Applying a neural network method to search for optimal air ionization conditions
DOI:
https://doi.org/10.15587/1729-4061.2022.270315Keywords:
air ionization, air quality, neural network, production facilities, air indicatorsAbstract
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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Copyright (c) 2022 Serhii Sukach, Volodymyr Chenchevoi, Natalja Fjodorova, Olga Chencheva, Volodymyr Bakharev, Olena Kortsova, Volodymyr Shevchenko, Ivan Petrenko
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