Separator Control of Tuba Degassing Using Model Predictive Control (MPC)

Seaar Al-Dabooni; Zainab A. Ashour

doi:10.52716/jprs.v16i3.889

Authors

Seaar Al-Dabooni Engineering Maintenance Directory, Instrumentation and Control Department, Basra Oil Company (BOC), Ministry of Oil, Basra, Iraq.
Zainab A. Ashour Engineering Maintenance Directory, Instrumentation and Control Department, Basra Oil Company (BOC), Ministry of Oil, Basra, Iraq.

DOI:

https://doi.org/10.52716/jprs.v16i3.889

Keywords:

PID, Model Predictive Controller (MPC), Three-Phase Separator, Control Valves.

Abstract

The Proportional-Integral-Derivative (PID) controller is widely used in the Basrah Oil Company (BOC) to control oil equipment, such as the inlet and outlet valves of separators in degassing sites. PID controllers are simple and do not require a full model of the equipment to be controlled. However, they can be difficult to tune, and the trial-and-error method used by BOC engineers can be risky and inefficient. Additionally, PID controllers are single-input and single-output, which limits their flexibility. Otherwise, Model Predictive Control (MPC) is a multi-input and multi-output (MIMO) controller that can be used to control complex systems with multiple interacting variables. MPC also requires a model of the system, but this model can be uncertain. In this paper, we use MPC to control the valves of a three-phase separator in the Tuba degassing station of the Tuba oil field in Basrah city. We use a Matlab program to derive a model of the separators’ valves from collected data stored in the Distributed Control System (DCS) network control system. We compare the responses of the PID and MPC and show that the MPC performs better. We also estimate that using an MPC instead of PID controllers could improve annual gas production by 58.5% and oil production by 87.2% (equivalent to 31,800 barrels of oil). This would not only increase oil and gas production but also reduce gas flaring and oil-water mixing. Therefore, MPC is a promising alternative to PID controllers for controlling oil equipment to improve performance, reduce risks, and increase efficiency.

References

Z. Yang, M. Juhl, and B. Løhndorf, "On the innovation of level control of an offshore three-phase separator," IEEE International Conference on Mechatronics and Automation, Xi'an, China, pp. 1348-1353, 2010, doi: https://doi.org/10.1109/ICMA.2010.5588340.

S. F. A. Bukhari, I. Ismail, and W. Q. Yang. "Visualizing oil separator vessel and decision-making for control," 4th World Congress in Industrial Process Tomography, pp. 855-860, 2005.

‏A. F. Sayda, and J H. Taylor. "Modeling and control of three-phase gravity separators in oil production facilities," 2007 American Control Conference, New York, USA, pp. 4847-4853, 2007, doi: https://doi.org/10.1109/ACC.2007.4282265.

S. Song, X. Liu, C. Li, Z. Li, S. Zhang, W. Wu, B. Shi, Q. Kang, H. Wu, and J. Gong, "Dynamic simulator for three-phase gravity separators in oil production facilities," ACS omega, no. 8, vol. 6, pp. 6078-6089, 2023, doi: https://doi.org/10.1021/acsomega.2c08267.

S. Al-Dabooni and H. A. M. Alshehab, “Self-Learning Controllers in the Oil and Gas Industry,” Journal of Petroleum Research and Studies, vol. 11, no. 1, pp. 18-35, 2021, doi: https://doi.org/10.52716/jprs.v11i1.427.

S. Al-Dabooni, A. A. Tawiq, and H. Alshehab, “Dual Heuristic Dynamic Programming in the Oil and Gas Industry for Trajectory Tracking Control,” SPE Conference at Oman Petroleum & Energy Show, Muscat, Oman, 21–23 March 2022, doi: https://doi.org/10.2118/200271-MS.

S. J. Al-Dabooni and H. A. Al-Shawi, “Using Fuzzy Inference System in Gas Turbine to Overcome a High Exhaust Temperature Problem,” Journal of Petroleum Research and Studies, vol. 12, no. 1(Suppl.), pp. 225-242, Apr. 2022, doi: https://doi.org/10.52716/jprs.v12i1(Suppl.).634.

L. Aimacaña-Cueva, O. Gahui-Auqui, J. Llanos-Proaño, and D. Ortiz-Villalba, "Advanced Control Algorithms for a Horizontal Three-Phase Separator in a Hardware in the Loop Simulation Environment," Springer Nature, International Conference on Applied Technologies, pp. 399-414. Cham, Switzerland, 2022, doi: https://doi.org/10.1007/978-3-031-24971-6_29.

J. Bhookya, M. V. Kumar, J. R. Kumar, and A. S. Rao, “Implementation of PID controller for liquid level system using mGWO and integration of IoT application,” Journal of Industrial Information Integration, vol. 28, Art. no. 100368, 2022, doi: https://doi.org/10.1016/j.jii.2022.100368.

P. A. Darwito, B. L. Widjiantoro, S. A. Prananda, D. O. Pradana, and S. W. Sudrajat, “Development of WSAN on a Prototype Three-Phase Separator Level Control System using LoRa RF Based on IoT,” 2022 IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM), Laguboti, North Sumatra, Indonesia, pp. 01-06, 2022, doi: https://doi.org/10.1109/ICOSNIKOM56551.2022.10034872.

Rao, PV Gopi Krishna, M. V. Subramanyam, and K. Satyaprasad. “Study on PID controller design and performance based on tuning techniques,” 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Kanyakumari, India, pp. 1411-1417, 2014, doi: https://doi.org/10.1109/ICCICCT.2014.699318.

J. Arroyo, C. Manna, F. Spiessens, and L. Helsen, “Reinforced model predictive control (RL-MPC) for building energy management,” Applied Energy, vol. 309, Art. no. 118346, 2022, doi: https://doi.org/10.1016/j.apenergy.2021.118346.

H. Wang, X. Zheng, X. Yuan, and X. Wu, “Low-complexity model-predictive control for a nine-phase open-end winding pmsm with dead-time compensation,” IEEE transactions on power electronics, vol. 37, no. 8, pp.8895-8908, 2022, doi: https://doi.org/10.1109/TPEL.2022.3146644.

S. Y. Chang, H. -C. Wu, Y. -C. Kuan and Y. Wu, "Tensor Levenberg-Marquardt Algorithm for Multi-Relational Traffic Prediction," IEEE Transactions on Vehicular Technology, vol. 72, no. 9, pp. 11275-11290, Sept. 2023, doi: https://doi.org/10.1109/TVT.2023.3270037.

W. Zhang, Z. Zheng, H. Liu, "Droop control method to achieve maximum power output of photovoltaic for parallel inverter system," CSEE Journal of Power and Energy Systems, vol. 8, no. 6, pp. 1636-1645, November 2022, doi: https://doi.org/10.17775/CSEEJPES.2020.05070.

S. Al-Dabooni, and D. C. Wunsch, “An Improved N-Step Value Gradient Learning Adaptive Dynamic Programming Algorithm for Online Learning,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 4, pp. 1155-1169, April 2020, doi: https://doi.org/10.1109/TNNLS.2019.2919338.

S. Al-Dabooni, and D. C. Wunsch, “Convergence of recurrent neuro-fuzzy value-gradient learning with and without an actor,” IEEE Transactions on Fuzzy Systems, vol. 28, no. 4, pp. 658-672, April 2020, doi: https://doi.org/10.1109/TFUZZ.2019.2912349.