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, 24 (6)
doi: 10.3390/ijms24065780
Artikel, Bibliographie
Dies ist die neueste Version dieses Eintrags.
Kurzbeschreibung (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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2023 |
| Autor(en): | Aghel, Babak ; Yahya, Salah I. ; Rezaei, Abbas ; Alobaid, Falah |
| Art des Eintrags: | Bibliographie |
| Titel: | A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Molecular Sciences |
| Jahrgang/Volume einer Zeitschrift: | 24 |
| (Heft-)Nummer: | 6 |
| Kollation: | 13 Seiten |
| DOI: | 10.3390/ijms24065780 |
| Zugehörige Links: | |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | biomass sample, higher heating value, Elman neural network, topology tuning, training algorithm |
| Zusätzliche Informationen: | This article belongs to the Collection Feature Papers in Physical Chemistry and Chemical Physics |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Institut für Energiesysteme und Energietechnik (EST) |
| Hinterlegungsdatum: | 02 Aug 2024 12:51 |
| Letzte Änderung: | 02 Aug 2024 12:51 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
-
A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass. (deposited 11 Apr 2023 11:37)
- A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass. (deposited 02 Aug 2024 12:51) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum