Parameter Optimization of Whole-Straw Returning Device Based on the Bp Neural Network

Abstract

To solve the poor fitting degree of errors in multiobjective parameter optimization and low accuracy, a multiobjective optimization method based on a BP neural network was proposed. By taking the 1ZT-210 type whole-straw returning device as the research object, a BP neural network model on power consumption, straw returning rate and the influencing factors was obtained. By optimizing the model by the proposed method, the optimal parameter combination of the test factors was as follows: the advancing speed of the device was 0.65 km/h, the blade roll rotating speed was 210 rpm, the blade installation angle was 55 o , the minimum power consumption was 9.82 kW and the maximum straw returning rate was 93.23%. Under such test conditions, the minimum power consumption was 10.75 kW, and the straw returning rate was 92.46%, which were all better than those obtained by the regression analysis method. Finally, a verification test was conducted on the results of BP neural network optimization. The power consumption of the test was 10.04 kW, the absolute error was 0.22 kW and the relative error was 2.24%. For a straw returning rate of 93.11%, the absolute error was -0.12% and the relative error was 0.13%. The test results indicated that the optimization method was feasible.

Optimization of extraction technology of alkaloids in lotus leaf based on BP neural network

“…The optimization research of two method all both establishes on the basic of function fitted. Theoretically, the fitting function with relatively small average errors is closer to the real function of the problem, and the obtained optimized results have a higher degree of accuracy (Dong et al, 2022).…”

“…The global optimization method for solving the optimal value of the lotus leaf alkaloid yield is as follows (Dong et al, 2021(Dong et al, , 2022.…”

“…The optimization method based on BP neural network is an optimization method proposed by the iterative optimization principle of mathematical programming method on the basis of BP neural network function fitting (Wang et al, 2017;Zhang et al, 2016;Zhao et al, 2020;Dong et al, 2022). This method provides a new idea and method for the optimization of the extraction process of lotus leaf alkaloids.…”

“…Dong et al (2018a) used this method to study the soybean planting model, and provided a set of optimal planting models for soybean planting in Shanhe Farm, which reduced soybean planting costs and improved soybean yield. Dong et al (2018bDong et al ( , 2022 improved the optimization method based on BP neural network and optimized the power consumption of the straw returning machine, and obtained the parameter combination of the minimum power consumption. Zhao et al (2018) used this method to optimize the parameters of the baling mechanism, and obtained the optimal combination of disc diameter, feed amount, steel roll speed, aspect ratio and minimum power consumption value, the optimization result was verified by experiments is correct and feasible.…”

Technological parameter optimization for walnut shell-kernel winnowing device based on neural network

“…At the same time, Michael Thomas Rex et al (2020) established the response surface method and artificial neural network prediction model by using the findings of finite element analysis and demonstrated the ability of the artificial neural network in modelling optimization. In order to address the poor fitting degree and low accuracy in multi-objective parameter optimization, Dong et al (2022) used a straw-returning device as the research object and obtained the optimal parameter combination of associated experimental factors through a neural network optimization model. Therefore, it is essential to apply neural networks having strong prediction ability and fast optimization capability for the optimization of parameters.…”