Integrating principal component analysis and vector quantization with support vector regression for sulfur content prediction in hydrodesulfurization process

An accurate prediction of sulfur content is very important for the proper operation and product quality control in hydrodesulfurization (HDS) process. For this purpose, a reliable data-driven soft sensor utilizing Support Vector Regression (SVR) was developed and the effects of integrating Vector Quantization (VQ) with Principle Component Analysis (PCA) were studied in the assessment of this soft sensor. First, in the pre-processing step the PCA and VQ techniques were used to reduce dimensions of the original input datasets. Then, the compressed datasets were used as input variables for the SVR model. Experimental data from the HDS setup were employed to validate the proposed integrated model. The integration of VQ/PCA techniques with SVR model was able to increase the prediction accuracy of SVR. The obtained results show that integrated technique (VQ-SVR) was better than (PCA-SVR) in prediction accuracy. Also, VQ decreased the sum of the training and test time of SVR model in comparison with PCA. For further evaluation, the performance of VQ-SVR model was also compared to that of SVR. The obtained results indicated that VQ-SVR model delivered the best satisfactory predicting performance (AARE = 0.0668 and R2 = 0.995) in comparison with investigated models.