Incremental identification of hybrid process models

Kahrs, Olaf; Marquardt, Wolfgang

doi:10.1016/j.compchemeng.2007.02.014

Cited by 45 publications

(41 citation statements)

References 13 publications

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“…the initial values of the parameters might be known, which can simplify the identification. Approaches explicitly dealing with this scenario are given in Vande Wouwer et al (2004), Kahrs and Marquardt (2008), Yang, Martin, and Morris (2011) the latter is shortly presented in Section 2.4.2.…”

Section: How Can Unknown Parts Be Identified? -Methods For Model Idenmentioning

confidence: 99%

“…Additionally, the objective function can contain a regulation term which e.g. can enhance the generalization capabilities of the model (Hu, Mao, He, & Yang, 2011;Kahrs & Marquardt, 2008;Vande Wouwer et al, 2004). While, in principle the same identification schema can be applied when also other parameters are unknown, e.g.…”

Section: How Can Unknown Parts Be Identified? -Methods For Model Idenmentioning

confidence: 99%

“…Convergence and success of the identification depend on the initialized parameter values (Kahrs & Marquardt, 2008;von Stosch, Oliveira, Peres, & Feyo de Azevedo, 2012). Relatively small weight values are preferential (because generally inheriting better generalization capabilities), wherefore the initialization values of the parameters are usually constrained, e.g.…”

Section: General Remarks About the Identificationmentioning

confidence: 99%

“…The incremental approach, proposed by Kahrs and Marquardt (2008), is ideal for relatively large systems, since the identification problem is at first decomposed into four smaller problems which are solved sequentially, thereby reducing the curse of dimensionality. During this phase standard training techniques for the identification of the nonparametric model can be used.…”

Section: The Incremental Approachmentioning

confidence: 99%

See 3 more Smart Citations

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

Stosch¹,

Oliveira²,

Peres³

et al. 2014

Computers & Chemical Engineering

359

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: How Can Unknown Parts Be Identified? -Methods For Model Idenmentioning

confidence: 99%

Section: How Can Unknown Parts Be Identified? -Methods For Model Idenmentioning

confidence: 99%

Section: General Remarks About the Identificationmentioning

confidence: 99%

Section: The Incremental Approachmentioning

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

359

201

View full text Add to dashboard Cite

show abstract

“…The systematic methods for identifying this type of models can be found in e.g. Kahrs and Marquardt (2008). However, in reality a constitutive relationship may be of a known mechanistic form but has unknown parameters that require to be estimated.…”

Section: Introductionmentioning

confidence: 99%