A Hybrid Intelligent Control Design for Improving the Quality of Petroleum Products

Ghada A. Mutuab; Mohammed Y. Hassan

doi:10.51173/jt.v8i2.2890

Authors

Ghada A. Mutuab Department of Control Engineering, College of Control and Systems Engineering, University of Technology-Iraq, Baghdad, Iraq
Mohammed Y. Hassan Department of Intelligent Control Engineering, College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq

DOI:

https://doi.org/10.51173/jt.v8i2.2890

Keywords:

Type-2 Fuzzy Logic Controller (T2FLC), Fluid Catalytic Cracking Unit (FCCU), Nonlinear Control, Petroleum Quality, Firefly Optimization

Abstract

One of the most crucial parts of the oil refining process is the Fluid Catalytic Cracking Unit (FCCU). This system is essential for transforming heavy hydrocarbon fractions into valuable lighter products such as propylene, diesel, and gasoline. This method helps to remove impurities, enhance the quality of finished goods, and increase the yield of desirable products. The unit encounters uncertainties in its model dynamics, cross-coupling between its axes and sub-units, and extremely nonlinear effects. This study discusses the design of four hybrid controllers comprising two PD-Like Interval Type-2 (PDLIT2) fuzzy logic controllers and two Nonlinear Proportional-Integral-Derivative (NPID) controllers for controlling the reactor and regenerator temperatures in FCCU processes. The parameters of the NPID controller and each Fuzzy controller are tuned using the Firefly Optimization algorithm. These controllers make up for the model's nonlinearities, uncertainty, chattering, and cross-coupling. The overshoot of the reactor temperature response is enhanced by up to 85%, while that of the generator is enhanced by up to 60%, compared with previous published work.

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Author Biographies

Ghada A. Mutuab, Department of Control Engineering, College of Control and Systems Engineering, University of Technology-Iraq, Baghdad, Iraq

The author awarded the M. Sc. degree in control Engineering from the control and Systems Engineering Department, University of Technology-Iraq

Mohammed Y. Hassan, Department of Intelligent Control Engineering, College of Artificial Intelligence Engineering, University of Technology-Iraq, Baghdad, Iraq

References

Z. Brijet, and N. Bharathi, "Design of type-2 fuzzy logic controller for fluid catalytic cracking unit," International Journal of Manufacturing Technology and Management, vol. 35, no. 1, 2021, https://doi.org/10.1504/IJMTM.2021.114700.

A. Dey and R. Ayyagari, "Robust PID controller design using fuzzy pole placement techniques," IFAC, Elsevier, vol. 49, no. 1, pp. 789–794, 2016, https://doi.org/10.1016/j.ifacol.2016.03.153.

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, vol. 105, pp. 480-485, 2017, https://doi.org/10.1016/j.egypro.2017.03.344.

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, vol. 51, no. 4, pp. 657-662, 2018, https://doi.org/10.1016/j.ifacol.2018.06.172.

S. Ahmed, S. Ali, and R. Tabasha, R., "The design and implementation of a fuzzy gain-scheduled PID controller for the Festo MPS PA compact workstation liquid level control," Engineering Science and Technology, an International Journal, vol. 23, no. 2, pp. 307–315, Apr. 2020, https://doi.org/10.1016/j.jestch.2019.05.014.

G. L. Demidova, D. V. Lukichev, A. Y. and Kuzin, "A genetic approach for auto-tuning of adaptive fuzzy PID control of a telescope’s tracking system," Procedia Computer Science, vol. 150, pp. 495–502, 2019, https://doi.org/10.1016/j.procs.2019.02.084.

I. Ganchev, A. Taneva, K. Kutryanski, and M. Petrov, "Decoupling fuzzy-neural temperature and humidity control in HVAC systems", IFAC PapersOnLine, vol. 52, no. 25, pp. 299-304. Nov. 2019, https://doi.org/10.1016/j.ifacol.2019.12.539.

