Monomer Balance Control in Precursor Preparation for Polyamidation Processes
A polyamide precursor, process control technology, applied in the direction of chemical/physical process, chemical/physical/physicochemical process, control/regulation process, etc., can solve the problem that the exact role is not fully understood
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Embodiment 1
[0079] Example 1: PLS model development using Raman spectroscopy
[0080] The one liter jacketed reactor was constructed with a side orifice suitable for holding a 1 / 2" diameter probe approximately at the height of the stirrer or at approximately the 300 mL fill line. Using the appropriate process fittings, 1 / 2" A diameter MarqMetrix PerformanceBall-probe (available from MarqMetrix Inc., Seattle, Washington) was held in place. The probe was connected to a HyperFlux PRO Plus Raman Spectrometer (available from Tornado Spectral Systems, Toronto, Ontario) via a 5 meter fiber optic cable. The HyperFlux PRO Plus utilizes a 785nm laser with 495mW of power. Each spectral file collected was a total of 25 spectra collected from a 750 ms exposure.
[0081] An initial solution was prepared by carefully weighing the components shown in Table 1 and heating the system to about 75°C with stirring. The hexamethylenediamine (HMD) used contained 20% water (as indicated by the table data). ...
Embodiment 2
[0096] Example 2: PLS model predictability of composition for laboratory analysis
[0097] A model approach according to the method of Example 1 was used to predict process samples collected during operation and analyzed offline using laboratory-scale pH / titration analysis.
[0098] Figure 4 Parity plot between DE determined from laboratory analysis [shown on the x-axis] and DE values [shown on the y-axis] predicted using a PLS model developed from the twelve-point composition data of Table 1. exist Figure 4 In , the solid line connects the square data points and the dashed line is their corresponding linear fit with a slope of about 0.994. It is evident from the quality of the data matches that the Example 1 model approach achieves excellent predictability and thus may be a useful technique for robust process control of monomer equilibria during polymerization. therefore, Figure 4 It is shown how the PLS model developed from the spectral data of Example 1 performs ...
Embodiment 3
[0099] Example 3: Alternative statistical model development using Raman spectroscopy
[0100] Using the Example 1 spectral data composed in Table 1, the following six specific ranges were identified as important in PLS modeling and selected for meaningful analysis:
[0101] Range 1[R1]: 1639-1762cm-1
[0102] Range 2[R2]: 2825-2901cm-1
[0103] Range 3[R3]: 1088-1125cm-1
[0104] Range 4[R4]: 895-914cm-1
[0105] Range 5[R5]: 961-985cm-1
[0106] Range 6[R6]: 825-836cm-1
[0107] For each range, pair with figure 2 The intensities corresponding to the six wavelength ranges are integrated. The integrals for each range were then used in a multivariate statistical analysis [MiniTab statistical package]. This analysis resulted in the following multivariate model for predicting DE values:
[0108] DE=-32845+1765[R1]-4.51[R2]-125.9[R3]+1095[R4]-94[R5]-379[R6]-30.2[R1] 2 -16.8[R4] 2
[0109] Figure 5 A representation of the predictability for analysis using the alterna...
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