A ferrous oxalate mediated photo-Fenton system: Toward an increased biodegradability of indigo dyed wastewaters

This study assessed the applicability of a ferrous oxalate mediated photo-Fenton pretreatment for indigo-dyed wastewaters as to produce a biodegradable enough effluent, likely of being derived to conventional biological processes. The photochemical treatment was performed with ferrous oxalate and hydrogen peroxide in a Compound Parabolic Concentrator (CPC) under batch operation conditions. The reaction was studied at natural pH conditions (5–6) with indigo concentrations in the range of 6.67–33.33 mg L−1, using a fixed oxalate-to-iron mass ratio (C2O42−/Fe2+ = 35) and assessing the system's biodegradability at low (257 mg L−1) and high (1280 mg L−1) H2O2 concentrations. In order to seek the optimal conditions for the treatment of indigo dyed wastewaters, an experimental design consisting in a statistical surface response approach was carried out. This analysis revealed that the best removal efficiencies for Total Organic Carbon (TOC) were obtained for low peroxide doses. In general it was observed that after 20 kJ L−1, almost every treated effluent increased its biodegradability from a BOD5/COD value of 0.4. This increase in the biodegradability was confirmed by the presence of short chain carboxylic acids as intermediate products and by the mineralization of organic nitrogen into nitrate. Finally, an overall decrease in the LC50 for Artemia salina indicated a successful detoxification of the effluent.

Laser shock peening without absorbent coating (LSPwC) effect on 3D surface topography and mechanical properties of 6082-T651 Al alloy

The influence of nanosecond laser pulses applied by laser shock peening without absorbent coating (LSPwC) with a Q-switched Nd:YAG laser operating at a wavelength of λ = 1064 nm on 6082-T651 Al alloy has been investigated. The first portion of the present study assesses laser shock peening effect at two pulse densities on three-dimensional (3D) surface topography characteristics. In the second part of the study, the peening effect on surface texture orientation and micro-structure modification, i.e. the effect of surface craters due to plasma and shock waves, were investigated in both longitudinal (L) and transverse (T) directions of the laser-beam movement. In the final portion of the study, the changes of mechanical properties were evaluated with a residual stress profile and Vickers micro-hardness through depth variation in the near surface layer, whereas factorial design with a response surface methodology (RSM) was applied. The surface topographic and micro-structural effect of laser shock peening were characterised with optical microscopy, InfiniteFocus® microscopy and scanning electron microscopy (SEM). Residual stress evaluation based on a hole-drilling integral method confirmed higher compression at the near surface layer (33 μm) in the transverse direction (σmin) of laser-beam movement, i.e. − 407 ± 81 MPa and − 346 ± 124 MPa, after 900 and 2500 pulses/cm2, respectively. Moreover, RSM analysis of micro-hardness through depth distribution confirmed an increase at both pulse densities, whereas LSPwC-generated shock waves showed the impact effect of up to 800 μm below the surface. Furthermore, ANOVA results confirmed the insignificant influence of LSPwC treatment direction on micro-hardness distribution indicating essentially homogeneous conditions, in both L and T directions.

A stochastic model updating method for parameter variability quantification based on response surface models and Monte Carlo simulation

Stochastic model updating must be considered for quantifying uncertainties inherently existing in real-world engineering structures. By this means the statistical properties,instead of deterministic values, of structural parameters can be sought indicating the parameter variability. However, the implementation of stochastic model updating is much more complicated than that of deterministic methods particularly in the aspects of theoretical complexity and low computational efficiency. This study attempts to propose a simple and cost-efficient method by decomposing a stochastic updating process into a series of deterministic ones with the aid of response surface models and Monte Carlo simulation. The response surface models are used as surrogates for original FE models in the interest of programming simplification, fast response computation and easy inverse optimization. Monte Carlo simulation is adopted for generating samples from the assumed or measured probability distributions of responses. Each sample corresponds to an individual deterministic inverse process predicting the deterministic values of parameters. Then the parameter means and variances can be statistically estimated based on all the parameter predictions by running all the samples. Meanwhile, the analysis of variance approach is employed for the evaluation of parameter variability significance. The proposed method has been demonstrated firstly on a numerical beam and then a set of nominally identical steel plates tested in the laboratory. It is found that compared with the existing stochastic model updating methods, the proposed method presents similar accuracy while its primary merits consist in its simple implementation and cost efficiency in response computation and inverse optimization.