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

Aghel, Babak and Yahya, Salah I. and Rezaei, Abbas and Alobaid, Falah (2023) A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass. International Journal of Molecular Sciences, 24 (6). p. 5780. ISSN 1422-0067

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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.

Item Type:	Article
Uncontrolled Keywords:	biomass sample; higher heating value; Elman neural network; topology tuning; training algorithm
Subjects:	Q Science > QA Mathematics > QA76 Computer software
Divisions:	Faculty of Engineering > Department of Software Engineering
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	08 May 2023 10:15
Last Modified:	08 May 2023 10:15
URI:	http://eprints.koyauniversity.org/id/eprint/366

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