7, 109216-109227.
  • Tian Y, Zhang J, Chen L, Geng Y, Wang X. Selective ensemble based on extreme learning machine for sensor-based human activity recognition. Sensors 2019, 19(16), 3468.
  • Zhou Z, Du N, Xu J, Li Z, Wang P, Zhang J. Randomized Kernel Principal Component Analysis for Modeling and Monitoring of Nonlinear Industrial Processes with Massive Data. Industrial & Engineering Chemistry Research 2019, 58(24), 10410-10417.
  • Cong LW, Bahadori A, Zhang J, Ahmad Z. Prediction of Water Quality Index (WQI) using Support Vector Machine (SVM) and Least Square-Support Vector Machine (LS-SVM). International Journal of River Basin Management 2019, 13(6), 1310-1318.
  • Yang Q, Zhang J, Yi Z. Predicting molten steel endpoint temperature using a feature-weighted model optimized by mutual learning cuckoo search. Applied Soft Computing 2019, 83, 105675.
  • Ahmad Z, Bahadori A, Zhang J. Prediction of equilibrium water dew point of natural gas in TEG dehydration systems using Bayesian Feedforward Artificial Neural Network (FANN). Petroleum Science and Technology 2018, 36(20), 1620-1626.
  • Li F, Zhang J, Shang C, Huang D, Oko E, Wang M. Modelling of a Post-combustion CO₂ Capture Process Using Deep Belief Network. Applied Thermal Engineering 2018, 130, 997-1003.
  • Osuolale FN, Zhang J. Thermodynamic optimization of atmospheric distillation unit. Computers & Chemical Engineering 2017, 103, 201-209.
  • Gao X, Zhang J, Yang F, Shang C, Huang D. Robust Proportional−Integral−Derivative (PID) Design for Parameter Uncertain Second-Order Plus Time Delay (SOPTD) Processes Based on Reference Model Approximation. Industrial & Chemical Engineering Research 2017, 56(41), 11903-11918.
  • Ahmad Z, Bahadori A, Zhang J. Prediction of combustion efficiency using multiple neural networks. Chemical Engineering Transactions 2017, 56, 85-90.
  • Li F, Zhang J, Oko E, Wang M. Modelling of a Post-combustion CO₂ Capture Process Using Extreme Learning Machine. International Journal of Coal Science & Technology 2017, 4(1), 33-40.
  • Ahmad Z, Rahim NA, Bahadori A, Zhang J. Improving Water Quality Index Prediction in Perak River Basin Malaysia through the Combination of Multiple Neural Networks. International Journal of River Basin Management 2017, 15(1), 79-87.
  • Ahmad Z, Rahim NA, Bahadori A, Zhang J. Air pollution index prediction using multiple neural net. IIUM Engineering Journal 2017, 18(1), 1-12.
  • Lawal SA, Zhang J. Actuator Fault Monitoring and Fault Tolerant Control in Distillation Columns. International Journal of Automation and Computing 2017, 14(1), 80-92.
  • Liu J, Liu T, Zhang J. Window-Based Stepwise Sequential Phase Partition for Nonlinear Batch Process Monitoring. Industrial & Engineering Chemistry Research 2016, 55(34), 9229-9243.
  • Ahmad Z, Zhang J, Kashiwao T, Bahadori A. Prediction of absorption and stripping factors in natural gas processing industries using feed forward artificial neural network. Petroleum Science and Technology 2016, 34(2), 105-113.
  • Osuolale FN, Zhang J. Energy efficiency optimisation for distillation column using artificial neural network models. Energy 2016, 106, 562-578.
  • Zhou Z, Yang C, Wen C, Zhang J. Analysis of PCA-based reconstruction method for fault diagnosis. Industrial & Engineering Chemistry Research 2016, 55(27), 7402-7410.
  • Hao S, Liu T, Zhang J, Sun X, Zhong C. Robust output feedback stabilization for discrete-time systems with time-varying input delay. Systems Science & Control Engineering: An Open Access Journal 2015, 3(1), 300-306.
  • Oko E, Wang M, Zhang J. Neural Network Approach for Predicting Drum Pressure and Level in Coal-fired Subcritical Power Plant. Fuel 2015, 151, 139-145.
