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Modeling of biotechnological objects

Palvan KalandarovNational Research University “Tashkent Institute of Irrigation and Agricultural Mechanization Engineers”, Tashkent, Republic of UzbekistanKh.Kh. AbdullaevBukhara Institute of Natural Resources Management of the National Research University of the Tashkent Institute of Irrigation and Agricultural Mechanization Engineers. Bukhara, Republic of UzbekistanA.N. KhaitovBukhara Institute of Natural Resources Management of the National Research University of the Tashkent Institute of Irrigation and Agricultural Mechanization Engineers. Bukhara, Republic of UzbekistanKh.S. SharifovBukhara Institute of Natural Resources Management of the National Research University of the Tashkent Institute of Irrigation and Agricultural Mechanization Engineers. Bukhara, Republic of UzbekistanG.F. MurodovaBukhara Institute of Natural Resources Management of the National Research University of the Tashkent Institute of Irrigation and Agricultural Mechanization Engineers. Bukhara, Republic of Uzbekistan
BIO Web of Conferencesjournal2024en
ABI

Аннотация

The article discusses the system of modeling biotechnological objects and biogas production in the process of fermentation of various agricultural wastes. The complexity of automation of biotechnological processes, which consists in the lack of sufficient knowledge about the phenomena associated with the process of fermentation and the synthesis of target materials, is shown. Solutions to control problems using intelligent methods, intelligent control systems, based on fuzzy logic and on the basis of on the basis of neural network technologies, which have proven themselves in the control of complex objects with parameters that vary widely, as well as the analysis of algorithms for the operation of programmable logic controllers that perform the functions of regulators in the systems of operational control of complex processes, adaptive neuro-fuzzy architecture. Distributed in management decision support systems, it is made in the form of a six-layer neural structure, i.e. systems with neuro-fuzzy regulators, are able to largely meet the modern requirements of the designed systems, as a result of which the systems, using the technology of synthesis of intelligent control algorithms, can optimize complex circuits of automatic control systems.

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