Development of soft sensors for isomerization process based on support vector machine regression and dynamic polynomial models

Abstract

A novel data-driven soft sensor models for application in the refinery isomerization process are presented. Soft sensor models based on support vector machine regression (SVM) and dynamic polynomial linear Finite Impulse Response (FIR), Autoregressive with Exogenous Inputs (ARX), Output Error (OE), and Nonlinear Dynamic Autoregressive with Exogenous Inputs (NARX) and Hammerstein–Wiener (HW) models are developed. They are intended for continuous estimation of key component contents in the products of a low-temperature isomerization process equipped with a deisohexanizer distillation column. Experimental data from the refinery distributed control system are employed. A significant attention is paid on collection, analysis and pre-processing of the data as well as selection of influential input variables. Developed models were evaluated on an independent data set, and the results show that selected models can reliably estimate the component contents. SVM regression model has better generalization ability in comparison with standard dynamic models on the data set with a low diversity. Developed soft sensors are suitable as analyser replacement and application in the deisohexanizer column advanced process control strategies.