The influence of medium constituents on the production of biosurfactants by Candida tropicalis cultivated in waste frying oil was investigated according to a fractional factorial 25‐1 design. The combined effect of the C/Ninorganic, C/Fe, C/Mg, and C/P ratios and yeast extract on surface tension reduction, biosurfactant yield, emulsification activity, and biomass were studied. The highest biosurfactant yield was reached when low C/Mg and low C/P ratio variables were combined, while the cell growth was favored by increasing the nitrogen concentration. The highest surface tension net decrease, on the other hand, was observed at low yeast extract concentration, low C/Fe, and high C/P ratios. Emulsification indices against lubrication and automobile waste oil of approximately 65 to 95% were observed. The crude biosurfactant produced in the medium—formulated with 2% waste frying oil, 0.067% NH4Cl, 0.025% MgSO4·7H2O, 0.067% KH2PO4, and 0.0026% FeCl3·6H2O—removed approximately 78 to 97% of the petroleum and motor oil adsorbed in sand samples.

Biomasses are renewable sources used in energy conversion processes to obtain diverse products through different technologies. The production chain, which involves delivery, logistics, pre-treatment, storage and conversion as general components, can be costly and uncertain due to inherent variability. Optimization methods are widely applied for modeling the biomass supply chain (BSC) for energy processes. In this qualitative review, the main aspects and global trends of using geographic information systems (GISs), linear programming (LP) and neural networks to optimize the BSC are presented. Modeling objectives and factors considered in studies published in the last 25 years are reviewed, enabling a broad overview of the BSC to support decisions at strategic, tactical and operational levels. Combined techniques have been used for different purposes: GISs for spatial analyses of biomass; neural networks for higher heating value (HHV) correlations; and linear programming and its variations for achieving objectives in general, such as costs and emissions reduction. This study reinforces the progress evidenced in the literature and envisions the increasing inclusion of socio-environmental criteria as a challenge in future modeling efforts.