https://doi.org/10.4081/jae.2021.1129
Operation technological process research in the cleaning system of the grain combine
Published: 28 June 2021
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
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The purpose of this study is to increase the efficiency of the technological process of a combine harvester cleaning system. It involved the simulation of the actual work process, the identification of problem areas and the uniform airflow distribution across the width of the sieve mill. The method for determining the class of ‘air-grain heap’ flow in the combine harvester cleaning system is presented in order to provide the mathematical description of its technical work process. As a result, the volumetric (Q = 8·10–6 ÷ 2.5·10–4) and mass (W = 0.01 ÷ 0.3) concentration of a grain heap in different sections of the sieve mill of the combine harvester cleaning system. The experimental measurements of the airflow speed on the sieve mill’s surface for the existing structures of the cleaning system in modern grain harvesters were 3.75 ÷ 10.2 m/s. The data obtained will be used in the future to implement a mathematical model for a complete description of the technological process of a combine harvester cleaning system using methods based on two-phase flow mechanics.
Aldoshin N.V., Lylin N.A. 2018. Improving the design of the grain combine harvester cleaner. Russ. Agric. Sci. 44:108-11.
DOI: https://doi.org/10.3103/S1068367418010032
Alferov S.A. 1987. Air sieve cleaning of grain harvesters. Agropromizdat, Moscow, Russia.
Badretdinov I.D., Mudarisov S.G. 2019. Scientific substantiation and improvement of pneumatic systems of agricultural machines based on modeling the technological process. Vestnik NGIEI. 9:5-16.
Badretdinov I., Mudarisov S., Lukmanov R., Permyakov V., Ibragimov R., Nasyrov R. 2019. Mathematical modeling and research of the work of the grain combine harvester cleaning system. Comput. Electron. Agric. 165:104966.
DOI: https://doi.org/10.1016/j.compag.2019.104966
Baran I.A., Popov V.B., Vyrskiy A.N., Trukhanovich S.V. 2016. Computer modeling of the process of separating grain and chaff into fractions in the cleaning system of a grain harvester. Vestnik GSTU im. P.O. Sukhoi. 3:3-9.
Borodin K., Zhangabay N.Z. 2019. Mechanical characteristics, as well as physical-and-chemical properties of the slag-filled concretes, and investigation of the predictive power of the metaheuristic approach. Curved Layer. Struct. 6:236-44.
DOI: https://doi.org/10.1515/cls-2019-0020
Ermoliev Y.I, Muratov D.K. 2011. Modeling the process of centrifugal fans functioning in the air-screen cleaning of a grain harvester. Vestnik DSTU. 11:38-46.
FAO Report. 2014. Crop prospects and food situation; the Trade and Markets Division of FAO under the Global Information and Early Warning System. No. 4, December 2014. FAO, Rome, Italy.
Feiffer A., Feiffer P., Kutschenreiter W., Rademacher T. 2005. Getreideernte - sauber, sicher, schnell. DLG Verlag, Frankfurt, Germany.
Kelemen Z., Komladi J., Peto V. 2005. Der Verlauf der Durchsatzleistung, der Kornverluste und des Treibstoffverbrauches bei Mahdreschern unterschiedlicher Konstruktion in der Weizenernte. Tagungsband VDI - MEG Kolloquium Landtechnik, Mahdrescher, H. 38:117-24.
Korn C. 2020. Application of coupled CFD-DEM simulation to separation process in combine harvester cleaning devices. Springer, Berlin, Germany.
DOI: https://doi.org/10.1007/978-3-662-61638-3
Korn C., Herlitzius T. 2017. Coupled CFD-DEM simulation of separation process in combine harvester cleaning devices. Landtechnik. 72:247-61.
DOI: https://doi.org/10.51202/9783181023006-199
Kotov A.V., Chaus V.P. 2010. Improvement of the cleaning system of the combine harvester when harvesting grain on the slopes. Bulletin of the Gomel State Technical University named after I.I. BY. Sukhoi. 2:3-10.
