జర్నల్ ఆఫ్ ఎనలిటికల్ & బయోఅనలిటికల్ టెక్నిక్స్

నైరూప్య

Optimization of operational parameters for adsorption of cellulases onto microcrystalline cellulose using REML and RSM methodologies.

Khurram Shahzad Baig

In most of the developed countries, the most of the transport running is on some form of biofuel. The main feedstocks used to produces biofuel are wheat and corn grans. The addition of bioethanol into motor fuels has increased the pressure to the grains supply market and food prices are on the increase due to a relation with the grains prices. A use of waste lignocellulosic materials (agricultural waste and /or forestry waste) would release this pressure.

The cost of biofuel production is based on the costs of two main reactant materials i) lignocellulosic materials, ii) enzymes. The cost of enzymes can be reduced by reuse of them. Apart from the redesigning of enzymes to increase enzymes stability at elevated temperature etc., it is also important to look into the optimization of operating parameters. The optimization of the adsorption parameters was performed by using statistical analysis tools such as Response Surface Methodology (RSM) and Restricted Maximum Likelihood Estimation (RMLE). The obtained binomial quadratic model predicted almost the same values of the cellulases adsorbed as that of experimental values within a percent error of x±6. The optimized values of the operating parameters were modified according to available practical knowledge and the model was validated. It was found to be in the agreement of the experimental values. The optimized conditions would help biofuel industry in designing their production process.

Highlights

The operating parameters for enzymatic adsorption such as adsorption temperature, enzymes concentration, and incubation time were optimized for production of biofuel.

The RSM response equation can be used for controlling efficiency of the adsorption reaction with multiple variables.

The interaction of operating parameters for the enzymatic adsorption and its efficiency was systemically analyzed for the first time using RSM and REML methods.