Polymeric polyols are prevalent in the production of polyurethanes and other specialized polymers. The hydroxyl number (OH#) measures the concentration of hydroxyl groups within the polyol.
The quantity of reactive hydroxyl groups (OH) on the polyol directly affects the number of urethane linkages, significantly influencing the physical attributes of the eventual polyurethane product. Monitoring and controlling this parameter is of great importance during polyol manufacturing.
The conventional laboratory approach for determining the hydroxyl number is time-consuming and involves using hazardous materials.
This article will elaborate on using the GUIDED WAVE™ hardware and software tools for assessing the hydroxyl number in polyols through fiber optic-based Near-Infrared (NIR) spectroscopy.
NIR can be applied in real-time directly within the manufacturing process or as part of a laboratory procedure. In either scenario, NIR presents itself as a time and cost-efficient alternative to traditional methods while enhancing safety.
This method aligns with ASTM D6342-22 (Standard Practice for Polyurethane Raw Materials: Determining Hydroxyl Number of Polyols by Near-Infrared (NIR) Spectroscopy).
Measurement Background
The NIR portion of the electromagnetic spectrum allows for utilizing the overtone and combination bands of C-H, O-H, and N-H fundamentals.
By analyzing the NIR spectra of a set of polyol samples with known hydroxyl numbers, it is possible to construct a quantitative model to determine the hydroxyl number in forthcoming samples solely based on their NIR spectrum.
The analyzer systems utilize fiber optics, permitting the sample probe to be positioned in remote locations, separate from the spectrometer.
Experimental
The NIR spectra of various process polyether polyol samples were recorded within the 1000 to 2100 nm wavelength range using a NIR-O Spectrometer. Figure 1 illustrates the absorbance spectra of select samples using an online process probe with a 10 mm path length.
The hydroxyl number for these samples spans from 9 to 44. Data preprocessing entailed a simple 2-point baseline correction for this application to eliminate offsets.
The spectra and concentration data were subjected to third-party chemometric software, developing a calibration model utilizing PLS regression methodology. For a more comprehensive discussion on PLS and other multivariate calibration techniques, please refer to Martens & Naes and ASTM E1855. 1,2
Figure 1. NIR Spectra of Polyester Polyol Samples. Image Credit: Process Insights
Analyzer Selection
To achieve similar results for well-established process measurements, a ClearView db multi-wavelength photometer can be used. These photometer systems provide robust measurement capabilities tailored to various applications. To choose between measurement systems, visit the Process Insights website.
Results
The model was employed to forecast hydroxyl number values within a laboratory context, with the outcomes presented in Figure 2 as a scatter plot. The model yielded an RMSEP (root mean square error of prediction) of 1.8, demonstrating a favorable alignment with the accuracy of the standard laboratory procedure.
Figure 2. OH Number Laboratory vs. Measured. Image Credit: Process Insights
Conclusion
The employment of NIR spectroscopy for OH number measurement of polyols is rapid and dependable, leveraging the hardware and software tools. This approach reduces the necessity for the conventional laboratory method, providing real-time results (within seconds).
This method conforms to ASTM D6342-22 (Standard Practice for Polyurethane Raw Materials: Determining Hydroxyl Number of Polyols by Near Infrared (NIR) Spectroscopy).
This technique can be applied within a batch reactor system to control this pivotal property during mixing, facilitating the determination of the reaction endpoint and resulting in substantial savings in terms of time and material costs.
For more comprehensive information concerning system specifications and the NIR-O process analyzer, please contact a Process Insights sales or technical specialist.
References and Further Reading
- H. Martens, T. Naes, Multivariate Calibration, John Wiley & Sons, 1989.
- ASTM E1655 Standard Practices for Infrared, Multivariate, Quantitative Analysis.
This information has been sourced, reviewed and adapted from materials provided by Process Insights.
For more information on this source, please visit Process Insights.