Affiliation:
1. School of Electromechanical Engineering, Henan University of Technology, Zhengzhou 450001, China
2. Henan Ancai Hi-Tech Limited Liability Company, Anyang 455000, China
3. School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China
Abstract
The main causes of high productional costs and greenhouse gas emissions in the chopping process of maize straws are high energy consumption and breaking force. Addressing these issues, this paper proposes a solution that leverages machine-learning algorithms to select appropriate operational parameters for chopping devices, thereby reducing energy consumption and the cutting force. In this study, the peak breaking force of the stalk (PB), the energy consumption of the stalk chopping (EC) and the slide-cutting momentum of the disc blade (SM) were set as dependent variables, and the rotation speed of the Y-type blade (RSY), transmission ratio (TR) and slide-cutting angle (SA) were set as independent variables. Various techniques, including back-propagation (BP), a radial basis function (RBF), an artificial neural network (ANN), support vector regression and a stepwise polynomial regression model, were applied using a 6-fold cross-validation approach to determine the most effective predictive models. The results indicated that the BP-ANN model performs best in predicting the PB (R2Test = 0.9860) and SM (R2Test = 0.9561), while the RBF-ANN model yields the highest accuracy in predicting the EC (R2Test = 0.9255) under the optimal parameters. Subsequently, a verification test was conducted using randomly selected training and testing data based on the selected predicted functions. The results demonstrated that the R2Train and R2Test data for PB, EC and SM are all above 0.95, indicating that the BP and RBF neural networks are capable of accurately predicting the nonlinear relationship between the dependent variables (EC, SM and PB) and independent variables (RSY, TR and SA) in practical applications.
Funder
Science and Technology Research Project of Henan Province
Scientific Research Foundation for Advanced Talents of Henan University of Technology
Cultivation Programme for Young Backbone Teachers in Henan University of Technology
Training Plan of Young Backbone Teachers in Colleges and Universities in Henan Province
Opening Subject of Henan Key Laboratory of Grain and Oil Storage Construction and Safety
Subject
Agronomy and Crop Science