Innovative aerodynamic grain separation system for plant harvesting in sloped areas: problems, research and optimization of parameters

• Autorzy: Bieniek Jerzy , Komarnicki Piotr , Detyna Jerzy

Identyfikatory

DOI

10.1007/s43452-021-00174-x

• Języki publikacji: EN

Abstrakty

EN

This article presents the main problems associated with cereal harvesting in sloping areas. The presented innovative aerodynamic system supporting the separating unit of combine harvester can be one of the ways to counteract the negative effects of harvesting machines work on slopes. The Monte Carlo numerical method, presented in this article, was applied in the optimization of an aerodynamic sieve separation process on an inclined terrain. The given variables are the transverse slope of separator α (of the sieve), longitudinal slope β and the output of the main and side fans. The Monte Carlo method makes it possible to determine an optimized set of parameters (α = 10°, β = 2.8°, δ = 9°), the output of the main fan (0.67 m3 s−1) and the output of the side fan (1.86 m3 s−1), allowing to obtain the best indicator values of 2.1% grain loss and 97.5% grain purity.

Słowa kluczowe

EN

cereal harvesting; combine harvester; sloping agricultural land; aerodynamic separation system; system optimization; grain loss; grain purity

PL

kombajn zbożowy; grunty rolne; czystość ziarna

• Wydawca: Springer ; Wrocław University of Science and Technology
• Czasopismo: Archives of Civil and Mechanical Engineering
• Rocznik: 2021
• Tom: Vol. 21, no. 1
• Strony: 448--460
• Opis fizyczny: Bibliogr. 57 poz., fot., rys., tab., wykr.

Twórcy

autor

Bieniek Jerzy

• Institute of Agricultural Engineering, The Faculty of Life Sciences and Technology, Wrocław University of Environmental and Life Sciences, 37a, Chełmońskiego Str., 51‑630 Wrocław, Poland

autor

Komarnicki Piotr

• Institute of Agricultural Engineering, The Faculty of Life Sciences and Technology, Wrocław University of Environmental and Life Sciences, 37a, Chełmońskiego Str., 51‑630 Wrocław, Poland

autor

Detyna Jerzy

• Department of Mechanics, Materials and Biomedical Engineering, Faculty of Mechanical Engineering, Wrocław University of Science and Technology, 25 Smoluchowskiego Str., 50‑372 Wrocław, Poland

