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Article dans une revue

Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming

Abstract : The higher heating value (HHV) is a significant parameter for the determination of fuel quality. However, its measurement is time-consuming and requires sophisticated equipment. For this reason, several researches have been interested to develop mathematical models for the prediction of HHV from fundamental composition. The purpose of this study is to develop new correlations to determine the biomass HHV from ultimate analysis. As a result, two models were elaborated. The first was developed using multiple variable regression analysis while the second has adopted genetic programming formalism. Data of 171 from various types of biomass samples were randomly used for the development (75%) and the validation (25%) of new equations. The accuracy of the established models was compared to previous literature works in terms of correlation coefficient (CC), average absolute error (AAE), and average bias error (ABE). The proposed models were more performing with the highest CC and the smallest errors.
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https://hal.univ-lille.fr/hal-02923573
Contributeur : Lilloa Université de Lille <>
Soumis le : jeudi 27 août 2020 - 11:32:28
Dernière modification le : mardi 13 octobre 2020 - 13:29:17

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Imane Boumanchar, Kenza Charafeddine, Younes Chhiti, Fatima Ezzahrae M’hamdi Alaoui, Abdelaziz Sahibed-Dine, et al.. Biomass higher heating value prediction from ultimate analysis using multiple regression and genetic programming. Biomass Conversion and Biorefinery, Springer, 2019, Biomass Conversion and Biorefinery, 9 (3), pp.499-509. ⟨10.1007/s13399-019-00386-5⟩. ⟨hal-02923573⟩

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