This paper reports on-line monitoring of the density of linear low-density polyethylene (LLDPE) by near-infrared (NIR) spectroscopy and chemometrics. The on-line monitoring was carried out not only in a laboratory but also in a real plant. We composed an on-line monitoring system for molten polymers consisting of a Fourier transform near-infrared (FT-NIR) spectrometer, input/output (I/O) module, a personal computer, and a sampling cell that we developed. We first compared NIR spectra of LLDPE in the solid and melt states and then developed calibration models that predict the density using partial least squares regression (PLS). The sample sets for developing prediction models were collected for three months at the plant, and the density of LLDPE was continuously monitored on-line for another three months using the model. The standard error of prediction (SEP) for the on-line monitoring of the density of LLDPE at the plant was +/-2.1 mg/cm(3) (range: 0.91-0.95 g/cm(3)).

Near infrared (NIR) diffuse reflectance spectra have been measured using a rotating drawer for pellets of 16 kinds of linear low-density polyethylene (LLDPE) with short branches and PE without any branches to propose a calibration model which predicts their density and to increase the understanding of NIR spectra of LLDPE. The density of the LLDPE samples investigated was in the range 0.911-0.925 g cm -3 . Partial least squares (PLS) regression has been applied to the original NIR spectra data set, their second derivatives and the spectra after multiplicative scatter correction (MSC) treatment to make up the models. The correlation coefficient was calculated to be 0.961, 0.965 and 0.970 for the original NIR spectra, their second derivatives and those with the MSC treatment, respectively, and the standard error of prediction (SEP) was found to be 0.001 g cm -3 for all the cases. The regression coefficients plot for the calibration models shows that bands at 1192, 1376 and 1698 nm due to the overtone and combination modes of the CH 3 groups play important roles in the prediction of density.

A near-infrared (NIR) and mid-infrared (mid-IR) dual-region spectrometer having two immersion probes, a transmission probe for NIR, and an attenuated total reflection (ATR) probe for mid-IR has been developed for highly reliable process monitoring and deep process understanding. This spectrometer facilitates sequential acquisition of both NIR (10,000-4000 cm(-1)) and mid-IR (5000-1200 cm(-1)) spectra by switching the light path leading to the probes without the need for probe replacement. The use of a single light source and a single beam splitter enables achievement of a permanent alignment of the optical system and sequential data acquisition. The transmission NIR and ATR mid-IR probes designed and developed in the present study facilitate the acquisition of NIR/mid-IR spectra with optimized absorption intensities in both regions by simply placing the probes into a sample solution. The performance of the developed spectrometer was demonstrated in monitoring the ethanol fermentation process. NIR/mid-IR spectra of the fermentation solution with multiplicative scatter correction (MSC) represent the relative changes in the concentrations of glucose and ethanol in both regions. Principal component analysis (PCA) was performed on the MSC-treated spectra in the regions 6300-5650 cm(-1), 4850-4300 cm(-1), and 3500-2880 cm(-1) to detect the end-point of the fermentation as an example of process monitoring. For all the regions, the score plot of the first principal component (PC) indicates that the fermentation progresses with the fermentation time and stops after 210 minutes and thus the end-point of the fermentation exists at around 210 minutes. The loading plot indicates that all of the first PCs are the relative changes in the concentrations of glucose and ethanol. This result reveals that the same chemical changes are observed in both transmission NIR and ATR mid-IR spectra. Multiple and simultaneous analysis was also performed, and intensity change in light scattering relating the growth of yeasts was monitored by the NIR spectra.

This paper reports on the influence of a change in sample temperature, and a method for its compensation, for the prediction of ethylene (C2) content in melt-state random polypropylene (RPP) and block polypropylene (BPP) by near-infrared (NIR) spectroscopy and chemometrics. Near-infrared (NIR) spectra of RPP in the melt and solid states were measured by a Fourier transform near-infrared (FT-NIR) on-line monitoring system and an FT-NIR laboratory system. There are some significant differences between the solid and melt-state RPP spectra. Moreover, we investigated the predicted values of the C2 content from the RPP or BPP spectra measured at 190 degrees C and 250 degrees C using the calibration model for the C2 content developed using the RPP or BPP spectra measured at 230 degrees C. The errors in the predicted values of the C2 content depend on the pretreatment methods for each calibration model. It was found that multiplicative signal correction (MSC) is very effective in compensating for the influence of the change of temperature for the RPP or BPP samples on the predicted C2 content. From the suggestion of principal component analysis (PCA) and difference spectrum analysis, we propose a new compensation method for the temperature change that uses the difference spectra between two spectra sets measured at different temperatures. We achieved good results using the difference spectra between the RPP/BPP spectra sets measured at 190 degrees C and 250 degrees C after correction and the calibration model developed with the spectra measured at 230 degrees C. The comparison between the method using MSC and the proposed method showed that the predicted error in the latter is slightly better than those in the former.