International Journal of Control Science and Engineering
pISSN: 21684952 eISSN: 21684960
2020; 10(1): 110
doi:10.5923/j.control.20201001.01
Rashid M. Ansari^{1}, Hasan Imran^{2}, Ali S. Hunaidy^{3}
^{1}Engineering Specialist, Refining Development, Oil Upgrading R&D Division, Research & Development Center, Saudi Aramco
^{2}Senior Scientist, Research & Development Center, Saudi Aramco
^{3}Research Engineer, Research & Development Center, Saudi Aramco
Correspondence to: Rashid M. Ansari, Engineering Specialist, Refining Development, Oil Upgrading R&D Division, Research & Development Center, Saudi Aramco.
Email: 
Copyright © 2020 The Author(s). Published by Scientific & Academic Publishing.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
This article presents the application of integrating realtime optimization with modelpredictive control on a hydrocracking unit on a model case refinery in the Middle East. Realtime optimization (RTO) provides technological excellence that helps to maximize the contribution of the plant to the business profit, provides bestinclass performance, optimizing the plant operation, enhancing safety and reliability. The main objective of RTO implementation on refinery processes was to optimize the operation by applying online rigorous nonlinear closedloop optimization technology. RTO contributed to optimize key process operating variables by shifting the unit margin toward the optimum, and operation was better placed to challenge targets and operating conditions, driving the plant toward a more profitable operating regime and bringing the higher benefits. The steadystate and kinetic models were developed and used by RTO to improve the yield of high value products by maximizing the economic objective function to enhance the yields of diesel and gasoline. Increasing the feed rate subject to unit constraints and catalyst run length was another objective of RTO implementation. In addition, potential RTO applications have been highlighted in this article for achieving CO_{2} emission reduction using two different approaches: improvement of energy efficiency and application of CO_{2} capture and conversion technologies. This application will integrate model predictive control (MPC) with RTO with an ultimate aim to maximize an economic objective function to reduce CO_{2} emission.
Keywords: Realtime optimization, Modelpredictive control, Hydrocracker, Refinery processes, Inferential model, Economic objective function, CO_{2} emissions
Cite this paper: Rashid M. Ansari, Hasan Imran, Ali S. Hunaidy, Integration of RealTime Optimization and ModelPredictive Control: Application to Refinery Processes, International Journal of Control Science and Engineering, Vol. 10 No. 1, 2020, pp. 110. doi: 10.5923/j.control.20201001.01.
Figure 1. Realtime optimization integrating with the system of automation 
Figure 2. System of inferential model and its development procedure 



Figure 3. Steps of online closed loop optimization 
Figure 4. A block diagram representing the RTO and regulatory feedback system 
Figure 5. Inferential model for gasoline (HN 95%  hydrocracking) 
Figure 6. Maximization of gasoline production with RTO application 
Figure 7. Diesel (HDO 90%) specification control between the limits (350352C) 