Characterization of mechanothermally processed nanostructured ZnO

In this paper, the Taguchi method with an L₉(34) orthogonal array was used as experimental design to determine the optimum conditions for preparing ZnO nanoparticles via a mechanothermal route. ZnSO₄·H₂O and Na₂CO₃ were used as starting materials. The effects of milling time, Na₂CO₃/ZnSO₄·H₂O molar ratio, and ball-to-powder mass ratio (BPR) on the bandgap (E_g) of ZnO nanoparticles were investigated. The ranges of the investigated experimental conditions were 5–15 h for the milling time (t), 1.0–1.2 for the Na₂CO₃/ZnSO₄·H₂O molar ratio (M), and 10–30 for BPR. The milling time and BPR exhibited significant effects; an increase in milling time reduced the bandgap. The optimum conditions from this study were t₃ = 15 h, M₁ = 1, and BPR₂ = 20. Only two significant factors (t₃, 15 h; BPR₂, 20) were used to estimate the performance at the optimum conditions. The calculated bandgap was 3.12 eV, in reasonable agreement with the experimental results obtained under the optimized conditions.     

Electrical annealing of severely deformed copper: microstructure and hardness

Commercial pure copper sheets were severely deformed after primary annealing to a strain magnitude of 2.32 through constrained groove pressing. After induction of an electrical current, the sheets were heated for 0.5, 1, 2, or 3 s up to maximum temperatures of 150, 200, 250, or 300℃. To compare the annealing process in the current-carrying system with that in the current-free system, four other samples were heated to 300℃ at holding times of 60, 90, 120, or 150 s in a salt bath. The microstructural evolution and hardness values of the samples were then investigated. The results generally indicated that induction of an electrical current could accelerate the recrystallization process by decreasing the thermodynamic barriers for nucleation. In other words, the current effect, in addition to the thermal effect, enhanced the diffusion rate and dislocation climb velocity. During the primary stages of recrystallization, the grown nuclei of electrically annealed samples showed greater numbers and a more homogeneous distribution than those of the samples annealed in the salt bath. In the fully recrystallized condition, the grain size of electrically annealed samples was smaller than that of conventionally annealed samples. The hardness values and metallographic images obtained indicate that, unlike the conventional annealing process, which promotes restoration phenomena with increasing heating time, the electrical annealing process does not necessarily promote these phenomena. This difference is hypothesized to stem from conflicts between thermal and athermal effects during recrystallization.     

Forecasting UK Industrial Production with Multivariate Singular Spectrum Analysis

In recent years the singular spectrum analysis (SSA) technique has been further developed and applied to many practical problems. The aim of this research is to extend and apply the SSA method, using the UK Industrial Production series. The performance of the SSA and multivariate SSA (MSSA) techniques was assessed by applying it to eight series measuring the monthly seasonally unadjusted industrial production for the main sectors of the UK economy. The results are compared with those obtained using the autoregressive integrated moving average and vector autoregressive models. We also develop the concept of causal relationship between two time series based on the SSA techniques. We introduce several criteria which characterize this causality. The criteria and tests are based on the forecasting accuracy and predictability of the direction of change. The proposed tests are then applied and examined using the UK industrial production series. Copyright © 2012 John Wiley & Sons, Ltd.     

An Organizational Cybernetics Framework for Designing a Viable Higher Education System

Systemic Practice and Action Research - The Viable System Model (VSM) is an Organizational Cybernetics framework mainly used to handle systems complexity. The fundamental role of the VSM is to...     

Polyimides Based on Flexible Diamines:Synthesis, Characterization and Thermal Properties

1 Results and DiscussionTwo diamines 1,4-phenylene di(oxy-4,4′-aniline) and 4-aminophenyloxy-N-4-[(4-amiophenyloxy)benzylidene]aniline were prepared via the nucleopilic substitution reaction and were polymerized with 3,3′,4,4′-benzophenonetetracarboxylic acid dianhydride (BP), 4,4′-(hexafluoroisopropylidene)diphthalic anhydride (HF) , 3,4,9,10-perylenetetracarboxylic acid dianhydride (PD) and pyromellitic dianhydride (PMDA) either by one step solution polymerization reaction or two step procedure. The l...     

Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices

Improving the prediction accuracy of agricultural product futures prices is important for investors, agricultural producers, and policymakers. This is to evade risks and enable government departments to formulate appropriate agricultural regulations and policies. This study employs the ensemble empirical mode decomposition (EEMD) technique to decompose six different categories of agricultural futures prices. Subsequently, three models—support vector machine (SVM), neural network (NN), and autoregressive integrated moving average (ARIMA)—are used to predict the decomposition components. The final hybrid model is then constructed by comparing the prediction performance of the decomposition components. The predicting performance of the combination model is then compared with the benchmark individual models: SVM, NN, and ARIMA. Our main interest in this study is on short-term forecasting, and thus we only consider 1-day and 3-day forecast horizons. The results indicate that the prediction performance of the EEMD combined model is better than that of individual models, especially for the 3-day forecasting horizon. The study also concluded that the machine learning methods outperform the statistical methods in forecasting high-frequency volatile components. However, there is no obvious difference between individual models in predicting low-frequency components.     

Estimation of froth flotation recovery and collision probability based on operational parameters using an artificial neural network

An artificial neural network and regression procedures were used to predict the recovery and collision probability of quartz flotation concentrate in different operational conditions. Flotation parameters, such as dimensionless numbers (Froude, Reynolds, and Weber), particle size, air flow rate, bubble diameter, and bubble rise velocity, were used as inputs to both methods. The linear regression method shows that the relationships between flotation parameters and the recovery and collision probability of flotation can achieve correlation coefficients (R2) of 0.54 and 0.87, respectively. A feed-forward artificial neural network with 3-3-3-2 arrangement is able to simultaneously estimate the recovery and collision probability as the outputs. In testing stages, the quite satisfactory correlation coefficient of 0.98 was achieved for both outputs. It shows that the proposed neural network models can be used to determine the most advantageous operational conditions for the expected recovery and collision probability in the froth flotation process.     

Investigation on the homogenization treatment and element segregation on the microstructure of a γ/γ′-cobalt-based superalloy

The aim of the present study was to investigate the effect of element segregation on the microstructure and γ′ phase in a γ/γ′ cobalt-based superalloy. Several samples were prepared from a cast alloy and homogenized at 1300°C for different times, with a maximum of 24 h. A microstructural study of the cast alloy using wavelength-dispersive spectroscopic analysis revealed that elements such as Al, Ti, and Ni segregated mostly within interdendritic regions, whereas W atoms were segregated within dendrite cores. With an increase in homogenization time, segregation decreased and the initial dendritic structure was eliminated. Field-emission scanning electron microscopy micrographs showed that the γ′ phases in the cores and interdendritic regions of the as-cast alloy were 392 and 124 nm, respectively. The size difference of γ′ was found to be due to the different segregation behaviors of constituent elements during solidification. After homogenization, particularly after 16 h, segregation decreased; thus, the size, chemical composition, and hardness of the precipitated γ′ phase was mostly uniform throughout the samples.     

Kinetic modeling of copper bioleaching from low-grade ore from the Shahrbabak Copper Complex

The copper recovery from low-grade copper sulfide ore was investigated using microbial leaching. Several parameters substantially affect the bioleaching of copper; among them, pulp density and nutrient media were selected for investigation. The optimum conditions for copper recovery were a pulp density of 5 g/mL, a mixed-mineral salt medium of Acidithiobacillus thiooxidans (70vol%) and Acidithiobacillus ferrooxidans (30vol%), and 10vol% of inoculum. Under these conditions, the maximum bioleaching capacity of the medium for copper recovery was determined to be approximately 99%. The effect of pulp density on the kinetics of the bioleaching process was surveyed using both da Silva's method and constrained multilinear regression analysis. The kinetics of copper dissolution followed the shrinking core model, and the process was diffusion controlled at a pulp density of 5 g/mL. Nevertheless, at higher pulp densities, the process was controlled by chemical reaction.