Steady-state detection of evaporation process based on multivariate data fusion

Area of Science:

• Chemical Engineering
• Process Control
• Data Science

Background:

• Alumina evaporation processes face instabilities due to frequent production condition changes.
• Accurate steady-state detection is crucial for process stability and efficiency.
• Existing methods may struggle with complex, variable-driven industrial operations.

Purpose of the Study:

• To develop an innovative multivariable data fusion strategy for adaptive steady-state detection.
• To address production instabilities in the alumina evaporation process.
• To enhance process control through advanced data analysis techniques.

Main Methods:

• Application of an adaptive denoising algorithm based on the Gaussian filter to eliminate erroneous data.
• Integration of the adaptive Gaussian filter with a multivariable R-test methodology for data fusion.
• Validation using actual industrial process data.

Main Results:

• The proposed strategy significantly enhances the stability and efficiency of the alumina evaporation process by 10%.
• Demonstrated precise and thorough steady-state detection through data fusion.
• Validated the effectiveness of the adaptive denoising and R-test integration.

Conclusions:

• The developed multivariable data fusion strategy effectively improves alumina evaporation process control.
• The approach offers potential applicability across various industrial settings facing similar challenges.
• Highlights the importance of adaptive strategies in managing complex industrial operations.