An investigation on the new operational parameter effective in Cr(VI) removal efficiency: a study on electrocoagulation by alternating pulse current

The performance of an electrocoagulation (EC) process with aluminum/iron electrodes for removal of chromium on laboratory scale was studied. The effect of operational parameters such as initial pH, current density (CD), reaction time, initial concentrations (50, 100, 500, 1000 mg/L), solution conductivity, electrical energy consumption (EEC) and type of circuit were studied in an attempt to reach higher Cr(VI) removal efficiency. Alternating pulse current (APC) was used to prevent the passivity or polarization of electrodes. Important operating parameters were optimized to access higher (99%) Cr(VI) removal efficiency as follows: EEC range: 4-58 kW h/m(3) wastewater, CD: 56-222 A/m(2), operating time: 20-110 min, pH 3-9 (pH(optimum) 5), voltage: 15-25 V. NaCl, KCl, PAC (poly aluminum chloride), NaNO(3) were used as supporting electrolytes. NaCl as well as KCl handled the EC with the best performance in every aspect; however, PAC and NaNO(3) did not have the same results (Applied conductivity is better than literature). The results of this work are comparable with those of recent studies. Equal removal efficiency was obtained in "direct current" (DC) and (APC); however, when "APC" was used, water recovery (0.92 m(3)/m(3) wastewater) was significant and the turbidity was 1 NTU. "APC" amazed our experimental team.     

Interfacial evaluation of epoxy/carbon nanofiber nanocomposite reinforced with glycidyl methacrylate treated UHMWPE fiber

Interface interactions of fiber–matrix play a crucial role in final performance of polymer composites. Herein, in situ polymerization of glycidyl methacrylate (GMA) on the ultrahigh molecular weight polyethylene (UHMWPE) fibers surface was proposed for improving the surface activity and adhesion property of UHMWPE fibers towards carbon nanofibers (CNF)‐epoxy nanocomposites. Chemical treatment of UHMWPE fibers was characterized by FTIR, XPS analysis, SEM, and microdroplet tests, confirming that the grafting of poly (GMA) chains on the surface alongside a significant synergy in the interfacial properties. SEM evaluations also exhibited cohesive type of failure for the samples when both GMA‐treated UHMWPE fiber and CNF were used to reinforce epoxy matrix. Compared with unmodified composite, a ～319% increase in interfacial shear strength was observed for the samples reinforced with both 5 wt % GMA‐grafted UHMWPE and 0.5 wt % of CNF. © 2016 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43751.     

Developing a new model to predict mass transfer coefficient of salicylic acid adsorption onto IRA-93: Experimental and modeling

An experimental breakthrough curve for Salicylic acid in an adsorption recovery process was determined by an anion-exchange resin IRA-93. The volumetric mass transfer coefficients were calculated by employing constant wave propagation theory. Meanwhile, the effects of volumetric feed flow rates on this break through curve and mass transfer coefficients at different flow rates were also studied in order to develop three new models to predict mass transfer coefficient. The results demonstrated that by the increase in the feed flow rates, the amount of adsorption reduces. However, while the volumetric feed flow rates increase the overall volumetric mass transfer coefficients will increase. This grows the feeling that the feed flow rate should be optimized. The optimum flow rate for the adsorption was found to be 7 mg/l in this study. In addition, three new models to predict the mass transfer coefficient in respect of feed rates were developed in this research work which showed very high fittings with R²>0.99. These models could fully support the experimental data obtained.     

