scholarly journals Application of iterative robust model-based optimal experimental design for the calibration of biocatalytic models

2017 ◽  
Vol 33 (5) ◽  
pp. 1278-1293 ◽  
Author(s):  
Timothy Van Daele ◽  
Krist V. Gernaey ◽  
Rolf H. Ringborg ◽  
Tim Börner ◽  
Søren Heintz ◽  
...  
Keyword(s):  
Experimental Design ◽  
Optimal Experimental Design ◽  
Model Based ◽  
Robust Model

scholarly journals The Impact of Global Sensitivities and Design Measures in Model-Based Optimal Experimental Design

Processes ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 27 ◽  
Author(s):  
René Schenkendorf ◽  
Xiangzhong Xie ◽  
Moritz Rehbein ◽  
Stephan Scholl ◽  
Ulrike Krewer
Keyword(s):  
Experimental Design ◽  
Mathematical Models ◽  
Chemical Engineering ◽  
Protein Kinase Inhibitors ◽  
Optimal Experimental Design ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Global Parameter ◽  
Model Based ◽  
The Impact

In the field of chemical engineering, mathematical models have been proven to be an indispensable tool for process analysis, process design, and condition monitoring. To gain the most benefit from model-based approaches, the implemented mathematical models have to be based on sound principles, and they need to be calibrated to the process under study with suitable model parameter estimates. Often, the model parameters identified by experimental data, however, pose severe uncertainties leading to incorrect or biased inferences. This applies in particular in the field of pharmaceutical manufacturing, where usually the measurement data are limited in quantity and quality when analyzing novel active pharmaceutical ingredients. Optimally designed experiments, in turn, aim to increase the quality of the gathered data in the most efficient way. Any improvement in data quality results in more precise parameter estimates and more reliable model candidates. The applied methods for parameter sensitivity analyses and design criteria are crucial for the effectiveness of the optimal experimental design. In this work, different design measures based on global parameter sensitivities are critically compared with state-of-the-art concepts that follow simplifying linearization principles. The efficient implementation of the proposed sensitivity measures is explicitly addressed to be applicable to complex chemical engineering problems of practical relevance. As a case study, the homogeneous synthesis of 3,4-dihydro-1H-1-benzazepine-2,5-dione, a scaffold for the preparation of various protein kinase inhibitors, is analyzed followed by a more complex model of biochemical reactions. In both studies, the model-based optimal experimental design benefits from global parameter sensitivities combined with proper design measures.

Local Optima in Model-Based Optimal Experimental Design

2010 ◽  
Vol 49 (20) ◽  
pp. 10059-10073 ◽  
Author(s):  
Jan C. Schöneberger ◽  
Harvey Arellano-Garcia ◽  
Günter Wozny
Keyword(s):  
Experimental Design ◽  
Optimal Experimental Design ◽  
Local Optima ◽  
Model Based

Handling Uncertainty in Model-Based Optimal Experimental Design

2010 ◽  
Vol 49 (12) ◽  
pp. 5702-5713 ◽  
Author(s):  
Tilman Barz ◽  
Harvey Arellano-Garcia ◽  
Günter Wozny
Keyword(s):  
Experimental Design ◽  
Optimal Experimental Design ◽  
Model Based
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