The hydrothermal carbonization (HTC) of wet organic residues can improve their fuel characteristics significantly by reducing the ratio of oxygen to carbon content of the solids, which in turn increases their higher heating value (HHV). However, since solids are lost in the process, assessment of whether the process is beneficial must include an evaluation of the energy yield, which depends on the behavior of the solid yield and change in HHV as a function of the HTC operating conditions. Therefore, correlations which can predict how operating conditions affect the HHV and solid yield are essential. The goal of this work was to develop correlations that can be used to predict the energy yield and energy densification based on a simple elemental analysis of the original biomass and operating conditions. A graph-based genetic programming method was used to explore the correlations for predicting the HHV and solid yield. The correlations were derived and further validated based on data collected from 26 and 4 literature sources, respectively.
