INVERSE NEURO-FUZZY MODEL BASED CONTROLLER DESIGN FOR A PH NEUTRALIZATION PROCESS

Year 2023, Issue: 052, 19 - 34, 29.03.2023

Talha Burak Akca , Cenk Ulu , Salih Obut

https://doi.org/10.59313/jsr-a.1197288

Abstract

Since pH neutralization processes have extremely nonlinear characteristics, controlling it might be difficult. Therefore, a special controller design is needed to handle the high nonlinearities of the process. In this study, an inverse neuro-fuzzy model-based controller (NFMBC) design is presented for control of a pH neutralization process (NP). Input-output (IO) data set of the process is collected by applying a proper excitation signal. Then, forward and inverse neuro-fuzzy models of the process are constructed by using this data set after a training process. In terms of design simplicity, a two-input-one-output model structure is chosen for both neuro-fuzzy models. These forward and inverse neuro-fuzzy models are used in a nonlinear internal model control (NIMC) structure in order to provide robustness against disturbances and model mismatches. To examine the proposed controller's performance, simulation studies are carried out under setpoint variation and disturbance conditions. Additionally, the performance of the inverse NFMBC is compared to that of a fuzzy proportional integral derivative (FPID) controller with a 7x7 rule base. The results demonstrate that the designed controller provides more effective control performance for setpoint variations and also exhibits higher robustness against disturbances in the acid flow rate than the FPID controller.

Keywords

Fuzzy Model, Inverse Controller, Adaptive Network Based Fuzzy Inference System, pH Neutralization Process, Internal Model Control

Thanks

This research has not received any grants.

References

• [1] Kumbasar, T., Eksin, I., Guzelkaya, M., and Yesil, E. (2012). Type-2 fuzzy model based controller design for neutralization processes. ISA transactions, 51(2), 277-287.
• [2] Estofanero, L., Edwin, R., and Claudio, G. (2019). Predictive controller applied to a pH neutralization process. IFAC-PapersOnLine, 52(1), 202-206.
• [3] Rose, T. P., and Devadhas, G. G. (2020). Detection of pH neutralization technique in multiple tanks using ANFIS controller. Microprocessors and Microsystems, 72, 102845.
• [4] Hall, R. C., and Seborg, D. E. (1989). Modelling and self-tuning control of a multivariable ph neutralization process part i: Modelling and multiloop control. In 1989 American Control Conference (pp. 1822-1827). IEEE.
• [5] Fuente, M. J., Robles, C., Casado, O., Syafiie, S., and Tadeo, F. (2006). Fuzzy control of a neutralization process. Engineering Applications of Artificial Intelligence, 19(8), 905-914.
• [6] Nyström, R. H., Sandström, K. V., Gustafsson, T. K., and Toivonen, H. T. (1998). Multimodel robust control applied to a pH neutralization process. Computers & chemical engineering, 22, S467-S474.
• [7] Gu, B., and Gupta, Y. P. (2008). Control of nonlinear processes by using linear model predictive control algorithms. ISA transactions, 47(2), 211-216.
• [8] Wright R. A., and Soroush M. (1991). Strong Acid Equivalent Control of pH Processes: An Experimental Study. Industrial & engineering chemistry research, 30(11), 2437-2444.
• [9] Obut, S., and Özgen, C. (2008). Online identification and control of pH in a neutralization system. Industrial & engineering chemistry research, 47(13), 4394-4404.
• [10] Wang, L. X., and Mendel, J. M. (1992). Fuzzy basis functions, universal approximation, and orthogonal least-squares learning. IEEE transactions on Neural Networks, 3(5), 807-814.
• [11] Babuska, R., Oosterhoff, J., Oudshoorn, A., and Bruijn, P. M. (2002). Fuzzy self-tuning PI control of pH in fermentation. Engineering Applications of Artificial Intelligence, 15(1), 3-15.
• [12] Nascimento Lima, N. M., Manenti, F., Filho, R. M., Embiruçu, M., and Wolf Maciel, M. R. (2009). Fuzzy model-based predictive hybrid control of polymerization processes. Industrial & Engineering Chemistry Research, 48(18), 8542-8550.
• [13] Babuška, R., Sousa, J. M., and Verbruggen, H. B. (1998). Inverse fuzzy model based predictive control. In Advances in fuzzy control (pp. 129-154). Physica, Heidelberg.
• [14] Ulu, C. (2014). Exact analytical inverse mapping of decomposable TS fuzzy systems with singleton and linear consequents. Applied Soft Computing, 23, 202-214..
• [15] Ulu, C. (2015). Exact analytical inversion of interval type-2 TSK fuzzy logic systems with closed form inference methods. Applied Soft Computing, 37, 60-70.
• [16] Kumbasar, T., Eksin, I., Guzelkaya, M., and Yesil, E. (2011). Adaptive fuzzy model based inverse controller design using BB-BC optimization algorithm. Expert Systems with Applications, 38(10), 12356-12364.
• [17] Jang, J. S. (1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE transactions on systems, man, and cybernetics, 23(3), 665-685..
• [18] Kampouropoulos, K., Andrade Rengifo, F., García Espinosa, A., and Romeral Martínez, J. L. (2014). A combined methodology of adaptive neuro-fuzzy inference system and genetic algorithm for short-term energy forecasting. Advances in Electrical and Computer Engineering, 14(1), 9-14.
• [19] Mota, A. S., Menezes, M. R., Schmitz, J. E., da Costa, T. V., da Silva, F. V., and Franco, I. C. (2016). Identification and online validation of a ph neutralization process using an adaptive network-based fuzzy inference system. Chemical Engineering Communications, 203(4), 516-526.
• [20] Sreekumar, S., Kallingal, A., and Mundakkal Lakshmanan, V. (2021). Adaptive neuro-fuzzy approach to sodium chlorate cell modeling to predict cell pH for energy-efficient chlorate production. Chemical Engineering Communications, 208(2), 256-270.
• [21] Sunori, S. K., Negi, P. B., Arora, S., Khan, F., Maurya, S., Juneja, P., ... and Ghai, K. (2022). Neuro-fuzzy Controller Design for pH Control in Sugar Refineries. 8th International Conference on Advanced Computing and Communication Systems (ICACCS) (Vol. 1, pp. 197-202). IEEE.