Abstract
Gieseler fluidity provides thermoplastic information and the compatibility of blended coals for the cokemaking. A novel soft computing method, random forest (RF), for prediction of the softening temperature (Ts), the temperature of maximum fluidity (Tf), resolidification temperature (Tr) and maximum fluidity (MF) [Gieseler parameters (Gp)] was conducted based on the coal proximate analysis. Variable importance measurements were performed by RF to select the most effective variables for the prediction of Gp. Selected variables have been used as an input set of RF model for the modelling and prediction. Results of models indicated that RF can provide a satisfactory prediction of Gp with the correlation of determination R2: 0.64, 0.82, 0.90, and 0.86 for Ts, Tf, Tr and MF, respectively. Based on these results, it can be proposed that RF as a reliable non-parametric reliable predictive tool can be used for modelling of complex relationships in the fuel and energy investigations. [Received: January 28, 2017; Accepted: March 28, 2017]
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