Modares Mechanical Engineering

Modares Mechanical Engineering

Application of Neuro-Fuzzy Network for Optimizing Steam Pipeline Expansion Loops in Process Industries

Document Type : Original Research

Authors
Yousef Shahsbi 1
Erfan Mirshekari 2
1 Ahvaz Branch, Islamic Azad University
2 Ahvaz Branch, Islamic Azad University,
10.48311/mme.25.1.23
Abstract
This research examines the optimization of expansion loops in steam pipeline systems using a neuro-fuzzy network. Stress analysis was conducted based on the ASME B31.3 design code using CAESAR II software. Additionally, a neuro-fuzzy network was developed and optimized in MATLAB. The results indicate that the neuro-fuzzy network outperforms traditional methods and the MLP neural network. Combining this network with the Bee Colony Optimization algorithm led to the identification of an optimal loop that minimizes pipeline length and reduces static and thermal stresses. The optimized loop obtained from the Perceptron network increased the loop length by 20 cm (1.14%) and reduced the total sum of standard stresses by 14.6%. In contrast, the optimized loop from the neuro-fuzzy network reduced the loop length by 120 cm (6.78%) and decreased the total sum of standard stresses by 9.5%. These findings demonstrate that the application of artificial intelligence techniques in expansion loop design significantly reduces thermal stresses and enhances design efficiency
Keywords
Optimization
Expansion Loop
Neuro-fuzzy network
CAESAR II
Bee Colony Optimization

Subjects

1. ASME B31.3-2020; Process Piping., New York, NY, USA, 2020: The American Society of Mechanical Engineering:, 2020.
2. Bisht, S.; Jahan, F., "An overview on pipe design using Caesar II," International Journal on Emerging Technologies, vol. 5, no. 2, pp. 114-118, 2014.
3. Sankar, K.S.S.; Kumaran, S.S., "Optimization of piping expansion loop in process plant," International Journal of Applied Engineering Research, vol. 10, no. 49, pp. 574-578, 2015.
4. M. Jamuna Rani, K. Ramanathan, "Design and analysis of piping system with supports using CAESAR-II," International Journal of Computer and Systems Engineering, vol. 10, no. 5, pp. 980-984, 2016.
5. Caponetto, R.; Fargione, G.; Giudice, F.; Schiavo, M, "Revamping Optimization of a Pressure Piping System Using Artificial Neural Networks," MDPI, Designs, Basel, Switzerland, vol. 6, pp. 103-120, 2022.
6. Chiba, T.; Okado, S.; Fujii, I.; Itami, K.; Hara, F, "Optimum support arrangement of piping systems using genetic algorithm," Journal of Pressure Vessel Technology, vol. 118, pp. 507-512, 1996.
7. Z Wei, J Wu, Z Li, S Cheng, X Yan, S Wang, "The Intelligent Layout of the Ship Piping System Based on the Optimization Algorithm," Applied Sciences, 2024, 14, 2694.
8. S. J. W. A. P. P. T. T. a. M. D. Hartono Yudo, "Numerical evaluation of expansion loops for pipe subjected to thermal displacements," Curved and Layer. Struct., vol. 9, pp. 72-80, 2022.
9. M. I. Nikola Jacimovic, "Shortcut Method for Pipe Expansion Loop Sizing," Journal of Pressure Vessel Technology, vol. 142, 2020.
10. L. A. ZADEH, "Fuzzy Sets," INFORMATION AND CONTROL, vol. 8, pp. 338-353, 1965.
11. L. A. ZADEH, "A fuzzy-algorithmic approach to the definition of complex or imprecise concepts," International Journal of Man-Machine Studies, vol. 8, pp. 249-291, 1976.
12. D. D. K. Balwant Kumar, "A review on Artificial Bee Colony algorithm," International Journal of Engineering and Technology, vol. 2, no. 3, pp. 175-186, 2013.
Modares Mechanical Engineering
Volume 25, Issue 1
January 2024
Pages 23-32