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8 results on '"Bousalham Srhir"'

2. 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)

Author

Fatima Ezzahra Yatim, Imane Boumanchar, Bousalham Srhir, Younes Chhiti, Charafeddine Jama, Fatima Ezzahrae M'hamdi Alaoui, Abdelmalek Essaadi University [Tétouan] (UAE), University Hassan II [Casablanca], Université Ibn Tofaïl (UIT), Université Mohammed VI Polytechnique [Ben Guerir] (UM6P), Unité Matériaux et Transformations - UMR 8207 (UMET), and Centrale Lille-Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)

Subjects
Artificial neural network, Heating, Physical Phenomena, [CHIM.POLY]Chemical Sciences/Polymers, Waste-to-energy, Higher heating value, Linear Models, [CHIM.MATE]Chemical Sciences/Material chemistry, Biomass, Neural Networks, Computer, Linear regression, Prediction models, Waste Management and Disposal
Abstract

International audience; 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.

Published
2022

4. Kinetic and Thermodynamic Studies, Mathematical Modeling of Experimental Data, and Investigation of Lead (Ii) in the Treatment of Synthetic Wastewater Using the Cationic Resin Am®Irc-50

Author

bensalah, jaouad, primary, Othmane, Hammani, additional, Abdelhay, El Amri, additional, Abdelkarim, Ouass, additional, Youness, Essaadaoui, additional, Hanae, Ouaddari, additional, Ahmed, Lebkiri, additional, Bousalham, Srhir, additional, Abdelkader, Zarrouk, additional, and El Housseine, Rifi, additional

Published
2022
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