• https://orcid.org/0000-0001-5750-2085

Bibliografia

• [1] Komarnicki P, Bieniek J, Banasiak J. Operational effectiveness of a sieve aerodynamic separator under the conditions of variable load of sieves. Eksploatacja i Niezawodnocść – Maintenance and Reliability. 2007;4:33–5.
• [2] Lewandowski B. Research of chevron separator system for application to a combine harvester for hilly and mountainous terrain. Wrocław: Wrocław University of Environmental and Life Sciences; 2004.
• [3] Kahrs J. Aerodynamic properties of weed seeds. Int Agrophysics. 1994;8:259–62.
• [4] Misener GC, Lee JHA. Aerodynamic separation of grain from straw and chaff in a dispersed stream. Can Agric Eng. 1973;15:62–5.
• [5] Gąsior K, Urbańska H, Grzesiek A, Zimroz R, Wyłomańska A. Identification, decomposition and segmentation of impulsive vibration signals with deterministic components–a sieving screen case study. Sensors. 2020;20:5648.
• [6] Bieniek J. Separation process of the cereal grain on the chevron sieve in change work condition. Wrocław: Wrocław University of Environmental and Life Sciences; 2003.
• [7] Detyna J. Maximum entropy as a theoretical criterion of statistical description of the granular matter separation. Wrocław: Wrocław University of Science and Technology Publishing; 2007.
• [8] Harrison HP. Grain separation and damage of an axial flow combine. Can Agric Eng. 1992;34:49–53. https://library.csbe scgab.ca/docs/journal/34/34_1_49_ocr.pdf.
• [9] Zhao Z, Li Y, Chen J, Xu J. Grain separation loss monitoring system in combine harvester. Comput Electron Agric. 2011;76:183–8.
• [10] Schulz W, Wember T, Schwarz M. Efficient and compact development of a combine harvester cleaning system. VDI MEG Tagung Landtechnik. 2011. p. 129–36. file://z/Ref_Works/Mahdrescher/Schulz et al_2011_efficient and compact devel opment of combine harvester cleaning system.pdf.
• [11] Kutzbach HD. Combine harvester cleaning systems. Landtech nik. 2001;56:392–3. file://z/Ref_Works/Mahdrescher/Kutzbach_2001_combine harvester cleaning systems.pdf.
• [12] Rothaug S, Böttinger S, Kutzbach HD. Combine grain clean ing unit with circular oscillation. Landtechnik. 2007;62:24–5. file://z/Ref_Works/Mahdrescher/Rothaug et al_2007_Reini gung im Mahdrescher durch Kreisschwinger.pdf.
• [13] Mullendore DL, Windt CW, Van As H, Knoblauch M. Sieve tube geometry in relation to phloem flow. Plant Cell. 2010;22:579–93. http://www.plantcell.org/content/22/3/579.short.
• [14] Craessaerts G, Saeys W, Missotten B, De Baerdemaeker J. Iden tification of the cleaning process on combine harvesters. Part I: A fuzzy model for prediction of the material other than grain (MOG) content in the grain bin. Biosyst Eng. 2008;101:42–9.
• [15] Krot P, Zimroz R, Michalak A, Wodecki J, Ogonowski S, Drozda M, et al. Development and verification of the diagnos tic model of the sieving screen. Shock Vib. 2020;2020:1–14. https://doi.org/10.1155/2020/8015465.
• [16] Banasiak J, Detyna J, Hutnik E, Szewczyk A, Zimny L. Agrotechnologia. Warszawa Wrocław: PWN Group; 1999.
• [17] Bohorquez P, Ancey C. Particle diffusion in non equilibrium bedload transport simulations. Appl Math Model. 2016;40:7474–92.
• [18] Krane MJM. Macrosegregation development during solidification of a multicomponent alloy with free floating solid particles. Appl Math Model. 2004;28:95–107.
• [19] Afonso Júnior PC, Corrêa PC, Pinto FAC, Queiroz DM. Aerodynamic properties of coffee cherries and beans. Biosyst Eng. 2007;98:39–46.
• [20] Grama SN, Bern CJ, Hurburgh CR Jr. Airflow resistance of mixtures of shelled corn and fines. Trans Am Soc Agric Eng. 1984;27:268–72.
• [21] Shamloo H, Pirzadeh B. Analysis of roughness density and flow submergence effects on turbulence flow characteristics in open channels using a large eddy simulation. Appl Math Model. 2015;39:1074–86.
• [22] Zamankhan P. Solid structures in a highly agitated bed of granular materials. Appl Math Model. 2012;36:414–29.
• [23] Jayanti S, Hewitt G. Response of turbulent flow to abrupt changes in surface roughness and its relevance in horizontal annular flow. Appl Math Model. 1996;20:244–51.
• [24] Bozzi S, Passoni G. Mathematical modelling of bottom evolution by particle deposition and resuspension. Appl Math Model. 2012;36:4186–96.
• [25] Komarnicki P, Banasiak J, Bieniek J. Distribution of aerodynamic parameters of the process equilibrium state of grain cleaning. Agric Eng. 2008;4:389–96.
• [26] Bieniek J, Banasiak J, Komarnicki P. Influence of selected parameters of air stream on the separation grain mass distribution. Agric Eng. 2007;8:13–20.
• [27] Zalewski R, Chodkiewicz P, Shillor M. Vibrations of a mass spring system using a granular material damper. Appl Math Model. 2016;40:8033–47.
• [28] Yuan ZB, Nie YF, Liu F, Turner I, Zhang GY, Gu YT. An advanced numerical modeling for Riesz space fractional advection–dispersion equations by a meshfree approach. Appl Math Model. 2016;40:7816–29.
• [29] Verdejo H, Escudero W, Kliemann W, Awerkin A, Becker C, Vargas L. Impact of wind power generation on a large scale power system using stochastic linear stability. Appl Math Model. 2016;40:7977–87.
