The efficiency of an oxypropylation process depends on several variables and operating conditions, which may not be generalized due to the diverse nature of subtracts. In lignocellulosic biomasses, the content of each fraction (lignin, cellulose and hemicellulose) can differ, as well as, the crystalline organization, which may limit reagent’s access to biomass. Also, the hydroxyl content can vary among biomasses; high values demand higher amounts of reactants, namely catalyst content, and more severe reaction conditions. Therefore, owing to biomass variability, selection of the operating conditions for oxypropylation their optimization is a key issue. In this context, one-factor-at-a-time approaches are commonly used to optimize processes; but it is well-known that optimal operating conditions or interactions between variables cannot be predicted by this simplistic method. Both problems may be overcome by employing the response surface methodology (RSM).info:eu-repo/semantics/publishedVersio
