Mathematical and Computer Modeling of Technological Parameters of Anaerobic Fermentation in Bioreactors

Authors

  • Kapan Shakerkhan Department of physics and informatics, Shakarim University, Glinka 20A, Semey, Kazakhstan Author
  • Vladimir Meshechkin Institute of Digital, Kemerovo State University, Krasnaya 6, Russia Author
  • Zhazira Taszhurekova Department of Applied Informatics and Programming, Dulaty University, Zhambyl, Kazakhstan Author
  • Ermek Abilmazhinov Department of physics and informatics, Shakarim University, Glinka 20A, Semey, Kazakhstan Author

DOI:

https://doi.org/10.47363/kgwgnk69

Keywords:

Mathematical Modeling, Efficient Energy, Correlation and Regression Analysis, Web Programming, Big Data Processing

Abstract

This research article examines computer and mathematical modeling of anaerobic digestion in livestock waste recycling. Several approaches to mathematical modeling of anaerobic digestion processes are considered, including ADM1 modeling. The creation of a computer program based on a self-developed mathematical model is described. A mathematical model of anaerobic fermentation has been proposed, which has proven its efficiency and reliability under industrial-scale conditions. This mathematical model was developed based on regression analysis and general correlation, with the identification of empirical relationships between biogas yield and 20 different process parameters that are measured by appropriate biosensors during industrial production.
The mathematical model we developed, taking into account a small volume of static data, and in our case for 5 positions of measured values, showed an average approximation error equal to 5.5% and a determination coefficient equal to 0.95, which proves to us a lower error, a high-quality and accurate measurement indicator. In this regard, the computer and mathematical model of anaerobic fermentation under industrial scale conditions that we proposed is considered effective and practical. The computer program was developed taking into account the concept of online statistical data transmission, using cellular communications and the Internet, and processing large amounts of statistical data, i.e., the Big Data concept. Flexibility, scalability, and accuracy are also considered. Machine learning technologies are proposed for using with this computer program, based on Big Data concepts.

Author Biographies

  • Kapan Shakerkhan, Department of physics and informatics, Shakarim University, Glinka 20A, Semey, Kazakhstan

    Department of physics and informatics, Shakarim University, Glinka 20A, Semey, Kazakhstan

  • Vladimir Meshechkin, Institute of Digital, Kemerovo State University, Krasnaya 6, Russia


    Institute of Digital, Kemerovo State University, Krasnaya 6, Russia

  • Zhazira Taszhurekova, Department of Applied Informatics and Programming, Dulaty University, Zhambyl, Kazakhstan

    Department of Applied Informatics and Programming, Dulaty University, Zhambyl, Kazakhstan

  • Ermek Abilmazhinov, Department of physics and informatics, Shakarim University, Glinka 20A, Semey, Kazakhstan

    Department of physics and informatics, Shakarim University, Glinka 20A, Semey, Kazakhstan 

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Published

2025-10-16