In this section
@article{Kucherenko:2025:10.1016/j.compchemeng.2025.109112,
author = {Kucherenko, S and Shah, N and Klymenko, OV},
doi = {10.1016/j.compchemeng.2025.109112},
journal = {Computers and Chemical Engineering},
title = {Analytical identification of process design spaces using R-functions},
url = {http://dx.doi.org/10.1016/j.compchemeng.2025.109112},
volume = {198},
year = {2025}
}
TY - JOUR
AB - A process design space (DS) is defined as the combination of process design and operational conditions that guarantees the assurance of product quality. This principle ensures that, as long as a process operates within its DS, it consistently yields a product that meets specifications. A novel DS identification method called the R-DS identifier has been developed in this work. It makes no assumptions about the underlying model - the only requirement is that each model constraint (e.g., defining product Critical Quality Attributes or process Key Performance Indicators) should be approximated by a closed-form function, e.g., a multivariate polynomial model. The method utilizes the methodology of V.L. 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 and inequalities defining the regions. 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 model.
AU - Kucherenko,S
AU - Shah,N
AU - Klymenko,OV
DO - 10.1016/j.compchemeng.2025.109112
PY - 2025///
SN - 0098-1354
TI - Analytical identification of process design spaces using R-functions
T2 - Computers and Chemical Engineering
UR - http://dx.doi.org/10.1016/j.compchemeng.2025.109112
UR - https://doi.org/10.1016/j.compchemeng.2025.109112
VL - 198
ER -