Optimization of Biosurfactant Production by Bacillus licheniformis STK 01 Grown Exclusively on Beta vulgaris Waste using Response Surface Methodology

Characterization, production and optimization of lipopeptide biosurfactant by new strain Bacillus pumilus 2IR isolated from an Iranian oil field

“…They consists of both polar and non-polar domains and are capable of showing various surface activities which among other roles, help solubilize hydrophobic substances (Amodu et al, 2014;Sari et al, 2014). Lipopeptides are the most known types of biosurfactants which are normally produced by Bacillus spp.…”

ExploringMusa paradisiacaPeel Extract as a Green Corrosion Inhibitor for Mild Steel Using Factorial Design Method

“…where X 1 , X 2 , X 3 , …, X n are the independent coded variables, α 0 is the offset term, and α i , α ii , and α ij account for the linear, squared, and interaction effects, respectively, and ε is the random error. A model reduction may be expedient, if there are many redundant model terms [40].…”

“…The adjusted R 2 is a useful tool for comparing the explanatory power of models with different numbers of predictors. P-value for each coefficient tests the null hypothesis that the coefficient has no effect [40].…”

Estimation of biosurfactant yield produced by Klebseilla sp. FKOD36 bacteria using artificial neural network approach

“…the other constant [15,16]; though this is more conventional and time consuming process. Other statistical tools for experimental design are Taguchi design [17][18][19], plackett-Burman design [20][21][22], response surface methodology using central composite design [23,24] and n-factor factorial design [25][26][27]. Though, in some cases, complex nonlinear biological interactions cannot be fully exploited by using second order polynomial model [28,29] based on response surface methodology and some other designs.…”