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Improving efficiency of pH control by balance-based adaptive control application

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This paper deals with the efficient control of the pH process. Considering the PI + gain scheduling (PI+GS) controller as the benchmark and its control performance as the base, we investigate experimentally the overall improvement in the control performance obtained by the application of the Balance-Based Adaptive Controller (B-BAC), which requires only the measurement data of the flow rates and pH values. The improvement of the control efficiency is investigated not only in terms of the controlled variable performance but also in terms of the manipulated variable performance considered as the considerable control cost. The application of the B-BAController can ensure lower controlled pH variability at the price of the control effort similar to the PI+GS approach and thus it can improve the overall efficiency of pH control. The second important contribution is the experimental validation of the very simple and intuitive tuning procedure for the B-BAController.
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  • Institute of Automatic Control, Silesian University of Technology Akademicka 16, 44-100 Gliwice, Poland
  • Institute of Automatic Control, Silesian University of Technology Akademicka 16, 44-100 Gliwice, Poland
  • Institute of Automatic Control, Silesian University of Technology Akademicka 16, 44-100 Gliwice, Poland
  • [1] K. J. Aström, T. Hä gglund, C. C. Hang and W. K. Ho: Automatic tuning and adaptation for PID controllers - a survey. Control Engineering Practice, 1(4), (1993), 699-714.
  • [2] G. Bastin and D. Dochain: On-line estimation and adaptive control of bioreactors. Elsevier Science Publishers B.V, 1990.
  • [3] M. Bauer, I. K. Craig: Economic assessment of advanced process control - a survey and framework. J. of Process Control, 18 (2008), 2-18.
  • [4] B. W. Bequette: Nonlinear control of chemical processes: A review. Ind. Eng. Chem. Res., 30 (1991), 1391-1413.
  • [5] S. I. Biagiola and J. L. Figueroa: State estimation in nonlinear processes. Application to pH process control. Ind. Eng. Chem. Res., 41 (2002), 4777-4785.
  • [6] J. Boling, D. E. Seborg and J. P. Hespanha: Multi-model adaptive control of a simulated pH neutralization process. Control Engineering Practice, 15 (2007), 663-672.
  • [7] K. L. Chien, J. A. Hrones and J. B. Reswick: On the automatic control of the generalized passive systems. Trans. Assoc. Soc. Mech. Eng., 74 (1952), 175-185.
  • [8] J. R. Cueli and C. Bordons: Iterative nonlinear model predictive control: Stability, robustness and applications. Control Engineering Practice, 16 (2008), 1023-1034
  • [9] J. Czeczot: Model-based Adaptive Predictive Control of fed-batch fermentation process with the substrate consumption rate application. Proc. IFAC Workshopon Adaptive Systems in Control and Signal Processing, University of Strathclyde, Glasgow, Scotland, UK, (1998), 357-362.
  • [10] J. Czeczot: Balance-Based Adaptive Control of the heat exchange process. Proc.7th IEEE International Conf. on Methods and Models in Automation and RoboticsMMAR 2001, Miedzyzdroje, Poland, (2001), 853-858.
  • [11] J. Czeczot: Balance-Based Adaptive Control of a neutralization process. InternationalJ. of Control, 79(12), (2006), 1581-1600.
  • [12] J. Deng and B. Huang: Identification of nonlinear parameter varying systems with missing output data. AIChE J., Published online inWiley Online Labrary (wileyonlinelibrary. com), (2012), DOI 10.1002/aic.13735.
  • [13] O. Galan, J. A. Romagnoli and A. Palazoglu: Real-time implementation of multi-linear model-based control strategy - An application to a benchscale pH neutralization reactor. J. of Process Control, 14 (2004), 571-579.
  • [14] M. A. Henson and D. E. Seborg: Adaptive input-output linearization of a pH neutralisation process. Int. J. of Adaptive Control and Signal Proc., 11 (1997), 171-200.
  • [15] A. Isidori: Nonlinear control systems. Springer-Verlag, New York, 1989.
  • [16] M. Jelali: An overview of control performance assessment technology and industrial applications. Control Engineering Practice, 14 (2006), 441-466.
  • [17] A. D. Kalafatis, L. Wang And W. R. Cluett: Linearizing feedforwardfeedback control of pH process based on Wiener model. J. of Process Control, 15 (2005), 103-112.
  • [18] T. Klopot, J. Czeczot and W. Klopot: Flexible function block for PLC-based implementation of the Balance-Based Adaptive Controller. Proc. American ControlConference ACC 2012, Montreal. Canada, (2012).
  • [19] C. Kravaris, J. Hahn and Y. Chu: Advances and selected recent developments in state and parameter estimation. Computers and Chemical Engineering, (2012),
  • [20] J. Lee and T. F. Edgar: Improved PI controller with delayed or filtered integral mode. AIChE J., 48(12), (2002), 2844-2850.
  • [21] Ł. Mikołajczyk: Identification of the properties of the pH laboratory process for PI+gain-scheduling technique. MSc Thesis, Silesian University of Technology, Gliwice, Polan, 2011, (in Polish).
  • [22] A. O’Dwyer: Handbook of PI and PID control tuning rules. 2nd Ed. Imperial Collage Press, 2006.
  • [23] M. R. Pishvaie (and) M. Shahrokhi: Control of pH processes using fuzzy modeling of titration curve. Fuzzy Sets and Systems, 157 (2006), 2983-3006.
  • [24] R.R. Rhinehart (and) J.B. Riggs: Process Control through nonlinear modeling. Control, 3(7), (1990), 86-90.
  • [25] R. R. Rhinehart, M. L. Darby and H. L. Wade: Editorial - Choosing advanced control. ISA Transactions, 50 (2010), 2-10.
  • [26] S. Salehi, M. Shahrokhi and A. Nejati: Adaptive nonlinear control of pH neutralization process using fuzzy approximators. Control Engineering Practice, 17 (2009), 1329-1337.
  • [27] A. Shahraz and R. B. Boozarjomehry: A Fuzzy sliding mode control approach for nonlinear chemical processes. Control Engineering Practice, 17 (2009), 541-550.
  • [28] J. J. Siirola and T. F. Edgar: Process energy systems: Control, economic, and sustainability objectives. Computers and Chemical Engineering, (2012),
  • [29] S. Syafiiea, F. Tadeoa, E. Martinez: Model-free learning control of neutralization processes using reinforcement learning. Engineering Applications of ArtificialIntelligence, 20(6), (2007), 767-782.
  • [30] R. A. Wright and C. Kravaris: On-line identification and nonlinear control of an industrial pH process. J. of Process Control, 11 (2001), 361-374.
  • [31] F. Xu, B. Huang and S. Akande: Performance assessment of model predictive control for variability and constraint tuning. Ind. Eng. Chem. Res., 46 (2007), 1208-1219.
  • [32] S. S. Yoon, T. W. Yoon, D. R. Yang and T. S. Kang: Indirect adaptive nonlinear control of a pH process. Computers and Chemical Engineering, 26 (2002), 1223-1230.
  • [33] Z. Yuan, B. Chen, G. Sin and R. Gani: State-of-the-art of progress in the optimization-based simultaneous design and control for chemical processes. AIChE J., Published online in Wiley Online Library (, (2012), DOI 10.1002/aic.13786.
  • [34] Y. Zhu, R. Patwardhan, S. B. Wagner and J. Zhao: Toward a low cost and high performance MPC: The role of system identification. Computers and ChemicalEngineering, (2012),
This work was supported by the Polish Ministry of Science and Higher Education, project no. N N514 146438, 2010-2012
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