Design, Construction, and Optimization of Performance of Electrodynamic Spraying Head of Atomizer Motorized Knapsack Sprayer

Document Type : Research Article

Authors

• M. Rezaei ¹
• J. Khodaei ¹
• B. Astinchap ²

¹ Department of Biosystems Engineering, Faculty of Agriculture, University of Kurdistan, Sanandaj, Iran

² Department of Physics, Faculty of Science, University of Kurdistan, Sanandaj, Iran

https://doi.org/10.22067/jam.2023.79865.1134

Abstract

Introduction
Due to the increasing need for agricultural products, protection of products against pathogens and preventing them from being wasted is important. Studies on droplet charging systems result in the reduction of chemical usage and an increase in the deposition of droplets on the target. Conventional sprayers used in Iran have numerous disadvantages such as drift, environmental pollution, lack of complete and homogeneous coverage of the spraying surface, phytotoxicity, and crop losses. Therefore, evaluation of new spraying methods and using a variety of electrical sprayers as alternatives to conventional spraying is essential. This study aims to design, construct, and optimize the performance of the electrodynamic head of an atomizer motorized knapsack sprayer, and study the effects of the angle of the target position, spraying distance, and wind speed on the performance of the electrodynamic sprayer.
Materials and Methods
Experiments were performed in an agricultural machinery workshop at The Department of Biosystems Engineering, the University of Kurdistan, Iran, with an atomizer motorized knapsack sprayer equipped with an electrodynamic head. The effect of some factors including wind speed, spraying angle, and spraying distance on deposition, coverage percentage, and uniformity of spraying were investigated. These effects were investigated to determine the uniformity coefficient of total spraying. Design Expert 8.0.6 Trial software was used to design the experiments based on central composite design and to analyze the data. The investigated factors and levels were: the distance of nozzles from the target (at three levels of 2, 4, and 6 m), the angle of the target position (at three levels of 0, 45, and 90 degrees), and wind speed (at three levels of 2.5, 3, and 3.5 m s^-1). Water-sensitive paper cards were used to evaluate the quality of the spraying. The cards were scanned and magnified with an Olympus SZX12 Stereo Microscope equipped with an objective lens of X1 and a total magnification of 7X. The characteristics of droplet size were determined using Mountains Map Trial and Deposit Scan software.
Results and Discussion
The maximum value of the total spraying uniformity coefficient was equal to 1.95 for the spraying angle of 0 degrees, the distance of 6 meters, and the speed of 3.5 meters per second. Meanwhile, the lowest value of the spray uniformity coefficient of 1.18 was obtained for the test conditions of 90 degrees, distance of 2 m, and speed of 2.5 m s^-1, respectively. Based on analysis of variance for the two-factor interactions model (P-value less than 0.0001, explanation coefficient 0.9383, absolute explanation coefficient 0.910, standard deviation 0.0590, and coefficient of variation 3.790%). It can be stated that this model is highly accurate in predicting the uniformity of the total spraying, and the linear components of spraying angle and spraying distance, as well as the interaction of spraying angle × spraying distance and spraying distance × wind speed, significantly affect the uniformity of the total spraying (p<0.05). Nevertheless, the linear component of wind speed and the interaction between wind speed and spraying angle had no significant effect on the changes in the uniformity coefficient of the total spray. According to the variance analysis table (F-values), spraying distance has a far greater effect on the spraying uniformity coefficient than the spraying angle.
It has been observed that the spraying uniformity coefficient will increase by increasing the spraying distance and decreasing the spraying angle. It can also be stated that the linear components of spraying angle and spraying distance, the interaction component of spraying angle × spraying distance, and the square power of the components of spraying distance and wind speed have a significant effect on surface coverage. The values of R², Adj-R², CV, and PRESS for the model adapted to the test data of leaf surface coverage percentage were obtained as 0.9929, 0.9865, 4.87%, and 188.61, respectively.
Among the three input variables, the spraying distance has the greatest effect on the coverage of water-sensitive papers. At larger spraying angles, especially 90 degrees, the coverage decreased with the increasing distance. At spray angle of 90 degrees, by increasing the distance from 2 to 4 m, the spray uniformity coefficient increased from 1.18 at a wind speed of 2.5 m s^-1 to 1.84 at a wind speed of 3.5 m s^-1. However, at smaller spraying angles (for example zero-degree angle), at first, the spraying coverage increases with the increase of the spraying distance from 2 to 3 m and then sharply decreases afterward. According to the contours of spray coverage, in the spray distance range of 4 to 6 m and regardless of wind speed, the spray coverage does not vary with the increase of the spraying angle (p< 0.05). Meanwhile, in the spray distance range of 2 to 4 m, with the increase of the spraying angle, the spraying coverage increases significantly (p<0.05). Overall, increasing the distance between the sprayer and the target decreased the surface coverage on the target, and in electrodynamic spraying, the uniformity of particle deposition on the underside of the target was relatively the same as on the upper side.
Conclusion
To improve the performance of the atomizer motorized knapsack sprayer, an electrodynamic spraying head was designed and built, and its performance was optimized using the response surface method (RSM) with a central composite design. During the research process, the influence of the independent parameters such as the distance between the nozzle and the target, the angle of the target position, and the wind speed on the variables including spraying uniformity, the percentage of the spraying coverage, and the percentage of changes in the total spraying coefficient were discussed and investigated. The results of the research led to the determination of the 3.5 m s^-1 wind speed, 2.5 m sprayer distance, and 90 degrees spraying angle with 0.792 desirability, which were considered as the optimal performance conditions of the electrodynamic spraying head. The results of laboratory validation for optimal conditions show that the uniformity of total spraying indicated by the total relative span factor (RSFT) and the percentage of spraying coverage (Cov) are equal to 1.65 and 28.27%, respectively.