Jalali, N., Razmi, H., and Doagou-Mojarrad, H., "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", Applied Soft Computing Journal, vol. 93, no. 106424, 2020, https://doi.org/10.1016/j.asoc.2020.106424.

P. N. Josiah, I. J. Otaraku, and B. O. Evbuomwan, “Servo and regulatory response of an industrial fluid catalytic cracking (FCC) unit under fuzzy logic supervisory control,” Engineering and Technology Journal, vol. 41, no. 9, pp. 1139-1151, 2023, https://doi.org/10.30684/etj.2023.139485.1432.

G. A. Mutuab and M. Y. Hassan, “Non-Linear PID control of fluid catalytic cracking unit,” Journal européen des systèmes automatisés, vol. 56, no. 5, pp. 793-800, Oct. 2023, https://doi.org/10.18280/jesa.560510.

Z. Guo and H. Yang, “Improving model-free control algorithms based on data-driven and model-driven approaches: a research study”, Mathematics, vol. 12, no. 1:24, 2023, https://doi.org/10.3390/math12010024.

J. Y. Lee, G. G. Jin, and G. B. So, “Adaptive nonlinear Proportional–Integral–Derivative control of a continuous stirred tank reactor process using a radial basis function neural network,” Algorithms, 1vol. 8, no. 7, pp. 442–442, July 2025, https://doi.org/10.3390/a18070442.

A. Rospawan and C. C. Tsai, "Recurrent polynomial-based FBLS for adaptive predictive PID control of nonlinear discrete-time systems: comparative studies on control performance and time complexity," 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Kuching, Malaysia, 2024, pp. 541-546, https://doi.org/10.1109/smc54092.2024.10832085.

Y. Sun and Z. Su, "Nonlinear finite-time PID control method with gain tuning by frequency domain analysis," 2023 5th International Conference on Intelligent Control, Measurement and Signal Processing (ICMSP), Chengdu, China, pp. 461-466, May 2023, https://doi.org/10.1109/ICMSP58539.2023.10170956.

A. Rajendran, M. Karthikeyan, G. Saravanakumar, “Implementation of FOPID controller with modified harmony search optimization for precise modelling and auto-tuning of nonlinear systems”, Automatika, vol. 65, no.; 3, pp. 881–893, 2024, https://doi.org/10.1080/00051144.2024.2307227.

A. Beşkardeş, Y. Hameş, and K. Kaya, “A comprehensive Review on Fuzzy logic Control Systems for All, Hybrid, and Fuel cell Electric Vehicles,” Soft Computing, vol. 28, pp. 8183–8221, 2024, https://doi.org/10.1007/s00500-023-09454-5.

M. G. Voskoglou, “Fuzzy logic in control theory”, In: Magdi, D.A., Helmy, Y.K., Mamdouh, M., Joshi, A. (eds) Digital Transformation Technology. Lecture Notes in Networks and Systems, vol. 224, Springer, Singapore, 2022, https://doi.org/10.1007/978-981-16-2275-5_13.

S. K. Atiyah, A. Y. A. Aljanabi, M. S. Ahmed, et al.,” Design of a ratio control algorithm for a fluid catalytic cracking system in an universal oil product context,” Petroleum Chemistry, vol 64, pp. 83–92, 2024, https://doi.org/10.1134/S0965544124020154.

A. Oloruntoba, Y. Zhang, and C. S. Hsu, “State-of-the-Art review of fluid catalytic cracking (FCC) catalyst regeneration intensification technologies,” Energies, vol. 15, no. 6:2061, 2022, https://doi.org/10.3390/en15062061.

W. Tian, S. Wang, S. Sun, C. Li, and Y. Lin, “Intelligent prediction and early warning of abnormal conditions for fluid catalytic cracking process,” Chemical Engineering Research and Design, vol. 181, pp. 304-320, 2022, https://doi.org/10.1016/j.cherd.2022.03.031.

