Monitoring Sulfuric Acid and Temperature Using Raman Spectroscopy and Multivariate Chemometrics

Abstract

Multivariate regression models were optimized for the quantification of sulfuric acid (H₂SO₄) [0–8 M] and temperature (20 °C–80 °C) in the presence of ammonium sulfate ((NH₄)₂SO₄ [0–0.6 M]) using Raman spectroscopy. Optical vibrational spectroscopy is a useful nondestructive technique for the in situ analysis of complex chemical systems notoriously difficult to monitor in situ and in real-time. Multivariate analysis, a chemometrics method, can be paired with these nondestructive optical methods for determining analyte concentration and speciation in complex solutions, such as dissociated species in polyprotic acids, e.g., H₂SO₄. The effect of temperature is often overlooked although it can have a major influence on speciation and the corresponding Raman spectra. Here, partial least squares regression models were optimized for the quantification of H₂SO₄ and its two deprotonated forms as a function of temperature. Measuring bisulfate as a function of temperature is particularly challenging owing to changes in the second dissociation constant. A designed training set effectively minimized the sample set size and trained a robust predictive model with percent root mean square error of <3% for H₂SO₄. The practical strategy employed here was demonstrated to be effective for building chemometric models that directly account for dynamic temperatures with static samples and is shown to be amenable to flow cell analysis applications with a simple calibration transfer for process monitoring applications.

Graphical abstract

This is a visual representation of the abstract.