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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
Article, Secondary publication, Publisher's Version

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Item Type: Article
Type of entry: Secondary publication
Title: Introducing a Linear Empirical Correlation for Predicting the Mass Heat Capacity of Biomaterials
Language: English
Date: 7 November 2022
Place of Publication: Darmstadt
Year of primary publication: 2022
Publisher: MDPI
Journal or Publication Title: Molecules
Volume of the journal: 27
Issue Number: 19
Collation: 12 Seiten
DOI: 10.26083/tuprints-00022842
Corresponding Links:
Origin: Secondary publication DeepGreen

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.

Uncontrolled Keywords: biomass sample, heat capacity, empirical correlation, biomass crystallinity, feature reduction
Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-228420
Additional Information:

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

Classification DDC: 500 Science and mathematics > 540 Chemistry
600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Institut für Energiesysteme und Energietechnik (EST)
Date Deposited: 07 Nov 2022 12:29
Last Modified: 14 Nov 2023 19:05
SWORD Depositor: Deep Green
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/22842
PPN: 501637958
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