Flame spray pyrolysis optimization via statistics and machine learning

Materials & Design

Joseph A. Libera¹  Marius Stan²  Noah H. Paulson³ 

[1] Applied Materials Division, Argonne National Laboratory, Argonne, IL 60439, United States of America;Applied Materials Division, Argonne National Laboratory, Argonne, IL 60439, United States of America;Corresponding author at: Argonne National Laboratory, 9700 Cass Avenue, Lemont, IL 60439, United States of America.;

关键词: Flame spray pyrolysis;    Nanoparticle synthesis;    Latin hypercube sampling;    Bayesian optimization;   

DOI  :  

来源: DOAJ

【 摘 要 】

Flame spray pyrolysis (FSP) is an important manufacturing process whereby nanomaterials are produced through the combustion of atomized fuel containing dissolved precursor elements. While FSP has the potential to enable the scalable production of a wide range of next generation energy materials, it also has a multi-scale, multi-physics character, and a large number of processing variables. Optimizing the process for desirable material outcomes by traditional approaches is challenging. In this work, the processing parameter space is explored via a new and efficient methodology that includes statistical methods such as Latin hypercube design of experiments, machine learning surrogate modeling, and Bayesian optimization. As a result, the FSP process is optimized for enhanced performance. Specifically, in-situ particle size measurements are used to tailor the production of silica nanoparticles for a low spread in particle diameters with respect to the mean particle diameter, resulting in an improvement of 25.5% over the baseline within 15 experimental trials. In the process, the analysis reveals distinct domains of primary particle and agglomerated particle formation.

【 授权许可】

Unknown