Skip to main content
Log in

Application of Fuzzy Control in a Wireless Liquid Level Simulator

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Liquid level control has great proposition in terms of chemical processes. It is important to make the level measurement in the tanks filled with industrial liquids with accurate and reliable equipment and to keep the liquid level at a certain level. In the studies conducted, wireless liquid level control was performed in a process control simulator system. For all computation and data processing procedures, the MATLAB program is used on-line connected to the system where the liquid level system is located. Then, the behavior of the output variable is examined by giving various effects to the liquid level valve opening selected as the setting variable. Fuzzy control of the system was performed by using the most suitable model found in the operating conditions obtained in dynamic studies. Wireless on-line computer control systems are used for this. The best control efficiency was obtained when the values were 4 dm.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

  1. Cara, D. (2008). Wireless networks for industrial automation. Pittsburgh: ISA-The Instrumentation Systems and Automatin Society.

    Google Scholar 

  2. Murari, A., & Lotto, L. (2004). Wireless communication using detectors located inside vacuum chambers. Vacuum, 72, 149–155.

    Article  Google Scholar 

  3. Aldemir, A., Altınten, A., Zeybek, Z., & Alpbaz, M. (2014). Application of wireless experimental fuzzy temperature control using MATLAB/Simulink. International Journal of Engineering Science and Innovative Technology, 3, 4.

    Google Scholar 

  4. González-Potes, A., Mata-López, W. A., Ibarra-Junquera, V., Ochoa-Brust, A. M., Martínez-Castro, D., & Crespo, A. (2016). Distributed multi-agent architecture for real-time wireless control networks of multiple plants. Engineering Applications of Artificial Intelligence, 56, 142–156.

    Article  Google Scholar 

  5. Dong, T., Hu, W., & Liao, X. (2016). Dynamics of the congestion control model in underwater wireless sensor networks with time delay. Chaos, Solitons & Fractals, 92, 130–136.

    Article  Google Scholar 

  6. Borges, L. M., Velez, F. J., & Lebres, A. S. (2014). Survey on the characterization and classification of wireless sensor network applications. IEEE Communications Surveys & Tutorials, 16(4), 1860–1890.

    Article  Google Scholar 

  7. Lebedev, V., Laukhina, E., Laukhin, V., Somov, A., Baranov, A. M., Rovira, C., et al. (2017). Investigation of sensing capabilities of organic bi-layer thermistor in wearable e-textile and wireless sensing devices. Organic Electronics, 42, 146–152.

    Article  Google Scholar 

  8. Azaza, M., Tanougast, C., Fabrizio, E., & Mami, A. (2016). ‘Smart greenhouse fuzzy logic based control system enhanced with wireless data monitoring. ISA Transactions, 61, 297–307.

    Article  Google Scholar 

  9. Almeida, L. A. L., SguareziFilho, A. J., Capovilla, C. E., Casella, I. R. S., & Costa, F. F. (2016). An impulsive noise filter applied in wireless control of wind turbines. Renewable Energy, 86, 347–353.

    Article  Google Scholar 

  10. Qi, Z., You, S., & Ren, N. (2017). Wireless electrocoagulation in water treatment based on bipolar electrochemistry. Electrochimica Acta, 229, 96–101.

    Article  Google Scholar 

  11. Song, S. H., Park, J. H., Chitnis, G., Siegel, R. A., & Ziaie, B. (2014). A wireless chemical sensor featuring iron oxide nanoparticle-embedded hydrogels. Sensors and Actuators B: Chemical, 193, 925–930.

    Article  Google Scholar 

  12. Altınten, A., Erdoğan, S., Hapoglu, H., Aliev, F., & Alpbaz, M. (2006). Application of fuzzy control method with genetic algorithm to a polymerization reactor at constant set point. Chemical Engineering Research and Design, 84, 1012–1018.

    Article  Google Scholar 

  13. Cetinkaya, S., Zeybek, Z., Hapoğlu, H., & Alpbaz, M. (2006). Optimal temperature control in a batch polymerization reactor using fuzzy-relational models-dynamics matrix control. Computers & chemical engineering, 30(9), 1315–1323.

