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

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Genetic programming Heat of combustion Mean squared error Exploit Municipal solid waste Regression analysis Gene expression programming Empirical modelling Computer science Statistics Engineering Mathematics Waste management Machine learning Combustion Simulation Chemistry Organic chemistry Computer security

Imane Boumanchar , Younes Chhiti , Fatima E.M. Alaoui , Abdelaziz Sahibed-Dine , Fouad Bentiss , Charafeddine Jama , M. Bensitel ·

YOU? · · 2018 · Open Access · · DOI: https://doi.org/10.1177/0734242x18816797 · OA: W2904875176

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).