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

• 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.
• 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.
• Wang S, Zhang J. A Fault Diagnosis Strategy based on Qualitative Trend Analysis Integrating Andrews Plot for Industrial Processes. In: 22nd IEEE International Conference on Industrial Technology (ICIT2021). 2021, Valencia, Spain: IEEE.
• 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.
• 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.
• Yang Q, Dong N, Zhang J. An enhanced adaptive bat algorithm for microgrid energy scheduling. Energy 2021, 232, 121014.
• 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.
• Wang S, Zhang J. An Intelligent Process Fault Diagnosis System based on Neural Networks and Andrews Plot. Processes 2021, 9(9), 1659.
• 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 2021, Epub ahead of print.
• 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.
• 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. Enhanced Data-Driven Fault Diagnosis of Chemical Process via Information Fusion in Multiple Neural Networks and Andrews Plot. In: 26th International Conference on Automation and Computing (ICAC’21). 2021, University of Portsmouth, Portsmouth, UK: IEEE.
• 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.
• Wang K, Zhang J, Huang D. Online temperature estimation of Shell coal gasification process based on extended Kalman filter. Chinese Journal of Chemical Engineering 2021, (ePub ahead of Print).
• 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.
• Kargbo HO, Zhang J, Phan AN. Optimisation of two-stage biomass gasification for hydrogen production via artificial neural network. Applied Energy 2021, 302, 117567.
• Qin C, Liu Z, Zhang J. Periodic Steady State Analysis of Lower Limb Rehabilitation Robot Based on Max-plus Algebra. In: 5th International Conference on Robotics and Automation Sciences (ICRAS 2021). 2021, Wuhan, China: IEEE.
• Zhu C, Zhang J. Reliable data-driven modelling and optimisation of a batch reactor using bootstrap aggregated deep belief networks. In: 50th Meeting of the Italian Statistical Society (SIS 2021). 2021, Pisa, Italy: Pearson.
• 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.
• 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.
• Wang S, Zhang J. An Intelligent Process Fault Diagnosis System Integrating Andrews Plot, PCA and Neural Networks. In: 25th International Conference on Automation and Computing (ICAC’19). 2020, Lancaster, United Kingdom: IEEE.
• 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.
• Qi R, Zhang J. Data-driven fault diagnosis and prognosis for process faults using principal component analysis and extreme learning. In: 18th IEEE International Conference on Industrial Informatics (INDIN2020). 2020, University of Warwick, UK: IEEE.
• Zhu C, Zhang J. Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks. AIMS Electronic and Electrical Engineering 2020, 4(3), 287–302.
• 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.
• Qi R, Zhang J. Fault Magnitude Prognosis in Chemical Process Based on Long Short-Term Memory Network. In: 4th International Symposium on Computer Science and Intelligent Control (ISCSIC2020). 2020, Newcastle University, UK: Association for Computing Machinery.
• Wang S, Zhang J. Improved Process Fault Diagnosis by Using Neural Networks with Andrews Plot and Autoencoder. In: 18th IEEE International Conference on Industrial Informatics (INDIN2020). 2020, University of Warwick, UK: IEEE.
• 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.
• Corrigan J, Zhang J. Integrating Dynamic Slow Feature Analysis with Neural Networks for Enhancing Soft Sensor Performance. Computers and Chemical Engineering 2020, 139, 106842.
• 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.
• Liu K, Zhang J. Modelling a Penicillin Fermentation Process Using Attention-based Echo State Networks Optimized by Covariance Matrix Adaption Evolutionary Strategy. In: 30th European Symposium on Computer Aided Chemical Engineering. 2020, Milano, Italy: Elsevier.
• Liu K, Zhang J. Nonlinear Process Modelling Using Echo State Networks Optimised by Covariance Matrix Adaption Evolutionary Strategy. Computers & Chemical Engineering 2020, 135, 106730.
• Tian Y, Zhang J. Optimizing sensor deployment for multi-sensor based HAR system with improved glowworm swarm optimization algorithm. Sensors 2020, 20(24), 7161.
• 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.
• Zhang P, Zhang J, Long Y, Hu B. An improved reinforcement learning control strategy for batch processes. In: 24th International Conference on Methods and Models in Automation and Robotics (MMAR2019). 2019, Miedzyzdroje, Poland: IEEE.
• Zhu C, Zhang J. Developing Robust Nonlinear Models through Bootstrap Aggregated Deep Belief Networks. In: 25th International Conference on Automation and Computing (ICAC’19). 2019, Lancaster University, Lancaster, UK: IEEE.
• Wang S, Zhang J. Enhanced Process Fault Diagnosis Through Integrating Neural Networks and Andrews Plot. In: 24th International Conference on Methods and Models in Automation and Robotics (MMAR2019). 2019, Miedzyzdroje, Poland: IEEE.
