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Título : A computational approach to partial least squares model inversion in the framework of the process analytical technology and quality by design initiatives
Autor : Ruiz Miguel, Santiago
Ortiz Fernández, Mª Cruz
Sarabia Peinador, Luis Antonio
Sánchez Pastor, Mª Sagrario
Publicado en: Chemometrics and Intelligent Laboratory Systems. 2018, V. 182, p. 70-78
Editorial : Elsevier
Fecha de publicación : nov-2018
Fecha de disponibilidad: nov-2020
ISSN : 0169-7439
DOI: 10.1016/j.chemolab.2018.08.014
Resumen : In the context of the paradigms founding the Quality by Design and Process Analytical Technology initiatives, the work herein presents a computational approach to support the decision-making process, in particular, about the feasibility of a product defined for some a priori given quality characteristics. The approach is based on the computation of the pareto-optimal front when simultaneously minimizing the expected differences between the predicted and the desired characteristics. Thus, the feasibility is tackled as an optimization problem with the novelty of doing so simultaneously for all the characteristics, preserving the correlation structure, but by separately handling each individual characteristic. With data from a low-density polyethylene production process, with fourteen process variables and five measured characteristics of the final polyethylene, solutions are found to define the Design Space for targeted quality characteristics on the product, and without the need of explicitly inverting the PLS (Partial Least Squares) prediction model fitted to the process.
Palabras clave: Process analytical technology
Quality by design
Partial least squares
Pareto optimality
Process decision making
Industrial processes
Materia: Química analítica
Chemistry, Analytic
Licencia: https://creativecommons.org/licenses/by-nc-nd/4.0/
URI : http://hdl.handle.net/10259/4973
Versión del editor: https://doi.org/10.1016/j.chemolab.2018.08.014
Aparece en las colecciones: Artículos Q&C

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