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

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Iranmanesh R., Pourahmad A., Faress F., Tutunchian S., Ariana M. A., Sadeqi H., ...More

MOLECULES, vol.27, no.19, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 27 Issue: 19
  • Publication Date: 2022
  • Doi Number: 10.3390/molecules27196540
  • Journal Name: MOLECULES
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Biotechnology Research Abstracts, CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, EMBASE, Food Science & Technology Abstracts, MEDLINE, Metadex, Veterinary Science Database, Directory of Open Access Journals, Civil Engineering Abstracts
  • Keywords: biomass sample, heat capacity, empirical correlation, biomass crystallinity, feature reduction, CO2 CAPTURE, ENERGY, CONFIGURATION, CARBONS
  • Istanbul Technical University Affiliated: Yes


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 similar to 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.