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Multifactor Prediction of Ecological Indicators of Production of Chemical Fibers Based on Neural Networks

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Fibre Chemistry Aims and scope

The problem of emission control in chemical fiber industry and construction of multifactor model of pollution index prediction is studied. A neural network method that makes it possible to derive regularities of a unidimensional series for PM 2.5 and simultaneously determine the influence of a set of factors of weather conditions is described. To reduce the complexity of the model, instead of the range of factors of influence, its main components are used. The interconnection of the number of factors of influence, depth of retrospective sampling, type and scale of neural network, and accuracy of prediction is studied.

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Authors and Affiliations

  1. Saint Petersburg State University of Industrial Technologies and Design (Sankt- Peterburgskii Gosudarstvennyi Universitet Promyshlennykh Tekhnologii i Dizaina), St. Petersburg, Russia

    V. I. Pimenov

  2. Admiral S.O. Makarov State University of Marine and River Fleet (Gosudarstvennyi Universitet Morskogo i Rechnogo Flota im. admirala S.O.Makarova), St. Petersburg, Russia

    I. V. Pimenov

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  1. V. I. Pimenov
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Correspondence to V. I. Pimenov.

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Translated from Khimicheskie Volokna, No. 3, pp. 50-55, May-June 2023

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Pimenov, V.I., Pimenov, I.V. Multifactor Prediction of Ecological Indicators of Production of Chemical Fibers Based on Neural Networks. Fibre Chem 55, 187–192 (2023). https://doi.org/10.1007/s10692-023-10458-y

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  • DOI: https://doi.org/10.1007/s10692-023-10458-y

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