Details

Autor(en) / Beteiligte

• Boumanchar, Imane
• Chhiti, Younes
• M'Hamdi Alaoui, Fatima Ezzahrae
• Sahibed-Dine, Abdelaziz
• Bentiss, Fouad
• Jama, Charafeddine
• Bensitel, Mohammed

Titel

Multiple regression and genetic programming for coal higher heating value estimation

Ist Teil von

• International journal of green energy, 2018-12, Vol.15 (14-15), p.958-964

Ort / Verlag

Taylor & Francis

Erscheinungsjahr

2018

Quelle

Taylor & Francis 数据库

Beschreibungen/Notizen

• The higher heating value (HHV) is an important characteristic for the determination of fuels quality. Nevertheless, its experimental measurement requires intricate technologies. In this work, the HHV of coal was predicted from ultimate composition using two methods: multiple regression and genetic programming. A dataset of 100 samples from literature was exploited (75% for training and 25% for testing). A comparative study was elaborated between the developed models and published ones in terms of correlation coefficient, root mean square error, and mean absolute percent error. The adopted models gave a good statistical performance. Abbreviations: C: Carbon; CC: Correlation coefficient; H: Hydrogen; HHV: Higher heating valueI; GT: Institute of gas technology; GP: Genetic programming; LHV: Lower heating value; MAPE: Mean absolute percent error; N: Nitrogen; O: Oxygen; RMSE: Root mean square error; S: sulfur; Wt: Weight percentage