Quality Enhancement of Dar Crude Oil Pre-processing Using Model Predictive Control

In large-scale industries such as oil refineries and petrochemicals, it is necessary to implement an advanced control strategy. This is of great importance, particularly in separation processes where high energy consumption is required during the operation. Thus, to implement a control system that guarantees high control efficiency with less energy and heat consumption, the first and second-stage separators in Central Processing Facilities (CPFs) operated with conventional PID controllers are replaced with a higher layer of Model Predictive Controller (MPC). In this paper, Aspen HYSYS software is used to characterize the Dar blend in the Sudanese oil field. Later, the existing PID controllers in the separators are simulated to reflect the current operation. The PID controllers are employed to control the liquid level in the first-stage separator and the bulk liquid temperature in the second-stage separator. Then, MATLAB System Identification Toolbox is used to identify the process model to be applied for the MPC controller. Finally, disturbance rejection and set-point tracking are applied for both PID and MPC controllers to assess and compare the performance of each controller quantitatively. The research revealed satisfactory performance in terms of disturbance rejection for both controllers with smoother operation and minimal load on the control valve in the MPC implementation case. Nevertheless, for the set-point tracking, the MPC controller exhibited a remarkably faster response that is nearly half the time required by the PID controller.