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

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Compressor map regression modelling based on partial least squares.

Xu Li¹, Chuanlei Yang¹, Yinyan Wang¹

¹College of Power and Energy Engineering, Harbin Engineering University, Harbin, People's Republic of China.

Royal Society Open Science

|September 19, 2018

Summary

This summary is machine-generated.

Two partial least squares (PLS) methods, PLS with power functions (PLSO) and PLS with trigonometric functions (PLSN), accurately predict compressor maps. PLSN offers superior accuracy and speed compared to PLSO, look-up tables, and artificial neural networks, especially for extrapolation.

Keywords:

compressor maps diesel engine partial least squares performance modelling regression modelling

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Area of Science:

Thermodynamics
Mechanical Engineering
Data Science

Background:

Compressor maps are crucial for engine performance modeling.
Accurate prediction of compressor maps is essential for optimizing thermodynamic models.
Existing methods like look-up tables and artificial neural networks have limitations in prediction accuracy and computational efficiency.

Purpose of the Study:

To evaluate two novel partial least squares (PLS) modeling methods for compressor map prediction: PLS with power functions (PLSO) and PLS with trigonometric functions (PLSN).
To compare the predictive capabilities of PLSO and PLSN against traditional methods (look-up table, artificial neural network) for both interpolated and extrapolated predictions.
To assess the computational efficiency of the proposed PLS methods.

Main Methods:

Development and application of PLSO using power function polynomials as basis functions.
Development and application of PLSN using trigonometric function polynomials as basis functions.
Comparative analysis of PLSO, PLSN, look-up table, and artificial neural network (ANN) performance on compressor map prediction tasks.
Evaluation of prediction accuracy and computational time for all methods.

Main Results:

Both PLSO and PLSN demonstrated superior prediction performance compared to look-up tables and ANNs.
PLSO and PLSN showed significant improvements in prediction accuracy, particularly for extrapolated data.
PLSN exhibited higher prediction accuracy and a shorter computational time than PLSO.
A sharp decrease in computational time was observed with the PLS methods.

Conclusions:

Partial least squares modeling, specifically PLSN, offers a more accurate and computationally efficient approach for predicting compressor maps.
PLSN provides significant advantages over traditional methods and PLSO, especially in extrapolation scenarios.
The findings suggest that PLSN can enhance the accuracy and reduce computational load in thermodynamic engine models, such as those for diesel engines.