DOI: https://doi.org/10.14710/teknik.v39i3.22087

Adaptive Neuro-Fuzzy Inference System (ANFIS) for Controlling Level and Pressure on Deaerator

*Sumardi Sumardi scopus  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Munawar A Riyadi  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
Lintang Nurlitha Aprivirly  -  Department of Electrical Engineering, Faculty of Engineering, Universitas Diponegoro, Indonesia
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Abstract

DDeaerator is one of the most widely used plants in the chemical industry and marine steam power plant. Deaerator is used to eliminate oxygen in water that enters the boiler to avoid corrosion of the boiler pipes. Control of pressure and level in deaerator needs to be done to keep the process well. The purpose of the research is to design a control system that can keep pressure and level of deaerator on the set point un the presence of changes in the load and input systems. Deaerator should be controlled to keep its safety and efficiency. Adaptive Neuro-Fuzzy Inference System (ANFIS) is a combination of fuzzy logic control and neural network. Design of ANFIS requires an input-output data set obtained from a PI controller that is considered as a "teacher" for ANFIS for the learning process. The results of the simulation show that the system using ANFIS controller for controlling pressure and level deaerator in normal set point can produce very small maximum overshoot that is equal to 0% and small IAE value that is 7.898 for pressure, and 157.7 for level compared to PI

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Keywords: Deaerator; level; pressure; ANFIS

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Language : EN
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  1. Darvill, J., Tisan, A., Cirstea, M. (2015). An ANFIS-PI based boost converter control scheme. 2015 IEEE 13th International Conference on Industrial
  2. Informatics (INDIN), 632–639
  3. https://doi.org/10.1109/INDIN.2015.7281809
  4. Gomathy, S., Anitha, T. (2015). Deaerator Storage Tank Level & Deaerator Pressure Control Using Soft Computing Deaerator Storage Tank Level &
  5. Deaerator Pressure Control Using Soft Computing. International Journal for Science and Advance Research in Technology, 1(January
  7. Jang, J.-S. R., Sun, C.-T., Mizutani, E. (1997). Neurofuzzy and soft computing: a computational approach to learning and machine intelligence. New Jersey: Prentice Hall
  8. Mahardhika, W. P., Soeprijanto, A., Syaiin, M., Wibowo, S., Kurniawan, R., Herijono, B., Kaloko, B. S. (2017). Design of Deaerator Storage Tank
  9. Level Control System at Industrial Steam Power Plant with Comparison of Neural Network ( NN ) and Extreme Learning Machine ( ELM ) Method,
  10. International Symposium on Electronics and Smart Devices, 40–45
  11. Narayan, J. (2017). ANFIS Based Kinematic Analysis of a 4-DOFs SCARA Robot. 4th IEEE International Conference on Signal Processing. Computing and Control (ISPCC 2k17)
  12. Opriş, I. O. A. N. A. (2013). A deaerator Model. In Recent Advances in Continuum Mechanics, Hydrology and Ecology, Energy, Environmental
  13. and Structural Engineering Series–14, WSEAS International Conference, Rhodes Island, Greece
  14. Stephanopoulos, G. (2001). Chemical Process Control An Introduction to Theory and Practice, Cambridge: Massachusets Institute of Technology
  15. Wang, P., Meng, H., Ji, Q. (2014). PID Neural Network Decoupling Control of Deaerator Pressure and Water Level Control System, Proceedings of the
  16. IEEE International Conference on Robotics and Biomimetics December 5-10, 2014, Bali, Indonesia, 2298–2303
  17. Wang, P., Meng, H., Dong, P., D. R. (2015). Decoupling Control Based on PID Neural Network for Deaerator and Condenser Water Level Control System. IEEE Proceedings of the 34th Chinese Control Conference July 28-30, 2015, Hangzhou: China, 2298–2303
  18. Zhang, Y., Chai, T., Wang, D., Chen, X., (2016). Virtual Unmodeled Dynamics Modeling for Nonlinear Multivariable Adaptive Control With Decoupling
  19. Design. IEEE Transactions On Systems, Man, And Cybernetics: Systems, 2168–2216
  20. Zhao, J., Yao, Z., Sun, L. (2014). New Type of Parallel Deaerator’s Water Level and Pressure Control. 2014 International Symposium on Computer,
  21. Consumer and Control, 143–145. https://doi.org/10.1109/IS3C.2014.48
  22. Zhou, H., Deng, H., & Duan, J. (2017). Hybrid Fuzzy Decoupling Control for a Precision Maglev Motion System. IEEE/ASME Transactions on
  23. Mechatronics, 23(1), 389–401. https://doi.org/10.1109/TMECH.2017.2771340

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Last update: 2024-11-07 00:24:00

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