Improvement of the prediction performance of a soft sensor model based on support vector regression for production of ultra-low sulfur diesel

Abstract

A novel data-driven, soft sensor based on support vector regression (SVR) integrated with a data compression technique was developed to predict the product quality for the hydrodesulfurization (HDS) process. A wide range of experimental data was taken from a HDS setup to train and test the SVR model. Hyper-parameter tuning is one of the main challenges to improve predictive accuracy of the SVR model. Therefore, a hybrid approach using a combination of genetic algorithm (GA) and sequential quadratic programming (SQP) methods (GA–SQP) was developed. Performance of different optimization algorithms including GA–SQP, GA, pattern search (PS), and grid search (GS) indicated that the best average absolute relative error (AARE), squared correlation coefficient (R2), and computation time (CT) (AARE = 0.0745, R2 = 0.997 and CT = 56 s) was accomplished by the hybrid algorithm. Moreover, to reduce the CT and improve the accuracy of the SVR model, the vector quantization (VQ) technique was used. The results also showed that the VQ technique can decrease the training time and improve prediction performance of the SVR model. The proposed method can provide a robust, soft sensor in a wide range of sulfur contents with good accuracy.