Batch process monitoring using on-line MIR spectroscopy.

Area of Science:

• Chemical Engineering
• Process Control
• Analytical Chemistry

Background:

• Industrial batch processes rely on precise recipes but are susceptible to variations from feedstock or impurities.
• Variations can lead to inconsistent end-product quality and potentially unsafe operating conditions.
• Traditional monitoring using physical parameters like temperature and pressure offers limited insight into chemical variations.

Purpose of the Study:

• To demonstrate the application of multivariate statistical process control (MSPC) for monitoring industrial batch processes.
• To highlight the benefits of integrating spectroscopic measurements with MSPC for enhanced process oversight.
• To develop a method for detecting process upsets and deviations from normal operating conditions in batch manufacturing.

Main Methods:

• Utilizing spectroscopic measurements to gain direct chemical insight into the batch process.
• Developing a calibration model based on spectroscopic data.
• Applying the principles of multivariate statistical process control (MSPC) to the developed model.
• Implementing statistical control charts for identifying process anomalies.

Main Results:

• Successfully developed a calibration model integrating spectroscopic data with MSPC principles.
• Demonstrated the effectiveness of the combined approach in detecting process perturbations.
• Statistical control charts were utilized to identify batches exhibiting process upsets.
• The method provides a simple yet powerful way to monitor batch process behavior.

Conclusions:

• Combining spectroscopic measurements with MSPC offers a robust solution for industrial batch process monitoring.
• This approach enables early detection of deviations, ensuring product quality and process safety.
• The developed methodology provides a valuable tool for quality control and operational stability in batch manufacturing.