Fourier Transform Near-Infrared Spectroscopy as a Reliable Method for Quick Real Time Analysis of Complex Samples in Industry

Abstract

Near-infrared spectroscopy (NIRS) in conjunction with multivariate calibration have proved to be adequate techniques to predict water content as well as methanol concentration in a industrial multicomponent mixture. The calibration models were developed using real industrial complex samples of water, methanol, and several other components in variable but lower concentrations such as sulfonic acids, acetone, and methyl cyanide. These samples come from industrial distillation of hydrated methanol. Prior to the establishment of the two calibration models, wavelength selection, original spectral data compression by means of the principal components analysis technique, and preprocessing optimization were performed. Calibration was modeled according to partial least squares (PLS) regression which was developed using the mean centering and the leave-one-out cross validation (LOOCV) method for the optimization of the principal components (PC) number. Both models were developed in the 5369−5153 cm −1 spectral region. Karl Fischer titration and gas chromatography were used as reference methods for water and methanol quantification, respectively. The suitability of the models was evaluated through the calculation of the standard error of calibration and standard error of prediction. For the water content determination the variance scaling pretreatment was applied with 12 PLS factors, the calibration and prediction errors being 2.56% and 1.76% respectively. As for the methanol concentration variance scaling was applied in conjunction with Savitzky−Golay's second order derivative with six PLS factors. In this case the calibration and prediction errors were 0.42% and 0.90%, respectively.

Intensification of esterification process in TBP synthesis by in situ vapor stripping

“…Hydrogen chloride, which was generated by dropwise addition of concentrated sulfuric acid on solid NaCl, was added to 1‐butanol for 5 h. All gases were dried over concentrated sulfuric acid. The water content in 1‐butanol was then measured by Karl Fischer titration at the end of the experiment (RSD = 1.56%) …”

Calibration Model Building for Online Monitoring of the Granule Moisture Content during Fluidized Bed Drying by NIR Spectroscopy

“…For online measurement, the acoustic emission technology was proposed to measure the granule moisture content based on acoustic signal analysis, which, however, could not provide good accuracy under different operating conditions of FBD processes, owing to the fact that acoustic emission signals are likely affected by the fluidizing air flow rate, the construction materials of FBD, and environmental acoustic sources . In contrast, the near-infrared (NIR) spectroscopy has been increasingly explored for in situ monitoring the moisture contents of various products in the recent years, − owing to the noninvasive and/or nondestructive measurement style along with no need for sample preparation.…”

“…In addition, it was demonstrated in ref that using the PLS based NIR spectra calibration for measuring the granule moisture in conjunction with the chamber humidity and temperature could facilitate improving monitoring and control performance of FBD processes. Note that the process operating conditions were almost not considered in the existing PLS spectral calibration methods for using the NIR or mid-infrared (MIR) spectroscopy to perform in situ measurement. ,, Meanwhile, the root-mean-square-of-cross-validation (RMSECV) index was primarily used to determine the number of retained factors in such a PLS model, which indeed could be sensitive to measurement outliers such as improper samples (unrepresentative of the granules under drying by FBD) taken for off-line measurement by the LOD method as often encountered in engineering applications. The resulting model prediction accuracy could be intangibly degraded under different operating conditions such as the FBD chamber temperature and heating energy.…”