Kutzbach H.D. 2001. Combine harvester cleaning systems. Landtechnik. 56:392-3.
Kutzbach H.D., Quick G.R. 1999. Harvesters and threshers. Grain. In: B.A. Stout, B. Cheze (Eds.), CIGR Handbook of Agricultural Engineering, Volume III - Plant Production Engineering. ASAE, St. Joseph, MI, USA, pp 311-47.
Mirenko V.V., Khizhenok V.F., Rodzevich P.E. 2011. Analysis of the fan operation of the cleaning system of a combine harvester. Mech. Eng. Eng. Sci. 1:3-10.
Mirzazadeh A., Abdollahpor S, Vahed M.M. 2015. Incorporating Skewness and Kurtosis in improvement of combine harvester cleaning system performance. Int. J. Agric. Innov. Res. 3:1412-6.
Miu P.I., Kutzbach H.D. 2008. Modeling and simulation of grain threshing and separation in threshing units - Part I. Comput. Electron. Agric. 60:96-104.
DOI: https://doi.org/10.1016/j.compag.2007.07.003
Mudarisov S.G., Badretdinov I.D. 2013. Numerical implementation of a mathematical model of the technological process of a diametrical fan in a rotating coordinate system. News Intern. Acad. Agrar. Educ. 17:79-83.
Ospanov A., Muslimov N., Timurbekova A., Jumabekova G., Almaganbetova A., Zhalelov D., Nurdan D. 2019. The study of indicators of the quality test of poly-cereal whole meal flour for making pasta. J. Hyg. Eng. Des. 27:32-8.
Ostrikov A.N., Ospanov A.A., Shevtsov A.Ð., Muslimov N.Z., Timurbekova A.K., Jumabekova G.B. 2020. Mathematical model of high-temperature tube-shaped pasta drying in a conveyer belt drier. Int. J. Food Eng. 1 [Epub ahead-of-print].
DOI: https://doi.org/10.1515/ijfe-2020-0101
Ostrikov A.N., Shakhov S.V., Ospanov A.A., Muslimov N.Z., Timurbekova A.K., Jumabekova G.B., Matevey Y.Z. 2018. Mathematical modeling of product melt flow in the molding channel of an extruding machine with meat filling feeding. J. Food Process Eng. 41:e12874.
DOI: https://doi.org/10.1111/jfpe.12874
Rademacher T. 2003. Mahdrescher. Die Qual der richtigen Wahl. Getreide Magazin. 3:186-91.
Sorochenko S.F.. 2017. Mathematical model of grain separation in the cleaning system of a slope grain harvester. Bull. Altai GAU. 12:134-40.
Å pokas L., AdamÄuk V., Bulgakov V., Nozdrovický L. 2016. The experimental research of combine harvesters. Res. Agric. Eng. 62:106-12.
DOI: https://doi.org/10.17221/16/2015-RAE
SteponaviÄius D., Å pokas L., PetkeviÄius S. 2008. The influence of position of the first straw walkers section on grain separation. Agron. Res. 6:377-85.
Vasilevskij M.V., Romandin V.I., Zykov E.G. 2013. Transportation and sedimentation of particles in the processing technologies of dispersed materials: monograph. Tomskij politehnicheskij universitet, Publishing House of Tomsk Polytechnic University, Tomsk, Russia.
Voicu G., Casandroiu T., Tarcolea C. 2008. Testing stochastic models for simulating the seeds separation process on the sieves of a cleaning system, and a comparison with experimental data. Agric. Conspec. Sci. 73:95-101.
Wang L., Zhang X., Leng J., Zhao G., Jiao Z., Qin Y. 2018. Fatigue life analysis of grain combine harvester cleaning device. Trans. Chinese Soc. Agricult. Machin. 49:282-7.
How to Cite
Badretdinov, I. (2021) “Operation technological process research in the cleaning system of the grain combine”, Journal of Agricultural Engineering, 52(2). doi: 10.4081/jae.2021.1129.
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