No Access

A Real-World Industrial Application of Particle Swarm Optimization: Baghouse Designing

Electrical Engineering Department, Final International University, Beşparmaklar Caddesi, No: 6 Çatalköy, Girne, Mersin 10, 99320 Turkey

E-mail Address: pouya.bolourchi@final.edu.tr

Corresponding author.

Search for more papers by this author

and

Mohammadreza Gholami

Electrical Engineering Department, Final International University, Beşparmaklar Caddesi, No: 6 Çatalköy, Girne, Mersin 10, 99320 Turkey

E-mail Address: mohammadreza.gholami@final.edu.tr

Search for more papers by this author

https://doi.org/10.1142/S1469026822500213Cited by:2 (Source: Crossref)

Abstract

Due to the high ability and flexibility of meta-heuristic algorithms (MAs), they can widely be used in many applications to solve different problems. Recently, real-world engineering applications of these optimization algorithms have attracted researchers’ attention. This paper applies particle swarm optimization (PSO) as an effective population-based MAs to design the baghouse (BH). BH filters are among the most commonly used devices in air pollution control systems in mining and food manufacturers and power plants. Designing the BH depends on several parameters such as its capacity or airflow (Nm³/h), air-to-cloth ratio ( $a ∕ c$ $a ∕ c$ ), cam velocity, and installation limitations. Generally, industrial designers select the number and length of bags and their arrangement based on the experimental observations to meet the parameters mentioned above. The minimum cost or total weight of equipment is utilized for proposing a competitive price for suppliers. In this paper, a PSO algorithm is used to minimize the total cost by finding the best possible design (the number, length, and arrangement of bags). In addition, a real example of installed BH in a pelletizing plant is given and compared with PSO results to investigate the efficiency of the proposed algorithm. The results suggest that PSO can find a better design with minimum total cost than an installed BH filter, and therefore, PSO is applicable to industrial designers.

Keywords:

Remember to check out the Most Cited Articles!
Check out these titles in artificial intelligence!

