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

Autor: Aghel, Reza Iranmanesh, Afham Pourahmad, Fardad Faress, Sevil Tutunchian, Mohammad Amin Ariana, Hamed Sadeqi, Saleh Hosseini, Falah Alobaid, Babak
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Molecules; Volume 27; Issue 19; Pages: 6540
ISSN: 1420-3049
DOI: 10.3390/molecules27196540
Popis: 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.
Databáze: OpenAIRE
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