Advertisement

Aerosol Science and Engineering

, Volume 2, Issue 4, pp 197–205 | Cite as

Predicting Fibrous Filter’s Efficiency by Two Methods: Artificial Neural Network (ANN) and Integration of Genetic Algorithm and Artificial Neural Network (GAINN)

  • Pooya Abdolghader
  • Fariborz Haghighat
  • Ali BahloulEmail author
Original Paper
  • 542 Downloads

Abstract

In this study, we used both methods of ANN and GAINN for predicting the fibrous filter’s efficiency. In this regard, we collected the experimental penetration data for particles in the range of 10.7–191.1 nm. Experimental data were collected with different constant flow rates and from one type of N95 filtering facepiece respirator. A satisfactory number of data from experimental setup were exploited to build up a database. These methods are according to the back-propagation algorithm to map two components, namely, particle diameter and constant air flow rates into the corresponding penetration. The developed ANN and GAINN methods were capable of predicting precise values of penetration from experimental data. Also by comparing the results of these two methods, it is understandable that ANN method can predict the penetration data from examples of the experimental setup more efficiently than GAINN within an acceptable computational time.

Keywords

Nanoparticles Filtration Artificial neural networks Genetic algorithm HVAC filters 

Notes

Acknowledgements

The authors would like to express their gratitude to the Concordia University for funding this work, and Ms. Farinaz Haghighat for her valuable comments and suggestions.

