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Article dans une revue

Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques

Abstract : Municipal solid waste (MSW) management presents an important challenge for all countries. In order to exploit them as a source of energy, a knowledge of their calorific value is essential. In fact, it can be experimentally measured by an oxygen bomb calorimeter. This process is, however, expensive. In this light, the purpose of this paper was to develop empirical models for the prediction of MSW higher heating value (HHV) from ultimate analysis. Two methods were used: multiple regression analysis and genetic programming formalism. Both techniques gave good results. Genetic programming, however, provides more accuracy compared to published works in terms of a great correlation coefficient (CC) and a low root mean square error (RMSE).
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https://hal.univ-lille.fr/hal-02922402
Contributeur : Lilloa Université de Lille <>
Soumis le : mercredi 26 août 2020 - 10:43:21
Dernière modification le : jeudi 27 août 2020 - 03:24:32

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Imane Boumanchar, Younes Chhiti, Fatima Ezzahrae M’hamdi Alaoui, Abdelaziz Sahibed-Dine, Fouad Bentiss, et al.. Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques. WASTE MANAGEMENT & RESEARCH, 2018, Waste Management & Research, 37 (6), pp.578-589. ⟨10.1177/0734242x18816797⟩. ⟨hal-02922402⟩

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