• Corrigan J, Zhang J. Nonlinear Data-Driven Process Modelling Using Slow Feature Analysis and Neural Networks. In: 16th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2019). 2019, Prague, Czech Republic: SciTePress.
• Zhang P, Zhang J, Hu B, Long Y. Optimization Control of a Fed-Batch Process Using an Improved Reinforcement Learning Algorithm. In: IEEE Conference on Control Technology and Applications (CCTA 2019). 2019, Hong Kong, China: IEEE.
• 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.
• 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.
• Qi R, Zhang J. Process Fault Detection and Reconstruction by Principal Component Analysis. In: 24th International Conference on Methods and Models in Automation and Robotics (MMAR2019). 2019, Miedzyzdroje, Poland: IEEE.
• 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.
• Cardona Baron CM, Zhang J. Reliable On-Line Re-Optimization Control of a Fed-Batch Fermentation Process Using Bootstrap Aggregated Extreme Learning Machine. In: 14th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2017). 2019, Madrid, Spain: Springer.
• 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.
• 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, Wang X, Yang P, Wang J, Zhang J. A Single Accelerometer-based Robust Human Activity Recognition via Wavelet Features and Ensemble Feature Selection. In: 24th International Conference on Automation and Computing (ICAC 2018). 2018, Newcastle upon Tyne, UK: IEEE.
• 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.
• 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.
• Al-Kalbani F, Zhang J. Inferential active disturbance rejection control of a heat integrated distillation column using dynamic principal component regression models. In: 9th IEEE-GCC Conference and Exhibition, GCCCE 2017. 2018, Manama, Bahrain: Institute of Electrical and Electronics Engineers Inc.
• Zhu C, Zhang J. Inferential Estimation of Polymer Melt Index Using Deep Belief Networks. In: 24th International Conference on Automation and Computing (ICAC 2018). 2018, Newcastle upon Tyne, UK: IEEE.
• 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.
• Liu K, Zhang J. Optimization of Echo State Networks by Covariance Matrix Adaption Evolutionary Strategy. In: 24th International Conference on Automation and Computing (ICAC 2018). 2018, Newcastle upon Tyne, UK: IEEE.
• Shen X, Xiong Z, Zhang J. Point-to-Point Iterative Learning Control with Piecewise Constant Inputs. In: 24th International Conference on Automation and Computing (ICAC 2018). 2018, Newcastle upon Tyne, UK: IEEE.
• 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.
• Zhang J, Fisher R. Reliable multi-objective on-line re-optimisation control of a fed-batch fermentation process using bootstrap aggregated neural networks. In: 2017 International Symposium on Computer Science and Intelligent Controls, ISCSIC 2017. 2018, Budapest, Hungary: Institute of Electrical and Electronics Engineers Inc.
• 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. Actuator Fault Monitoring and Fault Tolerant Control in Distillation Columns. International Journal of Automation and Computing 2017, 14(1), 80-92.
• Ahmad Z, Rahim NA, Bahadori A, Zhang J. Air pollution index prediction using multiple neural net. IIUM Engineering Journal 2017, 18(1), 1-12.
• 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.
• 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.
• Al Kalbani FK, Zhang J. Inferential active disturbance rejection control of a heat integrated distillation column. In: 2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV 2016). 2017, Phuket, Thailand: IEEE.
• 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.
• 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, Bahadori A, Zhang J. Prediction of combustion efficiency using multiple neural networks. Chemical Engineering Transactions 2017, 56, 85-90.
• Baron CMC, Zhang J. Re-optimisation control of a fed-batch fermentation process using bootstrap aggregated extreme learning machine. In: ICINCO 2017 - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics. 2017, SciTePress.
• 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.
• Osuolale FN, Zhang J. Thermodynamic optimization of atmospheric distillation unit. Computers & Chemical Engineering 2017, 103, 201-209.
• Al-Kalbani F, Zhang J, Bisgaard T, Huusom JK. Active Disturbance Rejection Control of a Heat Integrated Distillation Column. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). 2016, Miedzyzdroje, Poland: IEEE.
• 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.
• Xiong Z, Hong Y, Chen C, Zhang J, Zhong M. Convergence analysis of integrated predictive iterative learning control based on two-dimensional theory. In: American Control Conference (ACC). 2016, Boston, MA: Institute of Electrical and Electronics Engineers Inc.
• Osuolale FN, Zhang J. Energy efficiency optimisation for distillation column using artificial neural network models. Energy 2016, 106, 562-578.
• Lawal SA, Zhang J. Fault Monitoring and Fault Tolerant Control in Distillation Columns. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). 2016, Miedzyzdroje, Poland: IEEE.