Artificial neural network,ANN-PSO and ANN-ICA for modelling the Stirling engine

Nowadays, shortage of fossil fuels resources, especially oil, and also global attention to environmental hazards produced by the internal combustion process have caused extensive researches on the development of renewable energy engine technology. Among all kinds of renewable resources, solar energy Stirling engines have their own special situation for energy generation with lower pollutants and sustainable sources. The Stirling engine is an external combustion engine that uses any external heat source to generate mechanical power. Various parameters affect the performance of the Stirling engine. In this study, artificial neural network (ANN) was applied to estimate the power and torque values obtained from a Stirling heat engine (Philips M102C engine). It employs the Levenberg–Marquardt algorithm for training ANN with back propagation network for estimating the power and torque of the Stirling heat engine. The performances of the imperialist competitive algorithm (ICA)-ANN and ANN-particle swarm optimisation (PSO) are compared with the performance of the ANN based on mean square error (MSE) and correlation coefficient. PSO and ICAs are applied to determine the initial weights of the neural network. The obtained results indicate that ANN-PSO has a better performance than ICA-ANN and ANN alone; also the MSE for the ANN-PSO is lower as well. Considering the results obtained from this study, there is very good agreement between the output of the testing phase of the ANN-PSO model with experimental data and they are very close to each other.     

Accuracy improvement of high resolution satellite image georeferencing using an optimized line-based rational function model

The georeferencing procedure of high resolution satellite images (HRSIs) using proper mathematical models is an important step in 3D spatial information extraction. Since the 2000s, line-based mathematical models are considered more in photogrammetric communities. This could be due to the unique characteristics of linear features such as more reliable procedure of automatic matching as well as abundance of linear features in satellite images. In addition, the irreplaceable characteristics of Rational Function Models (RFM) such as generality and its independence to the sensor model makes it a proper mathematical model for this purpose. Although, traditional selection of the best order and terms’ combination of RFM due to the deficiency of physical interpretation of terms is not easily feasible. Hence, in this article, an optimization algorithm based on a binary particle swarm optimization (PSO) is developed to determine the optimum uncorrelated terms’ combination of a line-based RFM. For this purpose, a population of particles (representative of different RFM structures) is initialized randomly with a string of binary values, indicating the presence or omission of the corresponding terms. This is followed by directly using some conjugate lines in the image and object spaces as ground control lines (GCLs) to solve the unknown parameters of the RFM for each particle. The root mean square error (RMSE) of some check points (CPs) for each particle is considered to be its cost function and used to update the velocity of particles. The procedure is repeated to reach a stopping criteria. A comprehensive evaluation on the proposed model in comparison to the traditional line-based RFM is examined. Two different HRSIs (a GeoEye and an Ikonos Images) over different areas of Iran are used for this purpose. Based on the results, a traditional line-based RFM could not reach to sub-pixel accuracy. In contrast, the results show the potential of the proposed optimized line-based RFM to increase the accuracy to better than 0.8 pixel as well as reduce the systematic errors and the number of required control information, significantly.     

Influence of functionalized multi-layer graphene on adhesion improvement and corrosion resistance performance of zinc-rich epoxy primer

The influence of functionalised multi-layer graphene (FMLG) on anti-corrosion behaviour of zinc-rich primer (ZRP) was investigated. Electrochemical impedance spectroscopy, salt spray, cathodic delamination, pull-off adhesion tests and scanning electron microscopy analysis were evaluated. Samples were prepared using 0.25, 0.5, 0.75 and 1?wt-% of FMLG substituted by the same amount of zinc particles in epoxy containing 75?wt-% of zinc dust. Results revealed that sacrificial and barrier performances of ZRP were improved in the presence of FMLG. In addition, the adhesion and cathodic disbondment resistance showed significant improvement. The best results were obtained for ZRP containing 0.75?wt-% of FMLG.     

Enhancing and multi-objective optimising of the performance of Stirling engine using third-order thermodynamic analysis

Stirling engine is an external combustion engine which uses eternal heat sources like solar radiation for heating a compressible fluid inside cylinders. In the recent years, significant attention is drawn to Stirling engines due to the clear advantages, high efficiency potential, flexible fuel, lower nitrogen oxides, quiet and minimal vibration, high reliability and highest specific output work for any closed regenerative cycle. The third order thermal analysis is one of the analyses which has been applied in several studies which have been carried out on Stirling engines. NSGA-II algorithm is applied to optimise the differential regenerator pressure (bar) and the power output (kW) for a Stirling engine system. In this study, three decision-making techniques are utilised to optimise the solutions, obtained of the results. At last, the employed techniques are compared with the data of an experimental research work.