An integrated hybrid neural system for noise filtering, simulation and control of a fed-batch recombinant fermentation

Patnaik, Pratap R.

doi:10.1016/s1369-703x(02)00195-x

Cited by 26 publications

(36 citation statements)

References 42 publications

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“…The concept of using more than one neural network was also explored in serial hybrid semi-parametric models (Cao et al, 2004;Gnoth, Jenzsch, Simutis, & Luebbert, 2008;Gupta et al, 1999;Patnaik, 2001Patnaik, , 2003Patnaik, , 2010Piron, Latrille, & Rene, 1997;Preusting et al, 1996;Reuter et al, 1993;Silva, Cruz, Hokka, Giordano, & Giordano, 2000, 2001. Preusting et al (1996) used two ANNs in parallel to model separate phenomena, i.e.…”

Section: Nonparametric Models For Specific Problemsmentioning

confidence: 99%

“…done by Patnaik (2001Patnaik ( , 2003Patnaik ( , 2010, Piron et al (1997), Saraceno, Curcio, Calabro, and Iorio (2010) can help to make the model structure more transparent, and increase the accuracy of each predicted quantity. Differently Cao et al (2004) applied two individual nonparametric models to predict the same quantity with each model relying on different phenomena, i.e.…”

Section: Nonparametric Models For Specific Problemsmentioning

confidence: 99%

“…The hybrid process model can also be exploited to tune any kind of standard controller both, prior to application (off-line) (Georgieva & de Azevedo, 2009;Georgieva & Feyo de Azevedo, 2007;Schubert et al, 1994a;von Stosch et al, 2012); and under control (on-line) (Andrasik, Meszaros, & de Azevedo, 2004;Chen et al, 2004;Patnaik, 2003Patnaik, , 2004Patnaik, , 2008Patnaik, , 2010Schubert et al, 1994a;Wei, Hussain, & Wahab, 2007;Zhang et al, 2006). The utilization of a hybrid semiparametric model for controller tuning bears some advantages, because (i) the hybrid semi-parametric model might be identified from standard operational data, but the controllers can be tuned considering set-point changes and load disturbances, both scenarios in which the model might have to extrapolate (von Stosch et al, 2012); and (ii) the coupling of different control inputs and/or variables can be captured with little effort by the hybrid semi-parametric model and subsequently considered during the tuning (multivariate control) (von Stosch et al, 2012).…”

Section: Hybrid Semi-parametric Model Based Controller Tuningmentioning

confidence: 99%

“…The utilization of a hybrid semiparametric model for controller tuning bears some advantages, because (i) the hybrid semi-parametric model might be identified from standard operational data, but the controllers can be tuned considering set-point changes and load disturbances, both scenarios in which the model might have to extrapolate (von Stosch et al, 2012); and (ii) the coupling of different control inputs and/or variables can be captured with little effort by the hybrid semi-parametric model and subsequently considered during the tuning (multivariate control) (von Stosch et al, 2012). Controllers which are tuned in such a way comprise, for instance, neural net-work controllers (Patnaik, 2003(Patnaik, , 2004(Patnaik, , 2008(Patnaik, , 2010Schubert et al, 1994a;von Stosch et al, 2012;Zhang et al, 2006), the ANN models in a MPC schema (Georgieva & de Azevedo, 2009;Georgieva & Feyo de Azevedo, 2007) or hybrid controllers (Andrasik et al, 2004;Ng & Hussain, 2004;von Stosch et al, 2012;Wei et al, 2007), where the term hybrid semi-parametric controllers is an analogy to hybrid semi-parametric models. Frequently applied standard approaches which incorporate hybrid semi-parametric models comprise Internal Model Control (IMC) (Chen et al, 2004;Schubert et al, 1994a;Wei et al, 2007;Zhang et al, 2006) and Inverse Model Control (IVMC) (Ng & Hussain, 2004;Wei et al, 2007), see supplementary material - Table 9.…”

Section: Hybrid Semi-parametric Model Based Controller Tuningmentioning

confidence: 99%

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Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Stosch¹,

Oliveira²,

Peres³

et al. 2014

Computers & Chemical Engineering

336

201

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Hybrid semi-parametric models consist of model structures that combine parametric and nonparametric submodels based on different knowledge sources. The development of a hybrid semi-parametric model can offer several advantages over traditional mechanistic or data-driven modeling, as reviewed in this paper. These advantages, such as broader knowledge base, transparency of the modeling approach and cost-effective model development, have been widely recognized, not only in academia but also in the industry.In this paper, the most common hybrid semi-parametric modeling and parameter identification techniques are revisited. Applications in the areas of (bio)chemical engineering for process monitoring, control, optimization, scaleup and model-reduction are reviewed. It is outlined that the application of hybrid semi-parametric techniques does not automatically lead into better results but that rational knowledge integration has potential to significantly improve model-based process operation and design.

