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D. Process Modelling
- Improving the accuracy of flux balance analysis through the implementation of carbon availability constraints for intracellular reactions.
- Lularevic M, Racher A J, Jaques C, Kiparissides A.
- Biotechnology & Bioengineering 2019, 1-14
- https://doi.org/10.1002/bit.27025
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- Ship fuel consumption monitoring and fault detection via partial least squares and control charts of navigation data. Transportation Research Part D: Transport and Environment.
- Capezza C, Coleman, SY, Lepore A, Palumbo B, Vitiello L.
in press, 2019
D1 Kinetic modelling
- Improved method for kinetic studies in microreactors using flow manipulation and non-invasive Raman spectrometry
- Mozharov S, Nordon A, Littlejohn D, Wiles C, Watts P, Dallin P and Girkin JM
Journal of the American Chemical Society, 2011, 133, 3601-3608
- Multivariate kinetic hard-modelling of spectroscopic data: A comparison of the esterification of butanol by acetic anhydride on different scales and with different instruments
- Puxty, Neuhold Y-M, Ehly M, Gemperline PJ, Nordon A, Littlejohn D, Basford JK, De Cecco M and Hunderbuhler K
Chem. Eng. Sci.,, 2008, 63 (19), 4800-4809
- Nonlinear wave modeling and dynamic analysis of internal thermally coupled distillation columns
- Liu X, Zhou Y, Cong L, Zhang J.
AIChE Journal, 2012, Vol 58 No 4, 1146-1156
- Scale-up of batch kinetic models
- Ehly M, Gemperline PJ, Nordon A, Littlejohn D, Basford JK and De Cecco M
Anal Chim Acta, 2007, 595, 80-88
D2 Multi-block, predictive and multi-scale modelling methods
- Nonlinear process modelling using echo state networks optimised by covariance matrix adaption evolutionary strategy
- Liu K, Zhang J
- Computers & Chemical Engineering 2020, 135, 106730
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- Predicting molten steel endpoint temperature using a feature-weighted model optimized by mutal learning cuckoo research
- Yang Q, Zhang J, Zhi Y
- Applied Soft Computing Journal, 2019, 83, 105675
- Artifical intelligence techniques applied as estimator in chemical process systems - A literature survey
- Ali J M, Hussain M A, Tade M O and Zhang J.
Expert Systems with Applications, 2015, Vol 42 No 14, 5915-5913
- Batch to batch iterative learning control using updated models based on a moving window of historical data
- Jewaratnam J, Zhang J, Hussain A and Morris J
Procedia Engineering, 2012, Vol 42, 232-240
- Batch-to-batch control of fed-batch processes using control-affine feedforward neural network
- Xiong Z, Xu Y, Zhang J and Dong J
Neural Computing & Applications, 2008, 17, 425-432
- Effect of particle size distribution on spatially and angularly resolved diffuse reflectance measurement
- Chen Yi-Chieh, Tiernan-Vandermotten Sarra, Lue Leo, Ferreira Carla Sofia, Sefcik Jan, Thannadil Suresh
European Pharamaceutical Review, 2018, 23, 34-37
- Energy efficiency optimisation for distillation column using artificial neural network models
- Osuolale F, Zhang J.
Energy, 2016, Vol 106, 562-578
- Enhanced Predictive Modelling Using Multi Block Methods
- Jeong JJ, Zhang J and Morris AJ
19th Symposium on Computer Aided Process Engineering - ESCAPE 19, 2009
- Hybrid modeling as a QbD/PAT tool in Process Development: An industrial E.Coli case study
- von Stosch M, Hamelink J M, Oliveira R.
Journal of Bioprocess and Biosystems Engineering, 2016, 39 (5), 773-784
- Inferential estimation of kerosene dry point in refineries with varying crudes
- Zhou C, Liu Q, Huang D X, Zhang J.
Journal of Process Control, 2012, Vol 22 No 6, 1122-1126
- Iterative learning control of a crystallisation process using batch wise updated linearised models identified using PLS
- Zhang J, Nguyan J and Morris AJ
Computer Aided Chemical Engineering 26, Proceedings of the 19th European Symposium on Computer Aided Process Engineering, 2009, 387-392
- Modelling and control of reactive polymer composite moulding using bootstrap aggregated neural network models
- Zhang J, Pantelelis N G.
Chemical Product and Process Modeling, 2011, Vol 6 (2), Article 5
- Modelling of a post combustion CO2 capture process using neural networks
- Li F, Zhang J, Oko E and Wang M
Fuel, 2015, 151, 156-163
- Multi-scale Multiblock Batch Monitoring:Sensor and Process Drift and Degradation
- Alawi A, Zhang J and Morris J,
Org. Process Res. Dev., 2015, 19 (1), 145-157
- Neural network approach for predicting drum pressure and level in coal-fired subcritical power plant
- Oko E, Wang M and Zhang J.
Fuel, 2015, 151, 139-145
- Nonlinear multiscale modelling for fault detection and identification
- Choi SW, Morris J and Lee I-B
Chemical Engineering Science, 2008, 62 (22), 6191-6198
- Optimal control of fed-batch processess using particle swarm optimisation with staked neural network models
- Herrara F, Zhang J
Computers & Chemical Engineering, 2009, Vol 33, No 10, 1593-1601
- Optimal Iterative Learning Control for Batch Processes Based on Linear Time-varying Perturbation Model
- Xiong Z, Zhang J and Dong J
Chinese Journal of Chemical Engineering, 2008, 16, 235-240
- Prediction of absorption and stripping factors in natural gas processing industries using feed forward artificial neural network
- Ahmad Z, Zhang J, Kashiwao T and Bahadori A.
