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In Situ Microscopy for Real-time Determination of Single-cell Morphology in Bioprocesses

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Batch Process Monitoring Based on Quality-Related Time-Batch 2D Evolution Information.

Luping Zhao¹, Jiayang Yang¹

¹College of Information Science and Engineering, Northeastern University, Shenyang 110819, China.

Sensors (Basel, Switzerland)

|March 26, 2022

Summary

This summary is machine-generated.

This study introduces a novel online monitoring strategy for batch processes using two-dimensional time and batch evolution data. The method improves quality monitoring by analyzing distinct phases and time-varying characteristics, outperforming traditional single-dimension approaches.

Keywords:

batch process evolution information online monitoring partial least squares

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Area of Science:

Chemical Engineering
Process Monitoring
Data Analysis

Background:

Batch processes require robust quality monitoring systems.
Traditional methods often struggle with complex, time-varying process dynamics.
Analyzing both temporal and batch evolution is crucial for accurate process understanding.

Purpose of the Study:

To develop an online monitoring strategy for batch processes.
To leverage two-dimensional time and batch evolution information for enhanced quality control.
To address limitations of single-dimension analysis in traditional methods.

Main Methods:

Utilizing Partial Least Squares (PLS) model regression coefficients to divide batches into phases and parts based on temporal evolution.
Employing sliding windows on the score matrix (T) for mode division, considering slow time-varying characteristics.
Integrating phase and part division with batch evolution analysis for a comprehensive model.

Main Results:

The proposed strategy effectively combines time and batch two-dimensional evolution information.
Demonstrated superior monitoring results compared to traditional single-dimension analysis methods.
Successfully applied to injection molding processes, validating its effectiveness.

Conclusions:

The developed online monitoring strategy offers improved accuracy and robustness for batch processes.
Integrating multi-dimensional evolution data provides a more comprehensive understanding of process dynamics.
This approach enhances quality-related monitoring by overcoming the limitations of analyzing data in isolation.