M. K. Khaldi, M. Al-Dhaifallah, O. and Taha, “Artificial intelligence perspectives: a systematic literature review on modeling, control, and optimization of fluid catalytic cracking,” Alexandria Engineering Journal, vol. 80, pp. 294-314, 2023, https://doi.org/10.1016/j.aej.2023.08.066.

M. C. Mastry., L. Dorazio, J. C. Fu, J. P. Gómez, S. Sedano, S. S. Ail, M. J. Castaldi, and B. Yilmaz, “Processing renewable and Waste based feedstocks with fluid catalytic cracking: impact on catalytic performance and considerations for improved catalyst design,” Front. Chemistry, vol. 11, no. 1067488, 2023, https://doi.org/10.3389/fchem.2023.1067488.

A. E. Mavukwana, K. G. Burra, C. Sempuga, M. Castaldi, and A. K. Gupta, “Effect of spent fluid catalytic cracking (FCC) catalyst on synga production from pyrolysis and CO2-assisted gasification of waste tires,” Fuel, vol. 355, no. 129446, 2024, https://doi.org/10.1016/j.fuel.2023.129446.

M S. Akhtar and W. Zaman, “Advancing sustainable catalysis: catalytic solutions for green chemistry and the energy transition,” Catalysts, vol. 15, no. 6, pp. 511, 202,. https://doi.org/10.3390/catal15060511.

B. Maroua, Z. Laid, H. Benbouhenni, et al., ”Genetic algorithm type 2 fuzzy logic controller of microgrid system with a fractional-order technique,” Scientific Report, vol. 15, no. 6318, 2025, https://doi.org/10.1038/s41598-025-90239-1.

D. Çelik, N. Khosravi, M. A. Khan, m. Waseem, and H. Ahmed, “Advancements in Nonlinear PID Controllers: A Comprehensive Review”, Computers and Electrical Engineering, vol. 129 (Part A), no. 110775, 2026, https://doi.org/10.1016/j.compeleceng.2025.110775.

K. Alelg, A. Alfarraj, A. Tanimu, S. A. Ganiyu, and K. Alhooshani,” Data-Driven approach for predicting gasoline yield in an FCC unit charged with light and heavy feedstocks: A PCA-guided grouping for enhanced modeling experience,” Journal of Chemical Information and Modeling, vol. 66, no. 1, pp. 271-298, 2026, https://doi.org/10.1021/acs.jcim.5c02281.

B. Wei, Z. Wang, Y. Ye, L. Che, H. and Zhou, “Novel flexible model predictive control strategy to improve the yield of light products in fluid catalytic cracking unit,” Chemical Engineering Research and Design, vol. 217, pp. 467-482, 2025, https://doi.org/10.1016/j.cherd.2025.04.007.

F. Enache, D. Cursaru, and D, Danulescu, “Integration of wet scrubbing system and SOx additive technologies to reduce the SO2 emissions generated in FCCU”, Chemical Papers, vol. 76, pp. 6537–6549, 2022, https://doi.org/10.1007/s11696-022-02335-5.

T. W. Selalame, R. Patel, L. M. Mujtaba, and Y. M. John, “A Review of modelling of the FCC unit—Part II: The Regenerator,” Energies, vol. 15, no. 1:388, 2022, https://doi.org/10.3390/en15010388.

A. R. Nasser, A. T. Azar, A. J. Humaidi, A. K. Al-Mhdawi, I. K. Ibraheem, “Intelligent fault detection and identification approach for analog electronic circuits based on fuzzy logic classifier,” Electronics, vol. 10, no. 23:2888, 2021, https://doi.org/10.3390/electronics10232888.

A. K. Al Mhdawi, N. Wright, A. J. Humaidi and A. T. Azar, "Adaptive PI-Fuzzy like control of a stack pneumatic actuators testbed for multi-configuration small scale soft robotics," 2023 International Conference on Manipulation, Automation and Robotics at Small Scales (MARSS), Abu Dhabi, United Arab Emirates, 2023, pp. 1-8, https://doi.org/10.1109/MARSS58567.2023.10294122.

