Swift model for a lower heating value prediction based on wet-based physical components of municipal solid waste

To establish an empirical model for predicting a lower heating value (LHV) easily and economically by multiple regression analysis. A wet-based physical components model (WBPCM) was developed and based on physical component analysis without dewatering. Based on 497 samples of municipal solid waste (MSW) gathered from 14 incinerators in western parts of Taiwan from 2002 to 2009. The proposed model was verified by independent samples from other incinerators through parameters multiple correlation coefficients (R), relative percentage deviation (RPD) and mean absolute percentage error (MAPE). Experimental results indicated that R, RPD and MAPE were 0.976, 17.1 and 17.7, respectively. This finding implies that LHV predicted by the WBPCM could well explain the LHV characteristics of MSW. The WBPCM was also compared with existing prediction models of LHV on a dry basis. While more accurately predicting LHV predicting than those models based on proximate analysis, the WBPCM was comparable with models based on physical component analysis in term of RPD and MAPE. Experimental results further indicated that the prediction accuracy of the WBPCM varied with MSW moisture parabolically. No specific relation was observed in the results of the previous prediction model. The accuracy of the WBPCM was almost approached to that of ultimate analysis in moisture ranging from 40% to 55%. The model was applicable within this moisture range. We conclude that the WBPCM is a faster and more economical model for LHV predictions with comparable accuracy than those models based on physical component analysis. The proposed WBPCM is highly promising for use in designing and operating incinerators.

► We develop a wet-based physical components model (WBPCM).
► Lower heating value (LHV) predicted by the WBPCM can well explain the LHV characteristics of municipal solid waste (MSW).
► The accuracy of the WBPCM was almost approached to that of ultimate analysis in moisture ranging from 40% to 55%.
► WBPCM is a faster and more economical model for LHV predictions with comparable accuracy.