Machine Learning for Prediction of Energy Consumption and Broken Force in the Chopping Process of Maize Straw

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.