A PAT Solution for Automated Mill Control - Pharmaceutical Technology

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A PAT Solution for Automated Mill Control
The authors describe the implementation of an on-line particle-size analyzer on an active pharmaceutical ingredient milling operation at a commercial site.

Pharmaceutical Technology
Volume 34, Issue 1

Assessing the analytical solution

Figure 5: Real-time trend showing the impact of blade-rotation speed on particle size.
Following installation of the analyzer and optimization of the process interface, real-time particle-size data became available. In an initial study, the sensitivity of the analytical solution was investigated by systematically varying mill speed. Figure 5 shows data measured as rotor speed was changed between 2600 and 4000 rpm. The overlaid results (not shown) demonstrate the ability of the system to detect the impact of changes as small as 50 rpm, just 1.5% of the controlled variable range. With such a short trial, segregation of the mill feed can be a complicating factor, so under normal operating conditions, sensitivity is expected to be even higher.

Figure 6: Off- and on-line measurement of particle size as a function of mill rotor speed.
Although sensitivity is arguably the most relevant issue for process control, accuracy is also important. For this application, the off-line reference method used for in-process testing is a wet laser-diffraction measurement with a Malvern Mastersizer 2000 (Hydro S accessory), so a comparative study was carried out using this technique (see Figure 6). Analytical data were compared across the full operational range by manually extracting a sample for off-line analysis during each stable period of the variable speed trial previously described.

Figure 7: Correlation plot of on-line Dv50 (Insitec) versus off-line Dv50 (Mastersizer 2000). R2 = 0.9417.
The results show that both analytical methods successfully detect decreasing particle size as the blade speed increases. However, the on-line system gives a smoother correlation that more accurately reflects the expected trend. It exhibits better stability and greater sensitivity, highlighting the benefits of embedded analysis compared with an off-line method. Despite the short run time and the sampling errors introduced by manual analysis, it is possible to demonstrate a very stable correlation between off- and on-line results as shown in Figure 7.


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