The validity domain of hybrid models and its application in process optimization

Kahrs, Olaf; Marquardt, Wolfgang

doi:10.1016/j.cep.2007.02.031

Cited by 90 publications

(63 citation statements)

References 22 publications

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“…Here, it can be seen that the rectangular domain involves regions which do not involve any observation. The tight constraints of the convex hull prevent the model from taking these values, and therefore the reliability of the models is expected to increase as has been discussed in [11].…”

Section: Validity Domainmentioning

confidence: 99%

Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

Xenos

Kahrs

Cicciotti

et al. 2016

2016 European Control Conference (ECC)

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Abstract-The optimization of the operation of chemical plants may require the development of mathematical models of the process units of a plant. These mathematical models can be either first-principles or data-driven models. The former type of modeling may be complex for the use in optimization and especially for online applications such as real time optimization. Available measured process data can be used to develop the latter type of modeling. Although data-driven models offer several benefits for online applications, there are some very significant challenges related to their development in a practical industrial implementation. This paper discusses the important aspects of the building of data-driven models and demonstrates the effects of these types of models on the optimization results. The current work demonstrates the application of a real time optimization framework applied to an industrial air compressor station of an air separation plant when the models are based on operating data.

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Section: Validity Domainmentioning

confidence: 99%

Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

Xenos

Kahrs

Cicciotti

et al. 2016

2016 European Control Conference (ECC)

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“…Hybrid semi-parametric models have been used to optimize the control policy either to maximize some quantity Eslamloueyan & Setoodeh, 2011;Henriques et al, 1999;Ignova et al, 2002;Kahrs & Marquardt, 2007;Mahalec & Sanchez, 2012;Preusting et al, 1996;Psichogios & Ungar, 1992;Schubert et al, 1994a;Teixeira et al, 2005Teixeira et al, , 2006Tholudur & Ramirez, 1996, 1999Zuo & Wu, 2000) or to meet specific quality specifications (Doyle et al, 2003;Hermanto et al, 2011;Safavi et al, 1999;Tian et al, 2001;Zhang et al, 2012) (see Table 10 -supplementary material for a complete list). Theoretically, the control policy can be optimized off-line or on-line.…”

Section: Optimizationmentioning

confidence: 99%

“…Mahalec and Sanchez (2012) propose to constrain the optimization by two measures, one accounting for the distance of the current inputs to historical ones and a second ensuring that the residual and bias of the predictions in relation to the model plane do not exceed a certain threshold. Similarly, in Kahrs and Marquardt (2007) two complementary criteria to check the validity domain of hybrid semi-parametric models are proposed: (1) A convex-hull criteria to check whether each empirical model part only interpolates the data encountered during model identification; and (2) a confidence interval criterion with which the confidence intervals for the hybrid semi-parametric model are calculated. In comparison to the clustering technique, the convex-hull criteria has the advantage that it can be implemented as a set of linear constraints, while the clustering technique is a nonlinear constraint, but the convex-hull criteria might be too optimistic when the data distribution is strongly non-uniform, which is not the case for clustering.…”

Section: Measures For Model Extrapolationmentioning

confidence: 99%

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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“…Various industrial applications have been realized successfully [11][12][13][14], and software implementations are available as well.…”

Section: First Step: Modelling Of Hierarchical Functional Networkmentioning

confidence: 99%

Efficient reengineering of meso-scale topologies for functional networks in biomedical applications

Schuppert

2011

Mathematics in Industry

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Despite the deluge of bioinformatics data, the extraction of information with respect to complex diseases remains an open challenge. The development of efficient tools allowing the re-engineering of functional biological networks will therefore be crucial for the future of the pharmaceutical and biotech industry. In this paper we present a method for efficient re-engineering of meso-scale network topologies for biomedical systems from stationary data. We show that the meso-scale topology is related to functional structures of the input-output data of the entire system, which can be unravelled from high throughput screening experiments, without information with respect to intermediate variables. Analysis of the functional structure of the data provides a complementary approach to established network reengineering methods based on combinatorial optimization. A combination of both approaches will help to overcome the drawbacks of the established network reengineering algorithms.

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The validity domain of hybrid models and its application in process optimization

Cited by 90 publications

References 22 publications

Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

Challenges of the application of data-driven models for the real-time optimization of an industrial air separation plant

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

Efficient reengineering of meso-scale topologies for functional networks in biomedical applications

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