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Neural Network Model for Predicting the Resource Efficiency of the Defecosaturation Department of a Sugar Factory

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Intelligent Computing & Optimization (ICO 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 569))

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Abstract

The article proposes a model of the defecosaturation department of a sugar beet processing plant based on a neural network, which predicts the main indicators of the efficiency of diffusion juice purification. The predictive model allows you to adjust the operation mode of the department by analyzing the unmeasured qualitative indicator of the purification effect. The developed model also predicts the color of the semi-finished product, as well as the loss of sugar in the filter cake I and II saturation with an error of less than 5%. Unlike existing solutions, forecasting is performed in real time using a set of indicators of an automation system and an industrial laboratory. A predictive neural network model is an MLP universal for approximating non-linear complex functions with many variables. The model has 32 inputs, 4 outputs and one hidden layer with 23 neurons. The main technological variables from the automation system – temperatures, flow rates, pressure, pH in the apparatus, as well as from the industrial laboratory – alkalinity, SO content in the saturation gas will be fed into the network input. This allows to increase the informative support of the operator-technologist regarding the quality indicators of the enterprise without additional load on the industrial laboratory and without additional cost automatic quality devices.

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Author information

Authors and Affiliations

  1. State University of Trade and Economics, Kyiv, 02156, Ukraine

    Lidiia Vlasenko

  2. National University of Life and Environmental Sciences of Ukraine, Kiev, 03041, Ukraine

    Nataliia Zaiets & Olga Savchuk

  3. National University of Food Technologies, Kyiv, 01033, Ukraine

    Nataliia Lutska

Authors
  1. Lidiia Vlasenko
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  2. Nataliia Zaiets
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  3. Nataliia Lutska
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  4. Olga Savchuk
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Corresponding author

Correspondence to Nataliia Lutska .

Editor information

Editors and Affiliations

  1. Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Pandian Vasant

  2. Faculty of Engineering Management, Poznan University of Technology, Poznań, Wielkopolskie, Poland

    Gerhard-Wilhelm Weber

  3. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    José Antonio Marmolejo-Saucedo

  4. Department of Business Statistics and Operations Research, School of Economic Sciences, North West University, Mahikeng, South Africa

    Elias Munapo

  5. Department of Computer Science, UOW Malaysia KDU Penang University Colle, George Town, Malaysia

    J. Joshua Thomas

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Vlasenko, L., Zaiets, N., Lutska, N., Savchuk, O. (2023). Neural Network Model for Predicting the Resource Efficiency of the Defecosaturation Department of a Sugar Factory. In: Vasant, P., Weber, GW., Marmolejo-Saucedo, J.A., Munapo, E., Thomas, J.J. (eds) Intelligent Computing & Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-031-19958-5_12

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