Higher heating value prediction of high ash gasification-residues: Comparison of white, grey, and black box models

Abstract

The measurement of the higher heating value (HHV) in high-ash solid waste poses persistent challenges due to the inherent limitations of using an oxygen bomb calorimeter. HHV predictive models based on the components analysis were proved to be useful methods. To improve the accuracy and applicability of HHV models, this work selected 100 actual measured data of high ash gasification residues, and compared three types of predictive models from white, grey, and black box by the following procedures: Modeling, External-validation, and Extending study. In the Modeling and External-validation processes, eight models from linear regression, grey model, and machine learning models, were proposed with R2 > 0.95 and MAPE<8.42 %. Compared to existing research, the models proposed in this study provide appealing accuracies. In the Extending study for applicability evaluation, the UA-based models UOGM (1, 5) (R2 = 0.86 and MAPE = 12.96 %) and RL-Ult (R2 = 0.89 and MAPE = 11.52 %) outperformed the other six models by using the eight-expanding data of different solid wastes that collected from the literature. Based on these results, the above two models were applied to predict the HHV of remaining 53 gasifier residues, showing reliable predicting results. This work shows that the developed models have high accuracy for the HHV prediction of high-ash solid wastes.