Soft sensor design for hydrodesulfurization process using support vector regression based on WT and PCA

Abstract

A novel method for developing a reliable data driven soft sensor to improve the prediction accuracy of sulfur content in hydrodesulfurization (HDS) process was proposed. Therefore, an integrated approach using support vector regression (SVR) based on wavelet transform (WT) and principal component analysis (PCA) was used. Experimental data from the HDS setup were employed to validate the proposed model. The results reveal that the integrated WT-PCA with SVR model was able to increase the prediction accuracy of SVR model. Implementation of the proposed model delivers the best satisfactory predicting performance (E_AARE=0.058 and R²=0.97) in comparison with SVR. The obtained results indicate that the proposed model is more reliable and more precise than the multiple linear regression (MLR), SVR and PCA-SVR.

Keywords

soft sensor / support vector regression / principal component analysis / wavelet transform hydrodesulfurization process

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