Enhanced PH Neutralization Process Control Using Firefly-Optimized Artificial Neural Network Predictive Controller

  • Authors

    • M. Isai Vani Department of Electrical and Electronics Engineering, Vaigai College of Engineering, Madurai, India
    • M. Sivasubramanian Department of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College (Autonomous) Avadi, Chennai, India
    • M A Inayathullaah School of Electrical Engineering, Vellore Institute of Technology, Chennai, India
    • M. S. Malar Selvi Department of Science and Humanities, Chennai Institute of Technology, Chennai, 600069, India.
    • M. Pandikumar Department of Electrical Power & Energy Conversion, Saveetha School of Engineering, SIMATS, Chennai 602105, India.
    • N. Jothippriya Department of Electrical and Electronics Engineering, Erode Sengunthar Engineering College, Perundurai, Erode, India
    https://doi.org/10.14419/2ktkkt72

    Received date: March 3, 2025

    Accepted date: June 3, 2025

    Published date: October 19, 2025

  • pH process, FF optimized ANN controller, PI controller, PID controller

  • Abstract

    The pH neutralization is a challenging process, and it plays a significant role in the chemical process industry. Neutralization is the process of eradicating alkalinity or acidity by combining acids and bases to generate a neutral solution. To have the least amount of environmental impact, acidic or basic wastewater must be neutralized before discharge. Specifically, due to their inherent characteristics, the processes are difficult to control for high nonlinearity and sensitivity near the equilibrium point. To manage the difficult process of pH control, the traditional control techniques are utilized, including Proportional Integral (PI) and Proportional Integral Derivative (PID) controllers. Here, to standardize the pH process Artificial Neural Network (ANN) is employed and is optimized with the utilization of the Fire Fly Optimization (FFO) algorithm for tuning its input parameters. This paper aims to deploy a smoother control signal to achieve efficient load regulation and convenient set-point tracking. The suggested FF-ANN controller outperforms other existing control approaches with improved disturbance rejection, set-point tracking, and excellent sensitivity to changes in model parameters, according to the results of the determined simulation.

