IRUS Total

Computationally efficient identification of probabilistic design spaces through application of metamodeling and adaptive sampling

File Description SizeFormat 
paper_DS_August.docxAccepted version1.89 MBMicrosoft WordView/Open
Title: Computationally efficient identification of probabilistic design spaces through application of metamodeling and adaptive sampling
Authors: Kucherenko, S
Giamalakis, D
Shah, N
García-Muñoz, S
Item Type: Journal Article
Abstract: The design space (DS) is defined as the combination of materials and process conditions which provides assurance of quality for a pharmaceutical product (e.g. purity, potency, uniformity). A model-based approach to identify a probability-based design space requires simulations across the entire process parameter space (certain) and the uncertain model parameter space and material properties space if explicitly considered by the model. This exercise is a demanding task. A novel theoretical and numerical framework for determining probabilistic DS using metamodelling and adaptive sampling is developed. Several approaches were proposed and tested among which the most efficient is a new multi-step adaptive technique based using a metamodel for a probability map as an acceptance-rejection criterion to optimize sampling to identify the DS. It is shown that application of metamodel-based filters can significantly reduce model complexity and computational costs with speed up of two orders of magnitude observed here.
Issue Date: 4-Jan-2020
Date of Acceptance: 16-Oct-2019
URI: http://hdl.handle.net/10044/1/74719
DOI: 10.1016/j.compchemeng.2019.106608
ISSN: 0098-1354
Publisher: Elsevier BV
Start Page: 1
End Page: 9
Journal / Book Title: Computers & Chemical Engineering
Volume: 132
Copyright Statement: © 2019 Elsevier Ltd. All rights reserved. This manuscript is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence http://creativecommons.org/licenses/by-nc-nd/4.0/
Sponsor/Funder: Eli Lilly & Company (USA)
Funder's Grant Number: 4900606521
Keywords: 0904 Chemical Engineering
0913 Mechanical Engineering
Chemical Engineering
Publication Status: Published online
Article Number: 106608
Online Publication Date: 2019-10-22
Appears in Collections:Centre for Environmental Policy
Chemical Engineering
Faculty of Natural Sciences