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
Artikel, Zweitveröffentlichung, Verlagsversion
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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: | Zweitveröffentlichung |
| Titel: | A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass |
| Sprache: | Englisch |
| Publikationsjahr: | 2023 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2023 |
| 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: | 10.26083/tuprints-00023649 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/23649 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| 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 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-236497 |
| 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: | 11 Apr 2023 11:37 |
| Letzte Änderung: | 12 Apr 2023 05:02 |
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| Suche nach Titel in: | TUfind oder in Google |
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- A Dynamic Recurrent Neural Network for Predicting Higher Heating Value of Biomass. (deposited 11 Apr 2023 11:37) [Gegenwärtig angezeigt]
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