Title :

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

Author(s) :

Boumanchar, Imane [Auteur]
Université Chouaib Doukkali [UCD]
Charafeddine, Kenza [Auteur]

Chhiti, Younes [Auteur]
Université Chouaib Doukkali [UCD]
M’hamdi Alaoui, Fatima Ezzahrae [Auteur]
Université Chouaib Doukkali [UCD]
Sahibed-dine, Abdelaziz [Auteur]
Université Chouaib Doukkali [UCD]
Bentiss, Fouad [Auteur]
Université Chouaib Doukkali [UCD]
Jama, charafeddine [Auteur]
Unité Matériaux et Transformations - UMR 8207 [UMET]
Bensitel, Mohammed [Auteur]
Université Chouaib Doukkali [UCD]

Journal title :

Biomass Conversion and Biorefinery

Abbreviated title :

Biomass Conv. Bioref.

Volume number :

9

Pages :

499-509

Publisher :

Society for Mining, Metallurgy and Exploration Inc.

Publication date :

2019-02-07

HAL domain(s) :

Chimie/Polymères
Chimie/Matériaux

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Administrative institution(s) :

Université de Lille
CNRS
INRA
ENSCL

Collections :

• Unité Matériaux et Transformations (UMET) - UMR 8207

Research team(s) :

Ingénierie des Systèmes Polymères

Submission date :

2020-07-09T17:16:55Z
2020-08-27T09:32:19Z