Engineering and Technology Journal

Servo and Regulatory Response of an Industrial Fluid Catalytic Cracking (FCC) Unit under Fuzzy Logic Supervisory Control

Document Type : Research Paper

Authors

1 World Bank Africa Centre of Excellence, Centre for Oilfield Chemicals Research, University of Port Harcourt, Port-Harcourt, Nigeria.

2 Chemical Engineering Dept.,University of Port Harcourt, Port Harcourt, Nigeria.

Abstract

A self-tuning hierarchical controller in which a Fuzzy logic controller supervises the control actions of a conventional PID has been proposed, implemented and presented in this paper. The controller has been applied to a control study of Fluid Catalytic Cracking unit (FCCU) riser temperature, and regenerator temperature respectively. Comparison between the performance of the proposed Fuzzy-PID controller and the conventional PID was made in simulation studies of regulatory and servo performances of the two controller types. Six performance measures: Percent overshoot (OS), settling time (ST), integral absolute error (IAE), integral square error (ISE), integral time absolute error (ITAE) and integral time square error (ITSE) were employed as the tools for performance comparison between the conventional PID and the Fuzzy-PID controller. For the tracking of riser temperature with a set point at 524oC, the performance indicators under PID control gave the following results overshoot (14.5%); settling time (40 seconds) Integral absolute  error (8.246), integral square error (3.3); integral time absolute  error(1762);integral time square error (43.77) while  for the same indicators under Fuzzy-PID control the following values: overshoot (3.3%); settling time (40 seconds) ;Integral absolute  error (8.811); integral square error (14.5); integral time absolute error(280),;integral time square error (31.11) .The results allude to the superiority of the fuzzy-PID scheme  over the PID scheme in tracking the optimal set point of riser temperature.  More so, for tracking the regenerator set point temperature of 746oC, comparative study of step response under the two schemes gave the following results in six  performance indicators: overshoot (PID (12.6%) / Fuzzy-PID (6%)); settling time (PID (80 seconds) / Fuzzy-PID (20seconds)); Integral absolute error (PID (14.29) / Fuzzy-PID (8.63)); integral square error (PID(6.713). Fuzzy-PID (4.506)); integral time absolute  error(PID(2858)/Fuzzy-PID(305.9)), integral time square error (PID(77.55)/Fuzzy-PID(33.05)) . Moreover, the fuzzy-PID controller also showed superior performance over the conventional PID controller in terms disturbance rejection (regulatory response) of both riser and regenerator temperature. The results from this study suggest that the application of fuzzy-PID scheme to temperature control offers good promise of improved fluid catalytic cracking unit (FCCU) operations. 

Graphical Abstract

Highlights

  • Derive control law for classical PID.
  • Develop Fuzzy Logic rule-base and Fuzzy inference scheme.
  • Superiority of the fuzzy-PID scheme over the PID scheme in tracking the optimal set point of riser temperature.
  • The application of fuzzy-PID scheme to temperature control offers good promise of improved FCCU operations.

