Phenol biodegradation by a microbial consortium: application of artificial neural network (ANN) modelling

In this study, an effective microbial consortium for the biodegradation of phenol was grown under different operational conditions, and the effects of phosphate concentration (1.4 g L-1, 2.8 g L-1, 4.2 g L-1), temperature (25 degrees C, 30 degrees C, 35 degrees C), agitation (150 rpm, 200 rpm, 250 rpm) and pH (6, 7, 8) on phenol degradation were investigated, whereupon an artificial neural network (ANN) model was developed in order to predict degradation. The learning, recall and generalization characteristics of neural networks were studied using data from the phenol degradation system. The efficiency of the model generated by the ANN was then tested and compared with the experimental results obtained. In both cases, the results corroborate the idea that aeration and temperature are crucial to increasing the efficiency of biodegradation.

Total synthesis of (-)-indolizidine 167B via an unusual Wolff rearrangement from an alpha,beta-unsaturated diazoketone

A concise synthesis of the (-)-indolizidine alkaloid 167B and two formal syntheses of (-)-indolizidine 209D and (-)-coniceine are described in just three steps from an alpha,beta-unsaturated diazoketone, via an unusual photochemical Wolff rearrangement. Preparation of the unsaturated diazoketone is straightforward from N-Cbz-prolinal and a 3-diazo-2-oxopropylphosphonate, employing a Horner-Wadsworth-Emmons reaction. The strategy should be feasible and easily adaptable to the synthesis of other indolizidine alkaloids and analogues. (C) 2011 Elsevier Ltd. All rights reserved.

Niobium Effects on the Recrystallization Behavior on a Full Austenitic 15Cr-15Ni Stainless Steel Investigated Using Adaptive Neural Networks (ANN)

A new series of austenitic stainless steels-Nb stabilized, without Mo additions, non-susceptible to delta ferrite formation and devoid of intemetallic phases (sigma and chi), without deformation induced martensite is being developed, aiming at high temperature applications as well as for corrosive environments. The base steel composition is a 15Cr-15Ni with normal additions of Nb of 0.5, 1.0 and 2 wt%. Mechanical properties, oxidation and corrosion resistance already have been invetigated in previous papers. In this paper, the effects of Nb on the SFE, strain hardening and recrystallization resistance are evaluated with the help of Adaptive Neural Networks (ANN).