A systematic framework to monitor mulling processes using Near Infrared spectroscopy

• Agglomerate size does not affect NIR prediction accuracy.
• Smoothness and roughness of sample surface has little effect on NIR results.
• Sample–detector distance should ideally fall between 9 mm and 24 mm.

The optimal design of sensor location and setup is essential to ensure the accuracy and precision of in-line process monitoring of water/moisture content. This manuscript presents a systematic framework of using Near Infrared (NIR) spectroscopy to monitor moisture content in an alumina mulling process that is commonly used in the upstream operation of catalyst supports production. For this, the optimal conditions of NIR sensor setup and critical quality attributes (CQAs) of a mulling process have been first identified and then calibration models for monitoring moisture at various conditions were developed and validated. The results suggest that there is a strong relationship between sensor setup and prediction accuracy. Therefore, optimal conditions such as operating distance of the NIR sensor, sample thickness and acquisition number need to be identified prior to installment of the sensor into the manufacturing plant. In mulling processes, the particle size distribution (PSD) and surface roughness/smoothness can also vary during operation, making the monitoring of moisture content a difficult task. In this study, the effects of PSD and powder surface characteristics on moisture content measurement has been investigated and it has been found that if suitable raw data preprocessing has been applied, the effect of agglomerate size and sample surface characteristics on the accuracy of the in-line measurements can be significantly minimized. This will allow the use of a single calibration model for a range of PSDs and powder bed smoothness/roughness and that will save a significant amount of time and resources. Here, a mulling process where a microNIR sensor has been used for monitoring of moisture content has been considered as a demonstrative example. However, the approach is generic and can be applied for any combination of process and sensor.

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