Combination of data rectification techniques and soft sensor model for robust prediction of sulfur content in HDS process

A novel approach based on integration of data rectification techniques and support vector regression (SVR) is proposed to predict the sulfur content of treated product in gas oil hydrodesulfurization (HDS) process. Simultaneous approaches consisting of robust estimation method (REM) and wavelet transform (WT) were proposed to reduce outliers and noises of the input data for the SVR model. Results indicated that implementation of outlier detection and noise reduction techniques give a considerable improvement in the prediction error. Proposed approach delivered satisfactory predicting performance in computation time (CT) and prediction accuracy (AARE = 0.079 and CT = 74 s). The proposed method can provide a robust soft sensor for prediction of industrial treated gas oil's sulfur content.