  • Li F, Zhang J, Oko E, Wang M. Modelling of a Post-combustion CO₂ Capture Process Using Neural Networks. Fuel 2015, 151, 156-163.
  • Osuolale F, Zhang J. Distillation control structure selection for energy efficient operations. Chemical Engineering and Technology 2015, 38(5), 907-916.
  • Ali JM, Hussain MA, Tade MO, Zhang J. Artificial Intelligence techniques applied as estimator in chemical process systems - A literature survey. Expert Systems With Applications 2015, 42(14), 5915-5931.
  • Hong JJ, Zhang J, Morris J. Progressive multi-block modelling for enhanced fault isolation in batch processes. Journal of Process Control 2014, 24(1), 13-26.
  • Alawi A, Zhang J, Morris J. Multi-scale Multiblock Batch Monitoring: Sensor and Process Drift and Degradation. Organic Process Research & Development 2014.
  • Mohammed KR, Zhang J. Reliable Optimisation Control of a Reactive Polymer Composite Moulding Process Using Ant Colony Optimisation and Bootstrap Aggregated Neural Networks. Neural Computing & Applications 2013, 23(7-8), 1891–1898.
  • He B, Zhang J, Chen T, Yang XH. Penalized Reconstruction-Based Multivariate Contribution Analysis for Fault Isolation. Industrial & Engineering Chemistry Research 2013, 52(23), 7784-7794.
  • Stubbs S, Zhang J, Morris AJ. Multiway Interval Partial Least Squares for Batch Process Performance Monitoring. Industrial & Engineering Chemistry Research 2013, 52(35), 12399-12407.
  • He B, Yang XH, Chen T, Zhang J. Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach. Journal of Process Control 2012, 22(7), 1228-1236.
  • Liu XG, Zhou YX, Cong L, Zhang J. Nonlinear wave modeling and dynamic analysis of internal thermally coupled distillation columns. AIChE Journal 2012, 58(4), 1146-1156.
  • Zhou C, Liu QY, Huang DX, Zhang J. Inferential estimation of kerosene dry point in refineries with varying crudes. Journal of Process Control 2012, 22(6), 1122-1126.
  • Stubbs S, Zhang J, Morris AJ. Fault detection in dynamic processes using a simplified monitoring-specific CVA state space modelling approach. Computers & Chemical Engineering 2012, 41, 77-87.
  • Zhang J, Zhang E, Scott K, Burgess JG. Enhanced Electricity Production by Use of Reconstituted Artificial Consortia of Estuarine Bacteria Grown as Biofilms. Environmental Science and Technology 2012, 46(5), 2984-2992.
  • Hong JJ, Zhang J, Morris J. Fault Localization in Batch Processes through Progressive Principal Component Analysis Modeling. Industrial & Engineering Chemistry Research 2011, 50(13), 8153-8162.
  • Chen T, Zhang J. On-line multivariate statistical monitoring of batch processes using Gaussian mixture model. Computers and Chemical Engineering 2010, 34(4), 500-507.
  • Xiong Z, Xu Y, Dong J, Zhang J. Neural Network Based Iterative Learning Control for Product Qualities in Batch Processes. International Journal of Modelling, Identification and Control 2010, 11(1-2), 107-114.
  • Al-Mawali S, Zhang J. Compressor surge control using a variable area throttle and fuzzy logic control. Transactions of the Institute of Measurement and Control 2010, 32(4), 347-375.
  • Ahmad Z, Zhang J. Selective Combination of Multiple Neural Networks for Improving Model Prediction in Nonlinear Systems Modelling through Forward Selection and Backward Elimination. Neurocomputing 2009, 72(4-6), 1198-1204.
  • Morris J, Zhang J. Performance monitoring and batch to batch control of biotechnological processes. Studies in Computational Intelligence 2009, 218, 281-310.
  • Xiong ZH, Dong J, Zhang J. Optimal iterative learning control for end-point product qualities in semi-batch process based on neural network model. Science in China Series F: Information Sciences 2009, 52(7), 1136-1144.
