Adaptive consensus principal component analysis for on-line batch process monitoring

Area of Science:

• Environmental Engineering
• Chemical Engineering
• Data Science

Background:

• Increasingly stringent effluent quality regulations necessitate advanced on-line monitoring of wastewater treatment processes.
• Multivariate statistical process control, including principal component analysis (PCA), is crucial for fault detection and diagnosis in industrial processes.
• Existing PCA models can struggle with dynamic changes in operating conditions.

Purpose of the Study:

• To develop an adaptive algorithm for robust on-line monitoring of wastewater treatment processes.
• To address the challenge of changing operating conditions in process monitoring.
• To enable real-time detection and identification of process disturbances.

Main Methods:

• Proposing a novel consensus PCA algorithm.
• Implementing recursive updating of the covariance structure to adapt to changing conditions.
• Avoiding the need for estimation required by typical multiway PCA models.

Main Results:

• The consensus PCA algorithm successfully monitors wastewater treatment processes adaptively.
• Process disturbances are detected in real time.
• The specific measurements responsible for disturbances are directly identified.
• The methodology was validated on a pilot-scale sequencing batch reactor.

Conclusions:

• The consensus PCA algorithm offers an effective solution for adaptive wastewater treatment process monitoring.
• The method enhances real-time fault detection and diagnosis capabilities.
• This approach is suitable for environments with dynamic operating conditions.