Issue |
MATEC Web Conf.
Volume 377, 2023
Curtin Global Campus Higher Degree by Research Colloquium (CGCHDRC 2022)
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Article Number | 01025 | |
Number of page(s) | 11 | |
Section | Engineering and Technologies for Sustainable Development | |
DOI | https://doi.org/10.1051/matecconf/202337701025 | |
Published online | 17 April 2023 |
A comparison study between different kernel functions in the least square support vector regression model for penicillin fermentation process
Department of Chemical and Energy Engineering, Curtin University Malaysia, CDT 250, 98009 Miri, Sarawak, Malaysia
Soft sensors are becoming increasingly important in our world today as tools for inferring difficult-to-measure process variables to achieve good operational performance and economic benefits. Recent advancement in machine learning provides an opportunity to integrate machine learning models for soft sensing applications, such as Least Square Support Vector Regression (LSSVR) which copes well with nonlinear process data. However, the LSSVR model usually uses the radial basis function (RBF) kernel function for prediction, which has demonstrated its usefulness in numerous applications. Thus, this study extends the use of non-conventional kernel functions in the LSSVR model with a comparative study against widely used partial least square (PLS) and principal component regression (PCR) models, measured with root mean square error (RMSE), mean absolute error (MAE) and error of approximation (Ea) as the performance benchmark. Based on the empirical result from the case study of the penicillin fermentation process, the Ea of the multiquadric kernel (MQ) is lowered by 63.44% as compared to the RBF kernel for the prediction of penicillin concentration. Hence, the MQ kernel LSSVR has outperformed the RBF kernel LSSVR. The study serves as empirical evidence of LSSVR performance as a machine learning model in soft sensing applications and as reference material for further development of non-conventional kernels in LSSVR-based models because many other functions can be used as well in the hope to increase the prediction accuracy.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0 (http://creativecommons.org/licenses/by/4.0/).
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