  • Herrera F, Zhang J. Optimal Control of Batch Processes Using Particle Swarm Optimisation with Stacked Neural Network Models. Computers & Chemical Engineering 2009, 33(10), 1593-1601.
  • Geng H, Xiong Z, Xu Y, Zhang J. Iterative learning control with reference batch for linear time-variant system. Kongzhi yu Juece/Control and Decision 2009, 24(5), 648-652.
  • Stubbs S, Zhang J, Morris J. Fault detection of dynamic processes using a simplified monitoring-specific CVA state space approach. Computer Aided Chemical Engineering 2009, 26, 339-344.
  • Agustriyanto R, Zhang J. Control Structure Selection for the ALSTOM Gasifier Benchmark Process Using GRDG Analysis. International Journal of Modelling, Identification and Control 2009, 6(2), 126-135.
  • Xiong Z, Zhang J, Dong J. Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model. Chinese Journal of Chemical Engineering 2008, 16(2), 235-240.
  • Herrera F, Zhang J. Optimal control of batch processes using particle swam optimisation with stacked neural network models. Computer Aided Chemical Engineering 2008, 25, 375-380.
  • Zhang J, Nguyen J, Xiong Z. Iterative Learning Control of Batch Processes based on Time Varying Perturbation Models. Journal of Tsinghua University 2008, 48(S2), 1771-1774.
  • Zhang J. Batch-to-batch optimal control of a batch polymerisation process based on stacked neural network models. Chemical Engineering Science 2008, 63(5), 1273-1281.
  • Mukherjee A, Zhang J. A reliable multi-objective control strategy for batch processes based on bootstrap aggregated neural network models. Journal of Process Control 2008, 18(7-8), 720-734.
  • Xiong ZH, Zhang J, Wang X, Xu YM. Integrated tracking control strategy for batch processes using a batch-wise linear time-varying perturbation model. IET Control Theory and Applications 2007, 1(1), 179-188.
  • Li C, Zhang J, Wang G. Batch-to-batch Optimal control of batch processes based on recursively updated nonlinear partial least squares models. Chemical Engineering Communications 2007, 194(3), 261-279.
  • Mukherjee A, Zhang J. Reliable multi-objective optimal control of batch processes based on stacked neural network models. Computer Aided Chemical Engineering 2006, 21(C), 1407-1412.
  • Zhao SJ, Zhang J, Xu YM. Performance monitoring of processes with multiple operating modes through multiple PLS models. Journal of Process Control 2006, 16(7), 763-772.
  • Zhang J. Offset-free inferential feedback control of distillation compositions based on PCR and PLS models. Chemical Engineering and Technology 2006, 29(5), 560-566.
  • Zhao SJ, Zhang J, Xu YM, Xiong ZH. Nonlinear projection to latent structures method and its applications. Industrial and Engineering Chemistry Research 2006, 45(11), 3843-3852.
  • Zhang J. Modelling and Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-Fuzzy Networks. International Journal of Automation and Computing 2006, 3(1), 1-7.
  • Zhang J, Jin Q, Xu Y. Inferential estimation of polymer melt index using sequentially trained bootstrap aggregated neutral networks. Chemical Engineering and Technology 2006, 29(4), 442-448.
  • Zhang J. Improved on-line process fault diagnosis through information fusion in multiple neural networks. Computers and Chemical Engineering 2006, 30(3), 558-571.
  • Li C-F, Zhang J, Wang G-Z. Adaptive equality prediction of batch processes based on PLS model. Frontiers of Electrical and Electronic Engineering in China 2006, 1(2), 211-215.
  • Xiong Z, Zhang J, Wang X, Xu Y. Tracking control for batch processes through integrating batch-to-batch iterative learning control and within-batch on-line control. Industrial and Engineering Chemistry Research 2005, 44(11), 3983-3992.
  • Xiong Z, Zhang J. Optimal control of fed-batch processes based on multiple neural networks. Applied Intelligence 2005, 22(2), 149-161.
  • Xiong Z, Zhang J. Neural network model-based on-line re-optimisation control of fed-batch processes using a modified iterative dynamic programming algorithm. Chemical Engineering and Processing: Process Intensification 2005, 44(4), 477-484.
