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Determination of Nano-aerosol Size Distribution Using Differential Evolution

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Computational Intelligence in Emerging Technologies for Engineering Applications

Abstract

Nanotechnology characterizes an important area in engineering due to various applications that can be found, such as electronics and pharmaceutical industries, development of air filters, among others. From the environmental point of view, because nanometric particles provide special characteristics to the products, emission of these particles into the air must be limited. Among the approaches proposed in the literature, the electrical mobility technique is an emerging strategy used to ensure an aerosol stream with monodispersed particles. This technique is based on the ability of a charged particle to cross an electrical field. Thus, depending on the size of the particles, the bigger ones will arrive later in the central electrode than the smaller ones, and only a narrow band of sizes will be collected in a slit located at the bottom of the equipment. In order to characterize the relation between the monodispersed and polydispersed aerosol stream, an inverse problem is formulated and solved by using differential evolution. The objective function consists of determining transfer functions that minimize the sum of difference between predicted and experimental concentrations of NaCl obtained by a differential mobility analyzer. The results demonstrated that the proposed methodology was able to obtain a good approximation for two classical transfer functions.

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Acknowledgements

The authors would like to acknowledge the financial support from FAPEMIG and CNPq agencies.

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Correspondence to Fran Sérgio Lobato .

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Borges, L.C., Camargo, E.C.d.M., Damasceno, J.J.R., Arouca, F.d.O., Lobato, F.S. (2020). Determination of Nano-aerosol Size Distribution Using Differential Evolution. In: Llanes Santiago, O., Cruz Corona, C., Silva Neto, A., Verdegay, J. (eds) Computational Intelligence in Emerging Technologies for Engineering Applications. Studies in Computational Intelligence, vol 872. Springer, Cham. https://doi.org/10.1007/978-3-030-34409-2_7

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