• Jeffries SW, Al-Abri S, Zhang J. Inferential disturbance observer based control of a binary distillation column. In: IECON 2016 42nd Annual Conference of IEEE Industrial Electronics Society. 2016, Florence, Italy: IEEE Computer Society.
• Jeffries SW, Zhang J. Integrated disturbance observer based control and inferential control of a binary distillation column. In: 22nd International Congress of Chemical and Process Engineering (CHISA 2016) and 19th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES 2016). 2016, Prague, Czech Republic: Czech Society of Chemical Engineering.
• Muhsin WAS, Zhang J, Lee J. Modelling and Optimisation of a Crude Oil Hydrotreating Process Using Neural Networks. In: 19th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES 2016). 2016, Prague: Italian Association of Chemical Engineering.
• 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.
• Li F, Zhang J, Oko E, Wang MH. Modelling of a Post-combustion CO2 Capture Process Using Extreme Learning Machine. In: 2016 21st International Conference on Methods and Models in Automation and Robotics (MMAR). 2016, Miedzyzdroje, Poland: IEEE.
• Liu JX, Liu T, Zhang J. Phase partition for nonlinear batch process monitoring. In: 11th IFAC Symposium on Dynamics and Control of Process SystemsIncluding Biosystems DYCOPS-CAB 2016. 2016, Trondheim, Norway: Elsevier B.V.
• Qiu WW, Xiong ZH, Li WZ, Zhang J. Point-to-Point Tracking of Integrated Predictive Iterative Learning Control By Using Updating-Reference and CARIMA Model. In: 2016 Chinese Control and Decision Conference (CCDC). 2016, Yinchuan, China: IEEE.
• 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.
• 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.
• 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.
• Al-Kalbani F, Al-Hosni SM, Zhang J. Active Disturbance Rejection Control of a Methanol-Water Separation Distillation Column. In: 2015 IEEE 8th GCC Conference and Exhibition, GCCCE 2015. 2015, Muscat, Oman: IEEE.
• Lawal SA, Zhang J. Actuator Fault Monitoring and Fault Tolerant Control in Distillation Columns. In: 2015 21st International Conference on Automation and Computing (ICAC). 2015, Glasgow: IEEE.
• 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.
• Osuolale F, Zhang J. Distillation control structure selection for energy efficient operations. Chemical Engineering and Technology 2015, 38(5), 907-916.
• Al-Kalbani F, Zhang J. Inferential active disturbance rejection control of a distillation column. In: 9th IFAC Symposium on Advanced Control of Chemical Processes, ADCHEM 2015. 2015, Whistler, British Columbia, Canada: Elsevier.
• Al Kalbani F, Zhang J. Inferential Active Disturbance Rejection Control of a Distillation Column. In: 9th IFAC Symposium on Advanced Control of Chemical Processes ADCHEM 2015. 2015, Whistler, BC, Canada: Elsevier.
• Al Kalbani F, Zhang J. Inferential Active Disturbance Rejection Control of a Distillation Column using Dynamic Principal Component Regression Models. In: 2015 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO). 2015, Colmar, Alsasce, France: IEEE/ SCITEPRESS.
• Jung S, Zhang J, Oliveira R. Iterative Learning Control of Polyhydroxybutyrate Production in a Sequencing Batch Reactor. In: 20th International Conference on Methods and Models in Automation and Robotics (MMAR 2015). 2015, Miedzyzdroje, Poland: IEEE.
• Li F, Zhang J, Oko E, Wang M. Modelling of a Post-combustion CO₂ Capture Process Using Neural Networks. Fuel 2015, 151, 156-163.
• Osuolate FN, Zhang J. Multi-objective Optimisation of Atmospheric Crude Distillation System Operations Based on Bootstrap Aggregated Neural Network Models. In: 12th International Symposium on Process Systems Engineering and the 25th European Symposium on Computer Aided Process Engineering. 2015, Copenhagen, Denmark: Elsevier.
• 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.
• 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.
• Brown A, Zhang J. Active Disturbance Rejection Control of a Neutralisation Process. In: 24th European Symposium on Computer Aided Process Engineering. 2014, Budapest, Hungary: Elsevier.
• Osuolale FN, Zhang J. Energy Efficient Control and Optimisation of Distillation Column Using Artificial Neural Network. In: Pres 2014, 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction, Pts 1-3. 2014, Prague, Czech Republic: AIDIC Servizi.
• Osuolale FN, Zhang J. Energy efficient control and optimization of distillation process using artificial neural network. In: 21st International Congress of Chemical and Process Engineering (CHISA 2014) and 17th Conference on Process Integration, Modelling and Optimisation for Energy Saving and Pollution Reduction (PRES 2014). 2014, Prague, Czech Republic: Czech Society of Chemical Engineering.
