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).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kim, D.K.: Control of pH neutralization process using simulation based dynamic programming. Korean J. Chem. Eng. (2004)
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)
Henson, M.A., Seborg, D.E.: Adaptive nonlinear control of a pH neutralization process. IEEE Trans. Control Syst. Technol. 2, 169 (1994)
Ibrahim, R.: Practical modelling and control implementation studies on a pH neutralization process pilot plant. Doctorate thesis, University of Glasgow, March 2008
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)
Ahmed, D.F.: On-line control of the neutralization process based on fuzzy logic. Ph.D. thesis, University of Baghdad (2003)
Gomez, J.C., Baeyens, E.: Wiener model identification and predictive control of a pH neutralization process. IEEE Control Theory Appl. 151, 329 (2004)
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
Abd Al Kareem, D.I.: Implementation of neural control for neutralization process. Master’s thesis, University of Technology (2009)
Srinivasan, K., Anbarasan, K.: Fuzzy scheduled RTDA controller design. ISA Trans. 52, 252 (2013)
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)
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)
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)
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)
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)
Rodatz, P., Paganelli, G., Guzella, L.: Optimization air supply control of a PEM fuel cell system. Am. Control Conf. 2043 (2003)
Jacobs, O.L.R., Hewkin, M.A., While, C.: Online computer control of pH in an industrial process. IEE Proc. 127, 161 (1980)
Yua, Z., Wanga, J., Huangb, B., Bi, Z.: Performance assessment of PID control loops subject to setpoint changes. J. Process Control 21, 1164 (2011)
Prakash, J., Senthil, R.: Design of observer based nonlinear model predictive controller for a continuous stirred tank reactor. J. Process Control 18, 504 (2008)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
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
DOI: https://doi.org/10.1007/978-981-15-0184-5_37
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-0183-8
Online ISBN: 978-981-15-0184-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)