Generalized models to predict the lower heating value (LHV) of municipal solid waste (MSW)

Abstract

Accurately and efficiently predicting the LHV of MSW is vital for designing and operating a waste-to-energy plant. However, previous prediction models possess limited geographical applicability. In this paper, we employ multiple linear regression and artificial neural network (ANN) techniques to predict LHV. These data-driven models utilize 151 globally distributed datasets identified during a systematic literature review, describing the wet physical composition of MSW and measured LHV. The results show that models built via both methods exhibited acceptable and compatible levels of performance in predicting LHV, based on the multiple statistical indicators. However, the ANN model proved to be more robust in handling of datasets of diverse quality. Models developed from both methods demonstrate clearly that the wet proportion of food waste has a negative impact on LHV. Supported by the strong and significant correlation between food waste and moisture content, we concluded that the negative impact of high moisture content in food waste on LHV outweighed its calorific value. Separating food waste or any other waste with high moisture content from the MSW for incineration can significantly improve energy recovery efficiency. Contrary to expectation, the models also reveal a higher contribution of paper waste to the LHV of MSW than plastic waste.