Continuous estimation of kerosene cold filter plugging point using soft sensors

Abstract

Due to growing fuel quality demands, continuous measurements of process variables and product quality properties in the crude distillation unit (CDU) are necessary. One of the key diesel fuel properties is kerosene cold filter plugging point (CFPP). CFPP is usually determined only by laboratory assays. On the basis of available continuous measurements of temperatures and flows of appropriate process streams, soft sensor models for the estimation of kerosene CFPP have been developed. Data preprocessing includes: detection and outlier removal, generating additional output data by Multivariate Adaptive Regression Splines (MARSplines) algorithm, detrending data and filtering data. Soft sensors are developed using linear and nonlinear identification methods. Model structures are optimized by Genetic Algorithm (GA) and ANFIS (Adaptive Neuro-Fuzzy Inference System) algorithm. Results of the Output Error (OE) model, Hammerstein–Wiener (HW) model and neuro-fuzzy model are shown. Developed models were evaluated based on the final prediction error (FPE), root mean square error (RMSE), mean absolute error (AE) and FIT values. The best results are achieved with neuro-fuzzy model.