Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter July 10, 2018

Analysis of the Effects of Neuro-Fuzzy Control Configuration Parameters on PH Neutralization Process

  • D. G. Z. Mazzali , I. C. Franco and F. V. Silva EMAIL logo


The pH neutralization process is typical in chemical, biological and petrochemical industries. One of the major challenges to control it is to understand its nonlinearities and that requires several fine adjustments from conventional controls. Artificial Intelligence has been used to study these nonlinearities; one of them is Neuro-Fuzzy Logic, which was investigated in this work to develop controls dedicated to this process. These controls are formed by logical structures and may be adjusted to different configurations. In practical applications, it is highly important to adapt control parameters based on artificial intelligence to obtain better performance. The present work studied the effect of different configurations of a neuro-fuzzy control on the performance of a regulatory control to pH neutralization process by means of a virtual plant developed in both Indusoft© and Matlab© environments. For both variables, pH and reactor level control, membership function (MF) = [Gaussian], method “OR” = [probabilistic], method “E” = [product], type of MF output = [linear] and the optimization method = [hybrid], have improved control performance, which confirms the importance of configuration choices in neuro-fuzzy control adjustments. Moreover, the most determining factor in NFC performance is the types of membership functions.

Funding statement: This work was financially supported by CAPES – Coordination for higher Education Staff Development.


[1] Kramer CY. Extension of multiple range test to group means with unequal numbers of replications. Biometrics. 1956;12:307–10.10.2307/3001469Search in Google Scholar

[2] Geerlings MW. Plant and process characteristics. London: Butterworths Science Publications; 1957.Search in Google Scholar

[3] McAvoy TJ, Hsu E, Lowenthals S. Dynamics of pH in controlled stirred tank reactor. Ind Eng Chem Process Des Dev. 1972;11:68–78.10.1021/i260041a013Search in Google Scholar

[4] Gustafsson TK. Calculation of the pH value of a mixture solution an illustration of the use of chemical reaction invariants. Chem Eng Sci. 1982;37(9):1419–21.10.1016/0009-2509(82)85013-6Search in Google Scholar

[5] Gustafsson TK, Waller KV. Dynamic modeling and reaction invariant control of pH. Chem Eng Sci. 1983;38:389–98.10.1016/0009-2509(83)80157-2Search in Google Scholar

[6] Henson MA, Seborg DE. Adaptative nonlinear control of a pH neutralization process. IEEE Trans Control Syst Technol. 1994;2:169–82.10.1109/87.317975Search in Google Scholar

[7] Bhat NE, McAvoy T. Use of neural nets for dynamic modeling and control of chemical process systems. Comput Chem Eng. 1990;14:573–83.10.1016/0098-1354(90)87028-NSearch in Google Scholar

[8] Gustafsson TK, Skrifvars BO, Sandström KV, Waller V. Modeling of pH for control. Ind Eng Chem Res. 1995;34:820–7.10.1021/ie00042a014Search in Google Scholar

[9] Draeger A, Engell S, Ranke H. Model predictive control using neural networks. IEEE Control Syst. 1995;15:61–6.10.1109/37.466261Search in Google Scholar

[10] Palancar MC, Aragón JM, Torrecilla JS. pH-control system based on artificial neural networks. Ind Eng Chem Res. 1998;37:2729–40.10.1021/ie970718wSearch in Google Scholar

[11] Mota AS, Menezes MR, Schmitz JE, Costa TV, Silva FV, Franco IC. Identification and on-line validation of a ph neutralization process using an adaptive network based fuzzy inference system. Chem Eng Commun. 2015;203:516–26.10.1080/00986445.2015.1048799Search in Google Scholar

[12] Mwmbeshi MM, Kent CA, Salhi S. Flexible on-line modeling and control of pH in waste neutralization reactors. Chem Eng Technol. 2004;27:130–8.10.1002/ceat.200401660Search in Google Scholar

[13] Valarmathi K, Devaraj D, Radhakrishnan TK. Intelligent techniques for system identification and controller tuning in pH process. Braz J Chem Eng. 2009;26:99–111.10.1590/S0104-66322009000100010Search in Google Scholar

[14] Kumbasar T, Eksin I, Guzelkaya M, Yesil E. Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm. Expert Syst Appl. 2011;38:12356–64.10.1016/j.eswa.2011.04.015Search in Google Scholar

[15] Mamdani EH, Assilian S. An experiment in linguistic synthesis with a logic controller. Int J Man Mach Stud. 1975;7:1–13.10.1016/S0020-7373(75)80002-2Search in Google Scholar

[16] Takagi S, Sugeno M. Fuzzy identification of fuzzy systems and its application to modeling and control. IEEE Trans Syst Man Cybern. 1985;15:116–32.10.1016/B978-1-4832-1450-4.50045-6Search in Google Scholar

