Soft Sensor Development for the Estimation of Benzene Content in Catalytic Reformate

Abstract

As vehicle emission standards become more stringent, there is an increasing need for continual monitoring of benzene content in gasoline. Since on-line analyzers are often unavailable, and laboratory analyses are infrequently obtained, soft sensors for the estimation of benzene content of light reformate are developed. Soft sensors are developed using linear and nonlinear identification methods. Experimental data are acquired from the refinery distributed control system (DCS) and include continuously measured variables and analyzer assays available on-line. In the present work, the development of a finite impulse response (FIR) model, an output error (OE) model, and a Hammerstein−Wiener (HW) model is presented. To overcome the problem of selecting the best model parameters by trial and error, genetic algorithms and pattern (direct) search were used. On the basis of developed soft sensors, it is possible to entirely replace on-line analyzers with soft sensors by embedding the model in a DCS on-site.

Soft Sensor Models for a Fractionation Reformate Plant Using Small and Bootstrapped Data Sets

“…Also, mutual correlations between inputs as well as process engineer experience were taken into account during analysis (Ujević Andrijić et al, 2012).…”

“…Data preprocessing included detecting and removal of outliers and data filtering (Ujević Andrijić et al, 2012). The model development and the model validation used 80% and 20 percent of the overall data set, respectively.…”