• [30] Lopes P, Tabor G, Carvalho RF, Leandro J. Explicit calculation of natural aeration using a Volume of Fluid model. Appl Math Model. 2016;40:7504–15.
• [31] Zhang T, Ouyang H, Zhang YO, Lv BL. Nonlinear dynamics of straight fluid conveying pipes with general boundary conditions and additional springs and masses. Appl Math Model. 2016;40:7880–900.
• [32] Li D, Wang L, Wang Q, Liu G, Lu H, Zhang Q, et al. Simulations of dynamic properties of particles in horizontal rotating ellipsoidal drums. Appl Math Model. 2016;40:7708–23.
• [33] Detyna J, Bieniek J. Methods of statistical modeling in the process of sieve separation of heterogeneous particles. Appl Math Model. 2008;32:992–1002.
• [34] Li SP. A guided Monte Carlo method for optimization problems. Int J Mod Phys C. 2002;13:1365–74. https://doi.org/10.1142/S0129183102003978.
• [35] Homem de Mello T, Bayraksan G. Monte Carlo sampling based methods for stochastic optimization. Surv Oper Res Manag Sci. 2014;19:56–85.
• [36] Koch KR. Monte Carlo methods. In: Freeden W. (eds) Mathematische Geodäsie/Mathematical Geodesy. Springer Reference Naturwissenschaften. Berlin, Heidelberg: Springer Spektrum; 2020. https://doi.org/10.1007/978 3 662 55854 6_100.
• [37] Kleywegt AJ, Shapiro A, Homem de Mello T. The sample average approximation method for stochastic discrete optimization. SIAM J Optim. 2002;12:479–502. https://doi.org/10.1137/S1052623499363220.
• [38] Rota G C. Simulation and the Monte Carlo method. Adv Math (N Y). 1986;60:123. https://doi.org/10.1016/0001 8708(86)90009 5.
• [39] Tadeusiewicz R, Ogiela MR. Artificial intelligence techniques in retrieval of visual data semantic information. Lect Notes Artif Intell LNAI. 2003;2663:18–27.
• [40] Hesterberg T. Monte Carlo strategies in scientific computing. Technometrics. 2002;44:403–4. https://doi.org/10.1198/tech.2002.s85.
• [41] Lu J, Dai HC. Large eddy simulation of flow and mass exchange in an embayment with or without vegetation. Appl Math Model. 2016;40:7751–67.
• [42] Kroese DP, Taimre T, Botev ZI. Handbook of Monte Carlo methods. Hoboken, New Jersey: Wiley & Sons, Inc.; 2011. https://doi.org/10.1002/9781118014967.
• [43] McLeish D. A general method for debiasing a Monte Carlo estimator. Monte Carlo Methods Appl. 2011;17:301–15.
• [44] Giles M. Improved multilevel Monte Carlo convergence using the Milstein scheme. Monte Carlo Quasi Monte Carlo Methods 2006. 2006;2:343–58. http://eprints.maths.ox.ac.uk/1102/.
• [45] Dunn WL, Shultis JK. Exploring Monte Carlo methods. Elsevier; 2012. pp. 1–20. https://doi.org/10.1016/B978 0 444 51575 9.00001 4.
• [46] Dunn WL, Shultis JK. Exploring Monte Carlo methods. Elsevier; 2012. pp. 21–46. https://doi.org/10.1016/B978 0 444 51575 9.00002 6.
• [47] Rubinstein RY, Kroese DP. Simulation and the Monte Carlo method. Second Ed. Hoboken, New Jersey: Wiley & Sons, Inc.; 2016. http://doi.wiley.com/https://doi.org/10.1002/9780470230381.
• [48] Openshaw S, Whitehead P. A Monte Carlo simulation approach to solving multicriteria optimisation problems related to planmaking, evaluation, and monitoring in local planning. Environ Plan B Plan Des. 1985;12:321–34. https://doi.org/10.1068/b120321.
• [49] Kuczma MS. Viscoelastic plastic model for skeletal structural systems with clearances. Comput Assist Mech Eng Sci. 1999;83–106.
• [50] Iwaniec J. Output only technique for parameter identification of nonlinear systems working under operational loads. Key Eng Mater. 2007;347:467–72.
• [51] Iwaniec J, Iwaniec M. Output only identification of vibratory machine suspension parameters under exploitational conditions. Solid State Phenom. 2016;248:175–85.
• [52] Delgoda R, Pulfer JD. A guided Monte Carlo search algorithm for global optimization of multidimensional functions. J Chem Inf Comput Sci. 1998;38:1087–95. https://doi.org/10.1021/ci9701042.
• [53] Schleder AM, Araujo PC, Martins MR. Multicriteria optimization for system configuration using Monte Carlo simulation and RAM analysis. ASME 2016 35th Int Conf Ocean Offshore Arct Eng. Busan, South Korea; 2016. https://doi.org/10.1115/OMAE2016 54274.
• [54] Vrugt JA, Gupta HV, Bastidas LA, Bouten W, Sorooshian S. Effective and efficient algorithm for multiobjective optimization of hydrologic models. Water Resour Res. 2003;39:1–19. https://doi.org/10.1029/2002WR001746.
• [55] Wang W, Sebag M. Multi objective Monte Carlo tree search. Proc Asian Conf Mach Learn. 2012;25:507–22. http://proceedings.mlr.press/v25/wang12b/wang12b.pdf.
• [56] Cios KJ, Mamitsuka H, Nagashima T, Tadeusiewicz R. Computational intelligence in solving bioinformatics problems. Artif Intell Med. 2005;35:1–8. https://doi.org/10.1016/j.artmed.2005.07.001.
• [57] Kuczma MS, Świtka R. Bending of elastic beams on winkler type viscoelastic foundations with unilateral constraints. Comput Struct. 1990;34:125–36. https://doi.org/10.1016/0045 7949(90)90306 M.

Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021)