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

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@Article{Boumanchar:2018:WMR,

• author = "Imane Boumanchar and Younes Chhiti and Fatima Ezzahrae M'hamdi Alaoui and Abdelaziz Sahibed-Dine and Fouad Bentiss and Charafeddine Jama and Mohammed Bensitel",
• title = "Municipal solid waste higher heating value prediction from ultimate analysis using multiple regression and genetic programming techniques",
• journal = "Waste Management \& Research",
• year = "2018",
• volume = "37",
• number = "6",
• pages = "578--589",
• month = dec # "~19",
• keywords = "genetic algorithms, genetic programming, energy, higher heating value, multiple regression, municipal solid waste, prediction, chemical sciences/material chemistry, chemical sciences/polymers",
• bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
• contributor = "Unite Materiaux et Transformations - UMR 8207",
• description = "International audience",
• identifier = "hal-02922402; DOI: 10.1177/0734242x18816797; WOS: 000474405200003",
• language = "en",
• oai = "oai:HAL:hal-02922402v1",
• relation = "info:eu-repo/semantics/altIdentifier/doi/10.1177/0734242x18816797",
• URL = "https://hal.univ-lille.fr/hal-02922402",
• DOI = "doi:10.1177/0734242x18816797",
• 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).",
• annote = "Universite Chouaib Doukkali (UCD); Unite Materiaux et Transformations - UMR 8207 (UMET) ; Centre National de la Recherche Scientifique (CNRS)-Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Superieure de Chimie de Lille (ENSCL)-Universite de Lille",

}

Genetic Programming entries for Imane Boumanchar Younes Chhiti Fatima Ezzahrae M'hamdi Alaoui Abdelaziz Sahibed-Dine Fouad Bentiss Charafeddine Jama Mohammed Bensitel

Citations