• Alawi A, Zhang J, Morris J. Multi-scale Multiblock Batch Monitoring: Sensor and Process Drift and Degradation. Organic Process Research & Development 2014.
• Ganjian A, Zhang J, Oliveira R. Optimisation of a Sequencing Batch Reactor for Production of Polyhydroxybutyrate Using Process Characterisation Method and Neural Network Modelling. In: 24th European Symposium on Computer Aided Process Engineering. 2014, Budapest, Hungary: Elsevier.
• 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.
• 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.
• Ganjian A, Zhang J, Dias JML, Oliveira R. Modelling of a Sequencing Batch Reactor for Producing Polyhydroxybutyrate with Mixed Microbial Culture Cultivation Process Using Neural Networks and Operation Regime Classification. In: ICHEAP-11: 11th International Conference on Chemical & Process Engineering. 2013, Milan: AIDIC Associazione italiana di ingegneria chimica.
• 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, Zhang J, Chen T, Yang XH. Penalized Reconstruction-Based Multivariate Contribution Analysis for Fault Isolation. Industrial & Engineering Chemistry Research 2013, 52(23), 7784-7794.
• Kaunga DL, Zhang J, Ferguson K, Steele C. Reliable modeling of chemical duarability of high level waste glass using bootstrap aggregated neural networks. In: 2013 Ninth International Conference on Natural Computation (ICNC). 2013, Shenyang: IEEE.
• 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.
• Zhang J, Xiong ZH, Guillaume D, Lamande A. Batch to Batch Iterative Learning Control of a Fed-Batch Fermentation Process. In: Mechanical Engineering and Technology. 2012, London: Springer.
• 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.
• 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.
• 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.
• 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.
• Jewaratnam J, Zhang J, Hussain A, Morris J. Reliable Batch-to-Batch Iterative Learning Control of a Fed-batch Fermentation Process. In: 22 EUROPEAN SYMPOSIUM ON COMPUTER AIDED PROCESS ENGINEERING. 2012, SARA BURGERHARTSTRAAT 25, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS: ELSEVIER SCIENCE BV.
• 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.
• Zhang J, Pantelelis NG. Iterative learning control of a reactive polymer composite moulding process using batch-wise updated linearised models. In: 21st European Symposium on Computer Aided Process Engineering. 2011, Chalkidiki, Greece: Elsevier BV.
• Zhang J, Feng YT, Al-Mahrouqi MH. Reliable optimal control of a fed-batch fermentation process using ant colony optimisation and bootstrap aggregated neural network models. In: 21st European Symposium on Computer Aided Process Engineering. 2011, Chalkidiki, Greece: Elsevier BV.
• Stubbs S, Zhang J, Morris J. A Revised State Space Modelling Approach and Improved Fault Detection Using Combined Index Monitoring for Dynamic Processes. In: 9th International Symposium on Dynamics and Control of Process Systems - DYCOPS. 2010, Leuven, Belgium.
• Lingeson J, Zhang J, Hussain A, Morris AJ. Batch-to-Batch Iterative Learning Control of a Fed-batch Fermentation Process Using Incrementally Updated Models. In: Proceedings of the IFAC Symposium on Computer Applications in Biotechnology (CAB 2010). 2010, Leuven, Belgium: International Federation of Automatic Control.
• 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.
• 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.
• 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.
• Hong JJ, Zhang J. Progressive PCA Modeling for Enhanced Fault Diagnosis in a Batch Process. In: International Conference on Control, Automation and Systems (ICCAS). 2010, Gyeonggi do, South Korea: IEEE.
• Hong JJ, Zhang J. Quality Prediction for a Fed-Batch Fermentation Process Using Multi-Block PLS. In: EKC 2009 Proceedings of EU-Korea Conference on Science and Technology. 2010, London, UK: Springer.
• 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.
• Hong JJ, Zhang J, Morris J. Enhanced Predictive Modeling Using Multi Block Methods. In: 19th European Symposium on Computer Aided Process Engineering. 2009, Cracow, Poland: Elsevier BV.
• 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.
• Stubbs S, Zhang J, Morris J. Fault detection of dynamic processes using a simplified monitoring-specific CVA state space approach. In: 19th European Symposium on Computer Aided Process Engineering. 2009, Cracow, Poland: Elsevier BV.
• Zhou C, Liu Q, Huang D, Zhang J. Inferential estimation of kerosene dry point in refineries with varying crudes. In: Computer Aided Chemical Engineering: 19th European Symposium on Computer Aided Process Engineering. 2009, Cracow, Poland: Springer.
• Zhang J, Nguyan J, Xiong ZH, Morris J. Iterative Learning Control of a Crystallisation Process Using Batch Wise Updated Linearised Models. In: 21st Chinese Control and Decision Conference (CCDC). 2009, Guilin: IEEE.
