Inferential sensing of output quality in petroleum refinery using principal component regression and support vector regression

In this research, linear regression (ordinary least square and principal component) and non-linear regression (standard and least square support vector) models are developed for prediction of output quality from sulphur recovery unit. The hyper parameters associated with standard SVR and LS-SVR are determined analytically using the guidelines proposed in the literature. The relevant input-output data for process variables are taken from open source literature. The training set and validation set are statistically designed from the total data. The designed training data were used for design of the process model and the remaining validation data were used for model performance evaluation. Simulation results show superior performance of the standard SVR model over other models.