Generalizability of empirical correlations for predicting higher heating values of biomass

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dc.authoridGulec, Fatih/0000-0001-9045-4281
dc.contributor.authorDaskin, Mahmut
dc.contributor.authorErdogan, Ahmet
dc.contributor.authorGulec, Fatih
dc.contributor.authorOkolie, Jude A.
dc.date.accessioned2024-08-04T20:55:55Z
dc.date.available2024-08-04T20:55:55Z
dc.date.issued2024
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDesigning efficient biomass energy systems requires a thorough understanding of the physicochemical, thermodynamic, and physical properties of biomass. One crucial parameter in assessing biomass energy potential is the higher heating value (HHV), which quantifies its energy content. Conventionally, HHV is determined through bomb calorimetry, but this method is limited by factors such as time, accessibility, and cost. To overcome these limitations, researchers have proposed a diverse range of empirical correlations and machine-learning approaches to predict the HHV of biomass based on proximate and ultimate analysis results. The novelty of this research is to explore the universal applicability of the developed empirical correlations for predicting the Higher Heating Value (HHV) of biomass. To identify the best empirical correlations, nearly 400 different biomass feedstocks were comprehensively tested with 45 different empirical correlations developed to use ultimate analysis (21 different empirical correlations), proximate analysis (16 different empirical correlations) and combined ultimate-proximate analysis (8 different empirical correlations) data of these biomass feedstocks. A quantitative and statistical analysis was conducted to assess the performance of these empirical correlations and their applicability to diverse biomass types. The results demonstrated that the empirical correlations utilizing ultimate analysis data provided more accurate predictions of HHV compared to those based on proximate analysis or combined data. Two specific empirical correlations including coefficients for each element (C, H, N) and their interactions (C*H) demonstrate the best HHV prediction with the lowest MAE (similar to 0.49), RMSE (similar to 0.64), and MAPE (similar to 2.70%). Furthermore, some other empirical correlations with carbon content being the major determinant also provide good HHV prediction from a statistical point of view; MAE (similar to 0.5-0.8), RMSE (similar to 0.6-0.9), and MAPE (similar to 2.8-3.8%).en_US
dc.identifier.doi10.1080/15567036.2024.2332472
dc.identifier.endpage5450en_US
dc.identifier.issn1556-7036
dc.identifier.issn1556-7230
dc.identifier.issue1en_US
dc.identifier.scopus2-s2.0-85190064949en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage5434en_US
dc.identifier.urihttps://doi.org/10.1080/15567036.2024.2332472
dc.identifier.urihttps://hdl.handle.net/11616/101927
dc.identifier.volume46en_US
dc.identifier.wosWOS:001201385400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherTaylor & Francis Incen_US
dc.relation.ispartofEnergy Sources Part A-Recovery Utilization and Environmental Effectsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectBiomassen_US
dc.subjecthigher heating valueen_US
dc.subjectultimate analysisen_US
dc.subjectproximate analysisen_US
dc.subjectHHV predictionen_US
dc.titleGeneralizability of empirical correlations for predicting higher heating values of biomassen_US
dc.typeArticleen_US

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