Artificial Neural Network as a Tool for Estimation of the Higher Heating Value of Miscanthus Based on Ultimate Analysis

Abstract

Miscanthus is a perennial energy crop that produces high yields and has the potential to be converted into energy. The ultimate analysis determines the composition of the biomass and the energy value in terms of the higher heating value (HHV), which is the most important parameter in determining the quality of the fuel. In this study, an artificial neural network (ANN) model based on the principle of supervised learning was developed to predict the HHV of miscanthus biomass. The developed ANN model was compared with the models of predictive regression models (suggested from the literature) and the accuracy of the developed model was determined by the coefficient of determination. The paper presents data from 192 miscanthus biomass samples based on ultimate analysis and HHV. The developed model showed good properties and the possibility of prediction with high accuracy (R2 = 0.77). The paper proves the possibility of using ANN models in practical application in determining fuel properties of biomass energy crops and greater accuracy in predicting HHV than the regression models offered in the literature.

Prediction of Specific Fuel Consumption of a Tractor during the Tillage Process Using an Artificial Neural Network Method

“…The developed ANN model training speed was 85,761 K after being trained 100,000 times. During the ANN learning cycle, the network data were trained in order to ascertain the number of neurons and modify the weight coefficients in each neuron [43]. The final network included one hidden layer that included 22 neurons, 9 neurons for the input layer, and 1 neuron for the output layer, and the activation function was sigmoid.…”

“…The training error was 0.020675, which was achieved after 7295 iterations, and the sigmoid transfer function with training mode standards is shown in Figure 3. The matrices and vectors W1 and B1, and W2 and B2, respectively (Equation ( 7)), indicate the biases and weight coefficients associated with the ANN model's hidden and output layers [43]. Matrix notation can be used to depict the ANN model.…”

“…Matrix notation can be used to depict the ANN model. Equation ( 7) is used for computing the neural network's output data [43]:…”

“…where X is the input layer matrix; Y is the output value; and f1 and f2 are the transfer or The matrices and vectors W1 and B1, and W2 and B2, respectively (Equation ( 7)), indicate the biases and weight coefficients associated with the ANN model's hidden and output layers [43]. Matrix notation can be used to depict the ANN model.…”

Modification of Values for the Horizontal Force of Tillage Implements Estimated from the ASABE Form Using an Artificial Neural Network

“…The created ANN model can be assessed using multiple criteria by comparing the model predictions to the measured values in the testing, training, and validation datasets. The root mean square error (RMSE) and mean absolute error (MAE) are two examples of these criteria [64]. The measured and predicted values are visually compared using scatter plots.…”