  • References

    1. M. A. Henson, and D. E. Seborg, “Adaptive nonlinear control of a pH neutralization process,” IEEE transactions on control systems technology, vol. 2, no. 3, pp. 169-182, 1994, https://doi.org/10.1109/87.317975
    2. A. W. Hermansson, and S. Syafiie, “Model predictive control of pH neutralization processes: A review,” Control Engineering Practice, vol. 45, pp. 98-109, 2015, https://doi.org/10.1016/j.conengprac.2015.09.005
    3. N. H. Abdullah, M. N. Karsiti, and R. Ibrahim, “A review of pH neutralization process control,” In2012 4th International conference on intelligent and Advanced Systems (ICIAS2012), vol. 2, pp. 594-598, 2012, https://doi.org/10.1109/ICIAS.2012.6306084
    4. M. Han, B. Han, and W. Guo, “Process control of pH neutralization based on adaptive algorithm of universal learning network,” Journal of Process Control, vol. 16, no. 1, pp. 1-7, 2006, https://doi.org/10.1016/j.jprocont.2005.05.002
    5. S. D. Kambale, S. George, and R. G. Zope, “Controllers used in pH neutralization process: a review,” IRJET, vol. 2, no. 3, pp. 354-361, 2015.
    6. A. Altınten, “Generalized predictive control applied to a pH neutralization process”, Computers & chemical engineering, vol. 31, no. 10, pp. 1199-1204, 2007, https://doi.org/10.1016/j.compchemeng.2006.10.005
    7. R. C. Hall, and D. E. Seborg, “Modelling and self-tuning control of a multivariable pH neutralization process part i: Modelling and Multiloop con-trol,” In 1989 American control conference, pp. 1822-1827, 1989, https://doi.org/10.23919/ACC.1989.4790490
    8. L. Estofanero, R. Edwin, and G. Claudio, “Predictive controller applied to a pH neutralization process,” IFAC-PapersOnLine, vol. 52, no. 1, pp. 202-206, 2019, https://doi.org/10.1016/j.ifacol.2019.06.062
    9. J. M. Böling, D. E. Seborg, and J. P. Hespanha, “Multi-model adaptive control of a simulated pH neutralization process,” Control Engineering Prac-tice, vol. 15, no. 6, pp. 663-672, 2007, https://doi.org/10.1016/j.conengprac.2006.11.008
    10. J. Nie, A. P. Loh, and C. C. Hang, “Modeling pH neutralization processes using fuzzy-neural approaches,” Fuzzy Sets and Systems, vol. 78, no. 1, pp. 5-22, 1996, https://doi.org/10.1016/0165-0114(95)00118-2
    11. M. A. Henson, and D. E. Seborg, “Adaptive nonlinear control of a pH neutralization process,” IEEE transactions on control systems technology, Vol.2, No.3, (1994), pp:169-182, https://doi.org/10.1109/87.317975
    12. Y. K. Yeo, and T. I. Kwon, “A neural PID controller for the pH neutralization process,” Industrial & engineering chemistry research, vol. 38, no. 3, pp. 978-987, 1999, https://doi.org/10.1021/ie9805133
    13. N. T. Ghee, S. Kumaresan, and L. C. Fan Fuzzy, “PID controller to control the pH neutralization process,” In Student Conference on Research and Development, pp. 330-334, 2002, https://doi.org/10.1109/SCORED.2002.1033125
    14. S. S. Ram, D. D. Kumar, B. Meenakshipriya, and K. Sundaravadivu, “Designing and comparison of controllers based on optimization techniques for pH neutralization process,” In 2016 international conference on information communication and embedded systems (ICICES, pp. 1-5, 2016, https://doi.org/10.1109/ICICES.2016.7518847
    15. F. Buchholt, and M. Kümmel, “Self-tuning control of a pH-neutralization process,” Automatica, vol. 15, no. 6, pp. 665-671, 1976, https://doi.org/10.1016/0005-1098(79)90034-7
    16. Y. Yang, and Q. Wu, “A neural network pid control for pH neutralization process” In 2016 35th Chinese Control Conference (CCC), pp. 3480-3483, 2016, https://doi.org/10.1109/ChiCC.2016.7553893
    17. O. Galan, J. A. Romagnoli, and A. Palazoglu, “Real-time implementation of multi-linear model-based control strategies––an application to a bench-scale pH neutralization reactor,” Journal of Process Control, vol. 14, no. 5, pp. 571-579, 2004, https://doi.org/10.1016/j.jprocont.2003.10.003
    18. P. Rastogi, S. Chatterji, and D. S. Karanjkar, “Performance analysis of fractional-order controller for pH neutralization process,” In 2012 2nd Inter-national Conference on Power, Control and Embedded Systems, pp. 1-6, 2012, https://doi.org/10.1109/ICPCES.2012.6508116
    19. H. J. Sena, F. V. Da Silva, and A. M. Fileti, “ANN model adaptation algorithm based on extended Kalman filter applied to pH control using MPC,” Journal of Process Control, vol. 102, pp. 15-23, 2021, https://doi.org/10.1016/j.jprocont.2021.04.001
    20. M. G. Elarafi, and S. B. Hisham, “Modeling and control of pH neutralization using neural network predictive controller,” In2008 International Conference on Control, Automation and Systems, pp. 1196-1199, 2008 https://doi.org/10.1109/ICCAS.2008.4694329
    21. P. K. Singh, “Design and Implementation of Intelligent Control Schemes for pH Neutralization Process (Doctoral dissertation, BITS, Pilani,” http://dspace.bits-pilani.ac.in:8080/jspui/bitstream/123456789/4611/1/2007phxf432p_phdsynopsis.pdf
    22. M. Kandasamy, and S. Vijayachitra, “Design and Implementation of an Intelligent PI Controller for a Real Time Non Linear pH Neutralization Process”, https://core.ac.uk/download/pdf/322470446.pdf
    23. R. Murugesan, J. Solaimalai, and K. Chandran, “Computer-aided controller design for a nonlinear process using a Lagrangian-based state transition algorithm,” Circuits, Systems, and Signal Processing, vol. 39, pp. 977-96, 2020, https://doi.org/10.1007/s00034-019-01139-5
    24. J. A. Ruiz-Vanoye, and O. Díaz-Parra, “Similarities between meta-heuristics algorithms and the science of life”, Central European Journal of Opera-tions Research, vol. 19, pp. 445-66, 2011, https://doi.org/10.1007/s10100-010-0135-x
    25. L. Vidhyanandhan, N. Ikram, and M. Pravin Savaridass, “Design of CDM-PI Controller for pH Process,” International Journal for Research in Ap-plied Science & Engineering Technology (IJRASET).
    26. D. Njoku, J. Odii, E. Nwokorie, and O. Nwokonkwo, “A hybrid Intelligent Control Model for regulating pH in Industrial Chemical process,” Jour-nal of Electrical Engineering, Electronics, Control and Computer Science, vol. 8, no. 3, pp. 1-8, 2021.
    27. K. Rajasekhar, and K Raja Naguru Babu, “Firefly Optimization Algorithm Based PID Controller Tuning In Paper Machine”, IJCRT, vol. 9, no. 12, 2021.

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  • How to Cite

    Vani , M. I. ., Sivasubramanian, M. ., Inayathullaah , M. A. ., Selvi , M. S. M. ., Pandikumar, M. ., & Jothippriya, N. . . (2025). Enhanced PH Neutralization Process Control Using Firefly-Optimized Artificial Neural Network Predictive Controller. International Journal of Basic and Applied Sciences, 14(6), 374-383. https://doi.org/10.14419/2ktkkt72