Multiple regression and genetic programming for coal higher heating value estimation

ABSTRACT

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

KEYWORDS:

• Coal
• genetic programming
• higher heating value
• multiple regression
• prediction

Acknowledgments

Laboratory of catalysis and corrosion of materials (LCCM), science engineer laboratory for energy (LabSIPE) and Lille university are gratefully thanked.

Disclosure statement

No potential conflict of interest was reported by the authors.