TU Darmstadt / ULB / TUprints

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

Aghel, Babak ; Yahya, Salah I. ; Rezaei, Abbas ; Alobaid, Falah (2023)
A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass.
In: International Journal of Molecular Sciences, 2023, 24 (6)
doi: 10.26083/tuprints-00023649
Article, Secondary publication, Publisher's Version

[img] Text
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (4MB)
Item Type: Article
Type of entry: Secondary publication
Title: A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass
Language: English
Date: 11 April 2023
Place of Publication: Darmstadt
Year of primary publication: 2023
Publisher: MDPI
Journal or Publication Title: International Journal of Molecular Sciences
Volume of the journal: 24
Issue Number: 6
Collation: 13 Seiten
DOI: 10.26083/tuprints-00023649
Corresponding Links:
Origin: Secondary publication DeepGreen

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.

Uncontrolled Keywords: biomass sample, higher heating value, Elman neural network, topology tuning, training algorithm
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-236497
Additional Information:

This article belongs to the Collection Feature Papers in Physical Chemistry and Chemical Physics

Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institut für Energiesysteme und Energietechnik (EST)
Date Deposited: 11 Apr 2023 11:37
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/23649
PPN: 509109047
Actions (login required)
View Item View Item