Skip to main content

Takagi Sugeno Fuzzy-Tuned RTDA controller for pH Neutralization process Subject to Addictive Load Changes

  • Conference paper
  • First Online:
Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1057))

  • 1023 Accesses

Abstract

For the control of pH value in neutralization process, three closed-loop control schemes are designed in this work, namely Proportional–Integral Derivative (PID), model predictive control (MPC) and Robustness Tracking Disturbance Overall Aggressiveness (RTDA) controller. As the title of the paper implies, the neutralization process undergoes addictive changes in its process parameters that clearly indicate the necessity of introducing this advanced control technique for this process. RTDA controller is a next generation regulatory controller which is an alternative to the popular PID control scheme. It combines the simplicity of the PID controller with the versatility of MPC. The pH neutralization is a highly non-linear and time-varying process, which has different operating regimes. The control objective in neutralization process is to sustain pH value at the prescribed level by controlling the flow rate of both acid and base. Takagi Sugeno (TS) Fuzzy-Tuned RTDA controller is employed for this process to vary the controller parameters for each operating point so that the set-point can be tracked effectively in all the operating regimes. An additive load disturbance is applied in the flow rate of acid and base to obtain the regulatory response. Thus, the paper focuses on effective disturbance rejection in each operating region and robustness in tracking the desired output. The simulation results are compared using time domain specifications, computational time and performance index like integral square error (ISE).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Kim, D.K.: Control of pH neutralization process using simulation based dynamic programming. Korean J. Chem. Eng. (2004)

    Google Scholar 

  2. Asuero, A.G., Michalowski, T.: Comprehensive formulation of titration curves for complex acid-base systems and its analytical implications. Crit. Rev. Anal. Chem. 41, 151 (2011)

    Article  Google Scholar 

  3. Henson, M.A., Seborg, D.E.: Adaptive nonlinear control of a pH neutralization process. IEEE Trans. Control Syst. Technol. 2, 169 (1994)

    Article  Google Scholar 

  4. Ibrahim, R.: Practical modelling and control implementation studies on a pH neutralization process pilot plant. Doctorate thesis, University of Glasgow, March 2008

    Google Scholar 

  5. Chen, X., Chen, J., Lei, B.: Identification of pH neutralization process based on the T-S fuzzy model. Adv. Compu. Sci. Environ. Ecoinform. Educ. 2, 579 (2011)

    Google Scholar 

  6. Ahmed, D.F.: On-line control of the neutralization process based on fuzzy logic. Ph.D. thesis, University of Baghdad (2003)

    Google Scholar 

  7. Gomez, J.C., Baeyens, E.: Wiener model identification and predictive control of a pH neutralization process. IEEE Control Theory Appl. 151, 329 (2004)

    Article  Google Scholar 

  8. Sanaz Mahmoodia, Javad Poshtana, Mohammad Reza Jahed-Motlaghb, Allahyar Montazeria, “Nonlinear model predictive control of a pH neutralization process based on Wiener–Laguerre model,” Chemical Engineering Journal, 146(2009), 328

    Article  Google Scholar 

  9. Abd Al Kareem, D.I.: Implementation of neural control for neutralization process. Master’s thesis, University of Technology (2009)

    Google Scholar 

  10. Srinivasan, K., Anbarasan, K.: Fuzzy scheduled RTDA controller design. ISA Trans. 52, 252 (2013)

    Article  Google Scholar 

  11. Ogunnaike, B.A., Mukati, K.: An alternative structure for next generation regulatory controllers Part I: basic theory for design, development and implementation. J. Process Control 16, 499 (2006)

    Article  Google Scholar 

  12. Kapil, M., Ogunnaike, B.: An alternative structure for next generation regulatory controllers. Part II: stability analysis and tuning rules. J. Process Control 19 272 (2009)

    Google Scholar 

  13. Oral, O., Çetin, L., Uyar, E.: A novel method on selection of Q and R matrices in the theory of optimal control. Int. J. Syst. Control 1, 84 (2010)

    Google Scholar 

  14. Hasikos, J., Sarimveis, H., Zervas, P.L., Markatos, N.C.: Operational optimization and real-time control of fuel-cell systems. J. Power Sour. 193, 258 (2009)

    Article  Google Scholar 

  15. Mani, G., Pinagapani, A.K.: Design and implementation of a preemptive disturbance rejection controller for PEM fuel cell air-feed system subject to load changes. J. Electr. Eng. Technol. 11, 1449 (2016)

    Article  Google Scholar 

  16. Rodatz, P., Paganelli, G., Guzella, L.: Optimization air supply control of a PEM fuel cell system. Am. Control Conf. 2043 (2003)

    Google Scholar 

  17. Jacobs, O.L.R., Hewkin, M.A., While, C.: Online computer control of pH in an industrial process. IEE Proc. 127, 161 (1980)

    Article  Google Scholar 

  18. Yua, Z., Wanga, J., Huangb, B., Bi, Z.: Performance assessment of PID control loops subject to setpoint changes. J. Process Control 21, 1164 (2011)

    Article  Google Scholar 

  19. Prakash, J., Senthil, R.: Design of observer based nonlinear model predictive controller for a continuous stirred tank reactor. J. Process Control 18, 504 (2008)

    Article  Google Scholar 

  20. Prakash, J., Srinivasan, K.: Design of nonlinear PID controller and nonlinear model predictive controller for a continuous stirred tank reactor. ISA Trans. 48, 273 (2009)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Geetha Mani .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mani, G., Manochitra, G. (2020). Takagi Sugeno Fuzzy-Tuned RTDA controller for pH Neutralization process Subject to Addictive Load Changes. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_37

Download citation

Publish with us

Policies and ethics