Simplified multiple linear regression models for the estimation of heating values of refuse derived fuels

With the increasing concerns about fossil fuel?s impact on the environment and its expected depletion, the world strives for new and alternative energy sources. It is believed that biomass and residues are crucial tools to aid in the so required reduction of fossil fuels. One of those sources can be the municipal solid waste (MSW), particularly refused derived fuels (RDFs). With population growth, MSW management is a real problem that must be addressed. However, when carrying preliminary or screening evaluations of residue to assess its calorific value, the resources should be kept as minimum as possible to be the least expensive as possible. The development of empirical and numerical models, with high accuracy, to estimate de calorific value can be useful to decrease the need for experimental data. With a large dataset of RDFs (443 samples), this work developed predictive models based on linear regressions to estimate higher and lower heating value based on ultimate analysis. These models were then validated with a dataset of 15 samples. The models for estimating HHV and LHV have demonstrated excellent prediction accuracy. The absolute bias error was relatively low, 0.02% and 0.03%, for the HHV and LHV models, respectively, demonstrating a minor overestimation of the heating values. Besides that, the absolute average error, both for HHV and LHVmodels, were also low, 1.57% and 1.65%, respectively, indicating good accuracy. The low mean absolute error also confirmed the excellent precision of the developed models.

Automated summary

This study developed predictive models based on a large dataset of RDFs to estimate the heating values of municipal solid waste, demonstrating high accuracy and low errors.