M. K. Hamzah, R. S. Al-Azzawi, A. Al-Jodah, A. J. Humaidi, and A. F. Hasan, “Fuzzy logic-based chattering reduction in sliding mode control of single-link robot using muscle-like actuator,” ICIC Express Letters, vol. 18, no. 3, pp. 271-283, 2024, https://doi.org/10.24507/icicel.18.03.271.

Z. Brijet, and N. Bharathi, “Design of type-2 fuzzy logic controller for fluid catalytic cracking unit,” International Journal of Manufacturing Technology and Management, vol. 35, no. 1, pp. 51-68, 2021, https://doi.org/10.1504/IJMTM.2021.114700.

V. Karthika, Z. Brijet, N. and Bharathi, “Design of optimal controller for fluid catalytic cracking unit,” Procedia Engineering, vol. 38, pp. 1150-1160, 2012, https://doi.org/10.1016/j.proeng.2012.06.146.

S. K. Valluru M. and Singh,” Performance Investigations of APSO tuned linear and nonlinear PID controllers for a nonlinear dynamical system”, Journal of Electrical Systems and Information Technology, vol. 5, no. 3, pp. 442-452, 2018, https://doi.org/10.1016/j.jesit.2018.02.001.

M. Y. Hassan, H. I. Ali, and H. M. Jassim, "Hybrid h-infinity fuzzy logic controller design,” Journal of Engineering Science and Technology, vol. 15, no. 1, pp. 1–21, 2020.

S. S. Ghintab and M. Y. Hassan, “PID-like IT2FLC-based autonomous vehicle control in urban areas,” Arabian Journal for Science and Engineering, vol. 50, no. 14, pp. 11001-11017, 2025, https://doi.org/10.1007/s13369-024-09104-4

I. Fister, M. Perc, S. M. Kamal, and I. Fister, "A Review of chaos-based firefly algorithms: perspectives and research challenges," Applied Mathematics and Computation, vol. 252, pp. 155-165, Feb. 2015, https://doi.org/10.1016/j.amc.2014.12.006

M. Q. Kadhim and M. Y. Hassan, “Design and optimization of backstepping controller applied to autonomous quadrotor,” (2020) IOP Conference Series: Materials Science and Engineering, vol. 881, no. 1, art. no. 012128, 2020, https://doi.org/10.1088/1757-899X/881/1/012128.

A. J. Humaidi, A. A. Oglah, S. J. Abbas, and I. K. Ibraheem, "Optimal augmented linear and nonlinear PD control design for parallel robot based on PSO tuner," International Review on Modelling and Simulations, vol. 12, no. 5, pp. 281–291, 2019, https://doi.org/10.15866/iremos.v12i5.16298.

A. Al-Jodah, S. J. Abbas, A. F, Hasan, A. J. Humaidi, A. Sh. M. Al-Obaidi, A. A. Al-Qassar, and R. F. Hassan, "PSO-based optimized neural network PID control approach for a four wheeled omnidirectional mobile robot," International Review of Applied Sciences and Engineering, vol. 14, no. 1, pp. 58–67, 2023, https://doi.org/10.1556/1848.2022.00420.

M. Y. Hassan and S. S. Ezzaten, “PI-Like interval type-2 fuzzy logic control based social spider optimization for distillation column,” 2018 Third Scientific Conference of Electrical Engineering (SCEE), Baghdad, Iraq, art. no. 8684171, 2018, pp. 67 - 71, 2018, https://doi.org/10.1109/SCEE.2018.8684171.

R. G. Ghane and M. Y. Hassan, “Advanced hybrid nonlinear control for morphing quadrotors,” Mathematical Modelling of Engineering Problems, vol. 10, no. 4, pp. 1216-1224,2023, https://doi.org/10.18280/mmep.100414.