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Real-time adaptive input design for the determination of competitive adsorption isotherms in liquid chromatography

The adaptive input design (also called online redesign of experiments) for parameter estimation is very effective for the compensation of uncertainties in nonlinear processes. Moreover, it enables substantial savings in experimental effort and greater reliability in modeling. We present theoretical...

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Hauptverfasser: Barz, Tilman (VerfasserIn) , López C., Diana C. (VerfasserIn) , Cruz Bournazou, M. Nicolás (VerfasserIn) , Körkel, Stefan (VerfasserIn) , Walter, Sebastian F. (VerfasserIn)
Dokumenttyp: Article (Journal)
Sprache:Englisch
Veröffentlicht: 30July2016
In: Computers & chemical engineering
Year: 2016, Jahrgang: 94, Pages: 104-116
ISSN:0098-1354
DOI:10.1016/j.compchemeng.2016.07.009
Online-Zugang:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1016/j.compchemeng.2016.07.009
Verlag, lizenzpflichtig, Volltext: http://www.sciencedirect.com/science/article/pii/S0098135416302290
Volltext
Verfasserangaben:Tilman Barz, Diana C. López C., M. Nicolás Cruz Bournazou, Stefan Körkel, Sebastian F. Walter
Beschreibung
Zusammenfassung:The adaptive input design (also called online redesign of experiments) for parameter estimation is very effective for the compensation of uncertainties in nonlinear processes. Moreover, it enables substantial savings in experimental effort and greater reliability in modeling. We present theoretical details and experimental results from the real-time adaptive optimal input design for parameter estimation. The case study considers separation of three benzoate by reverse phase liquid chromatography. Following a receding horizon scheme, adaptive D-optimal input designs are generated for a precise determination of competitive adsorption isotherm parameters. Moreover, numerical techniques for the regularization of arising ill-posed problems, e.g. due to scarce measurements, lack of prior information about parameters, low sensitivities and parameter correlations are discussed. The estimated parameter values are successfully validated by Frontal Analysis and the benefits of optimal input designs are highlighted when compared to various standard/heuristic input designs in terms of parameter accuracy and precision.
Beschreibung:Gesehen am 19.08.2020
Beschreibung:Online Resource
ISSN:0098-1354
DOI:10.1016/j.compchemeng.2016.07.009

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