ANN-based Modeling of Sunflower Threshing Process and Defining the Optimal Operation Point for Separation Efficiency

ماشین های کشاورزی

Article 7, Volume 10, Issue 1 - Serial Number 19, 2020, Pages 73-82   

Document Type: Research Article-en

DOI: 10.22067/jam.v10i1.76264

Authors

P. Ghiasi^{* 1}; A. Masoumi¹; A. Hemmat¹; Gh. Najafi²

¹Department of Biosystems Engineering, Isfahan University of Technology, Isfahan, Iran

²Department of Biosystems Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

Harvesting is one of the most important field operations in sunflower production. Seed damage and low separation efficiency are the top concerns of harvesting sunflower. In this study, a threshing cylinder with rubber teeth and a concave for harvesting sunflower were designed and evaluated. The variable parameters were threshing cylinder speed (TCS), threshing space (TS) and moisture content (MC) of sunflower head. Azargol variety was used to evaluate the threshing unit. The tests were performed at three cylinder speed levels (280, 380 and 480 rpm), two threshing spaces (8 and 10 cm) and two moisture content of sunflower head based on the crop condition (20% and 45% wet basis). An ANN model was developed to predict the amount of materials in each part of the concave. Results showed that the sunflower seeds had no damage during the threshing process and the presented model could predict the amount of materials in each part of the concave with a regression coefficient R2=0.95. Based on the ANN model, with a decrease in MC and TS, and an increase in TCS, the separation efficiency was increased. Furthermore, optimal parameters for the threshing unit which were suggested by Design Expert software to maximize the separation efficiency were 18% w.b, 450 rpm and 10.5 cm for MC, TSC, and TS, respectively and in this condition separation efficiency was determined to be 94.92%.

Keywords

Sunflower harvesting; Threshing model; Threshing unit

References

1. El-khateeb, H., and M. I. Saad. 2008. Operating factors affecting using two different threshing machines for threshing sunflower heads. Agricultural Mechanization and Engineering 251-270.

2. Farokhi, E., A. Nabipor, and J. Daneshian. 2013. Guidelines sunflower production in different regions of the country. Agricultural and Natural Resource Research and Education Center. Tehran. Iran. (In Farsi).

3. Ghiasi, P., A. Masoumi, and A. Hemmat. 2016. Design, development, and evaluation of a threshing cylinder and concave for harvesting sunflower. The 10th national congresss on biosystems Eng. (Agr. Machinery). Mashhad, Iran. (In Farsi).

4. Giner, S. A., and M. C. Gely. 2005. Sorptional parameters of sunflower seeds of use in drying and storage stability studies. Biosystems Engineering 92: 217-227.

5. Griffin, G. A., and Deere and Company. 1981. Combine harvesting. Deere & Co.

6. Hiregoudar, S., R. Udhaykumar, K. T. Ramappa, B. Shreshta, V. Meda, and M. Anantachar. 2011. Artificial Neural Network for Assessment of Grain Losses for Paddy Combine Harvester a Novel Approach. Control, Computation and Information Systems 140: 221-231 Springer, Berlin, Heidelberg.

7. Inna, P. 2010. Agribusiness handbook: Sunflower refined and crude oils. FAO Investment Centre Division: 40-40.

8. KhajePor, M. 2012. Industrial plants. first edition. Isfahan University of Technology Publication Center. Isfahan. Iran. (In Farsi).

9. Lizhang, X., L. Yaoming, M. Zheng, Z. Zhan, and W. Chenghong. 2013. Theoretical analysis and finite element simulation of a rice kernel obliquely impacted by a threshing tooth. Biosystems Engineering 114: 146-156.

10. Mirzazadeh, A., Sh. Abdollahpour, A. Mahmoudi, and A. Ramazani. 2012. Intelligent modeling of material separation in combine harvester ’ s thresher by ANN. International Journal of Agriculture and Crop Sciences 23: 1767-1777. (In Farsi).

11. Miu, P. I., and H. D. Kutzbach. 2007. Mathematical model of material kinematics in an axial threshing unit. Computers and Electronics in Agriculture 58: 93-99.

12. Ning, X., J. Chen, Y. Li, K. Wang, Y. Wang, and X. Wang. 2015. Kinetic model of combine harvester threshing system and simulation and experiment of speed control. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 31: 25-34.

13. Saeidirad, M., and A. Javadi. 2011. Study on machine-crop parameters of cylinder threshers for cumin threshing. Agricultural Engineering International: CIGR Journal 13.

14. Salari, K., R. Amiri Chayjan, J. Khazaei, and J. Amiri Parian. 2013. Optimization of Independent Parameters for Chickpea Threshing Using Response Surface Method (RSM). Journal of Agricultural Science and Technology 15: 467-477.

15. Schneiter, A. A., and J. F. Miller. 1981. Description of sunflower growth stages. Crop Science.

16. Shahgoli, G., H. GhafouriChyane, and M. Tarahom. 2018. Modeling of Soil Compaction Beneath the Tractor Tire using Multilayer Perceptron Neural Networks. Journal of Agricultural Machinery 8: 105-118. (In Farsi).

17. Sudajan, S., V. M. Salokhe, and K. Triratanasirichai. 2002. PM-Power and Machinery: effect of type of drum, drum speed and feed rate on sunflower threshing. Biosystems Engineering 83: 413-421.

18. Uno, Y., S. O. Prasher, R. Lacroix, P. K. Goel, Y. Karimi, A. Viau, and R. M. Patel. 2005. Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data. Computers and Electronics in Agriculture 47: 149-161.

19. Vejasit, A., and V. Salokhe. 2004. Machine-Crop Parameters Affecting Performance of an Axial-Flow Soya Bean Thresher. Agricultural Engineering International: The CIGR Journal of Scientific Research and Development, Manuscript PM 04, 004.

20. Zurada, J. M. 1992. Introduction to artificial neural systems. West.