Real-time adaptive input design for the determination of competitive adsorption isotherms in liquid chromatography |
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| Affiliation: | 1. AIT Austrian Institute of Technology GmbH, Giefinggasse 2, 1210 Wien, Austria;2. Technische Universität Berlin, Chair of Process Dynamics and Operation, Str. des 17. Juni 135, 10623 Berlin, Germany;3. Technische Universität Berlin, Institute of Biotechnology, Department of Bioprocess Engineering, Ackerstr. 71-76, D-13355 Berlin, Germany;4. University of Mannheim, School of Business Informatics and Mathematics, B6, 26, 68131 Mannheim, Germany;5. Universität Heidelberg, Interdisciplinary Center for Scientific Computing, Im Neuenheimer Feld 368, 69120 Heidelberg, Germany;1. Mechanical, Energetics and Materials Engineering Department, Public University of Navarra, 31006, Pamplona, Navarra, Spain;2. Mechanical and Materials Engineering Department, Polytechnic University of Valencia, C/Camino de Vera s/n, 46022, Valencia, Spain |
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| Abstract: | 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. |
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| Keywords: | Adaptive input design Regularization techniques Model based optimal experimental re-design Parameter estimation |
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