References

1. N. V. P. R. D. Prasad, Automatic control and management of electrostatic precipitator, IEEE Trans. Ind. Appl. 35(3) (1999) 561–567. https://doi.org/10.1109/28.767002. Crossref, Google Scholar
2. R. Gebert et al., A new filter system, combining a fabric filter and electrostatic precipitator for effective pollution control behind cement kilns, in IEEE Cement Industry Techincal Conf., May, 2003, pp. 285–294. https://doi.org/10.1109/citcon.2003.1204729. Google Scholar
3. G. Kaelin, Reduce operating and energy costs and simultaneously assure EPA regulatory compliance with integration of intelligent baghouse control and sensing, IEEE Cement Industry Technical Conf. (2013), pp. 1–10. https://doi.org/10.1109/CITCON.2013.6525270. Crossref, Google Scholar
4. V. Bansal, R. W. Taylor, P. Maly, M. Johnson and B. Yetter, Enhanced capture of mercury in baghouse by using novel filtration media and filter design, AISTech — Iron and Steel Technology Conf. Proc., Vol. 1 (2013), pp. 175–184. Google Scholar
5. G. Kaelin and J. Workman, Automated baghouse troubleshooting diagnostics maximize efficiency, in 2018 IEEE-IAS/PCA Cement Industry Conf. IAS/PCA 2018, December, 2019, pp. 1–9. https://doi.org/10.1109/CITCON.2018.8373092. Google Scholar
6. X. Y. Yuan, H. G. Shen, Z. H. Wang and W. L. Hou, Research on filtration performance of filter media used in bag filter for industrial furnaces,” in 2010 Int. Conf. E-Product E-Service E-Entertainment, ICEEE2010 (2010), pp. 4–7. https://doi.org/10.1109/ICEEE.2010.5660385. Crossref, Google Scholar
7. P. Shah, R. Sekhar, A. J. Kulkarni and P. Siarry, Metaheuristic Algorithms in Industry 4.0 (2021), https://doi.org/10.1201/9781003143505. Google Scholar
8. M. O. Okwu and L. K. Tartibu, Particle swarm optimisation, Stud. Comput. Intell. 927 (2021) 5–13. https://doi.org/10.1007/978-3-030-61111-8_2. Crossref, Google Scholar
9. T. M. Shami, A. A. El-Saleh, M. Alswaitti, Q. Al-Tashi, M. A. Summakieh and S. Mirjalili, Particle swarm optimization: A comprehensive survey, IEEE Access 10 (2022) 10031–10061. https://doi.org/10.1109/ACCESS.2022.3142859. Crossref, Google Scholar
10. B. Almeida, Particle swarm optimisationparticle swarm optimization: A powerful technique for solving engineering problems, Intech 32 (2013) 137–144. http://www.intechopen.com/books/trends-in-telecommunications-technologies/gps-total-electron-content-tec-prediction-at-ionosphere-layer-over-the-equatorial-region%0AInTec%0Ahttp://www.asociatiamhc.ro/wp-content/uploads/2013/11/Guide-to-Hydropower.pdf Google Scholar
11. X. Rao and X. Yan, Particle swarm optimization algorithm based on information sharing in industry 4.0, Wirel. Commun. Mob. Comput. 2022 (2022) 1–11. https://doi.org/10.1155/2022/4328185. Crossref, Google Scholar
12. E. Tapoglou, I. C. Trichakis, Z. Dokou, I. K. Nikolos and G. P. Karatzas, Prévision du niveau des eaux souterraines dans les scénarios de changement climatique utilisant un réseau de neurones artificiels formé avec optimisation par essaim de particules, Hydrol. Sci. J. 59(6) (2014) 1225–1239. https://doi.org/10.1080/02626667.2013.838005. Crossref, Google Scholar
13. C. Sudheer, R. Maheswaran, B. K. Panigrahi and S. Mathur, A hybrid SVM-PSO model for forecasting monthly streamflow, Neural Comput. Appl. 24(6) (2014) 1381–1389. https://doi.org/10.1007/s00521-013-1341-y. Crossref, Google Scholar
14. H. M. I. Pousinho, V. M. F. Mendes and J. P. S. Catalão, A hybrid pso-anfis approach for short-term wind power prediction in Portugal, Energy Convers. Manag. 52(1) (2011) 397–402. https://doi.org/10.1016/j.enconman.2010.07.015. Crossref, Google Scholar
15. R. Keshavarzi, M. Akhlaghi and F. Emami, Binary PSO algorithm assisted to investigate the optical sensor based plasmonic nano-bi-domes, Optik (Stuttg). 127(19) (2016) 7670–7675. https://doi.org/10.1016/j.ijleo.2016.05.098. Crossref, Google Scholar
16. M. R. Tanweer, S. Suresh and N. Sundararajan, Dynamic mentoring and self-regulation based particle swarm optimization algorithm for solving complex real-world optimization problems, Inf. Sci. (Ny). 326 (2016) 1–24. https://doi.org/10.1016/j.ins.2015.07.035. Crossref, Google Scholar
17. A. Al-Maamari and F. A. Omara, Task scheduling using PSO algorithm in cloud computing environments, Int. J. Grid Distrib. Comput. 8(5) (2015) 245–256. https://doi.org/10.14257/ijgdc.2015.8.5.24. Crossref, Google Scholar
18. X. Li, J. Guo and J. Hu, An improved PSO algorithm and its application in GNSS ambiguity resolution, Appl. Sci. 8(6) (2018). https://doi.org/10.3390/app8060990. Crossref, Google Scholar
19. J. Zhang, Y. Zhai, Z. Han and J. Lu, Improved particle swarm optimization based on entropy and its application in implicit generalized predictive control, Entropy 24(1) (2022). https://doi.org/10.3390/e24010048. Crossref, Google Scholar
20. S. Cheng, Population diversity in particle swarm optimization: Definition, observation, control, and application, Ph. D. thesis (2013). Google Scholar
21. S. K. S. Fan and C. H. Jen, An enhanced partial search to particle swarm optimization for unconstrained optimization, Mathematics 7(4) (2019). https://doi.org/10.3390/math7040357. Crossref, Google Scholar
22. A. Ardiansyah, R. Ferdiana and A. E. Permanasari, MUCPSO: A modified chaotic particle swarm optimization with uniform initialization for optimizing software effort estimation, Appl. Sci. 12(3) (2022). https://doi.org/10.3390/app12031081. Crossref, Google Scholar
23. Z. D. Liang and W. J. Yi, Application of improved particle swarm optimization algorithm in TDOA, AIP Adv. 12(2) (2022). https://doi.org/10.1063/5.0082778. Crossref, Google Scholar
24. Q. Dai, H. Zhang and B. Zhang, An improved particle swarm optimization based on total variation regularization and projection constraint with applications in ground-penetrating radar inversion: A model simulation study, Remote Sens. 13(13) (2021). https://doi.org/10.3390/rs13132514. Crossref, Google Scholar
25. Z. Zhou, F. Li, J. H. Abawajy and C. Gao, Improved PSO algorithm integrated with opposition-based learning and tentative perception in networked data centres, IEEE Access 8 (2020) 55872–55880. https://doi.org/10.1109/ACCESS.2020.2981972. Crossref, Google Scholar
26. C. Leivo, J. Lange and D. Ramshaw, Reduced baghouse maintenance with LPHV pulse cleaning technology, in IEEE Cement Industry Technical Conf. (2014), pp. 6–7. https://doi.org/10.1109/CITCon.2014.6820120. Crossref, Google Scholar
27. C. Polizzi, Pursuing a lower total cost of ownership in a large kiln baghouse, in 2018 IEEE-IAS/PCA Cement Industry Conf. IAS/PCA 2018 (2018), pp. 1–12. https://doi.org/10.1109/CITCON.2018.8373110. Crossref, Google Scholar
28. A. Kalwar, F. Kurdziel and K. Pytel, The impact of changes in operating parameters on the bag filter in the WR - 25 water boiler, in Proc. 2019 20th Int. Carpathian Control Conf. ICCC 2019 (2019). https://doi.org/10.1109/CarpathianCC.2019.8765955. Crossref, Google Scholar
29. G. Kaelin and J. Workman, Automated baghouse troubleshooting diagnostics maximize efficiency, in 2018 IEEE-IAS/PCA Cem. Ind. Conf. IAS/PCA 2018 (2018), pp. 1–9. https://doi.org/10.1109/CITCON.2018.8373092. Crossref, Google Scholar
30. D. C. Jagtap, Air pollution controller — fabric filter, Irjet 5(2) (2018) 6–9. Google Scholar