Željka Ujević Andrijić

Prediction of dibenzothiophene conversion in the ultrasound assisted oxidative desulfurization process

ABSTRACT In order to produce ultra-low sulfur diesel, ultrasound-assisted oxidation desulfurization of dibenzothiophene (DBT) was carried out with acetic acid and hydrogen peroxide. Due to its complexity, ultrasound-assisted oxidation process lacks a precise analytical solution. This paper explores the application of linear multiple regression and neural network for the prediction of dibenzothiophene conversion. Models were employed with respect to hydrogen peroxide dosage, temperature, reaction time, initial DBT concentration, and rate constant. The most accurate results were achieved by neural network model. Developed models facilitate future research in terms of better understanding the influence of process conditions of DBT conversion.

Development of Soft Sensors for Crude Distillation Unit Control

Abstract Soft sensors for distillation end point (D95) on-line estimation in crude distillation unit (CDU) are developed. Experimental data are acquired from the refinery distributed control system (DCS) and include on-line available continuously measured variables and laboratory assays. Soft sensors are developed using different linear and nonlinear identification methods. Additional laboratory data for model identification are generated by Multivariate Adaptive Regression Splines (MARSplines). The models are evaluated based on Route Mean Square (RMS), Absolute Error (AE), FIT and Final Prediction Error (FPE) criteria. The best results are achieved with Box Jenkins (BJ), Output Error (OE) and Hammerstein–Wiener (HW) model. Based on developed soft sensors it is possible to estimate fuel properties in continuous manner and apply inferential control. By real plant application of developed soft sensors considerable savings could be expected, as well as compliance with strict law regulations for product quality specifications.

Soft sensors model optimization and application for the refinery real-time prediction of toluene content

ABSTRACT Industrial facilities nowadays show an increasing need for continuous measurements, monitoring and controlling many process variables. The on-line process analyzers, being the key indicators of process and product quality, are often unavailable or malfunction. This paper describes development of soft sensor models based on the real plant data that could replace an on-line analyzer when it is unavailable, or to monitor and diagnose an analyzer’s performance. Soft sensors for continuous toluene content estimation based on the real aromatic plant data are developed. The autoregressive model with exogenous inputs, output error, the nonlinear autoregressive model consisted of exogenous inputs and Hammerstein–Wiener models were developed. In case of complex real-plant processes a large number of model regressors and coefficients need to be optimized. To overcome an exhaustive trial-and-error procedure of optimal model regressor order determination, differential evolution optimization method is applied. In general, the proposed approach could be, of interest for the development of dynamic polynomial identification models. The performance of the models are validated on the real-plant data.