  • Zhang J. Modeling and optimal control of batch processes using recurrent neuro-fuzzy networks. IEEE Transactions on Fuzzy Systems 2005, 13(4), 417-427.
  • Ahmad Z, Zhang J. Combination of multiple neural networks using data fusion techniques for enhanced nonlinear process modelling. Computers and Chemical Engineering 2005, 30(2), 295-308.
  • Ahmad Z, Zhang J. Bayesian selective combination of multiple neural networks for improving long-range predictions in nonlinear process modelling. Neural Computing and Applications 2005, 14(1), 78-87.
  • Zhang J. Batch process modelling and optimal control based on neural network models. Zidonghua Xuebao/Acta Automatica Sinica 2005, 31(1), 19-31.
  • Li C, Ye H, Wang G, Zhang J. A recursive nonlinear PLS algorithm for adaptive nonlinear process modeling. Chemical Engineering and Technology 2005, 28(2), 141-152.
  • Zhang J. A neural network-based strategy for the integrated batch-to-batch control and within-batch control of batch processes. Transactions of the Institute of Measurement and Control 2005, 27(5), 391-410.
  • Xiong Z, Zhang J. A batch-to-batch iterative optimal control strategy based on recurrent neural network models. Journal of Process Control 2005, 15(1), 11-21.
  • Xiong Z, Zhang J. Trajectory tracking of batch processes with varying control interval and incrementally updated models. Computer Aided Chemical Engineering 2004, 18(C), 853-858.
  • Shen M, Zhang J, Scott K. The general rule of power converted from chemical energy to electrical energy. Fuel Cells 2004, 4(4), 388-393.
  • Shen M-Z, Zhang J, Scott K. Regulation of power conversion in fuel cells. Chemical Research in Chinese Universities 2004, 20(4), 466-469.
  • Di LQ, Zhang J, Yang XH. MWMPCA with application to batch processes monitoring. Journal of Jilin University: Information Science 2004, 22(4), 397-400.
  • Zhao SJ, Zhang J, Xu YM. Monitoring of processes with multiple operating modes through multiple principle component analysis models. Industrial and Engineering Chemistry Research 2004, 43(22), 7025-7035.
  • Zhao SJ, Zhang J, Xu YM. Monitoring of Processes with Multiple Operating Modes through Multiple PCA Models. Industrial & Engineering Chemistry Research 2004, 43(22), 7025-7035.
  • Xiong Z, Zhang J. Modelling and optimal control of fed-batch processes using a novel control affine feedforward neural network. Neurocomputing 2004, 61(1-4), 317-337.
  • Tian Y, Zhang J, Morris J. Dynamic on-line reoptimization control of a batch MMA polymerization reactor using hybrid neural network models. Chemical Engineering and Technology 2004, 27(9), 1030-1038.
  • Chen L, Hontoir Y, Huang D, Zhang J, Morris AJ. Combining first principles with black-box techniques for reaction systems. Control Engineering Practice 2004, 12(7), 819-826.
  • Xiong Z, Zhang J. Batch-to-batch optimal control of nonlinear batch processes based on incrementally updated models. IEE Proceedings: Control Theory and Applications 2004, 151(2), 158-165.
  • Li C, Zhang J, Wang G. Adaptive quality prediction for batch processes based on the PLS model. Qinghua Daxue Xuebao/Journal of Tsinghua University 2004, 44(10), 1360-1363.
  • Zhang J. A Reliable Neural Network Model Based Optimal Control Strategy for a Batch Polymerization Reactor. Industrial and Engineering Chemistry Research 2004, 43(4), 1030-1038.
  • Zhao S, Xu Y, Zhang J. A multiple PCA model based technique for the monitoring of processes with multiple operating modes. Computer Aided Chemical Engineering 2004, 18(C), 865-870.
  • Xiong Z, Zhang J. Product Quality Trajectory Tracking in Batch Processes Using Iterative Learning Control Based on Time-Varying Perturbation Models. Industrial and Engineering Chemistry Research 2003, 42(26), 6802-6814.
  • Zhang J, Agustriyanto R. Multivariable Inferential Feed Forward Control. Industrial & Engineering Chemistry Research 2003, 42(18), 4186-4197.
