Analysis of optimum drop size distribution of greenhouse air-assisted mist sprayer by using response surface method and genetic algorithm
Zhong Liping1,2, Qi Lijun1,3, Ji Ronghua4, Yuan Xue5, Mei Yincheng1, Gao Chunhua1
1.College of Engineering, China Agricultural University, Beijing 100083, China; 2.College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, Guangxi 541004, China; 3. Beijing Key Laboratory of Optimization Design of Modern Agricultural Equipment, Beijing 100083, China; 4.College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China; 5.Chinese Academy of Agricultural Sciences, Beijing 100083, China
In order to clarify the influences of operational parameters on drop size distribution issued by a greenhouse air-assisted mist sprayer, a series of spraying experiments are conducted, then two response surface models about the drop size distribution coefficient are correlated to the operational parameters by using quadratic polynomial regression and BP neural network method to obtain the optimum operational parameters combination. It is shown that the correlation coefficient and root-mean-square error are 0.987 1 and 0.134 0 respectively for the BP neural network model, compared with 0.928 2 and 0.215 9 for the response surface model established by the quadratic polynomial regression, suggesting the former is the best model. According to the quadratic polynomial response surface model, the minimum drop size distribution coefficient is as high as 1.47%, compared with 1.58% experimental measurement. Based on the BP neural network model, however, the minimum drop size distribution coefficient predicted by genetic algorithm is just 1.21% against 1.28% experimental observation. Ob-viously, the BP neural network method combined with genetic algorithm exhibits a better accuracy than the response surface method in the operational parameters optimization of air-assisted mist sprayer.
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