Titre :

Waste-to-energy as a tool of circular economy: Prediction of higher heating value of biomass by artificial neural network (ANN) and multivariate linear regression (MLR)

Auteur(s) :

Ezzahra Yatim, Fatima [Auteur]
Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan] [UAE]
Boumanchar, Imane [Auteur]

Srhir, Bousalham [Auteur]
Université Ibn Tofaïl [UIT]
Chhiti, Younes [Auteur]
Université Mohammed VI Polytechnique = Mohammed VI Polytechnic University [Ben Guerir] [UM6P]
Université Ibn Tofaïl [UIT]
Jama, charafeddine [Auteur]
Unité Matériaux et Transformations (UMET) - UMR 8207
Ezzahrae M'hamdi Alaoui, Fatima [Auteur]
Abdelmalek Essaadi University [Tétouan] = Université Abdelmalek Essaadi [Tétouan] [UAE]

Titre de la revue :

Waste Management

Nom court de la revue :

Waste Management

Numéro :

153

Pagination :

293-303

Éditeur :

Elsevier BV

Date de publication :

2022-11

ISSN :

0956-053X

Mot(s)-clé(s) en anglais :

Artificial neural network
Biomass
Prediction models
Higher heating value
Linear regression
Waste-to-energy

Discipline(s) HAL :

Chimie/Matériaux
Chimie/Polymères

Résumé en anglais : [en]

Circular economy is a global trend as a promising strategy for the sustainable use of natural resources. In this context, waste-to-energy presents an effective solution to respond to the ever-increasing waste generation ...
Lire la suite >Circular economy is a global trend as a promising strategy for the sustainable use of natural resources. In this context, waste-to-energy presents an effective solution to respond to the ever-increasing waste generation and energy demand duality. However, waste diversity makes their management a serious challenge. Among their categories, biomass waste valorization is an attractive solution energy regarding its low cost and raw materials availability. Nevertheless, the knowledge of biomass waste characteristics, such as composition and energy content, is a necessity. In this research, new models are developed to estimate biomass wastes higher heating value (HHV) based on the ultimate analysis using linear regression and artificial neural network (ANN). The quality-measure of the two models for new dataset was evaluated with statistical metrics such as coefficient of correlation (R), root mean squared error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). The methods developed in this work provided attractive accuracies comparing to other literature models. Additionally, it is found that the ANN, as machine learning method, is the best model for biomass HHV prediction (R = 0.75377, RMSE = 1.17527, MAE = 0.93315 and MAPE = 5.73%). Therefore, obtained results can be widely employed to design and optimize the reactors of combustion. In fact, the developed ANN software is a simple and accurate tool for HHV estimation based on ultimate analysis. Indeed, ANN is one of the most applicable and widely used software in the field of waste-to-energy.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
CNRS
INRAE
ENSCL

Collections :

• Unité Matériaux et Transformations (UMET) - UMR 8207

Équipe(s) de recherche :

Procédés de Recyclage et de Fonctionnalisation (PReF)

Date de dépôt :

2022-10-17T10:14:17Z
2022-10-19T07:17:30Z