Near-infrared in-line monitoring of polymer processing means using a fiber-optic-assisted spectrometer to obtain spectra of the polymer melt flowing through commonly used processing equipment (an extruder). Conditions in the extruder are typically 200 °C and 20 MPa. This paper shows the design of interfaces between the spectrometer and the molten polymer. Three designs are shown, each permitting monitoring at a different location in the process: a melt-at-die interface, a melt-in-barrel interface, and a strand interface. These designs are for monitoring just before the extruder exit, in the main barrel of the extruder, and after the product exits from the extruder as a strand, respectively. All these interfaces protect the inserted fiber-optic probe from the harsh environment within the extruder while permitting easy replacement of a probe without interrupting the process. This latter characteristic is very important because it permits easy probe repair as well as the use of other types of probes (for monitoring color or particles, for example) to be used during a run. Examples of near-infrared spectra obtained with each of the interfaces used with an immiscible blend of polyethylene and polypropylene are shown. Large differences in the spectra demonstrate that the design of the interface will affect multivariate analysis directed at composition prediction. Subsequent papers are directed at using the melt-at-die interface for composition prediction and accounting for nonlinear relationships between absorbance and concentration.
The objective of this work was to examine the application of various multivariate methods to determine the composition of a flowing, molten, immiscible, polyethylene–polypropylene blend from near-infrared spectra. These spectra were acquired during processing by monitoring the melt with a fiber-optic-assisted in-line spectrometer. Undesired differences in spectra obtained from identical compositions were attributed to additive and multiplicative light scattering effects. Duplicate blend composition data were obtained over a range of 0 to 100% polyethylene. On the basis of previously published approaches, three data preprocessing methods were investigated: second derivative of absorbance with respect to wavelength (d2), multiplicative scatter correction (MSC), and a combination consisting of MSC followed by d2. The latter method was shown to substantially improve superposition of spectra and principal component analysis (PCA) scores. Also, fewer latent variables were required. The continuum regression (CR) approach, a method that encompasses ordinary least squares (OLS), partial least squares (PLS), and principle component regression (PCR) models, was then implemented and provided the best prediction model as one based on characteristics between those of PLS and OLS models.
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