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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
 
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
 
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
 
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
 
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