Optimization and Modelling of Delignification Process for Nanocrystalline Cellulose Production from Rice Husk Biomass

Nanocrystalline cellulose (NCC) has been attributed as a noble material due to its superior properties for miscellaneous applications in medical science, pharmaceutical production and engineering fields. The major problems associated with the production of NCC are low production rate and inefficient process. Alkaline delignification plays an important role for the mass production of NCC from lignocellulosic biomass. In order to fulfil these research gaps a fractal kinetic model of alkaline delignification of rice husk biomass (RHB) was studied with process optimization. Fifteen kinetic experiments of alkaline delignification for NCC production from RUB, as designed by the Minitab (R) 2014b software using the Box-Belmken method, were performed in a 2L jacketed glass reactor under three process parameters (time, temperature and alkali concentration) with ranges of 2-10 hours, 40-100 degrees C and 1-4M, respectively. The nucleic growth model of fractal kinetics, developed by Nguyen & Dang (2007), has been successfully applied to the data sets available from this experimental work. The process parameters estimation for the fractal kinetic model was carried out using Matlab (R) 2014b software. From this study it has been found that the most efficient delignification process with first order kinetics was obtained at 15 h, 95 degrees C and 2.5 M alkali concentration. It has also been found that the kinetic rate coefficient of alkaline delignification is dependent on tune instead of alkali concentration. The correlation coefficients R-2 for this model was found to be 0.94, which indicated that the model can be considered with high level of confidence. From this hypothesis it can be concluded that the optimised condition gave maximum cellulose pulp for producing high quality NCC.