A framework of hybrid model development with identification of plant‐model mismatch

[1]  C Vervaet,et al.  Continuous direct compression as manufacturing platform for sustained release tablets. , 2017, International journal of pharmaceutics.

[2]  D. Kirschner,et al.  A methodology for performing global uncertainty and sensitivity analysis in systems biology. , 2008, Journal of theoretical biology.

[3]  Joseph Sang-Il Kwon,et al.  Deep hybrid modeling of chemical process: Application to hydraulic fracturing , 2020, Comput. Chem. Eng..

[4]  G. Reklaitis,et al.  Perspectives on the continuous manufacturing of powder‐based pharmaceutical processes , 2016 .

[5]  Marianthi Ierapetritou,et al.  Model development and prediction of particle size distribution, density and friability of a comilling operation in a continuous pharmaceutical manufacturing process , 2018, International journal of pharmaceutics.

[6]  Bernold Fiedler,et al.  Local identification of scalar hybrid models with tree structure , 2008 .

[7]  P. A. Minderman,et al.  INTEGRATING NEURAL NETWORKS WITH FIRST PRINCIPLES MODELS FOR DYNAMIC MODELING , 1992 .

[8]  Gary A. Montague,et al.  Modelling pressure drop in water treatment , 1997, Artif. Intell. Eng..

[9]  Marianthi G. Ierapetritou,et al.  Modeling of Particulate Processes for the Continuous Manufacture of Solid-Based Pharmaceutical Dosage Forms , 2013 .

[10]  Bryan Stanfill,et al.  Quantifying model-structure- and parameter-driven uncertainties in spring wheat phenology prediction with Bayesian analysis , 2017 .

[11]  Sebastião Feyo de Azevedo,et al.  Hybrid semi-parametric modeling in process systems engineering: Past, present and future , 2014, Comput. Chem. Eng..

[12]  Selen Cremaschi,et al.  An algorithm to determine sample sizes for optimization with artificial neural networks , 2013 .

[13]  Andreas Lübbert,et al.  Hybrid Process Modeling for Advanced Process State Estimation, Prediction, and Control Exemplified in a Production-Scale Mammalian Cell Culture , 1996 .

[14]  W. Fred Ramirez,et al.  Dynamic hybrid neural network model of an industrial fed-batch fermentation process to produce foreign protein , 2007, Comput. Chem. Eng..

[15]  Marianthi Ierapetritou,et al.  Effect of material properties on the residence time distribution (RTD) characterization of powder blending unit operations. Part II of II: Application of models , 2019, Powder Technology.

[16]  Sharifah Rafidah Wan Alwi,et al.  A generic hybrid model development for process analysis of industrial fixed-bed catalytic reactors , 2017 .

[17]  Teresa B. Ludermir,et al.  Comparison of new activation functions in neural network for forecasting financial time series , 2011, Neural Computing and Applications.

[18]  Marianthi G. Ierapetritou,et al.  Modeling the effects of material properties on tablet compaction: A building block for controlling both batch and continuous pharmaceutical manufacturing processes , 2018, International journal of pharmaceutics.

[19]  Pierantonio Facco,et al.  A Methodology to Diagnose Process/Model Mismatch in First-Principles Models , 2014 .

[20]  James B. Rawlings,et al.  Identification for decentralized model predictive control , 2006 .

[21]  Atharv Bhosekar,et al.  Advances in surrogate based modeling, feasibility analysis, and optimization: A review , 2018, Comput. Chem. Eng..

[22]  Dafydd Evans,et al.  A computationally efficient estimator for mutual information , 2008, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[23]  Andrew J. King,et al.  Measuring the Performance of Neural Models , 2016, Front. Comput. Neurosci..

[24]  Ron S. Kenett,et al.  Assessing the value of information of data-centric activities in the chemical processing industry 4.0 , 2018, AIChE Journal.

[25]  Selen Cremaschi,et al.  Adaptive sequential sampling for surrogate model generation with artificial neural networks , 2014, Comput. Chem. Eng..

[26]  Gintaras V. Reklaitis,et al.  Robust state estimation of feeding-blending systems in continuous pharmaceutical manufacturing. , 2018, Chemical engineering research & design : transactions of the Institution of Chemical Engineers.

