Pretreatment of Leucaena leucocephala wood by acidified glycerol: optimization, severity index and correlation analysis.

In this study, Leucaena leucocephala wood was pretreated with aqueous glycerol having H2SO4 as the catalyst. Response surface methodology (RSM) and artificial neural network (ANN) were used to optimize the process parameters, catalyst concentration (1-3%), duration (120-300 min) and temperature (100-150 °C). ANN gave more accurate predictions for total reducing sugar yield than RSM. ANN also had lower values for error functions. Severity index (SI) was calculated based on the temperature, duration and catalyst concentration. Increase in SI from 0.21 * 103 to 2.06 * 103 increased total reducing sugar (TRS) production from 39.97 g/kg to 321.8 g/kg. Further increase in SI reduced the TRS and this change positively correlates with the loss of cellulose content. Correlation analysis showed that severity index can also be used to describe pretreatment process.