Keywords

• Electrodynamic head
• Response surface method
• Spraying angle
• Sprayer
• Uniformity coefficient
• Wind speed

Main Subjects

• Design and Construction

©2023 The author(s). This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0).

References

1. Ahmad, F., Khaliq, A., Qiu, B., & Sultan, M. (2021). Advancements of spraying technology in agriculture. In book: Technology in Agriculture (pp.19). Publisher: IntechOpen Limited, London, UK. https://doi.org/10.5772/intechopen.98500
2. Amirshaghaghi, F., & Safari, M. (2016). Comparison and technical evaluation of electrostatic, micronair and tractor mounted lance sprayers in order to control (Carpocasa pomonella ) in apple orchards. Journal of Agricultural Machinery, 6(2), 376-383. (in Persian with English abstract). https://doi.org/10.22067/jam.v6i2.36084
3. Asaei, H., Jafari, A., & Loghavi, M. (2016). Development and evaluation of a targeted orchard sprayer using machine vision technology. Journal of Agricultural Machinery, 6(2), 362-375. (in Persian with English abstract). https://doi.org/10.22067/jam.v6i2.37220
4. Bayvel, L. P., & Orzechowski, Z. (1993). Liquid atomization. Taylor & Francis, Washington, DC.462P.
5. Behzadi Pour, F., Ghasemi Nejad Raeeni, M., Asoodar, M. A., Marzban, A., & Abdanan Mehdizadeh, S. (2017). Study of the operational parameters of crops turbine sprayer (turbo liner) on spray quality and diameter of droplets, using image processing. Journal of Agricultural Machinery, 7(1), 61-72. (in Persian with English abstract). https://doi.org/10.22067/jam.v7i1.48194
6. Cunha, M., Carvalho, C., & Marcal, A. R. S. (2012). Assessing the ability of image processing software to analysis spray quality on water-sensitive papers used as artificial targets. Biosystems Engineering, 3(1), 11-23. https://doi.org/10.1016/j.biosystemseng.2011.10.002
7. Derringer, G., & Suich, R. (1980) (Published online: 22 Feb 2018). Simultaneous optimization of several response variables. Journal of Quality Technology, 12(4), 214-219. https://doi.org/10.1080/00224065.1980.11980968 (accessed 30-01, 2023).
8. Farooq, M., Walker, T. W., Heintschel, B. P., & English, T. (2010). Impact of electrostatic and conventional sprayers characteristics on dispersion of barrier spray. Journal of the American Mosquito Control Association, 26(4), 422-429. https://doi.org/10.2987/09-5891.1
9. Ferguson, J. C., O’Donnell, Ch. C., Chauhan, B. S., Adkins, S. W., Kruger, G. R., Wang, R., Ferreira, P. H. U., & Hewitt, A. J. (2015). Determining the uniformity and consistency of droplet size across spray drift reducing nozzles in a wind tunnel. Crop Protection, 76, 1-6. https://doi.org/10.1016/j.cropro.2015.06.008
10. Haji Agha Alizadeh, H., Pourvosoughi Gregari, H., & Bakhtiari, A. A. (2018). Evaluation of the functional factors of electrostatic spraying on the top and back surfaces of leaves, using image processing.Iranian Journal of Biosystem Engineering, 47(1), 39-49. https://doi.org/10.22059/ijbse.2016.58476
11. Halliday, D., Resnick, R., & Walker, J. (2010). Fundamentals of Physics, 9^th Edition, 1136P.
12. Hoffmann, W. C., & Hewitt, A. J. (2005). Comparison of three imaging systems for water sensitive papers. American Society of Agricultural Engineers, 21(6), 961-964. https://doi.org/10.13031/2013.20026
13. Harrington, E. C. (1965). The Desirability Function. Industrial Quality Control, 21, 494-498.
14. Indu Baghel, A. S., Bhardwaj, A., & Ibrahim, W. (2022). Optimization of pesticides spray on crops in agriculture using machine learning. Computational Intelligence and Neuroscience, Article ID 9408535, 10 pages. https://doi.org/10.1155/2022/9408535
15. Jahannama, M. R., & Salehi, H. (2011). Patterns of spray attraction and deposition due to electrical charging. Sharif Mechanical Engineering Journal, 3-27(1), 3-14. (in Persian with English abstract).
16. Kathleen, M., Carley, N., Kamneva, Y., & Reminga, J. (2004). Response surface methodology. CASOS. Technical Report. CMU-ISRI-04, 136P.
