No abstract

In this work eighteen red yeasts were screened for carotenoids production on glycerol containing medium. Strain C2.5t1 of Rhodotorula glutinis, that showed the highest productivity, was UV mutagenized. Mutant 400A15, that exhibited a 280 % increase in β-carotene production in respect to the parental strain, was selected. A central composite design was applied to 400A15 to optimize carotenoids and biomass productions. Regression analyses of the quadratic polynomial equations obtained (R(2) = 0.87 and 0.94, for carotenoids and biomass, respectively) suggest that the models are reliable and significant (P < 0.0001) in the prediction of carotenoids and biomass productions on the basis of the concentrations of crude glycerol, yeast extract and peptone. Accordingly, total carotenoids production achieved (14.07 ± 1.45 mg l(-1)) under optimized growth conditions was not statistically different from the maximal predicted (14.64 ± 1.57 mg l(-1)) (P < 0.05), and it was about 100 % higher than that obtained under un-optimized conditions. Therefore mutant 400A15 may represent a biocatalyst of choice for the bioconversion of crude glycerol into value-added metabolites, and a tool for the valorization of this by-product of the biodiesel industry.

The use of forest biomass for energy production requires a careful attention to the sustainable silvicultural practices. This is a complex task because of the different environmental and economic issues to be taken into account. To this aim, suitable tools must be used as regards the representation of the dynamics of forest biomass and the economic assessment. In this paper, a user-friendly optimization-based decision support system (DSS) that can help decision makers in the optimal management of forest biomass use for energy production is presented. Attention is focused on the forest system in order to take into account sustainable silvicultural practices and on the minimization of costs for the collection plans over years. Specifically, a non linear optimization model (that includes forest growth models) is formalized, aiming at determining, over a certain period, the optimal exploitation policy of forest biomass through a single plant whose location and size are assumed known, in order to minimize costs and to respect silvicultural constraints. The decision model is solved through a receding horizon approach and is applied to the case study of Val Bormida (Savona Province, Italy). Different tests and sensitivity analysis have been performed to validate the model and the approach. From an application point of view, observing the obtained results, it is evident that results are strongly influenced by the old average age of the vegetation in the specific case study. However, depending on the species, different trends for the results of annual mean increment and harvesting plans are observed