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Section: Nonparametric Models For Specific Problemsmentioning

confidence: 99%

Section: Nonparametric Models For Specific Problemsmentioning

confidence: 99%

Section: Hybrid Semi-parametric Model Based Controller Tuningmentioning

confidence: 99%

Section: Hybrid Semi-parametric Model Based Controller Tuningmentioning

confidence: 99%

See 2 more Smart Citations

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Stosch¹,

Oliveira²,

Peres³

et al. 2014

Computers & Chemical Engineering

336

201

View full text Add to dashboard Cite

show abstract

“…Hybrid models have already been applied to bioreactors (Tholudur and Ramirez, 1996;Chen et al, 2000;James et al, 2002;Patnaik, 2003), ethanol production by Sacharomyces cerevisae (Harada et al, 2000), and enzymatic production (Tholudur et al, 1996;Gonçalves, et al, 2002) and other processes. In this work, neural networks were utilized to describe the behavior of the inhibition parameter ki.…”

Section: Neural Network and Hybrid Modelingmentioning

confidence: 99%

Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling

Corazza

Calsavara

Moraes

et al. 2005

Braz. J. Chem. Eng.

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-Neural networks and hybrid models were used to study substrate and product inhibition observed in the enzymatic hydrolysis of cellobiose at 40ºC, 50ºC and 55ºC, pH 4.8, using cellobiose solutions with or without the addition of exogenous glucose. Firstly, the initial velocity method and nonlinear fitting with Statistica ® were used to determine the kinetic parameters for either the uncompetitive or the competitive substrate inhibition model at a negligible product concentration and cellobiose from 0.4 to 2.0 g/L. Secondly, for six different models of substrate and product inhibitions and data for low to high cellobiose conversions in a batch reactor, neural networks were used for fitting the product inhibition parameter to the mass balance equations derived for each model. The two models found to be best were: 1) noncompetitive inhibition by substrate and competitive by product and 2) uncompetitive inhibition by substrate and competitive by product; however, these models' correlation coefficients were quite close. To distinguish between them, hybrid models consisting of neural networks and first principle equations were used to select the best inhibition model based on the smallest norm observed, and the model with noncompetitive inhibition by substrate and competitive inhibition by product was shown to be the best predictor of cellobiose hydrolysis reactor behavior.

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Machine learning‐based model predictive controller design for cell culture processes

Rashedi

Rafiei

Demers

et al. 2023

Biotech & Bioengineering

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The biopharmaceutical industry continuously seeks to optimize the critical quality attributes to maintain the reliability and cost‐effectiveness of its products. Such optimization demands a scalable and optimal control strategy to meet the process constraints and objectives. This work uses a model predictive controller (MPC) to compute an optimal feeding strategy leading to maximized cell growth and metabolite production in fed‐batch cell culture processes. The lack of high‐fidelity physics‐based models and the high complexity of cell culture processes motivated us to use machine learning algorithms in the forecast model to aid our development. We took advantage of linear regression, the Gaussian process and neural network models in the MPC design to maximize the daily protein production for each batch. The control scheme of the cell culture process solves an optimization problem while maintaining all metabolites and cell culture process variables within the specification. The linear and nonlinear models are developed based on real cell culture process data, and the performance of the designed controllers is evaluated by running several real‐time experiments.

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An integrated hybrid neural system for noise filtering, simulation and control of a fed-batch recombinant fermentation

Cited by 26 publications

References 42 publications

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Hybrid semi-parametric modeling in process systems engineering: Past, present and future

Determination of inhibition in the enzymatic hydrolysis of cellobiose using hybrid neural modeling

Machine learning‐based model predictive controller design for cell culture processes

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