Synthesis of a Fe3O4-rGO-ZnO-catalyzed photo-Fenton system with enhanced photocatalytic performance

In this work, we designed a magnetically-separable Fe₃O₄-rGO-ZnO ternary catalyst, ZnO anchored on the surface of reduced graphene oxide (rGO)-wrapped Fe₃O₄ magnetic nanoparticles, where rGO, as an effective interlayer, can enhance the synergistic effect between ZnO and Fe₃O₄. The effects of three operational parameters, namely irradiation time, hydrogen peroxide dosage, and the catalyst dosage, on the photo-Fenton degradation of methylene blue and methyl orange were investigated. The results showed that the Fe₃O₄-rGO-ZnO had great potential for the destruction of organic compounds from wastewater using the Fenton chemical oxidation method at neutral pH. Repeatability of the photocatalytic activity after 5 cycles showed only a tiny drop in the catalytic efficiency.     

Fuzzy Bayesian system reliability assessment based on Pascal distribution

The main purpose of this paper is to provide a methodology for discussing the fuzzy. Bayesian system reliability from the fuzzy component reliabilities, actually we discuss on the Fuzzy Bayesian system reliability assessment based on Pascal distribution, because the data sometimes cannot be measured and recorded precisely. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayes estimation method will be used to create the fuzzy Bayes point estimator of system reliability by invoking the well-known theorem called ‘Resolution Identity’ in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four sub-problems for the purpose of simplifying computation. Finally, the sub problems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.     

Optimal seismic design of steel structures by an efficient soft computing based algorithm

The main aim of this study is to propose advanced soft computing techniques for the optimal seismic design of real steel structures subjected to natural ground motion records. For the solution of the optimization problem an efficient combination of the particle swarm optimization (PSO) and adaptive virtual sub-population (AVSP) algorithms is proposed. Also an efficient combination of the adaptive neuro-fuzzy inference system (ANFIS), wavelet transforms (WT) and radial basis function (RBF) neural networks, termed as fuzzy wavelet radial basis function (FWRBF), is proposed to accurately predict the structural responses. The numerical results demonstrate the computational advantages of the proposed methodology.     

Optimum design of structures by an improved genetic algorithm using neural networks

Optimum design of large-scale structures by standard genetic algorithm (GA) makes the computational burden of the process very high. To reduce the computational cost of standard GA, two different strategies are used. The first strategy is by modifying the standard GA, called virtual sub-population method (VSP). The second strategy is by using artificial neural networks for approximating the structural analysis. In this study, radial basis function (RBF), counter propagation (CP) and generalized regression (GR) neural networks are used. Using neural networks within the framework of VSP creates a robust tool for optimum design of structures.     

Comparing the performance of multispectral vegetation indices and machine-learning algorithms for remote estimation of chlorophyll content: a case study in the Sundarbans mangrove forest

Optical vegetation indices (VIs) have been used to retrieve and assess biophysical variables from satellite reflectance data. These indices, however, also are sensitive to a number of confounding factors, such as canopy geometry, soil optical properties, and solar position. This suggests that VIs should be used cautiously for biophysical parameter estimation. Among biophysical variables, chlorophyll content is of particular importance as an indicator of photosynthetic activity. The goal of this study is to investigate the performance of multispectral optical VIs for chlorophyll content estimation in the world’s largest mangrove forest, the Sundarbans, and to compare these with machine-learning algorithms (MLAs). To this end, we have investigated the performance of 15 multispectral VIs and six state-of-the-art MLAs that are widely used for adaptive data fitting. The MLAs are Artificial Neural Networks (ANNs), Genetic Algorithm (GA), Gaussian Processes for Machine Learning (GPML), Kernel Ridge Regression (KRR), Locally Weighted Polynomials (LWP), and Multivariate Adaptive Regression Splines (MARS). We use an in situ data set of reflectance and chlorophyll measurements to develop and validate our models. Each MLA was evaluated 500 times with random partitions of training and validation data. Results showed that the weight optimization and term selection used within GA produce the most reliable chlorophyll content estimation. However, green normalized difference VI (GNDVI) is a simple and computationally efficient VI that produces results that are nearly as accurate as GA in terms of model fit and performance. Results also show that all methods except ANNs and MARS produce a quasi-linear relationship between spectral reflectance and chlorophyll content. Statistical transformations of GNDVI and chlorophyll content have the capability of further reducing model error.     

Corrosion Inhibition of Carbon Steel in Aqueous HCl Solutions by Acid Pre-Magnetization Technique: Experimental Study and Modelling

Protection of Metals and Physical Chemistry of Surfaces - The effect of magnetization of acid before usage (pre-magnetization technique) on the corrosion inhibition of carbon steel (CS) in 7.5, 10...     

Optimal placement of steel plate shear walls for steel frames by bat algorithm

Layout optimization of steel frames with steel plate walls (SPWs) using a meta‐heuristic search algorithm is the main aim of the present study. SPWs are lateral load‐resisting systems, especially against earthquake excitation. These systems offer significant advantages in terms of cost, performance and ease of design compared with other systems. In this study, orthotropic membrane model is used to model the behaviour of steel plate shear walls. The newly developed bat algorithm, which is based on the echolocation behaviour of bats, is employed as the present study optimizer. Design variables of the optimization problem consist of the cross sections of beams and columns of the frame, the web plate thicknesses of SPWs and the placement of SPW in the frame. The bat algorithm performs suitable selection of sections from the AISC wide‐flange (W) shapes list. Strength constraints of the American Institute of Steel Construction Load and Resistance Factor Design and displacement constraints are checked during the optimization process. The results reveal the effectiveness of the proposed method for optimization of steel frames with SPWs. Copyright © 2014 John Wiley & Sons, Ltd.     

Experimental investigation of the effect of vibration on mechanical properties of 304 stainless steel welded parts

Some of the problems that occur during the welding process include the creation of coarse grains in the weld structure and the hardening of the weld region, which reduce the strength and impact resistance of the welded parts. One technique to improve the mechanical properties of weld is the application of mechanical vibration to the molten pool. In this article, the effect of vibrating the part during welding on the mechanical properties of steel plates has been investigated in the tungsten inert gas (TIG) welding process. The plate is made of stainless steel 304 with 2 mm in thickness. A filler material has also been used for welding so that the effect of vibration can be observed on the weld pool region. The experimental tests have been performed under different welding conditions with respect to voltage, current, welding speed, vibrations amplitude, and frequency. Then, the resultant mechanical properties of the tested parts were measured. Also, the microstructure obtained by applying the vibration has been examined. Based on these experimental results, the effect of mechanical vibration on mechanical properties of the weld was investigated. Moreover, considering the mechanical properties obtained from these experiments, the optimum values of amplitude, frequency, and welding speed were determined.     

Fault detection and identification using combination of EKF and neuro-fuzzy network applied to a chemical process (CSTR)

Pattern Analysis and Applications - In this paper, a new algorithm is proposed for fault detection and identification (FDI) in a class of nonlinear systems by combining the extended Kalman filter...