References

  1. Abdolghader P, Brochot C, Haghighat F, Bahloul A (2018) Airborne nanoparticles filtration performance of fibrous media: a review. Sci Technol Built Environ.  https://doi.org/10.1080/23744731.2018.1452454 CrossRefGoogle Scholar
  2. Awad WH, Herzallah R (2015) Driving licensing renewal policy using neural network-based probabilistic decision support system. Int J Comput Appl Technol 51(3):155–163CrossRefGoogle Scholar
  3. Bahloul A, Mahdavi A, Haghighat F, Ostiguy C (2014) Evaluation of N95 filtering facepiece respirator efficiency with cyclic and constant flows. J Occup Environ Hyg 11(8):499–508.  https://doi.org/10.1080/15459624.2013.877590 CrossRefGoogle Scholar
  4. Brochot C, Abdolghader P, Haghighat F, Bahloul A (2018) Filtration of nanoparticles applied in general ventilation. Sci Technol Built Environ.  https://doi.org/10.1080/23744731.2018.1500396 CrossRefGoogle Scholar
  5. Brown RC (1993) Air filtration: an integrated approach to the theory and applications of fibrous filters. Pergamon, New YorkGoogle Scholar
  6. Brown M, Harris CJ (1994) Neurofuzzy adaptive modelling and control. Prentice Hall, Englewood CliffsGoogle Scholar
  7. Dongare AD, Kharde RR, Kachare AD (2012) Introduction to artificial neural network. Int J Eng Innov Technol (IJEIT) 2(1):189–194Google Scholar
  8. Filletti ÉR, Da Silva JM, Ferreira VG (2015) Predicting of the fibrous filters efficiency for the removal particles from gas stream by artificial neural network. Adv Chem Eng Sci 5(03):317CrossRefGoogle Scholar
  9. García-Pedrajas N, Hervás-Martínez C, Muñoz-Pérez J (2003) COVNET: a cooperative coevolutionary model for evolving artificial neural networks. IEEE Trans Neural Netw 14(3):575–596CrossRefGoogle Scholar
  10. Givehchi R, Tan Z (2014) An overview of airborne nanoparticle filtration and thermal rebound theory. Aerosol Air Qual Res 14(1):46–63.  https://doi.org/10.4209/aaqr.2013.07.0239 CrossRefGoogle Scholar
  11. Hinds WC (1999) Aerosol technology: properties, behavior, and measurement of airborne particles. Wiley, New York. http://www.loc.gov/catdir/toc/onix02/98023683.html
  12. Holland JH (1975) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. University of Michigan Press, Ann ArborGoogle Scholar
  13. Huang W, Lam HN (1997) Using genetic algorithms to optimize controller parameters for HVAC systems. Energy Build 26(3):277–282CrossRefGoogle Scholar
  14. Kanaoka C, Emi H, Otani Y, Iiyama T (1987) Effect of charging state of particles on electret filtration. Aerosol Sci Technol 7(1):1–13.  https://doi.org/10.1080/02786828708959142 CrossRefGoogle Scholar
  15. Kasper G, Preining O, Matteson M (1978) Penetration of a multistage diffusion battery at various temperatures. J Aerosol Sci 9(4):331–338CrossRefGoogle Scholar
  16. Kaushik A, Soni A, Soni R (2015) A hybrid approach for software cost estimation using polynomial neural networks and intuitionistic fuzzy sets. Int J Comput Appl Technol 52(4):292–304CrossRefGoogle Scholar
  17. Krenker A, Bešter J, Kos A (2011) Introduction to the artificial neural networks. In: Suzuki K (ed) Artificial Neural Networks-Methodological Advances and Biomedical Applications. InTech, Rijeka, Carotia, pp 3–18Google Scholar
  18. Lahanas M, Schreibmann E, Milickovic N, Baltas D (2003) Intensity modulated beam radiation therapy dose optimization with multiobjective evolutionary algorithms. In: Fonseca CM, Fleming PJ, Zitzler E, Thiele L, Deb K (eds) Evolutionary multi-criterion optimization: second international conference, EMO 2003, Faro, Portugal, April 8–11, 2003. Proceedings. Springer, Berlin, pp 648–661.  https://doi.org/10.1007/3-540-36970-8_46 CrossRefGoogle Scholar
  19. Lathrache R, Fissan H (1987) Enhancement of particle deposition in filters due to electrostatic effects. Filtr Sep 24(6):418–422Google Scholar
  20. Lee K, Liu B (1982) Theoretical study of aerosol filtration by fibrous filters. Aerosol Sci Technol 1(2):147–161CrossRefGoogle Scholar
  21. Lu L, Cai W, Xie L, Li S, Soh YC (2005) HVAC system optimization—in-building section. Energy Build 37(1):11–22CrossRefGoogle Scholar
  22. Madaeni SS, Hasankiadeh NT, Kurdian AR, Rahimpour A (2010) Modeling and optimization of membrane fabrication using artificial neural network and genetic algorithm. Sep Purif Technol 76(1):33–43CrossRefGoogle Scholar
  23. Magnier L, Haghighat F (2010) Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network. Build Environ 45(3):739–746CrossRefGoogle Scholar
  24. Mahdavi A, Haghighat F, Bahloul A, Brochot C, Ostiguy C (2015) Particle loading time and humidity effects on the efficiency of an N95 filtering facepiece respirator model under constant and inhalation cyclic flows. Ann Occup Hyg 59(5):629–640.  https://doi.org/10.1093/annhyg/mev005 CrossRefGoogle Scholar
  25. Mostofi R, Wang B, Haghighat F, Bahloul A, Jaime L (2010) Performance of mechanical filters and respirators for capturing nanoparticles—Limitations and future direction. Ind health 48(3):296–304CrossRefGoogle Scholar
  26. Mostofi R, Bahloul A, Lara J, Wang B, Cloutier Y, Haghighat F (2011) Investigation of potential affecting factors on performance of N95 respirator. J Int Soc Respir Prot 28(1):26–39Google Scholar
  27. Mostofi R, Noël A, Haghighat F, Bahloul A, Lara J, Cloutier Y (2012) Impact of two particle measurement techniques on the determination of N95 class respirator filtration performance against ultrafine particles. J Hazard Mater.  https://doi.org/10.1016/j.jhazmat.2012.02.058 CrossRefGoogle Scholar
  28. Pala M, Caglar N, Elmas M, Cevik A, Saribiyik M (2008) Dynamic soil-structure interaction analysis of buildings by neural networks. Constr Build Mater 22(3):330–342CrossRefGoogle Scholar
  29. Parsian A, Ramezani M, Ghadimi N (2017) A hybrid neural network-gray wolf optimization algorithm for melanoma detection. Biomed Res 28(8):3408–3411Google Scholar
  30. Payet S, Boulaud D, Madelaine G, Renoux A (1992) Penetration and pressure drop of a HEPA filter during loading with submicron liquid particles. J Aerosol Sci 23(7):723–735CrossRefGoogle Scholar
  31. Sakamoto Y, Nagaiwa A, Kobayasi S, Shinozaki T (1999) An optimization method of district heating and cooling plant operation based on genetic algorithm. ASHRAE Trans 105:104Google Scholar
  32. Stephens B, Siegel JA (2013) Ultrafine particle removal by residential heating, ventilating, and air-conditioning filters. Indoor Air 23(6):488–497.  https://doi.org/10.1111/ina.12045 CrossRefGoogle Scholar
  33. Su H, Xie W, Zeng H (2014) Multiple response optimization based on the ANN theory of complex injection moulding process. Int J Comput Appl Technol 50(3–4):186–190CrossRefGoogle Scholar
  34. Tennal K, Mazumder M, Siag A, Reddy R (1991) Effect of loading with AIM oil aerosol on the collection efficiency of an electret filter. Part Sci Technol 9(1–2):19–29CrossRefGoogle Scholar
  35. Wang C, Otani Y (2013) Removal of nanoparticles from gas streams by fibrous filters: a review. Ind Eng Chem Res 52(1):5–17.  https://doi.org/10.1021/ie300574m CrossRefGoogle Scholar
  36. Wetter M, Wright J (2004) A comparison of deterministic and probabilistic optimization algorithms for nonsmooth simulation-based optimization. Build Environ 39(8):989–999CrossRefGoogle Scholar
  37. Yang J, Rivard H, Zmeureanu R (2005). Building energy prediction with adaptive artificial neural networks. In: Proceedings of the 9th international IBPSA conference, Montreal, Quebec, CanadaGoogle Scholar
  38. Yengui F, Labrak L, Frantz F, Daviot R, Abouchi N, O’Connor I (2012) A hybrid GA-SQP algorithm for analog circuits sizing. Circuits Syst 3(02):146CrossRefGoogle Scholar
  39. Zhu M, Han J, Wang F, Shao W, Xiong R, Zhang Q, Zhang F (2017) Electrospun nanofibers membranes for effective air filtration. Macromol Mater Eng 302(1):1600353CrossRefGoogle Scholar

Copyright information

© Institute of Earth Environment, Chinese Academy Sciences 2018

Authors and Affiliations

  1. 1.Department of Building, Civil and Environmental EngineeringConcordia UniversityMontrealCanada
  2. 2.Institut de recherche Robert-Sauvé en santé et en sécurité du travail (IRSST)MontrealCanada

Personalised recommendations