• Zhang J, Nguyan J, Morris J. Iterative learning control of a crystallisation process using batch wise updated linearised models identified using PLS. In: Computer Aided Chemical Engineering: 19th European Symposium on Computer Aided Process Engineering. 2009, Cracow, Poland: Springer.
• Geng H, Xiong Z, Xu Y, Zhang J. Iterative learning control with fixed reference batch and exponential learning gain for linear systems. In: Chinese Control and Decision Conference (CCDC). 2009, Guilin, China: IEEE.
• 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.
• 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.
• 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.
• 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.
• Morris J, Zhang J. Performance monitoring and batch to batch control of biotechnological processes. Studies in Computational Intelligence 2009, 218, 281-310.
• 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.
• 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.
• Al-Mawali S, Zhang J. A novel fuzzy logic control strategy for compressor surge control using a variable area throttle. In: 22nd IEEE International Symposium on Intelligent Control, ISIC. Part of IEEE Multi-conference on Systems and Control. 2008, Singapore: IEEE.
• 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.
• Zhang J, Nguyan J, Morris J, Xiong ZH. Batch to Batch Iterative Learning Control of A Fed-batch Fermentation Process Using Linearised Models. In: 10th International Conference on Control Automation Robotics & Vision (Icarcv 2008). 2008, Hanoi, Vietnam: IEEE.
• Xiong Z, Xu Y, Zhang J, Dong J. Batch-to-batch control of fed-batch processes using control-affine feedforward neural network. In: Neural Computing and Applications: 4th International Symposium on Neural Networks. 2008, Nanjing, China: Springer.
• 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.
• Xiong Z, Zhang J. Integrated tracking control for batch processes in the presence of model uncertainties. In: 2007 IEEE International Conference on Control and Automation, ICCA. 2008, Singapore: IEEE Control Systems Society.
• 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.
• Geng H, Xiong Z, Xu Y, Zhang J. Iterative Learning Control with Reference Batch for Linear Time-Variant Systems. In: Tenth International Conference on Control, Automation, Robotics and Vision (ICARCV). 2008, Hanoi, Vietnam: IEEE.
• 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.
• Herrera F, Zhang J. Optimal Control of Batch Processes Using Particle Swam Optimisation with Stacked Neural Network Models. In: Computers & Chemical Engineering: 18th European Symposium on Computer Aided Process Engineering (ESCAPE). 2008, Lyon, France: Pergamon.
• 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.
• Mukherjee A, Zhang J. Reliable Multi-Objective On-line Re-Optimisation Control of Batch Processes Based on Bootstrap Aggregated Neural Networks. In: 17th IFAC World Congress. 2008, COEX, South Korea: IFAC.
• Al-Mahrouqi M, Zhang J. Reliable Optimal Control of a Fed-Batch Bio-Reactor Using Ant Colony Optimization and Bootstrap Aggregated Neural Networks. In: 17th IFAC World Congress. 2008, COEX, South Korea: IFAC.
• 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.
• Antelo FS, Zhang J. Improved bleach plant control using internal model control with smith predictor. In: IEEE International Conference on Control Applications. 2007, Singapore: IEEE.
• 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.
• Agustriyanto R, Zhang J. Obtaining the worst case RGA and RDGA for uncertain systems via optimization. In: Proceedings of the American Control Conference. 2007, IEEE Press Books.
• Al-Mawali S, Zhang J. A Fuzzy Approach to Active Surge Control of Centrifugal Compressors. In: Proceedings of the International Conference Control 2006. 2006, Glasgow, Scotland: University of Strathclyde Publishing.
• Ahmad Z, Zhang J. A nonlinear model predictive control strategy using multiple neural network models. In: Advances in Neural Networks (ISNN): Third International Symposium on Neural Networks. 2006, Chengdu, China: Springer Berlin / Heidelberg.
• Ahmad Z, Zhang J. A nonlinear model predictive control strategy using multiple neural network models. In: Advances in Neural Networks - ISNN 2006. 2006, Chengdu, China: Springer.
• 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.
• Agustriyanto R, Zhang J. Control Structure Selection for the Alstom Gasifier Benchmark Process Using GRDG Analysis. In: Proceedings of the International Conference Control 2006. 2006, Glasgow, Scotland.
• Zhou Q, Xiong Z, Zhang J, Xu Y. Hierarchical neural network based product quality prediction of industrial ethylene pyrolysis process. In: Advances in Neural Networks (ISNN): Third International Symposium on Neural Networks. 2006, Chengdu, China: Springer Berlin / Heidelberg.
• Zhang J. Improved on-line process fault diagnosis through information fusion in multiple neural networks. Computers and Chemical Engineering 2006, 30(3), 558-571.
