Experimental evaluation and application of genetic programming to develop predictive correlations for hydrochar higher heating value and yield to optimize the energy content

Abstract

The hydrothermal carbonization (HTC) process has been found to consistently improve biomass fuel characteristics by raising the higher heating value (HHV) of the hydrochar as process severity is increased. However, this is usually associated with a decrease in the solid yield (SY) of hydrochar, making it difficult to determine the optimal operating conditions to obtain the highest energy yield (EY), which combines the two parameters. In this study, a graph-based genetic programming (GP) method was used for developing correlations to predict HHV, SY, and EY for hydrochars based on published values from 42 biomasses and a broad range of HTC experimental systems and operating conditions, i.e., 5 ≤ holding time (min) ≤ 2208, 120 ≤ temperature (°C) ≤ 300, and 0. 0096 ≤ biomass to water ratio ≤ 0.5. In addition, experiments were carried out with 5 pomaces at 4 temperatures and two reactor scales, 1 L and 18.75 L. The correlations were evaluated using this experimental data set in order to estimate prediction errors in similar experimental systems. The use of the correlations to predict HTC conditions to achieve the maximum EY is demonstrated for three common feedstocks, wheat straw, sewage sludge, and a fruit pomace. The prediction was confirmed experimentally with pomace at the optimized HTC conditions; we observed 6.9 % error between the measured and predicted EY %. The results show that the correlations can be used to predict the optimal operating conditions to produce hydrochar with the desired fuel characteristics with a minimum of actual HTC runs.