Petroleum Science and Technology, 2016, Vol 34 No 2, 105-113
- Progressive multi-block modelling for enhanced fault isolation in batch processes
- Hong JJ, Zhang J, Morris J
Journal of Process Control, 2014, 24(1), 13-26
- Reliable optimal control of a fed-batch fermentation process using ant colony optimisation and bootstrap aggregated neural network models
- Zhang J, Feng M
Appl. Metaheuristics Process. Eng., 2014, 183-200
- Reliable optimisation control of a reactive polymer composite moulding process using ant colony optimisation and bootstrap aggregated neural networks
- Mohammed K R, Zhang J.
Neural computing & Applications, 2013, Vol 23, 1891-1898
- Selective Combination of Multiple Neural Networks for Improving Model Prediction in Nonlinear Systems Modelling through Forward Selection and Backward Elimination
- Ahmad Z and Zhang J
Neurocomputing, 2008, 72 (4-6), 1198-1204
- Spatially and angularly resolved spectroscopy for in-situ estimation of concentration and particle size in colloidal suspensions
- Chen Yi-Chieh, Foo David, Dehanov Nicolau, Thennadil Suresh N
Analytical and Bioanalytical Chemistry, 2017, 409, 6975-6988
- Towards intensifying Design of Experiements in upstream bioprocess development: An industrial E. coli feasibility study
- von Stosch M, Hamelink J M, Oliveira R.
Biotechnology Progress, 2016
D3 Performance monitoring
- A MATLAB toolbox for data pre-processing and multivariate statistical process control
- Yi G, Herdsman C, Morris J
- Chemometics and Intelligent Laboratory Systems 194 (2019) 103863
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- Randomised Kernel Principal Component Analysis for Modelling and Monitoring of Nonlinear Industrial Processes with Massice Data
- Zhou Z, Du N, Xu J, Li Z, Wang P, Zhang J
- Industrial and Engineering Chemistry Research 2019, 58, 10410-10417
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- A comparative investigation of the combined effects of pre processing, wavelength selection and regression methods on near-infrared calibration model performance
- Wan Jian, Chen Yi-Chieh, Morris A Julian, Thennadil N Suresh
Appl. Spectrosc., 2017, On-Line 30/03/17
- A comparative investigation of the combined effects of pre-processing, wavelength selection and regression methods on near infrared calibration model performance,
- Wan J., Chen, Y.-C., Morris, J. A. and Thennadil, S. N
Applied Spectroscopy, 2017, Jul:71 (7), 1432-1446
- Disturbance Attenuation in Fault Detection of Gas Turbine Engines: A Discrete Robust Observer Design
- Dai X, Gao Z, Breikin T and Wang H
IEEE T. Syst. Man Cybernet. Part C, 2009, 39(2), 234-239
- Entropy Optimization Filtering for Fault Isolation of nonlinear Non-Gaussian Stochastic Systems
- Guo L, Yin L, Wang H and Chai TY
IEEE T. Automat. Cont., 2009, 54:, 804-810
- Fault detection in dynamic processes using a simplified monitoring-specific CVA state space modelling approach
- Stubbs S, Zhang J, Morris J.
Computers & Chemical Engineering, 2012, Vol 41, 77-87
- Fault detection of dynamic processes using a simplified monitoring-specific CVA state space approach
- Stubbs S, Zhang J and Morris AJ
Eur. Sym. Comput. Aided Process Eng. ESCAPE 19, 2009
- Fault localization in batch processes through progressive principal component analysis modeling
- Hong JJ, Zhang J, Morris J
Ind Eng Chem Res, 2011, Vol 50 (13), 8153-8162
- Hybrid modeling as a QbD/PAT tool in Process Development: An industrial E.Coli case study
- von Stosch M, Hamelink J M, Oliveira R.
Journal of Bioprocess and Biosystems Engineering, 2016, 39 (5), 773-784
- Multiway interval partial least squares for batch process performance
- Stubbs S, Zhang J, Morris J.
Ind Eng Chem Res, 2013, Vol 52 (35), 12399-12407
- Nonlinear multiscale modelling for fault detection and identification
- Choi SW, Morris J and Lee I-B
Chemical Engineering Science, 2008, 62 (22), 6191-6198
- On-line multivariate statistical monitoring of batch processes using Gaussian mixture model
- Chen T, Zhang J.
Computers & Chemical Engineering, 2010, Vol 34, 500-507
- Penalized reconstruction-based multivariate contribution analysis for fault isolation
- He B, Zhang J, Chen T and Yang X
Ind Eng Chem Res, 2013, Vol 52 (23), 7784-7794
- Progressive multi-block modelling for enhanced fault isolation in batch processes
- Hong JJ, Zhang J, Morris J
Journal of Process Control, 2014, 24(1), 13-26
- Reconstruction-based multivariate contribution analysis for fault isolation: A branch and bound approach
- He B, Ynag X, Chen T, Zhang J
Journal of Process Control, 2012, Vol 22, 1228-1236
- Towards intensifying Design of Experiements in upstream bioprocess development: An industrial E. coli feasibility study
- von Stosch M, Hamelink J M, Oliveira R.
Biotechnology Progress, 2016
- Zero assignment for robust H_2/ H_infinity fault detection filter design
- Dai X, Gao Z, Breikin T and Wang H
IEEE T. Sig. Pro. Sys., 2009, 57, 1363-1372