Pretreatment of Leucaena leucocephala wood by acidified glycerol: optimization, severity index and correlation analysis. (October 2018) - Singhal, Anjali; Kumar, Madan; Bhattacharya, Mallika; Kumari, Neeta; Jha, Pawan Kumar; Chauhan, Devendra Kumar; Thakur, Indu Shekhar
Graphical abstract: Highlights: Acidified aqueous glycerol pretreatment of Leucaena leucocephala wood. ANN model gave more accurate predicted values than RSM-BBD model. After optimization 8-fold increase in total reducing sugar yield. Pretreatment process can also be represented by SI. Strong correlation existed between SI, TRS and biomass components. Abstract: In this study, Leucaena leucocephala wood was pretreated with aqueous glycerol having H2 SO4 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 * 10 3 to 2.06 * 10 3 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.