[27]  Andreas A. Schuppert,et al.  Extrapolability of structured hybrid models: a key to optimization of complex processes , 2000 .

[28]  Lyle H. Ungar,et al.  A hybrid neural network‐first principles approach to process modeling , 1992 .

[29]  Min Xu,et al.  Practical generalized predictive control with decentralized identification approach to HVAC systems , 2007 .

[30]  Marianthi G. Ierapetritou,et al.  An integrated approach for dynamic flowsheet modeling and sensitivity analysis of a continuous tablet manufacturing process , 2012, Comput. Chem. Eng..

[32]  Alexander Mitsos,et al.  Economic nonlinear model predictive control using hybrid mechanistic data-driven models for optimal operation in real-time electricity markets: In-silico application to air separation processes , 2019 .

[33]  Marianthi Ierapetritou,et al.  Effect of tracer material properties on the residence time distribution (RTD) of continuous powder blending operations. Part I of II: Experimental evaluation , 2019, Powder Technology.

[34]  Venkat Venkatasubramanian,et al.  A neural network methodology for process fault diagnosis , 1989 .

[35]  Zilong Wang,et al.  Process analysis and optimization of continuous pharmaceutical manufacturing using flowsheet models , 2017, Comput. Chem. Eng..

[36]  Yang Lu,et al.  Industry 4.0: A survey on technologies, applications and open research issues , 2017, J. Ind. Inf. Integr..

[37]  Eric S. Fraga,et al.  A diagnostic procedure for improving the structure of approximated kinetic models , 2020, Comput. Chem. Eng..

[38]  Hector M. Budman,et al.  Simultaneous model identification and optimization in presence of model-plant mismatch , 2015 .

[39]  André Elisseeff,et al.  A Partial Correlation-Based Algorithm for Causal Structure Discovery with Continuous Variables , 2007, IDA.

[40]  Alexander Mitsos,et al.  Reduced dynamic modeling approach for rectification columns based on compartmentalization and artificial neural networks , 2019, AIChE Journal.

[41]  D. Hamby.  A comparison of sensitivity analysis techniques. , 1995, Health physics.

[42]  Ali Lohi,et al.  Applications of hybrid models in chemical, petroleum, and energy systems: A systematic review , 2018, Applied Energy.

[43]  Jian Chu,et al.  Detecting Model–Plant Mismatch of Nonlinear Multivariate Systems Using Mutual Information , 2013 .

[44]  G. P. Rangaiah,et al.  First-Principles, Data-Based, and Hybrid Modeling and Optimization of an Industrial Hydrocracking Unit , 2006 .

[45]  A. Kraskov,et al.  Estimating mutual information. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[46]  Henk B. Verbruggen,et al.  Semi-mechanistic modeling of chemical processes with neural networks , 1998 .

[47]  Fernanda de Castilhos Corazza,et al.  DETERMINATION OF INHIBITION IN THE ENZYMATIC HYDROLYSIS OF CELLOBIOSE USING HYBRID NEURAL MODELING , 2005 .

[48]  Venkat Venkatasubramanian,et al.  Hidden representations in deep neural networks: Part 1. Classification problems , 2020, Comput. Chem. Eng..

[49]  Shankar Narasimhan,et al.  Detection of model-plant mismatch and model update for reaction systems using concept of extents , 2018, Journal of Process Control.

[50]  Michael Baldea,et al.  Plant-model mismatch evaluation for unconstrained MPC with state estimation , 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC).

[51]  Jarka Glassey,et al.  Hybrid Modeling in Process Industries , 2018 .

[52]  V. Venkatasubramanian.  The promise of artificial intelligence in chemical engineering: Is it here, finally? , 2018, AIChE Journal.

[53]  Mark A. Kramer,et al.  Modeling chemical processes using prior knowledge and neural networks , 1994 .

[54]  Sirish L. Shah,et al.  Detection of model-plant mismatch in MPC applications ☆ , 2009 .

[55]  Johannes Khinast,et al.  Detailed modeling and process design of an advanced continuous powder mixer , 2018, International journal of pharmaceutics.

[56]  Karel Ch. A. M. Luyben,et al.  Strategy for dynamic process modeling based on neural networks in macroscopic balances , 1996 .