Looking for a hyper polyhedron within the multidimensional space of Design Space from the results of Designs of Experiments - Aix-Marseille Université Access content directly
Journal Articles Chemometrics and Intelligent Laboratory Systems Year : 2023

Looking for a hyper polyhedron within the multidimensional space of Design Space from the results of Designs of Experiments

Abstract

In pharmaceutical studies, the Quality by Design (QbD) approach is increasingly being implemented to improve product development. Product quality is tested at each step of the manufacturing process, allowing a better process understanding and a better risk management, thus avoiding manufacturing defects. A key element of QbD is the construction of a Design Space (DS), i.e., a region in which the specifications on the output parameters should be met. Among the various possible construction methods, Designs of Experiments (DoE), and more precisely Response Surface Methodology, represent a perfectly adapted tool. The DS obtained may have any geometrical shape; consequently, the acceptable variation range of an input may depend on the value of other inputs. However, the experimenters would like to directly know the variation range of each input so that their variation domains are independent. In this context, we developed a method to determine the “Proven Acceptable Independent Range” (PAIR). It consists of looking for all the hyper polyhedra included in the multidimensional DS and selecting a hyper polyhedron according to various strategies. We will illustrate the performance of our method on different DoE cases.
Fichier principal
Vignette du fichier
Looking for a hyper polyhedron within the multidimensional space of Design Space from the results of Designs of Experiments_2023.pdf (11.18 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04021786 , version 1 (23-08-2023)

Identifiers

Cite

Diane Manzon, Badih Ghattas, Magalie Claeys-Bruno, Sophie Declomesnil, Christophe Carité, et al.. Looking for a hyper polyhedron within the multidimensional space of Design Space from the results of Designs of Experiments. Chemometrics and Intelligent Laboratory Systems, 2023, 232, pp.104712. ⟨10.1016/j.chemolab.2022.104712⟩. ⟨hal-04021786⟩
71 View
12 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More