Eleni D. Koronaki, Luise F. Kaven, Johannes M. M. Faust, Ioannis G. Kevrekidis, Alexander Mitsos
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Nonlinear manifold learning determines microgel size from Raman spectroscopy
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.
期刊介绍:
The AIChE Journal is the premier research monthly in chemical engineering and related fields. This peer-reviewed and broad-based journal reports on the most important and latest technological advances in core areas of chemical engineering as well as in other relevant engineering disciplines. To keep abreast with the progressive outlook of the profession, the Journal has been expanding the scope of its editorial contents to include such fast developing areas as biotechnology, electrochemical engineering, and environmental engineering.
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Articles are categorized according to the following topical areas:
Biomolecular Engineering, Bioengineering, Biochemicals, Biofuels, and Food
Inorganic Materials: Synthesis and Processing
Particle Technology and Fluidization
Process Systems Engineering
Reaction Engineering, Kinetics and Catalysis
Separations: Materials, Devices and Processes
Soft Materials: Synthesis, Processing and Products
Thermodynamics and Molecular-Scale Phenomena
Transport Phenomena and Fluid Mechanics.