“Feedforward, Cascade and Model Predictive Control Algorithms for De-Oiling Hydrocyclones: Simulation Study”

Authors: Mishiga Vallabhan, Jose Matias and Christian Holden,
Affiliation: NTNU
Reference: 2021, Vol 42, No 4, pp. 185-195.

Keywords: De-oiling hydrocyclones, Control Schemes, Simulation

Abstract: Maintaining the efficiency of the produced-water treatment system is important for the oil and gas industry, especially taking into consideration the environmental impact caused of the produced-water. De-oiling hydrocyclones are one of the most common type of equipment used in the produced-water treatment system. The low residence time of this device makes it difficult for a control system to maintain efficiencies at different plant disturbances. In this paper, a control-oriented hydrocyclone model with a traditional pressure drop ratio (PDR) controller is analysed, and the inability of the PDR controller to maintain the efficiency when increasing the inlet concentration is shown experimentally as well as in simulation. Then, we propose three control schemes for dealing with this issue: a feed-forward, a feed-back/cascade and a model predictive controller. We show in simulation that all proposed schemes are able to improve and maintain the efficiency of hydrocyclones considering the upstream disturbances, such as variations in inlet oil concentrations and inflow rates. We also discuss the characteristics of the three methods and propose guidelines for choosing the appropriate scheme based on the available resources at the industrial site (such as measurements, hardware and software at hand).

