Energy recovery from municipal solid wastes (MSW) offers economic benefits together with improved management of wastes. In the literature, attempts have been made to understand and quantify the potential energy benefits of MSW but the implications of the proportion of the elemental constituents on the heating value of the wastes are rarely discussed. In this investigation, novel linear and nonlinear equations were developed from artificial neural network (ANN) to predict the higher heating values (HHV) of MSW. The new equations perform equally well in comparison with the existing models in the literature for different HHV data from various MSW sources. They also showed consistency in satisfactory performances for predicting HHV values from new data as well as altered elemental compositions. Furthermore, it was found that the change in the proportion of elemental compositions have interesting relation to the magnitude of the HHV for different wastes. Results show that a change in percent hydrogen (%H) changes the HHV in some wastes that possess the thresholds of both HHV magnitude and the carbon to energy ratio (C/HHV). For the waste with low HHV but relatively high C/HHV value, increasing the %H does not significantly alter their HHV value. For those with high HHV value and moderate C/HHV value, HHV increases as the %H increases. Wastes with high HHV value but low C/HHV undergo reverse in the trend of HHV as the %H increases. Typical example of this is found in plastic wastes with high percentage carbon (%C) but low C/HHV. In this waste, as the %H increases the corresponding HHV decreases. Keywords: Municipal solid wastes, linear, nonlinear, artificial neural network, carbon to energy ratio, higher heating values.
