Incremental Learning in Modelling Process Analysis Technology (PAT)-An Important Tool in the Measuring and Control...

Area of Science:

• Analytical Chemistry
• Process Analytical Technology (PAT)

Background:

• Existing measurement and evaluation methods in the chemical and pharmaceutical industries require improvement for accuracy, selectivity, and cost-effectiveness.
• Raman spectroscopy offers potential for process monitoring but requires advanced evaluation techniques.

Purpose of the Study:

• To develop and demonstrate a novel, user-friendly evaluation method for Raman spectroscopy systems.
• To enhance measurement accuracy and product selectivity for industrial process applications.
• To validate the method using a Raman spectrometer and an alcohol-water mixture.

Main Methods:

• Development of a self-learning algorithm for spectral data evaluation.
• Application of the algorithm to a Raman spectrometer system.
• Demonstration using an alcohol-water mixture to determine concentrations.
• Comparison of results with classical evaluation methods, such as those in Unscrambler software.

Main Results:

• The self-learning algorithm provides more precise results compared to classical methods.
• The method demonstrates increased accuracy in detecting concentrations.
• The computation time is reduced, particularly for the alcohol/water example.
• The approach validates the concept of Raman process monitoring for continuous column applications.

Conclusions:

• The developed self-learning evaluation method significantly improves the accuracy of Raman spectroscopy for process monitoring.
• This technique enables the transformation of Raman spectra into cost-effective and robust Raman photometers.
• The method is suitable for real-time concentration determination and continuous process monitoring in the chemical and pharmaceutical industries.