[17] Jang JSR. ANFIS: adaptive network based fuzzy inference system. IEEE Trans Syst Man Cybern. 1993;23:665–85.10.1109/21.256541Search in Google Scholar

[18] Zhang J, Morris AJ. Recurrent neuro-fuzzy networks for nonlinear process modeling. IEEE Trans Neural Networks. 1999;10:313–26.10.1109/72.750562Search in Google Scholar PubMed

[19] Franco IC, Dall’Agnol M, Fileti AMF, Silva FV. A neuro-fuzzy identification of non-linear transient systems: application to a pilot refrigeration plant. Int J Refrig. 2011;34:2063–75.10.1016/j.ijrefrig.2011.04.009Search in Google Scholar

[20] Sivakumar R, Sahana C, Savitha PA. Design of ANFIS based estimation and control for MIMO systems. Int J Eng Res Appl. 2012;2:2803–09.Search in Google Scholar

[21] Fonseca RR, Fileti AMF, Franco IC, Silva FV. Experimental fuzzy/split-range control: novel strategy for biodiesel batch reactor temperature control. Chem Eng Commun. 2016;203:1251–9.10.1080/00986445.2016.1172484Search in Google Scholar

[22] Franco IC, Schmitz JE, Fileti AMF, Silva FV. Development of a predictive control based on takagi-sugeno model applied in a non-linear system of industrial refrigeration. Chem Eng Commun. 2016;204:39–54.10.1080/00986445.2016.1230850Search in Google Scholar

[23] Vidal SFS, Schmitz JE, Franco IC, Fileti AMF, Silva FV. Fuzzy multivariable control strategy applied to a refrigeration system. Chem Prod Process Model. 2016;12:1–8.Search in Google Scholar

[24] Ardabilia SF, Najafia B, Shamshirband S, Bidgolid BM, Deoe RC, Chau KW. Computational intelligence approach for modeling hydrogen production: a review. Eng Appl Comput Fluid Mech. 2018;12:438–58.Search in Google Scholar

[25] Ameli F, Hemmati-Sarapardeh A, Shaffieb M, Huseinc MM, Shamshirband S. Modeling interfacial tension in N 2/n-alkane systems using corresponding state theory: application to gas injection processes. Fuel. 2018;222:779–91.10.1016/j.fuel.2018.02.067Search in Google Scholar

[26] Hemmati-Sarapardeh A, Ameli F, Varamesh A, Shamshirband S, Mohammadi AH, Dabir B. Toward generalized models for estimating molecular weights and acentric factors of pure chemical compounds. Int J Hydrogen Energy. 2018;43:2699–717.10.1016/j.ijhydene.2017.12.029Search in Google Scholar

[27] Lotfi A, Tsoi AC. Importance of membership functions: a comparative study on different learning methods for fuzzy inference systems. In: IEEE World Congress on Computational Intelligence Proceedings, 26-29 Jun 1994, Orlando, FL.10.1109/FUZZY.1994.343588Search in Google Scholar

[28] Mohamad S, Ishak AA, Aishah SSA Design of fuzzy logic controller for overdamped temperature response of a process air heater system. In: International Conference Modeling, Simulation and Applied Optimization (ICMSAO), 19-21 Apr 2011, Kuala Lumpur, MY.10.1109/ICMSAO.2011.5775523Search in Google Scholar

[29] Usta MA, Akyazi O, Altas IH Design and performance of solar tracking system with fuzzy logic controller used different membership functions. In: International Conference Electrical and Electronics Engineering (ELECO), 1-4 Dec 2011, Bursa, TR.Search in Google Scholar

[30] Garrido R, Adroer R, Poch M. Wastewater neutralization control based in fuzzy logic: simulation results. Ind Eng Chem Res. 1997;36:1665–74.10.1021/ie950654uSearch in Google Scholar

[31] Adroer M, Alsina A, Aumatell J, Poch M. Wastewater neutralization control based in fuzzy logic: experimental results. Ind Eng Chem Res. 1999;38:2709–19.10.1021/ie980268nSearch in Google Scholar

[32] Li HX, Gatland HD. Conventional fuzzy control and its enhancement. IEEE Trans Syst Man Cybern Part B Cybern. 1996;26:791–7.10.1109/3477.537321Search in Google Scholar PubMed

[33] Jang JSR, Gulley N. Fuzzy logic toolbox – for use matlab®/simulink. Natick, MA: The MathWorks, Inc, 1995Search in Google Scholar

Received: 2018-03-06
Revised: 2018-06-06
Accepted: 2018-06-25
Published Online: 2018-07-10

© 2018 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 3.2.2023 from
Scroll Up Arrow