• Ahmad Z, Zhang J, Syukor S. Improving multi step-ahead model prediction using multiple neural networks combination through forward selection (FS) technique. In: International Conference on Computing and Informatics (ICOCI '06). 2006, Kuala Lumpur: IEEE.
• 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.
• Liu Y, Xiong Z, Yang XH, Zhang J. Kernel regression modeling based batch process optimal control. In: DYNAMICS OF CONTINUOUS DISCRETE AND IMPULSIVE SYSTEMS-SERIES B-APPLICATIONS & ALGORITHMS. 2006, C/O DCDIS JOURNAL, 317 KAREN PLACE, WATERLOO, ONTARIO N2L 6K8, CANADA: WATAM PRESS.
• 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. 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. Performance monitoring of processes with multiple operating modes through multiple PLS models. Journal of Process Control 2006, 16(7), 763-772.
• 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.
• Mukherjee A, Zhang J. Reliable Multi-Objective Optimal Control of Batch Processes based on Stacked Neural Network Models. In: 16th European Symposium on Computer Aided Process Engineering and 9th International Symposium on Process Systems Engineering. 2006, Garmisch-Partenkirchen, Germany: Elsevier.
• 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.
• 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.
• 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.
• Xiong Z, Zhang J, Wang X, Xu Y. An integrated batch-to-batch iterative learning control and within batch control strategy for batch processes. In: Proceedings of the American Control Conference. 2005, Portland, Oregon, USA: IFAC.
• Zhang J. Batch process modelling and optimal control based on neural network models. Zidonghua Xuebao/Acta Automatica Sinica 2005, 31(1), 19-31.
• Liu Y, Yang X, Xiong Z, Zhang J. Batch-to-batch optimal control based on support vector regression model. In: Advances in Neural Networks – ISNN 2005. Second International Symposium on Neural Networks. 2005, Chongqing, China: Springer.
• Liu Y, Yang X, Xiong Z, Zhang J. Batch-to-Batch Optimal Control Based on Support Vector Regression Model. In: Advances in Neural Networks (ISNN). 2005, Chongqing, China: Springer-Verlag GmbH.
• 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.
• 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.
• Xiong Z, Zhang J, Wang X, Xu YM. Integrated Batch-to-Batch Iterative Learning Control and within Batch Control of Product Quality for Batch Processes. In: Proceedings of the 16th IFAC World Congress. 2005, Prague, Czech Republic: Elsevier.
• Zhang J. Modeling and optimal control of batch processes using recurrent neuro-fuzzy networks. IEEE Transactions on Fuzzy Systems 2005, 13(4), 417-427.
• Xiong Z, Zhang J, Wang X, Xu Y. Neural network based on-line shrinking horizon re-optimization of fed-batch processes. In: Advances in Neural Networks – ISNN 2005. Second International Symposium on Neural Networks. 2005, Chongqing, China: Springer.
• Xiong Z, Zhang J, Wang X, Xu YM. Neural Network Based on-line Shrinking Horizon Re-optimization of Fed-batch Processes. In: Advances in Neural Networks (ISNN). 2005, Chongqing, China: Springer-Verlag GmbH.
• 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.
• 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, 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.
• 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.
• Zhao SJ, Xu YM, Zhang J. A Multiple PCA Model based Technique for the Monitoring of Processes with Multiple Operating Modes. In: Computer-Aided Chemical Engineering 18, European Symposium on Computer-Aided Process Engineering - 14. 2004, Lisbon, Portugal: Elsevier.
• Liu Y, Yang XH, Zhang J. A neural network modeling method for batch process. In: Advances in Neural Networks: International Symposium on Neural Networks (ISNN 2004). 2004, Dalian, China: Springer.
• 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.
• Zhao SJ, Xu YM, Zhang J. A novel nonlinear projection to latent structures algorithm. In: COMPSTAT 2004 - Proceedings in Computational Statistics. 2004, Prague, Czech Republic: Springer.
• Zhao SJ, Xu YM, Zhang J. A novel nonlinear projection to latent structures algorithm. In: Advances in Neural Networks: International Symposium on Neural Networks (ISNN 2004). 2004, Dalian, China: Springer.
• Zhao SJ, Xu YM, Zhang J. A Novel Nonlinear Projection to Latent Structures Algorithm. In: Advances in Neural Networks: International Symposium on Neural Networks (ISNN). 2004, Dalian, China: Springer.
• 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.
• 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.
• 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.
• 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.
• 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.
• Ahmad Z, Zhang J. Improving Long Range Prediction in Nonlinear Process Modelling through Bayesian Combination of Multiple Neural Networks. In: European Symposium on Computer-Aided Process Engineering. 2004, Lisbon, Portugal: Elsevier.
