Issue |
BIO Web Conf.
Volume 105, 2024
IV International Conference on Agricultural Engineering and Green Infrastructure for Sustainable Development (AEGISD-IV 2024)
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Article Number | 02005 | |
Number of page(s) | 8 | |
Section | Biosystems Engineering and Waste Management | |
DOI | https://doi.org/10.1051/bioconf/202410502005 | |
Published online | 26 April 2024 |
Bioengineering, waste processing and fermentation process control for biogas production
1 National Research University «Tashkent Institute of Irrigation and Agricultural Mechanization Engineers», Tashkent, 100000, Uzbekistan
2 Urgench branch of Tashkent University of Information Technologies named after Muhammad Al-Al-Khwarizmi, Urgench, Republic of Uzbekistan
3 Jizzakh Polytechnic Institute, Jizzakh, Republic of Uzbekistan
4 Kazakh National Research Technical University named after K.I. Satpayev, Almaty, Republic of Kazakhstan
* Corresponding author: eest_uz@mail.ru
The article discusses the analysis of the state of control of the processes of biogas production from animal waste by methane digestion. The article discusses the problems of synthesis of automated control systems of biotechnological processes under conditions of information uncertainty. The analysis of the current state of control of fermentation stage processes shows that insufficient attention is paid to the problem of synthesis of second-tier banks under conditions of information uncertainty. Construction of mathematical modeling of biosynthetic processes is a kinetic model, where experimental and analytical methods are used due to the difficulty of identifying patterns in microbiological processes. The article discusses the application of methods and algorithms for intellectualization of problem solving in ACS for the synthesis of complex biotechnological objects in conditions of lack of information, that they should be attributed to priority tasks. The results of research on the application of a neuro-fuzzy system for controlling fermentation processes under conditions of uncertainty and multimode of processes, as well as a forecasting algorithm using nonlinear sets and neural networks are presented.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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