Analytical identification of design and multidimensional spaces using R-functions
File(s)
Preprint version
OA Location
Author(s)
Kucherenko, Sergei
Klymenko, Oleksiy
Shah, Nilay
Type
preprint
Abstract
The design space (DS) is defined as the combination of materials and process conditions that guarantees the assurance of quality. This principle ensures that as long as a process operates within DS, it consistently produces a product that meets specifications. A novel DS identification method called the R-DS identifier has been developed. It makes no assumptions about the underlying model-the only requirement is that each model constraint (CQA) should be approximated by a multivariate polynomial model. The method utilizes the methodology of Rvachev's R-functions and allows for explicit analytical representation of the DS with only a limited number of model runs. R-functions provide a framework for representing complex geometric shapes and performing operations on them through implicit functions. The theory of R-functions enables the solution of geometric problem such as identification of DS through algebraic manipulation. It is more practical than traditional sampling or optimization-based methods. The method is illustrated using a batch reactor system.
Date Issued
2024-07-18
Citation
arXiv, 2024
Journal / Book Title
arXiv
Copyright Statement
Copyright © 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Description
Preprint version
Subjects
Design Space
R-functions
Multivariate Polynomial Model
Pharmaceutical Manufacturing