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Inline Raman Spectroscopy and Indirect Hard Modeling for Concentration Monitoring of Dissociated Acid Species.

Authors
  • Echtermeyer, Alexander1
  • Marks, Caroline1
  • Mitsos, Alexander1, 2, 3
  • Viell, Jörn1
  • 1 Process Systems Engineering (AVT.SVT), 9165RWTH Aachen University, Aachen, Germany. , (Germany)
  • 2 Energy Systems Engineering, Institute for Energy and Climate Research IEK-10, Jülich, Germany. , (Germany)
  • 3 JARA-ENERGY, Aachen, Germany. , (Germany)
Type
Published Article
Journal
Applied Spectroscopy
Publisher
SAGE Publications
Publication Date
May 01, 2021
Volume
75
Issue
5
Pages
506–519
Identifiers
DOI: 10.1177/0003702820973275
PMID: 33107761
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

We propose an approach for monitoring the concentration of dissociated carboxylic acid species in dilute aqueous solution. The dissociated acid species are quantified employing inline Raman spectroscopy in combination with indirect hard modeling (IHM) and multivariate curve resolution (MCR). We introduce two different titration-based hard model (HM) calibration procedures for a single mono- or polyprotic acid in water with well-known (method A) or unknown (method B) acid dissociation constants pKa. In both methods, spectra of only one acid species in water are prepared for each acid species. These spectra are used for the construction of HMs. For method A, the HMs are calibrated with calculated ideal dissociation equilibria. For method B, we estimate pKa values by fitting ideal acid dissociation equilibria to acid peak areas that are obtained from a spectral HM. The HM in turn is constructed on the basis of MCR data. Thus, method B on the basis of IHM is independent of a priori known pKa values, but instead provides them as part of the calibration procedure. As a detailed example, we analyze itaconic acid in aqueous solution. For all acid species and water, we obtain low HM errors of < 2.87 × 10-4mol mol-1 in the cases of both methods A and B. With only four calibration samples, IHM yields more accurate results than partial least squares regression. Furthermore, we apply our approach to formic, acetic, and citric acid in water, thereby verifying its generalizability as a process analytical technology for quantitative monitoring of processes containing carboxylic acids.

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