  • Huang D, Jin Y, Zhang J, Morris AJ. Non-linear chemical process modelling and application in epichlorhydrine production plant using wavelet networks. Chinese Journal of Chemical Engineering 2002, 10(4), 435-443.
  • Tian Y, Zhang J, Morris J. Modeling and optimal control of a batch polymerization reactor using a hybrid stacked recurrent neural network model. Industrial and Engineering Chemistry Research 2001, 40(21), 4525-4535.
  • Zhang J. Developing robust neural network models by using both dynamic and static process operating data. Industrial and Engineering Chemistry Research 2001, 40(1), 234-241.
  • Zhang J. A nonlinear gain scheduling control strategy based on neuro-fuzzy networks. Industrial and Engineering Chemistry Research 2001, 40(14), 3164-3170.
  • Zhang J, Morris AJ. Long range predictive control of nonlinear processes based on recurrent neuro-fuzzy network models. Neural Computing and Applications 2000, 9(1), 50-59.
  • Zhang J, Morris AJ. Recurrent Neuro-Fuzzy Networks for Nonlinear Process Modeling. IEEE Transactions on Neural Networks 1999, 10(2), 313-326.
  • Zhang J. Inferential estimation of polymer quality using bootstrap aggregated neural networks. Neural Networks 1999, 12(6), 927-938.
  • Zhang J. Developing robust non-linear models through bootstrap aggregated neural networks. Neurocomputing 1999, 25(1-3), 93-113.

• Book Chapters

  • Djarum DH, Ahmad Z, Zhang J. River Water Quality Prediction in Malaysia Based on Extra Tree Regression Model Coupled with Linear Discriminant Analysis (LDA). In: 31st European Symposium on Computer Aided Process Engineering. Amsterdam: Elsevier B.V, 2021, pp.1491-1496.
  • Djarum DH, Ahmad Z, Zhang J. Comparing Different Pre-processing Techniques and Machine Learning Models to Predict PM10 and PM2.5 Concentration in Malaysia. In: Zaini MAA; Jusoh M; Othman N, ed. Proceedings of the 3rd International Conference on Separation Technology (ICoST 2020). Berlin: Springer, 2021, pp.353-374.
  • Alli K, Zhang J. Adaptive Optimal Control of Baker's Yeast Fermentation Process with Extreme Learning Machine and Recursive Least Square Technique. In: 31st European Symposium on Computer Aided Process Engineering. Amsterdam: Elsevier B.V, 2021, pp.1241-1246.
  • Liu K, Zhang J. A Dual-Layer Attention-Based LSTM Network for Fed-batch Fermentation Process Modelling. In: 31st European Symposium on Computer Aided Process Engineering. Amsterdam: Elsevier B.V, 2021, pp.541-547.
  • Osuolale FN, Zhang J. Exergetic optimisation of atmospheric and vacuum distillation system based on bootstrap aggregated neural network models. In: Exergy for A Better Environment and Improved Sustainability 1: Fundamentals. Cham: Springer Verlag, 2018, pp.1033-1046.
  • Stubbs S, Zhang J, Morris J. BioProcess performance monitoring using multiway interval partial least squares. In: Computer Aided Chemical Engineering. Elsevier BV, 2018, pp.243-259.
  • Muhsin WAS, Zhang J. Modelling and Optimal Operation of a Crude Oil Hydrotreating Process with Atmospheric Distillation Unit Utilising Stacked Neural Networks. In: Espuña A; Graells M; Puigjaner L, ed. Computer Aided Chemical Engineering. Barcelona: Elsevier, 2017, pp.2479-2484.
  • von Stosch M, Zhang J, Willis M. Hybrid neural network modelling for process monitoring and control. In: Artificial Neural Networks in Chemical Engineering. New York: Nova Science Publishers, Inc, 2017, pp.205-228.
  • Lawal SA, Zhang J. Actuator and Sensor Fault Tolerant Control of a Crude Distillation Unit. In: Computer Aided Chemical Engineering. Elsevier B.V, 2017, pp.1705-1710.
  • Lawal SA, Zhang J. Sensor Fault Detection and Fault Tolerant Control of a Crude Distillation Unit. In: Computer Aided Chemical Engineering. Elsevier B.V, 2016, pp.2091-2096.
