Issue 1, 2022

Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization

Abstract

Autonomous chemical process development and optimization methods use algorithms to explore the operating parameter space based on feedback from experimentally determined exit stream compositions. Measuring the compositions of multicomponent streams is challenging, requiring multiple analytical techniques to differentiate between similar chemical components in the mixture and determine their concentration. Herein, we describe a universal analytical methodology based on multitarget regression machine learning (ML) models to rapidly determine chemical mixtures' compositions from Fourier transform infrared (FTIR) absorption spectra. Specifically, we used simulated FTIR spectra for up to 6 components in water and tested seven different ML algorithms to develop the methodology. All algorithms resulted in regression models with mean absolute errors (MAE) between 0–0.27 wt%. We validated the methodology with experimental data obtained on mixtures prepared using a network of programmable pumps in line with an FTIR transmission flow cell. ML models were trained using experimental data and evaluated for mixtures of up to 4-components with similar chemical structures, including alcohols (i.e., glycerol, isopropanol, and 1-butanol) and nitriles (i.e., acrylonitrile, adiponitrile, and propionitrile). Linear regression models predicted concentrations with coefficients of determination, R2, between 0.955 and 0.986, while artificial neural network models showed a slightly lower accuracy, with R2 between 0.854 and 0.977. These R2 correspond to MAEs of 0.28–0.52 wt% for mixtures with component concentrations between 4–10 wt%. Thus, we demonstrate that ML models can accurately determine the compositions of multicomponent mixtures of similar species, enhancing spectroscopic chemical quantification for use in autonomous, fast process development and optimization.

Graphical abstract: Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization

Supplementary files

Article information

Article type
Paper
Submitted
26 Dit 2021
Accepted
20 Kax 2021
First published
23 Kax 2021
This article is Open Access
Creative Commons BY license

Digital Discovery, 2022,1, 35-44

Machine learning enhanced spectroscopic analysis: towards autonomous chemical mixture characterization for rapid process optimization

A. Angulo, L. Yang, E. S. Aydil and M. A. Modestino, Digital Discovery, 2022, 1, 35 DOI: 10.1039/D1DD00027F

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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