17. Kumar Narang, M., Mishra, A., Kumar, V., Singh Thakur, S., & Singh, M. (2015). Comparative evaluation of spraying technology in cotton belt of Punjab (India). Journal of Agricultural Engineering, 1, 61-71.
18. Matthews, G., Bateman, A., & Miller, P. (2014). Pesticide Application Methods, 4^th John Wiley and Sons, Ltd. 536 P.
19. Mahmoudi, F., Heidarbeigi, K., & Azizpanah, A. (2019). Evaluation of the effect of pressure and wind speed on the amount of drift through the image processing method. Iranian Journal of Biosystem Engineering, 50, 213-221. (in Persian with English abstract). https://doi.org/10.22059/ijbse.2018.262469.665076
20. Mozafari, M. (2010). Technical and economic investigation and comparison of the performance of sprayers with different mechanisms and their effect on onion thrips control. Agricultural Engineering Research Institute, Karaj, Iran. 46P. (In Persian).
21. McNearney, E. J., & Hons, B. E. (2020). Analysis of droplet-target interactions in electrostatically charged spraying systems. A Thesis of Master of Engineering, in Electrical and Electronic Engineering, University of Canterbury, Christchurch, New Zealand. 94 P.
22. Mishra, P. K., Singh, M., Sharma, A., Sharma, K., & Singh, B. (2014). Studies on effect of electrostatic spraying in orchards. Agricultural Engineering International, CIGR Journal, 16(3), 60-69.
23. Mostafaei Minagh, B., Ghobadian, B., & Jahannama, M. R. (2008). Design and development of a greenhouse electrostatic sprayer and evaluation. Journal of Agricultural Science, 18(1), 229-242. (in Persian with English abstract).
24. Mousavi, A. M., & Baradaran Motie, J. (2021).Design of an electrostatic attachment set for use in drone sprayers. 13^th National Congress on Biosystems Engineering and Agricultural Mechanization, Tehran, Iran.
25. Patel, M. K. (2016). Technological improvements in electrostatic spraying and its impact to agriculture during the last decade and future research perspectives– A review. Engineering in Agriculture, Environment and Food, 9(1), 92-100. https://doi.org/10.1016/j.eaef.2015.09.006
26. Moltó, E., Chueca, P., Garcerá, C., Balsari, P., Gil, E., & van de Zande, J. C. (2017). Engineering approaches for reducing spray drift. Biosystems Engineering, 154, 1-2. https://doi.org/10.1016/j.biosystemseng.2017.01.002
27. Sasaki, S. R., Teixeira, M. M., Fernandes, H. C., Monterio, P. M. B., Rodrigues, D. E., & Alvarenga, C. B. (2012). Effect of space on droplets electrical charge during electrostatic spraying. International Conference of Agricultural Engineering- CIGR-AgEng: agriculture and engineering for a healthier life, Valencia, Spain, 8-12 July 2012.
28. Sayınci, B., Bastaban, S., & Sánchez-Hermosilla, J. (2012). Determination of optimal spot roundness variation interval for droplet size analysis on water sensitive paper. Journal of Agricultural Science and Technology, 14, 285-298. https://dorl.net/dor/20.1001.1.16807073.2012.14.2.11.3
29. Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., Dawiec, A., & Pokomeda, K. (2014). Application of response surface methodology and artificial neural network methods in modeling and optimization of biosorption process. Bioresource Technology, 160, 150-160. https://doi.org/1016/j.biortech.2014.01.021
30. Yang, Z., Niu, M., Li, J., Xu, X., Xu, J., & Chen, Z. (2015). Design and experiment of an electrostatic sprayer with online mixing system for orchard. Transactions of the Chinese Society of Agricultural Engineering, 31, 60-67. https://doi/org/10.11975/j.issn.1002-6819.2015.21.008
31. Zhang, Y. L., Lian, Q., & Zhang, W. (2017). Design and test of a six-rotor unmanned aerial vehicle (UAV) electrostatic spraying system for crop protection. International Journal of Agricultural and Biological Engineering, 10, 68-76. https://doi.org/10.25165/j.ijabe.20171006.3460
32. Zhao, S., Castle, G. S. P., & Adamiak, K. (2008). Factors affecting deposition in electrostatic pesticide spraying. Journal of Electrostatics, 66(11-12), 594-601. https://doi.org/10.1016/j.elstat.2008.06.009