    Article  Google Scholar 

  14. Kamesh, R., & Rani, K. Y. (2016). Parameterized data-driven fuzzy model based optimal control of a semi-batch reactor. ISA transactions, 64, 418–430.

    Article  Google Scholar 

  15. Azadeh, A., Salehi, V., Arvan, M., & Dolatkhah, M. (2014). Assessment of resilience engineering factors in high-risk environments by fuzzy cognitive maps: A petrochemical plant. Safety Science, 68, 99–107.

    Article  Google Scholar 

  16. Altınten, A., Erdoğan, S., Hapoglu, H., & Alpbaz, M. (2003). Control of a polymerization reactor by fuzzy control method with genetic algorithm. Computers & Chemical Engineering, 27, 1031–1040.

    Article  Google Scholar 

  17. Andújar, José M., & Bravo, José M. (2005). Multivariable fuzzy control applied to the physical–chemical treatment facility of a cellulose factory. Fuzzy Sets and Systems, 150(3), 475–492.

    Article  MathSciNet  Google Scholar 

  18. Sahebjamnia, Navid, Tavakkoli-Moghaddam, Reza, & Ghorbani, Narges. (2016). Designing a fuzzy Q-learning multi-agent quality control system for a continuous chemical production line—a case study. Computers & Industrial Engineering, 93, 215–226.

    Article  Google Scholar 

  19. Chuanxin, Y., & Xuefeng, Y. (2011). A fuzzy-based adaptive genetic algorithm and its case study in chemical engineering. Chinese Journal of Chemical Engineering, 19, 299–307.

    Article  Google Scholar 

  20. Azadeh, Ali, Salehi, Vahid, & Mirzayi, Mahsa. (2016). The impact of redundancy and teamwork on resilience engineering factors by fuzzy mathematical programming and analysis of variance in a large petrochemical plant. Safety and Health at Work, 7(4), 307–316.

    Article  Google Scholar 

  21. Bello, Oladipupo, Hamam, Yskandar, & Djouani, Karim. (2014). Control of a coagulation chemical dosing unit for water treatment plants using MMPC based on fuzzy weighting. Journal of Water Process Engineering, 4, 34–46.

    Article  Google Scholar 

  22. Bahita, M., & Belarbi, K. (2016). Model reference neural-fuzzy adaptive control of the concentration in a chemical reactor (CSTR). IFAC-Papers OnLine, 49–29, 158–162.

    Article  Google Scholar 

  23. Abilov, A. G., Zeybek, Z., Tuzunalp, O., & Telatar, Z. (2002). Fuzzy temperature control of industrial refineries furnaces through combined feedforward/feedback multivariable cascade systems. Chemical Engineering and Processing: Process Intensification, 41(1), 87–98.

    Article  Google Scholar 

  24. Bayram, İ. (2015). Wireless liquid level control with advanced control methods. Ph.D. Thesis, Ankara University, Institute of Science, Ankara.

  25. Vural, İ. H., Altinten, A., Hapoğlu, H., Erdoğan, S., & Alpbaz, M. (2015). Application of pH control to a tubular flow reactor. Chinese Journal of Chemical Engineering, 23(1), 154–161.

    Article  Google Scholar 

  26. Graham, B. P., & Newell, R. B. (1989). Fuzzy adaptive control of a first-order process. Fuzzy Sets and Systems, 31, 47–65.

    Article  MathSciNet  Google Scholar 

  27. Newell, R. B., & Lee, P. L. (1989). Applied process control —A case study (pp. 97–111). Upper Saddle River: Prentice Hall.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to İsmail Bayram.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bayram, İ., Zeybek, Z., Altinten, A. et al. Application of Fuzzy Control in a Wireless Liquid Level Simulator. Wireless Pers Commun 109, 211–222 (2019). https://doi.org/10.1007/s11277-019-06560-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06560-2

Keywords

Navigation