• Zhang J. Integrated Batch-to-Batch Control and within Batch Control of Batch Processes Using Neural Network Models. In: Proceedings of The 2004 IEEE International Symposium on Intelligent Control. 2004, Taipei, Taiwan, China: IEEE.
• 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.
• Bezas K, Farrugia D, Richardson A, Musicka T, Zhang J, Martin EB. Modelling the distortion of long product sections after hot rolling using finite elements and neural networks. In: 5th International Conference on Quality, Reliability, and Maintenance (QRM 2004). 2004, Oxford, UK: Professional Engineering Publishing.
• 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.
• 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.
• Di LQ, Zhang J, Yang XH. MWMPCA with application to batch processes monitoring. Journal of Jilin University: Information Science 2004, 22(4), 397-400.
• Zhou Q, Zhang J, Xu Y. Predicting the product yield profile and cracking degrees in an industrial ethylene pyrolysis furnace. In: 8th International Conference on Control, Automation, Robotics and Vision (ICARCV 2004). 2004, Kunming, China: IEEE.
• Xiong Z, Zhang J. Recurrent neural network model based batch-to-batch iterative optimising control. In: IASTED International Conference on Neural Networks and Computational Intelligence. 2004, Grindelwald, Switzerland: ACTA Press.
• Shen M-Z, Zhang J, Scott K. Regulation of power conversion in fuel cells. Chemical Research in Chinese Universities 2004, 20(4), 466-469.
• 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.
• Xiong Z, Zhang J, Wang X, Xu YM. Run-to-Run Iterative Optimization Control of Batch Processes Based on Recurrent Neural Networks. In: Advances in Neural Networks: International Symposium on Neural Networks (ISNN). 2004, Dalian, China: Springer.
• 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.
• 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.
• Xiong Z, Zhang J. Trajectory Tracking of Batch Processes with Varying Control Interval and Incrementally Updated Models. In: Computer-Aided Chemical Engineering 18, European Symposium on Computer-Aided Process Engineering - 14. 2004, Lisbon, Portugal: Elsevier.
• Xiong Z, Zhang J. Batch-to-Batch Model-based Iterative Optimisation Control for a Batch Polymerisation Reactor. In: Proceedings of the American Control Conference. 2003, Denver, Colorado, USA: IEEE.
• Ahmed MH, Zhang J. Improved Inferential Feedback Control through Combining Multiple PCR Models. In: IEEE International Symposium on Intelligent Control. 2003, Houston, Texas, USA: IEEE.
• Ahmad Z, Zhang J. Improving Data based Nonlinear Process Modelling through Bayesian Combination of Multiple Neural Networks. In: Proceedings of the International Joint Conference on Neural Networks. 2003, Portland, Oregon, USA: IEEE.
• Zhang J, Xu YM. Inferential Estimation of Polymer Melt Index Using Bootstrap Aggregated Neural Networks with Sequential Training. In: Proceedings of the IFAC International Conference on Intelligent Control and Signal Processing. 2003, Faro, Portugal: Academic Press.
• Norris DJ, Cullis AG, Olsen SH, O'Neill AG, Zhang J. Measurements of gate-oxide interface roughness in strained-Si virtual substrate SiGe/Si MOSFET device structures. In: Microscopy of Semiconducting Materials: Conference on Microscopy of Semiconducting Materials. 2003, Cambridge, UK: Institute of Physics Publishing.
• Zhang J. Multi-Objective Optimal Control of Batch Processes Using Recurrent Neuro-fuzzy Networks. In: Proceedings of the International Joint Conference on Neural Networks. 2003, Portland, Oregon, USA: IEEE.
• Zhang J, Agustriyanto R. Multivariable Inferential Feed Forward Control. Industrial & Engineering Chemistry Research 2003, 42(18), 4186-4197.
• Ahmed MH, Zhang J. Multivariable Inferential Feedback Control of Distillation Compositions Using Dynamic Principal Component Regression Models. In: Proceedings of the American Control Conference. 2003, Denver, Colorado, USA: IEEE.
• Zhang J. Neural Network Model based Batch-to-Batch Optimal Control. In: IEEE International Symposium on Intelligent Control. 2003, Houston, Texas, USA: IEEE.
• Zhang J. Neural Network Model Based Batch-to-Batch Optimal Control. In: IEEE International Symposium on Intelligent Control. 2003, Houston, Texas, USA: IEEE.
• 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. Reliable Optimal Control of a Batch Polymerisation Reactor Based on Neural Network Model with Model Prediction Confidence Bounds. In: Process Systems Engineering 2003: 8th International Symposium on Process Systems Engineering. 2003, Kunming, China: Computer Aided Chemical Engineering: Elsevier BV.
