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Parametric and mathematical model of raw cotton storage in cotton stacks

Field of Science:Industrial and Manufacturing EngineeringEnvironmental EngineeringMaterials Science
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“TEXTILE JOURNAL OF UZBEKISTAN” Scientific-technical journal

“TEXTILE JOURNAL OF UZBEKISTAN” Scientific-technical journal

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ARTICLE ANNOTATION

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The process of storing raw cotton is an indispensable and important part of the technological process of primary processing of cotton. Due to proper storage of cotton, labor productivity increases in the processes after storage, however, different types of cotton have different effects on the storage process. The article studies that the quality of cotton storage in open cotton bunches depends on the degree of contamination of cotton, grade, humidity, density and external factors such as temperature and humidity. The mathematical model of the raw cotton storage process is initially presented as a dynamic graph, the analysis of which shows that the initial state of raw cotton yi(t) is determined by the algebraic sum of the results of atmospheric influences and the reaction of the product to these influences. In the Matlab graph modeling program, the initial state is modeled by adders, integral reflection integrators of the graph, and proportional reflection arcs with appropriate coefficients. To define a mathematical model of the raw cotton storage process in the classical differential form, a new variable Z(t) was introduced and converted to the classical differential form. The mathematical model developed in the article characterizes the functional structure of the raw cotton storage process and makes it possible to determine a parametric transfer function for analyzing the characteristics of the transient process.

AUTHORS

S.Yuldashev

TOSHKENT TO'QIMACHILIK VA YENGIL SANOAT INSTITUTI

Tags

# temperature# humidity# mathematical model# raw cotton# storage# parametric model# dynamic graph# atmospheric influences

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References

R.A.Sailov.Vliyaniye protsessa xraneniya na posleuborochnoye sostoyaniye xlopkasirsa. Texnologiya tektstilnoy promishlennosti.2009g.№5. (320)

Gulyayev R.A, Lugachev A.YE., Mardonov B.M. Razrabotka optimizirovannoy texnologii uvlajneniya xlopka- sirsa i issledovaniye yeye vliyaniya na soxraneniye prirodnix i pryadilnix svoystv xlopkovogo volokna. Jurnal Texnologiya legkoy promishlennosti. Uzbekistan. 2017 god.str.15-19.

Zikriyoyev E.Z. «Paxtani dastlabki qayta ishlash» O`quv qo`llanma. Toshkent, «Mexnat» 2002 y.

Salimov A.M., Lugachev A.YE., M.T.Xodjiyev “Texnologiya pervichnoy obrabotki xlopka”. Uchebnoye posobiy.T.: “Adabiyot uchqunlari”, 2018. -148str.

Uskov A.A., Kuzmin A.V. Intellektualniye texnologii upravleniY. Iskusstvenniye neyronniye seti i nechetkaya logika.-M.: Goryachaya liniya - Telekom, 2004. -143 s.

Soldatov V.V., Sudnik Y.A., Savosin S.I. Mikroprotsessornaya sistema avtomatizirovannogo kontrolya vlajnosti zerna i vozduxa v xranilishe / Avtomatizatsiya i informatsionnoye obespecheniye proizvodstvennix protsessov v selskom xozyaystve. Sbornik dokladov X mejdunarodnoy nauchno-prakticheskoy konferensii (16-17 sentabrya 2008 g., g. Uglich). Chast 1.-M.: izd-vo «Izvestiya», 2008. - S. 373 - 379.

Donskoy, D. A. Modelirovaniye iskusstvennix neyronnix setey v sisteme MATLAB. Chast 3. Radialniye bazisniye seti: metod, ukazaniya k vipolneniyu laboratornix rabot / D. A. Donskoy [i dr ].-Penza : Penzenskiy gos. un-t, 2005.-63 s.

Oppoqov, Y. P. Oddiy differensial tenglamalardan misol va masalalar to‘plami : o‘quv qo‘llanma / Y. P. Oppoqov [va boshq.].-Toshkent : 2009.-156 b.

O‘razboyev, G. Differensial tenglamalardan masala va mashqlar to‘plami : o‘quv qo‘llanma / G. O‘razboyev, U. A. Xoitmetov. - Urganch : 2019.-196 b.

Yakushenko S.Y. Sovremenniye informatsionniye sistemi. - http: www.rlda.ru/index.htm

Haykin S. Neural Networks and Learning Machines. - New York: Prentice Hall, 2009. - 936 p.

Bishop C. M. Pattern Recognition and Machine Learning. - New York: Springer, 2006.- 738 p.

Ogata K. Modern Control Engineering. - 5th ed. - New Jersey: Prentice Hall, 2010.894 p.

Boylestad R., Nashelsky L. Electronic Devices and Circuit Theory. - 11th ed. Boston: Pearson, 2013. - 912 p.

Kreyszig E. Advanced Engineering Mathematics. - 10th ed. - New York: Wiley, 2011. - 1280 p.

Haykin S., Network S. Neural networks: a comprehensive foundation // IEEE Transactions on Neural Networks.- 2008. - Vol. 19, No. 5.- P. 705–720.