A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass

Abstract

The higher heating value (HHV) is the main property showing the energy amount of biomass samples. Several linear correlations based on either the proximate or the ultimate analysis have already been proposed for predicting biomass HHV. Since the HHV relationship with the proximate and ultimate analyses is not linear, nonlinear models might be a better alternative. Accordingly, this study employed the Elman recurrent neural network (ENN) to anticipate the HHV of different biomass samples from both the ultimate and proximate compositional analyses as the model inputs. The number of hidden neurons and the training algorithm were determined in such a way that the ENN model showed the highest prediction and generalization accuracy. The single hidden layer ENN with only four nodes, trained by the Levenberg–Marquardt algorithm, was identified as the most accurate model. The proposed ENN exhibited reliable prediction and generalization performance for estimating 532 experimental HHVs with a low mean absolute error of 0.67 and a mean square error of 0.96. In addition, the proposed ENN model provides a ground to clearly understand the dependency of the HHV on the fixed carbon, volatile matter, ash, carbon, hydrogen, nitrogen, oxygen, and sulfur content of biomass feedstocks.

Applying feature selection and machine learning techniques to estimate the biomass higher heating value

“…The subsequent sections comprehensively evaluate the MLPNN performance utilizing graphical and numerical analyses. In addition, the MLPNN accuracy will be compared with another model recently proposed in the literature 61 .…”

“…The literature recently applied recurrent neural networks (RNN) to predict biomass HHV from all proximate and ultimate compositional analyses 61 . Therefore, it is a good idea to compare the prediction accuracy of this RNN with the proposed MLPNN in the current study.…”

Biomass Higher Heating Value Estimation: A Comparative Analysis of Machine Learning Models

“…These results show that the ANN is the best model for predicting the value of the HHV variable. Aghel et al (2023) [6] reported the model accuracy for various developed ANN models as R 2 0.83-0.88 and MAE 0.66-0.85, while Afolabi et al (2022) [5] reported 1.21 for the ANN model and 1.01 for the RF model.…”

“…The higher heating value is used in the design of energy systems of different sizes and is considered an extremely important parameter for the processes [5]. Aghel et al (2023) [6] state that the relationship between the variables of biomass proximity analysis is not linear, so nonlinear modeling is a better alternative in finding a solution. In addition to the analysis of the HHV as a measure of fuel quality, proximate analysis can provide a detailed insight into the physicochemical composition of the biomass, which is of crucial importance for further modeling.…”

“…The developed method showed a high degree of accuracy (R 2 > 0.91). In the study conducted by Aghel et al (2023) [6], a dynamic Elman recurrent neural network (ENN) was developed to predict biomass HHV based on the input data from the terminal and proximate analysis. It was found that the ENN model built from a hidden layer with four nodes was the most accurate and had a relatively low modeling error.…”