PDF PDF (1125 Kb)        DOI: 10.4173/mic.2021.4.4

DOI forward links to this article:
[1] Mishiga Vallabhan K G, Christian Holden and Sigurd Skogestad (2022), doi:10.2118/209576-PA
[2] Jaroslav Hlava and Shereen Abouelazayem (2022), doi:10.3390/s22082847
[3] Stefan Jespersen, Zhenyu Yang, Dennis Severin Hansen, Mahsa Kashani and Biao Huang (2023), doi:10.3390/en16207095
[4] Stefan Jespersen, Mahsa Kashani and Zhenyu Yang (2023), doi:10.1109/CoDIT58514.2023.10284508
[5] Kacper Filip Pajuro, Lasse Bonde Hansen, Michael Keenan Odena, Stefan Jespersen and Zhenyu Yang (2023), doi:10.1109/IECON51785.2023.10311791
[1] Andersson, J. A.E., Gillis, J., Horn, G., Rawlings, J.B., and Diehl, M. (2019). CasADi -- A software framework for nonlinear optimization and optimal control, Mathematical Programming Computation. 11(1):1--36. doi:10.1007/s12532-018-0139-4
[2] Beyer, J., Bakke, T.H., Lichtenthaler, R., and Klungsoyr, J. (2019). Environmental effects of offshore produced water discharges evaluated for the barents sea, NIVA-rapport.
[3] Bock, H.G. and Plitt, K.-J. (1984). A multiple shooting algorithm for direct solution of optimal control problems, IFAC Proceedings Volumes. 17(2):1603--1608. doi:10.1016/S1474-6670(17)61205-9
[4] Bram, M.V., Hansen, L., Hansen, D.S., and Yang, Z. (2018). Hydrocyclone separation efficiency modeled by flow resistances and droplet trajectories, 3rd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production. 51(8):132--137. doi:10.1016/j.ifacol.2018.06.367
[5] Bram, M.V., Jespersen, S., Hansen, D.S., and Yang, Z. (2020). Control-oriented modeling and experimental validation of a deoiling hydrocyclone system, Processes. 8(9):1010. doi:10.3390/pr8091010
[6] Das, T. and Jaschke, J. (2018). Modeling and control of an inline deoiling hydrocyclone, 3rd IFAC Workshop on Automatic Control in Offshore Oil and Gas Production OOGP. 51(8):138--143. doi:10.1016/j.ifacol.2018.06.368
[7] Durdevic, P., Pedersen, S., Bram, M., Hansen, D., Hassan, A., and Yang, Z. (2015). Control oriented modeling of a de-oiling hydrocyclone, 17th IFAC Symposium on System Identification SYSID. 48(28):291--296. doi:10.1016/j.ifacol.2015.12.141
[8] Durdevic, P. and Yang, Z. (2018). Application of hinf robust control on a scaled offshore oil and gas de-oiling facility, Energies. 11(2):287. doi:10.3390/en11020287
[9] Hansen, L., Durdevic, P., Jepsen, K.L., and Yang, Z. (2018). Plant-wide optimal control of an offshore de-oiling process using mpc technique, Ifac-papersonline. 51(8):144--150. doi:j.ifacol.2018.06.369
[10] Husveg, T., Rambeau, O., Drengstig, T., and Bilstad, T. (2007). Performance of a deoiling hydrocyclone during variable flow rates, Minerals Engineering. 20(4):368--379. doi:10.1016/j.mineng.2006.12.002
[11] MATLAB. (2021). Gaussian process regression models, https://se, mathworks.com/help/stats/gaussian-process-regression-models.html. 2021. (accessed Sep , 2021).
[12] Meldrum, N. (1988). Hydrocyclones: A solution to produced-water treatment, SPE Production Engineering. 3(04):669--676. doi:10.2118/16642-PA
[13] Orlowski, R., Euphemio, M. L.L., Euphemio, M.L., Andrade, C.A., Guedes, F., Tostada Silva, L.C., Pestana, R.G., deCerqueira, G., Lourenco, I., and Pivari, A. (2012). Marlim 3 phase subsea separation system-challenges and solutions for the subsea separation station to cope with process requirements, In Offshore Technology Conference. Offshore Technology Conference. doi:10.4043/23552-MS
[14] Pereira, R.M., Campos, M. C. M. M.d., deOliveira, D.A., deSouza, R. d. S.A., Filho, M. M.C., Orlowski, R., Duarte, D.G., Raposo, G.M., Lillebrekke, C., Ljungquist, D., Carvalho, A., and Fares, M. (2012). Ss: Marlim 3 phase subsea separation system: Controls design incorporating dynamic simulation work, paper presented at the offshore technology conference, houston, texas, usa, 30 april – 3 may. Houston, Texas, USA. doi:10.4043/23564-MS
[15] Qin, S.J. and Badgwell, T.A. (2003). A survey of industrial model predictive control technology, Control engineering practice. 11(7):733--764. doi:10.1016/S0967-0661(02)00186-7
[16] Rawlings, J.B., Mayne, D.Q., and Diehl, M. (2017). Model predictive control: theory, computation, and design, volume2, Nob Hill Publishing Madison, WI.
[17] Seborg, D.E., Mellichamp, D.A., Edgar, T.F., and DoyleIII, F.J. (2010). Process dynamics and control, third edition. John Wiley & Sons.
[18] Vallabhan, M. and Holden, C. (2020). Non-linear control algorithms for de-oiling hydrocyclones, In 2020 28th Mediterranean Conference on Control and Automation (MED). IEEE, pages 85--90. doi:10.1109/MED48518.2020.9183115
[19] Vallabhan K G, M., Holden, C., and Skogestad, S. (2020). A first-principles approach for control-oriented modeling of de-oiling hydrocyclones, Industrial & Engineering Chemistry Research. 59(42):18937--18950. doi:10.1021/acs.iecr.0c02859
[20] Wachter, A. and Biegler, L.T. (2006). On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Mathematical programming. 106(1):25--57. doi:10.1007/s10107-004-0559-y
[21] Williams, C.K. and Rasmussen, C.E. (2006). Gaussian processes for machine learning, volume2, MA:MIT press Cambridg.

  title={{Feedforward, Cascade and Model Predictive Control Algorithms for De-Oiling Hydrocyclones: Simulation Study}},
  author={Vallabhan, Mishiga and Matias, Jose and Holden, Christian},
  journal={Modeling, Identification and Control},
  publisher={Norwegian Society of Automatic Control}