A three dimensional kinetic Monte Carlo defect-free crystal dissolution model for biological systems, with application to uncertainty analysis and robust optimization. (January 2022)
- Record Type:
- Journal Article
- Title:
- A three dimensional kinetic Monte Carlo defect-free crystal dissolution model for biological systems, with application to uncertainty analysis and robust optimization. (January 2022)
- Main Title:
- A three dimensional kinetic Monte Carlo defect-free crystal dissolution model for biological systems, with application to uncertainty analysis and robust optimization
- Authors:
- Chaffart, Donovan
Ricardez-Sandoval, Luis A. - Abstract:
- Highlights: A 3D kinetic Monte Carlo (kMC) model is developed to simulate crystal dissolution. The novel kMC model is applied to simulate CaCO3 dissolution within the human body. Polynomial chaos expansions used to propagate uncertainty through the kMC model. Low-order models developed to predict PCE coefficients for various crystal designs. Low-order models used to perform robust optimization on crystal dissolution process. Abstract: The objective of this study was to construct a novel kinetic Monte Carlo (kMC) model to predict the complete dissolution of nanoscale crystals. The developed framework was designed to predict the dissolution of a wide variety of crystalline minerals, regardless of their composition and crystal structure. The proposed framework was used to explore ways of enhancing crystal dissolution processes by assessing the variability from environmental uncertainties and by performing robust optimization to improve the dissolution performance. The approach was used to simulate calcium carbonate dissolution within the human gastrointestinal system. Polynomial chaos expansions (PCEs) were used to propagate the parametric uncertainty through the kMC model. Robust optimization was subsequently performed to determine the crystal design parameters that achieve target dissolution specifications using low-order PCE coefficient models. The results showcased the applicability of the kMC crystal dissolution model and the need to account for dissolution uncertaintyHighlights: A 3D kinetic Monte Carlo (kMC) model is developed to simulate crystal dissolution. The novel kMC model is applied to simulate CaCO3 dissolution within the human body. Polynomial chaos expansions used to propagate uncertainty through the kMC model. Low-order models developed to predict PCE coefficients for various crystal designs. Low-order models used to perform robust optimization on crystal dissolution process. Abstract: The objective of this study was to construct a novel kinetic Monte Carlo (kMC) model to predict the complete dissolution of nanoscale crystals. The developed framework was designed to predict the dissolution of a wide variety of crystalline minerals, regardless of their composition and crystal structure. The proposed framework was used to explore ways of enhancing crystal dissolution processes by assessing the variability from environmental uncertainties and by performing robust optimization to improve the dissolution performance. The approach was used to simulate calcium carbonate dissolution within the human gastrointestinal system. Polynomial chaos expansions (PCEs) were used to propagate the parametric uncertainty through the kMC model. Robust optimization was subsequently performed to determine the crystal design parameters that achieve target dissolution specifications using low-order PCE coefficient models. The results showcased the applicability of the kMC crystal dissolution model and the need to account for dissolution uncertainty within key biological applications. … (more)
- Is Part Of:
- Computers & chemical engineering. Volume 157(2022)
- Journal:
- Computers & chemical engineering
- Issue:
- Volume 157(2022)
- Issue Display:
- Volume 157, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 157
- Issue:
- 2022
- Issue Sort Value:
- 2022-0157-2022-0000
- Page Start:
- Page End:
- Publication Date:
- 2022-01
- Subjects:
- Kinetic Monte Carlo -- Crystal dissolution -- Robust optimization -- Polynomial chaos expansion -- Pharmaceutical drug delivery
Chemical engineering -- Data processing -- Periodicals
660.0285 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00981354 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.compchemeng.2021.107586 ↗
- Languages:
- English
- ISSNs:
- 0098-1354
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.664000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 20368.xml