Keywords

Main Subjects

References
[1] Y. Rang and V. Du, An Integrated methodology for the modelling of Fluid Catalytic Cracking (FCC) riser reactor, Appl. Petrochem. Res., 4 (2014) 423-433. https://doi.org/10.1007/s13203-014-0084-8
[2] A, Dey and R. Ayyagan, Robust PID Controller design using Fuzzy Pole Placement Techniques, IFAC-PapersonLine, 49 (2016) 789–794. https://doi.org/10.1016/j.ifacol.2016.03.153
[3] A.A. El-Samahy and M.A. Shamseldin, Brushless DC motor tracking control using self-tuning fuzzy PID control and model reference adaptive control, Ain Shams Eng. J., 9 (2018) 341-352. https://doi.org/10.1016/j.asej.2016.02.004
[4] S. Dettori, V. Iannino , V. Colla and A. Signorini  , A Fuzzy Logic-based Tuning Approach of PID Control for Steam Turbines for Solar Applications, Energy Procedia, 105 (2017) 480–485. https://doi.org/10.1016/j.egypro.2017.03.344 
[5] Y. I. Kudinov, V.A. Kolesnikov , F.F. Pashchenko , A.F. Pashchenko and L. Papic ,Optimization of Fuzzy PID Controller’s Parameters, Procedia Comput. Sci., 103 (2017) 618–622. https://doi.org/10.1016/j.procs.2017.01.086
[6] D. Vrecko, M. Nerat , B. Dolenc , D. Vrančić , F. Meyer and Đ. Juričić  , Optimizing the operation of solid oxide fuel cell power system with a supervisory controller based on extremum-seeking approach, Energy Conserv. Manag., 187 (2019) 53-62. https://doi.org/10.1016/j.enconman.2019.03.012
[7] Z. Fan, X. Yu, M.Yan and C. Hong, Oxygen Excess Ratio Control of PEM Fuel Cell Based on Self-adaptive Fuzzy PID, IFAC-PapersOnLine, 51 (2018) 15–20. https://doi.org/10.1016/j.ifacol.2018.10.004 
[8] J.E. Rodríguez-Castellanos , V. H. Grisales-Palacio and J.  E.  Cote-Ballesteros , A tuning proposal for direct fuzzy PID controllers oriented to industrial continuous processes, IFAC-PapersOnLine, 51 (2018) 657–662. https://doi.org/10.1016/j.ifacol.2018.06.172
[9] A. Kumar and V. Kumar, A novel interval type-2 fractional order fuzzy PID controller: Design, performance evaluation, and its optimal time domain tuning, ISA Trans., 68 (2017) 251–275. https://doi.org/10.1016/j.isatra.2017.03.022 
[10] S. Ahmad, S. Ali and R. Tabasha, The design and implementation of a fuzzy gain-scheduled PID controller for the Festo MPS PA compact workstation liquid level control, Eng. Sci. Technol. Int. J., 23 (2020) 307-315. https://doi.org/10.1016/J.JESTCH.2019.05.014 
[11] G.L. Demidova , D.V. Lukichev, A.Yu. Kuzin and  A. Genetic , Approach for Auto-Tuning of Adaptive Fuzzy PID Control of a Telescope’s Tracking SystemProcedia Comput. Sci., 150 (2019) 495–502. https://doi.org/10.1016/j.procs.2019.02.084
[12] I. Ganchev, A. Taneva, K. Kutryanski and M. Petrov, Decoupling Fuzzy-Neural Temperature and Humidity Control in HVAC Systems, IFAC-PapersOnLine, 52 (2019) 299–304. https://doi.org/10.1016/j.ifacol.2019.12.539
[13] N. Jalali,  H. Razmi and H. Doagou-Mojarrad, Optimized fuzzy self-tuning PID controller design based on Tribe-DE optimization algorithm and rule weight adjustment method for load frequency control of interconnected multi-area power systems, Appl.  Soft Comput., 93 (2020) 106424. https://doi.org/10.1016/j.asoc.2020.106424
[14] H. H. Manap, A. K. Abdul Wahab and F. M. Zuki,Control for Carbon Dioxide Exchange Process in a Membrane Oxygenator  Using  Online  Self-Tuning Fuzzy-PID Controller, Biomed. Signal Process. Control, 64 (2021) 102300. https://doi.org/10.1016/j.bspc.2020.102300
[15] D. Sharma, Designing and Modeling of Fuzzy Control Systems, Int. J. Comput.  Appl., 16 (2011) 46-53. https://doi.org/10.5120/1973-2644
[16] Z.  Has and M.F. Rahmat, Application of Self-Tuning Fuzzy-PID Controller on Industrial Hydraulic Actuator using System Identification Approach, Int. J. Smart Sens. Intell. Syst., 2 (2009) 246-267. https://doi.org/10.21307/ijssis-2017-349
[17] S.R. Vaishnav and  Z.J. Khan, Design and Performance of PID and Fuzzy Logic Controller with Smaller Rule Set for Higher Order System, Proc. World Congr. Eng. Comp.  Sci., (2007) 1-4.      
[18] D. F. Ahmed and S. K. Ateya , Modelling and Simulation of Fluid Catalytic Cracking Unit, J. Chem.  Eng. Process Technol., 7 (2016) 2-13 . https://doi.org/10.4172/2157-7048.1000308
[19] B. Olufemi and K. Latinwo, A. Olukayode, Riser Reactor Simulation in a Fluid Catalytic Cracking Unit, Chem.  Process Eng.  Res., 7 (2013) 12 – 24.  
[20] O. A. Olafadehan, O. M. Daramola, O. P. Sunmola and G. O. Abatan, Modelling and Simulation of Riser Reactor for a commercial Fluid Catalytic cracking unit using 6-lump kinetics of Vacuum Gas Oil, Pet. Petroch. Eng.  J., 3 (2019) 194. https://doi.org/10.23880/ppej-16000194
[21] P. N, Josiah, J.U. Nwalor and T.O. Ajayi, Basic Modelling of the Riser-Stripper- Regenerator   Unit of a Fluid Catalytic Cracker, J. Niger. Soc. Eng., 29 (2019) 31-48. https://doi.org/10.1007/s13203-018-0212-y
[22] P. T. Yendamuri and C. S. Rao, Design of Robust PI controller with Decoupler for a Fluid Catalytic Cracking Unit, Ind. Eng. Chem. Res.,58 (2019) 20722-20733. https://doi.org/10.1021/acs.iecr.9b04770
[23] A.K.D. Velayudhaan, Design of a supervisory fuzzy  logic controller for monitoring the inflow and purging of gas through lift bags for safe and viable salvaging operations, Ocean Eng., 171 (2019) 193-201. https://doi.org/10.1016/j.oceaneng.2018.10.049
[24] M. Lofti ,M. B. Menhaj, S. A. Hosseini and A. S. Shirani, Design of switching Supervisory Control based on Fuzzy-PID Controllers for VVER-100 Pressurizer System with RELAP5 and MATLAB Coupling, Ann. Nucl. Energy,147 (2020) 107-118. https://doi.org/10.1016/j.anucene.2020.107625
[25] M. Van and X.  P. Do , M. Mavrovouniotis , Self-tuning fuzzy PID-nonsingular fast terminal sliding mode control for robust fault tolerant control of robot manipulators, ISA Trans., 96 (2020) 60-68.  https://doi.org/10.1016/j.isatra.2019.06.017
[26] H. Rani, S. Navasree, S. George and S. Ashok , Fuzzy logic supervisory controllers for Multi-input non-isolated DC to DC converters connected to DC grid, Electr. Power Power Syst., 12 (2019) 49-60. https://doi.org/10.1016/j.ijepes.2019.04.018
Engineering and Technology Journal
Volume 41, Issue 9
Chemical Engineering
5 articles
September 2023
Page 1139-1151
Files
Share
Export Citation
Statistics