Degradation of dye containing in textile wastewater by sequential process: photocatalytic and biological treatment.

In this research, a combined photocatalytic and biological treatment is proposed for the elimination of pollutants present in textile wastewater using a natural erionite zeolite (PE) and aluminum oxide (PA) synthesized by the sol-gel method as photocatalysts, and solar radiation. Both catalysts were characterized by XRD, SEM, and EDS. For biological treatment two bacterial consortium were used: BC1 (Escherichia coli N16, Serratia k120, Pseudomonas putida B03 and Enterobacter hormaechei), and consortium BC2 (Escherichia coli N16, Serratia Mc107, Enterobacter N9, Enterobacter hormaechei Mc9). The photocatalytic and microbiological treatments were carried out initially separately and subsequently in a sequential manner, first the photocatalytic followed by the microbiological to determine if a synergistic effect was achieved. Comparing the photocatalytic performance, erionite showed higher performance of dyes degradation (54.75%) than alumina (28.62%). While in the biological process, BC1 decreased the dye concentration to 56.93% and BC2 to 53.56%. Finally, the best combined process was PA+BC1 reaching pollutants degradation 64.62%, showing that the application of both processes promotes a decolorization in textile wastewater. The water resulting from the combined photocatalysis-microbiological degradation processes was tested for toxicity using Daphnia magna, obtaining that none of the effluents shows toxicity.

Structural Health Monitoring for Jacket-Type Offshore Wind Turbines: Experimental Proof of Concept.

Structural health monitoring for offshore wind turbines is imperative. Offshore wind energy is progressively attained at greater water depths, beyond 30 m, where jacket foundations are presently the best solution to cope with the harsh environment (extreme sites with poor soil conditions). Structural integrity is of key importance in these underwater structures. In this work, a methodology for the diagnosis of structural damage in jacket-type foundations is stated. The method is based on the criterion that any damage or structural change produces variations in the vibrational response of the structure. Most studies in this area are, primarily, focused on the case of measurable input excitation and vibration response signals. Nevertheless, in this paper it is assumed that the only available excitation, the wind, is not measurable. Therefore, using vibration-response-only accelerometer information, a data-driven approach is developed following the next steps: (i) the wind is simulated as a Gaussian white noise and the accelerometer data are collected; (ii) the data are pre-processed using group-reshape and column-scaling; (iii) principal component analysis is used for both linear dimensionality reduction and feature extraction; finally, (iv) two different machine-learning algorithms, k nearest neighbor (k-NN) and quadratic-kernel support vector machine (SVM), are tested as classifiers. The overall accuracy is estimated by 5-fold cross-validation. The proposed approach is experimentally validated in a laboratory small-scale structure. The results manifest the reliability of the stated fault diagnosis method being the best performance given by the SVM classifier.