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Related Experiment Video

Updated: Feb 5, 2026

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

Published on: November 8, 2019

Performance monitoring method based on balanced partial least square and Statistics Pattern Analysis.

Jian Yang¹, Zheng Lv¹, Hongbo Shi¹

¹Key Laboratory of Advanced Control and Optimization for Chemical Process of the Ministry of Education, East China University of Science and Technology, Shanghai 200237, PR China.

ISA Transactions

|September 16, 2018

Summary

This study introduces a new method for process monitoring using predicted performance indexes. It enhances efficiency by classifying variables and using a balanced Partial Least Square algorithm for accurate predictions.

Keywords:

Partial least square Performance monitoring Quality prediction Statistics pattern analysis Variable classification

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Area of Science:

Chemical Engineering
Process Control
Industrial Monitoring

Background:

Effective process monitoring is crucial for maintaining quality, economy, and security in industrial operations.
Traditional methods may struggle with online measurement of all critical performance indexes.
Accurate prediction of unmeasurable performance indexes is needed to enhance process monitoring efficiency.

Purpose of the Study:

To propose a novel performance monitoring method based on predicting key performance indexes.
To improve the efficiency and accuracy of industrial process monitoring.
To validate the proposed method using simulation and practical examples.

Main Methods:

Classifying process variables based on their correlation with performance indexes.
Developing a balanced Partial Least Square (bPLS) algorithm with an enhanced objective function for index prediction.
Utilizing Statistics Pattern Analysis (SPA) on prediction residuals for monitoring variations.

Main Results:

The proposed bPLS algorithm effectively predicts online unmeasurable performance indexes.
Statistics Pattern Analysis successfully captures variations in performance indexes through prediction residuals.
The method demonstrated effectiveness in both Simulink and practical industrial examples.

Conclusions:

The novel method enhances process monitoring efficiency by predicting critical performance indexes.
The integration of bPLS and SPA provides a robust approach for real-time industrial process analysis.
This predictive monitoring strategy offers significant advantages for quality, economic, and security management.