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Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials

Iranmanesh, Reza ; Pourahmad, Afham ; Faress, Fardad ; Tutunchian, Sevil ; Ariana, Mohammad Amin ; Sadeqi, Hamed ; Hosseini, Saleh ; Alobaid, Falah ; Aghel, Babak (2022)
Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials.
In: Molecules, 2022, 27 (19)
doi: 10.26083/tuprints-00022842
Artikel, Zweitveröffentlichung, Verlagsversion

Kurzbeschreibung (Abstract)

This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was more straightforward than the five-parameter correlation and provided better predictions (AARD = 0.98%). The proposed three-parameter correlation predicted the heat capacity of four different biomass classes with residual errors between −0.02 to 0.02 J/g∙K. The literature related biomass Cp to temperature using quadratic and linear correlations, and ignored the effect of the chemistry of the samples. These quadratic and linear correlations predicted the biomass Cp of the available database with an AARD of 39.19% and 1.29%, respectively. Our proposed model was the first work incorporating sample chemistry in biomass Cp estimation.

Typ des Eintrags: Artikel
Erschienen: 2022
Autor(en): Iranmanesh, Reza ; Pourahmad, Afham ; Faress, Fardad ; Tutunchian, Sevil ; Ariana, Mohammad Amin ; Sadeqi, Hamed ; Hosseini, Saleh ; Alobaid, Falah ; Aghel, Babak
Art des Eintrags: Zweitveröffentlichung
Titel: Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials
Sprache: Englisch
Publikationsjahr: 2022
Ort: Darmstadt
Publikationsdatum der Erstveröffentlichung: 2022
Verlag: MDPI
Titel der Zeitschrift, Zeitung oder Schriftenreihe: Molecules
Jahrgang/Volume einer Zeitschrift: 27
(Heft-)Nummer: 19
Kollation: 12 Seiten
DOI: 10.26083/tuprints-00022842
URL / URN: https://tuprints.ulb.tu-darmstadt.de/22842
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Herkunft: Zweitveröffentlichung DeepGreen
Kurzbeschreibung (Abstract):

This study correlated biomass heat capacity (Cp) with the chemistry (sulfur and ash content), crystallinity index, and temperature of various samples. A five-parameter linear correlation predicted 576 biomass Cp samples from four different origins with the absolute average relative deviation (AARD%) of ~1.1%. The proportional reduction in error (REE) approved that ash and sulfur contents only enlarge the correlation and have little effect on the accuracy. Furthermore, the REE showed that the temperature effect on biomass heat capacity was stronger than on the crystallinity index. Consequently, a new three-parameter correlation utilizing crystallinity index and temperature was developed. This model was more straightforward than the five-parameter correlation and provided better predictions (AARD = 0.98%). The proposed three-parameter correlation predicted the heat capacity of four different biomass classes with residual errors between −0.02 to 0.02 J/g∙K. The literature related biomass Cp to temperature using quadratic and linear correlations, and ignored the effect of the chemistry of the samples. These quadratic and linear correlations predicted the biomass Cp of the available database with an AARD of 39.19% and 1.29%, respectively. Our proposed model was the first work incorporating sample chemistry in biomass Cp estimation.

Freie Schlagworte: biomass sample, heat capacity, empirical correlation, biomass crystallinity, feature reduction
Status: Verlagsversion
URN: urn:nbn:de:tuda-tuprints-228420
Zusätzliche Informationen:

This article belongs to the Special Issue Sustainable Development and Application of Renewable Chemicals from Biomass and Waste

Sachgruppe der Dewey Dezimalklassifikatin (DDC): 500 Naturwissenschaften und Mathematik > 540 Chemie
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: 07 Nov 2022 12:29
Letzte Änderung: 08 Nov 2022 06:10
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