  • Bai Z, Li F, Zhang J, Oko E, Wang M, Xiong Z, Huang D. Modelling of a Post-combustion CO₂ Capture Process Using Bootstrap Aggregated Extreme Learning Machines. In: Computer Aided Chemical Engineering. Amsterdam, Netherlands: Elsevier BV, 2016, pp.2007-2012.
  • Zhang J, Feng Y, Al-Mahrouqi MH. Reliable optimal control of a fed-batch fermentation process using ant colony optimization and bootstrap aggregated neural network models. In: Valadi,J; Siarry,P, ed. Applications of Metaheuristics in Process Engineering. Cham: Springer International Publishing, 2014, pp.183-200.
  • Morris AJ, Zhang J. Performance Monitoring and Batch to Batch Control of Biotechnological Processes. In: Nicoletti, M.C., Jain, L.C, ed. Computational Intelligence Techniques for Bioprocess Modelling, Supervision and Control. Berlin; New York: Springer-Verlag, 2009, pp.281-310.
  • Xiong Z, Zhang J, Wang X, Xu Y. Run-to-run iterative optimization control of batch. In: Advances in Neural Networks - ISNN 2004. Berlin: Springer, 2004, pp.97-103.
  • Liu Y, Yang X, Zhang J. A neural network modeling method for batch process. In: Advances in Neural Networks - ISNN 2004. Berlin: Springer, 2004, pp.874-879.
  • Zhang J, Morris AJ. Inferential Estimation and Optimal Control of a Batch Polymerisation Reactor Using Stacked Neural Networks. In: I. M. Mujtaba and M. A. Hussain, ed. Application of Neural Network and Other Learning Technologies in Process Engineering. London: Imperial College Press, 2001, pp.243-266.

• Conference Proceedings (inc. Abstracts)

  • Djarum DH, Ahmad Z, Zhang J. Performance Analysis of Neural Network Architecture in Developing Real-Time Malaysian River Water Quality Model. In: 33rd Symposium of Malaysian Chemical Engineers (SOMChE 2022). 2022, Online: IOP.
  • Djarum DH, Anuar NH, Ahmad Z, Zhang J. Analyzing different dynamically modelled data structures and machine learning algorithms to predict PM2.5 concentration in China. In: Proceedings of the 14th International Symposium on Process Systems Engineering (PSE2021+). 2022, Kyoto: Elsevier.
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  • Tian Y, Zhang J, Morris AJ. On-line Re-optimisation Control of a Batch Polymerisation Reactor based on a Hybrid Recurrent Neural Network Model. In: Proceedings of American Control Conference 2001. 2001, Arlington, Virginia, U.S.A: IEEE.
  • Zhang J, Morris J. Nonlinear model predictive control based on multiple local linear models. In: Proceedings of the American Control Conference 2001. 2001, Arlington, Virginia, U.S.A: Institution of Electronic and Electrical Engineers.
  • Zhang J, Agustriyanto R. Inferential Feedforward Control of a Distillation Column. In: Proceedings of American Control Conference 2001. 2001, Arlington, Virginia, U.S.A: IEEE.
  • Zhang J. Inferential Feedback Control of Distillation Composition based on PCR and PLS Models. In: Proceedings of American Control Conference 2001. 2001, Arlington, Virginia, U.S.A: IEEE.
  • Patton RJ, Lopez CJ, Simani S, Morris J, Martin E, Zhang J. Actuator Fault Diagnosis in a Continuous Stirred Tank Reactor Using Identification Techniques. In: Proceedings of European Control Conference 2001. 2001, Porto, Portugal.

• Reviews

  • Ahmad Z, Noor RAM, Zhang J. Multiple neural networks modeling techniques in process control: a review. Asia-Pacific Journal of Chemical Engineering 2009, 4(4), 403-419.
  • Tian Y, Zhang J, Morris J. Optimal control of a fed-batch bioreactor based upon an augmented recurrent neural network model. Neurocomputing 2002, 48, 919-936.
  • Tian Y, Zhang J, Morris J. Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models. Chemical Engineering and Processing 2002, 41(6), 531-538.