Published December 30, 2022 | Version v1
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Applying a neural network method to search for optimal air ionization conditions

  • 1. Kremenchuk Mykhailo Ostrohradskyi National University
  • 2. National Institute of Chemistry
  • 3. Scientific and Technical Center of Promekology Ltd
  • 4. Institute of Geotechnical Mechanics named by N. Poljakov of National Academy of Sciences of Ukrain

Description

This paper reports measuring, modeling, and determining the optimized air ionic composition of the air at industrial premises to ensure safe living and working conditions for workers.

The possibility of using saline solutions with different degrees of concentration to increase the number of negative ions in the airspace, as well as the variability of the air flow rate for the process of ionization of the air of industrial premises, has been investigated. Analysis of experimental data revealed that an increase in the concentration of saline solutions leads to a decrease in the release of the number of air ions into the vapor-air space of the room.

It is proved that in order to improve air quality, it is advisable to enable air ionization using an ultrasonic air ion generator and the use of demineralized water. The optimal input parameters established for the ultrasonic installation are: s –distance to the ultrasonic installation, 40 cm; v ‒ airflow rate, 6.00 m/s; and c ‒ concentration of salt water solution, 3.3 %.

The result reported here could be used in the design and development of a control system for an ultrasonic generator of air ions of ventilation systems and microclimate systems in order to create the most comfortable high-quality ionized air at industrial premises.

To find the optimal mode of operation of the ionization process, a representation procedure for a neural network was applied, which was most accurate to determine the optimal parameters for ionizing the airspace of the working room.

Optimization was performed using a Feed Forward Bottle Neck Neural Network (FFBN NN) representation. This approach allows one to determine several optimal conditions for the process under study on the basis of a compromise solution.

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References

  • Standard of Building Biology Testing Methods: SBM-2015. BAUBIOLOGIE MAES / Institut für Baubiologie + Nachhaltigkeit IBN. Available at: https://buildingbiology.com/site/wp-content/uploads/standard-2015-englisch.pdf
  • Wang, H., Wang, B., Niu, X., Song, Q., Li, M., Luo, Y. et al. (2020). Study on the change of negative air ion concentration and its influencing factors at different spatio-temporal scales. Global Ecology and Conservation, 23, e01008. doi: https://doi.org/10.1016/j.gecco.2020.e01008
  • Yue, C., Yuxin, Z., Nan, Z., Dongyou, Z., Jiangning, Y. (2020). An inversion model for estimating the negative air ion concentration using MODIS images of the Daxing'anling region. PLOS ONE, 15 (11), e0242554. doi: https://doi.org/10.1371/journal.pone.0242554
  • Kaiser, F.-J., Bassler, N., Tölli, H., Jäkel, O. (2011). Initial recombination in the track of heavy charged particles: Numerical solution for air filled ionization chambers. Acta Oncologica, 51 (3), 368–375. doi: https://doi.org/10.3109/0284186x.2011.626452
  • Rubenstone, J. (2021). Cleaning the Air, Fighting The Virus. ENR: Engineering News-Record, 286 (8), 8–11.
  • Wei, W., Ramalho, O., Malingre, L., Sivanantham, S., Little, J. C., Mandin, C. (2019). Machine learning and statistical models for predicting indoor air quality. Indoor Air, 29 (5), 704–726. doi: https://doi.org/10.1111/ina.12580
  • Sukach, S., Kozlovs'ka, T., Serhiienko, I., Khodakovskyy, O., Liashok, I., Kipko, O. (2018). Studying and substantiation of the method for normalization of air­ionic regime at industrial premises at the ultrasonic ionization of air. Eastern-European Journal of Enterprise Technologies, 4 (10 (94)), 36–45. doi: https://doi.org/10.15587/1729-4061.2018.141060
  • Noakes, C. J., Sleigh, P. A., Beggs, C. B. (2007). Modelling the air cleaning performance of negative air ionisers in ventilated rooms. Proceeding of the 10 th Int. Conference on Air Distribution in Rooms (Roomvent 2007). Helsinki.
  • Sydorov, O. V., Hlyva, V. A. (2013). Vplyv elektrostatychnykh poliv na kontsentratsiyi lehkykh aeroioniv na robochomu mistsi operatora PEOM. Stroitel'stvo, materialovedenie, mashinostroenie. Seriya: Bezopasnost' zhiznedeyatel'nosti, 71 (2), 176–183. Available at: https://dspace.nau.edu.ua/handle/NAU/29609
  • Biliaiev, M. M., Tsygankova, S. G. (2016). Complex of numerical models for computation of air ion concentration in premises. Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, 2 (62), 16–24. doi: https://doi.org/10.15802/stp2016/67281
  • Rusakova, T. I. (2019). Method for predicting parameters of the aeroionic mode in open terrain ground areas. Science and Transport Progress. Bulletin of Dnipropetrovsk National University of Railway Transport, 3 (81), 16–26. doi: https://doi.org/10.15802/stp2019/170273
  • Podorozhniak, A., Liubchenko, N., Kvochka, M., Suarez, I. (2021). Usage of intelligent methods for multispectral data processing in the field of environmental monitoring. Advanced Information Systems, 5 (3), 97–102. doi: https://doi.org/10.20998/2522-9052.2021.3.13
  • Sobchuk, V., Zamrii, I., Olimpiyeva, Y., Laptiev, S. (2021). Functional stability of technological processes based on nonlinear dynamics with the application of neural networks. Advanced Information Systems, 5 (2), 49–57. doi: https://doi.org/10.20998/2522-9052.2021.2.08
  • Sukach, S. V., Sydorov, O. V. (2016). Metodolohichni zasady pidvyshchennia yakosti kontroliu aeroionnoho skladu povitria vyrobnychoho seredovyshcha. Problemy okhorony pratsi v Ukraini, 32, 127–133.
  • Daszykowski, M. (2003). A journey into low-dimensional spaces with autoassociative neural networks. Talanta, 59 (6), 1095–1105. doi: https://doi.org/10.1016/s0039-9140(03)00018-3
  • Eriksson, L., Johansson, E., Kettaneh-Wold, N., Wikström, C., Wold, S. (2008). Design of Experiments: Principles and Applications. Umetrics AB, Umeе.