• Ahmad Z, Zhang J, Taip FS. Selective Combination of Multiple Neural Networks for Improving Long Range Prediction in Nonlinear Process Modelling based on Correlation Analysis. In: International Conference on Chemical and Bioprocess Engineering. 2003, Kota Kinabalu, Sabah, Malaysia.
• Ahmad Z, Zhang J. Selective Combination of Multiple Neural Networks for Improving Long Range Prediction in Nonlinear Process Modelling through Correlation Coefficient Analysis. In: International Conference on Robotics, Vision, Information and Signal Processing (ROVISP). 2003, Penang, Malaysia.
• Ahmad Z, Zhang J. A comparison of different methods for combining multiple neural networks models. In: International Joint Conference on Neural Networks (IJCNN 02). 2002, Honolulu, Hawaii, USA: IEEE.
• Jiang J, Xu YM, Zhang J. A Steady State Model for Propylene Polymerization in an Industrial Loop Reactor and Its Application in Melt Index Predication. In: IEEE Conference on Control Applications. 2002, Glasgow, Scotland: IEEE.
• Zhang J. A training method for enhancing neural network model generalisation. In: International Joint Conference on Neural Networks. 2002, Honolulu, Hawaii, USA: IEEE.
• Wei J, Fan S, Xu YM, Zhang J. Dynamic Modelling of an Industrial Polypropylene Reactor and Its Application in Melt Index Prediction During Grade Transitions. In: American Control Conference. 2002, Anchorage, Alaska, USA: IEEE.
• Zhang J. Improved on-line process fault diagnosis using stacked neural networks. In: International Conference on Control Applications. 2002, Glasgow, UK: IEEE.
• Ahmad Z, Zhang J. Improving long range prediction for nonlinear process modelling through combining multiple neural networks. In: International Conference on Control Applications. 2002, Glasgow, UK: IEEE.
• Zhang M, Xu YM, Liu XG, Zhang J. Industrial Application of Non-equilibrium Model: Simulation and Analysis of Ethylene Fractionator. In: IEEE Conference on Control Applications. 2002, Glasgow, Scotland: IEEE.
• Xiong Z, Zhang J. Modeling and optimal control of fed-batch processes using control affine feedforward neural networks. In: Proceedings of the American Control Conference. 2002, Anchorage, Alaska: IEEE.
• Xiong Z, Zhang J. Neural Network Based On-Line Re-Optimisation Control of Fed-Batch Processes Using Iterative Dynamic Programming for Discret-Time Systems. In: 15th IFAC World Congress. 2002, Barcelona, Spain: Elsevier.
• Wei J, Xu YM, Zhang J. Neural Networks based Model Predictive Control of an Industrial Polypropylene Process. In: IEEE Conference on Control Applications. 2002, Glasgow, Scotland: IEEE.
• 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. Optimal control of a batch emulsion copolymerisation reactor based on recurrent neural network models. Chemical Engineering and Processing 2002, 41(6), 531-538.
• 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.
• Xiong Z, Zhang J. Optimal control of batch processes incorporating model prediction confidence bounds based on multiple neural networks. In: International Conference on Control Applications. 2002, Glasgow, UK: IEEE.
• Liu XG, Xu YM, Zhang J, Qian JX. Optimal Energy Cost in Ideal Internal Thermally Coupled Distillation Columns. In: American Control Conference. 2002, Anchorage, Alaska, USA: IEEE.
• Zhang J. Sequential training of bootstrap aggregated neural networks for nonlinear systems modelling. In: Proceedings of the American Control Conference. 2002, Anchorage, Alaska: IEEE.
• Zhang J. A nonlinear gain scheduling control strategy based on neuro-fuzzy networks. Industrial and Engineering Chemistry Research 2001, 40(14), 3164-3170.
• 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.
• 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, 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.
• 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.
• 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.
• 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, 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.
• 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. Recurrent neuro-fuzzy networks for the modelling and optimal control of batch processes. In: Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS). 2001, Vancouver, British Columbia, Canda: IEEE.
• Zhang J. Recurrent Neuro-Fuzzy Networks for the Modelling and Optimal Control of Batch Processes. In: Proceedings of the joint 9th IFSA World Congress and the 20th NAFIPS International Conference. 2001, Vancouver, Canada: IEEE.
• 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. Developing robust non-linear models through bootstrap aggregated neural networks. Neurocomputing 1999, 25(1-3), 93-113.
• Zhang J. Inferential estimation of polymer quality using bootstrap aggregated neural networks. Neural Networks 1999, 12(6), 927-938.
• Zhang J, Morris AJ. Recurrent Neuro-Fuzzy Networks for Nonlinear Process Modeling. IEEE Transactions on Neural Networks 1999, 10(2), 313-326.