Global uncertainty-sensitivity analysis on mechanistic kinetic models: From model assessment to theory-driven design of nanoparticles. (2nd September 2020)

• Record Type: Journal Article
• Authors: Andalibi, M. Reza; Bowen, Paul; Carino, Agnese; Testino, Andrea
• Is Part Of: Computers & chemical engineering. Volume 140(2020)
• Publication Date: 2020-09-02
• Subjects: Reactive crystallization -- Population balance -- Direct quadrature method of moments -- Uncertainty/sensitivity analysis -- Nanoparticles -- Calcium silicate hydrate (C-S-H); Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.106971
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A population balance modeling workflow for nanoparticle formation is presented. Equations are solved via a robust variation of direct quadrature method of moments. Thermodynamic driving force for precipitation is calculated by coupling to PHREEQC. The model behavior is examined using global uncertainty/sensitivity analysis. Guidelines for rationalizing the synthesis toward a desirable product are provided. Abstract: Recently, we developed a population balance framework describing the precipitation of calcium-silicate-hydrate, a key nanomaterial in the construction industry and with potential applications in biomedicine, environmental remediation, and catalysis. In this article, we first refine our computational workflow by developing a more efficient and robust method for the solution of the moment-transformed population balance equations. Then, we generalize our framework by coupling to PHREEQC, a widely used open-source speciation solver, to enhance the adaptability of the framework to new systems. Using this improved computational model, we perform global uncertainty/sensitivity analysis (UA/SA) to understand the effect of variations in the model parameters and experimental conditions on the properties of the product. With the specific surface area of particles as an example, we show that UA/SA identifies the factors whose control would allow a fine-tuning of the desired properties. This general approach can be transferred to other nanoparticle synthesis schemes as well.