Nonlinear manifold learning determines microgel size from Raman spectroscopy

AIChE Journal, vol. 70

Abstract

Abstract Polymer particle size constitutes a crucial characteristic of product quality in polymerization. Raman spectroscopy is an established and reliable process analytical technology for in‐line concentration monitoring. Recent approaches and some theoretical considerations show a correlation between Raman signals and particle sizes but do not determine polymer size from Raman spectroscopic measurements accurately and reliably. With this in mind, we propose three alternative machine learning workflows to perform this task, all involving diffusion maps, a nonlinear manifold learning technique for dimensionality reduction: (i) directly from diffusion maps, (ii) alternating diffusion maps, and (iii) conformal autoencoder neural networks. We apply the workflows to a data set of Raman spectra with associated size measured via dynamic light scattering of 47 microgel (cross‐linked polymer) samples in a diameter range of 208–483 nm. The conformal autoencoders substantially outperform state‐of‐the‐art methods and results for the first time in a promising prediction of polymer size from Raman spectra.

Authors 5

1. Eleni D. Koronaki

   University of Luxembourg

   Affiliation as printed

   Faculté des Sciences, de la Technologie et de la Communication Université de Luxembourg Esch‐sur‐Alzette Luxembourg

2. Luise F. Kaven Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

3. Johannes M.M. Faust Aachen Process Systems Engineering (AVT.SVT)

   RWTH Aachen University

   Affiliation as printed

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany

4. Ioannis G. Kevrekidis

   Johns Hopkins University

   Affiliation as printed

   Department of Chemical and Biomolecular Engineering and Department of Applied Mathematics and Statistics, Whiting School of Engineering Johns Hopkins University Baltimore Maryland USA

   Department of Chemical and Biomolecular Engineering and Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, USA

5. Alexander Mitsos corresponding Aachen Process Systems Engineering (AVT.SVT)

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Institute of Energy and Climate Research, Energy Systems Engineering (IEK‐10) Forschungszentrum Jülich GmbH Jülich Germany

   JARA‐CSD Aachen Germany

   Process Systems Engineering (AVT.SVT) RWTH Aachen University Aachen Germany

   JARA-CSD, Aachen, Germany

   Process Systems Engineering (AVT.SVT), RWTH Aachen University, Aachen, Germany