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.

Solvation in aqueous binary mixtures: consequences of the hydrophobic character of the ionic liquids and the solvatochromic probes

Information on the solvation in mixtures of water, W, and the ionic liquids, ILs, 1-allyl-3-R-imidazolium chlorides; R = methyl, 1-butyl, and 1-hexyl, has been obtained from the responses of the following solvatochromic probes: 2,6-dibromo-4-[(E)-2-(1-R-pyridinium-4-yl)ethenyl] phenolate, R = methyl, MePMBr2; 1-octyl, OcPMBr(2), and the corresponding quinolinium derivative, MeQMBr(2). A model developed for solvation in binary mixtures of W and molecular solvents has been extended to the present mixtures. Our objective is to assess the relevance to solvation of hydrogen-bonding and the hydrophobic character of the IL and the solvatochromic probe. Plots of the medium empirical polarity, E-T(probe) versus its composition revealed non-ideal behavior, attributed to preferential solvation by the IL and, more efficiently, by the IL-W hydrogen-bonded complex. The deviation from linearity increases as a function of increasing number of carbon atoms in the alkyl group of the IL, and is larger than that observed for solvation by W plus molecular solvents (1-propanol and 2-(1-butoxy)ethanol) that are more hydrophobic than the ILs investigated. This enhanced deviation is attributed to the more organized structure of the ILs proper, which persists in their aqueous solutions. MeQMBr(2) is more susceptible to solvent lipophilicity than OcPMBr(2), although the former probe is less lipophilic. This enhanced susceptibility agrees with the important effect of annelation on the contributions of the quinonoid and zwitterionic limiting structures to the ground and excited states of the probe, hence on its response to both medium composition and lipophilicity of the IL.

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).