Robust Optimization in Nanoparticle Technology: A Proof of Principle by Quantum Dot Growth in a Residence Time Reactor

Dienstbier J, Aigner KM, Rolfes J, Peukert W, Segets D, Pflug L, Liers F (2021)

Publication Status: Submitted

Publication Type: Journal article, Original article

Future Publication Type: Journal article

Publication year: 2021

Journal

Computers & Chemical Engineering Elsevier

URI: http://www.optimization-online.org/DB_HTML/2021/02/8262.html

DOI: 10.1016/j.compchemeng.2021.107618

Abstract

Knowledge-based determination of the best-possible experimental setups for nanoparticle design is highly challenging. Additionally, such processes are accompanied by noticeable uncertainties. Therefore, protection against these uncertainties is needed. Robust optimization helps determining such best possible processes. The latter guarantees quality requirements regardless of how the uncertainties, e.g. with regard to variations in raw materials, heat and mass transport characteristics, material properties and (growth) rates, manifest within predefined ranges. To approach this huge task, in this paper we exemplarily model a particle synthesis process with seeded growth by population balance equations and study different growth kinetics. We determine the mean residence time maximizing the product mass subject to a guaranteed yield. Additionally, we hedge against uncertain growth rates and derive an algorithmically tractable reformulation for the robustified problem. We evaluate our approach for seeded growth synthesis of zinc oxide quantum dots
and demonstrate computationally that a guaranteed yield is met for all growth rates within previously defined regions. The protection against uncertainties only reduces the maximum amount of product that can be obtained by a negligible margin.

Authors with CRIS profile

Jana Dienstbier Professur für Optimization under Uncertainty & Data Analysis Kevin-Martin Aigner Professur für Optimization under Uncertainty & Data Analysis Jan Rolfes Economics - Discrete Optimization - Mathematics (EDOM) Wolfgang Peukert Lehrstuhl für Partikelsynthese (LFG) Lukas Pflug Lehrstuhl für Angewandte Mathematik (Kontinuierliche Optimierung) Frauke Liers-Bergmann Professur für Optimization under Uncertainty & Data Analysis

Related research project(s)

Unifying mathematical framework for synthesis and chromatographic separation of nanoparticles (SFB 1411 - D03) Design of particulate products (SFB 1411) Jan. 1, 2020 - Dec. 31, 2023

Involved external institutions

Universität Duisburg-Essen (UDE) DE Germany (DE)

How to cite

APA:

Dienstbier, J., Aigner, K.-M., Rolfes, J., Peukert, W., Segets, D., Pflug, L., & Liers, F. (2021). Robust Optimization in Nanoparticle Technology: A Proof of Principle by Quantum Dot Growth in a Residence Time Reactor. Computers & Chemical Engineering. https://doi.org/10.1016/j.compchemeng.2021.107618

MLA:

Dienstbier, Jana, et al. "Robust Optimization in Nanoparticle Technology: A Proof of Principle by Quantum Dot Growth in a Residence Time Reactor." Computers & Chemical Engineering (2021).

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