Beneficiation of low-grade iron ore fines by multi-gravity separator (MGS) using optimization studies

There is an urgent need for development of environmental friendly processes through which iron ore fines can be beneficiated and utilized effectively. For processing of low-grade iron ores, ground to finer size necessitates the use of centrifugal force. This is because the settling rate of the particles in centrifugal force is 500–600 times more than that noticed for the nominal gravitational force. Multi-gravity separator (MGS) is the one such unit used for recovering hematitic fines without addition of chemicals. In the present article, an approach has been made to beneficiate low-grade hematitic iron ore of Jilling mine, Odisha, India, using MGS. The effect of three important variables was studied and their influences were analyzed through statistical approach to optimize grade, recovery, and separation efficiency. Use of response surface methodology (RSM) based on Box–Behnken design has also been adopted for analysis purpose. The results obtained indicate that it is possible to beneficiate low-grade hematitic iron ore from a feed of 50.74% Fe to 65.11% Fe with an acceptable recovery of 71.88%. Optimization of process variables was done for each response, optimized independently irrespective of other responses. Further optimization of the variables was carried out with a multi-objective target.     

航天光学遥感器信噪比的人工神经网络评价

提出基于人工神经网络进行航天光学遥感器信噪比评价的方法,首先对航天遥感图像进行分析,从图像中将与景物结构和噪声有关的特征向量分别提取出来,作为ANN的输入。网络通过对大量信噪比已知的图像样本训练后,可完成对航天光学遥感器传输下来的任意一幅地面景物图像进行系统的信噪比测试,从而避免了采用特定景物目标进行测量中的诸多弊端,测量平均误差低于10%。     

Wavelet neural network (WNN) approach for calibration model building based on gasoline near infrared (NIR) spectra

In this paper we have compared the abilities of two types of artificial neural networks (ANN): multilayer perceptron (MLP) and wavelet neural network (WNN) — for prediction of three gasoline properties (density, benzene content and ethanol content). Three sets of near infrared (NIR) spectra (285, 285 and 375 gasoline spectra) were used for calibration models building. Cross-validation errors and structures of optimized MLP and WNN were compared for each sample set. Four different transfer functions (Morlet wavelet and Gaussian derivative – for WNN; logistic and hyperbolic tangent – for MLP) were also compared. Wavelet neural network was found to be more effective and robust than multilayer perceptron.     

Predicting of mechanical properties of Fe–Mn–(Al, Si) TRIP/TWIP steels using neural network modeling

In this work, an artificial neural network (ANN) model was established in order to predict the mechanical properties of transformation induced plasticity/twinning induced plasticity (TRIP/TWIP) steels. The model developed in this study was consider the contents of Mn (15–30 wt%), Si (2–4 wt%) and Al (2–4 wt%) as inputs, while, the total elongation, yield strength and tensile strength are presented as outputs. The optimal ANN architecture and training algorithm were determined. Comparing the predicted values by ANN with the experimental data indicates that trained neural network model provides accurate results.     

Nonlinear constitutive models for FRP composites using artificial neural networks

This paper presents a new approach to generate nonlinear and multi-axial constitutive models for fiber reinforced polymeric (FRP) composites using artificial neural networks (ANNs). The new nonlinear ANN constitutive models are complete and have been integrated with displacement-based FE software for the nonlinear analysis of composite structures. The proposed ANN constitutive models are trained with experimental data obtained from off-axis tension/compression and pure shear (Arcan) tests. The proposed ANN constitutive model is generated for plane–stress states with assumed functional response in some parts of the multi-axial stress space with no experimental data. The ability of the trained ANN models to predict material response is examined directly and through FE analysis of a notched composite plate. The experimental part of this study involved coupon testing of thick-section pultruded FRP E-glass/polyester material. Nonlinear response was pronounced including in the fiber direction due to the relatively low overall fiber volume fraction (FVF). Notched composite plates were also tested to verify the FE, with ANN material models, to predict general non-homogeneous responses at the structural level.     

Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks

Various low-cost adsorbents have been used for removing Cu(II) ions from aqueous solutions for the treatment of copper containing wastewaters to remove organic compounds and color. Sawdust is an impressive adsorbent in terms of adsorption efficiency, cost and availability; hence the use of sawdust as biosorbent has been widely studied. Many earlier investigations tried to correlate the experimental data with available models or some modified empirical equations, but these results were unable to predict the values of parameters from a single equation. Artificial neural networks (ANN) are effective in modeling and simulation of highly non-liner multivariable relationships. A well-designed and very well trained network can converge even on multiple number of variables at a time without any complex modeling and empirical calculations. In this present work ANN is applied for the prediction of percentage adsorption efficiency for the removal of Cu(II) ions from aqueous solutions by sawdust. Artificial neural network model, based on multilayered partial recurrent back-propagation algorithm has been used. The performance of the network for predicting the sorption efficiency of sawdust for copper is found to be very impressive.     

Biotreatment of zinc-containing wastewater in a sulfidogenic CSTR: Performance and artificial neural network (ANN) modelling studies

Sulfidogenic treatment of sulfate (2-10g/L) and zinc (65-677mg/L) containing simulated wastewater was studied in a mesophilic (35 degrees C) CSTR. Ethanol was supplemented (COD/sulfate=0.67) as carbon and energy source for sulfate-reducing bacteria (SRB). The robustness of the system was studied by increasing Zn, COD and sulfate loadings. Sulfate removal efficiency, which was 70% at 2g/L feed sulfate concentration, steadily decreased with increasing feed sulfate concentration and reached 40% at 10g/L. Over 99% Zn removal was attained due to the formation of zinc-sulfide precipitate. COD removal efficiency at 2g/L feed sulfate concentration was over 94%, whereas, it steadily decreased due to the accumulation of acetate at higher loadings. Alkalinity produced from acetate oxidation increased wastewater pH remarkably when feed sulfate concentration was 5g/L or lower. Electron flow from carbon oxidation to sulfate reduction averaged 83+/-13%. The rest of the electrons were most likely coupled with fermentative reactions as the amount of methane production was insignificant. The developed ANN model was very successful as an excellent to reasonable match was obtained between the measured and the predicted concentrations of sulfate (R=0.998), COD (R=0.993), acetate (R=0.976) and zinc (R=0.827) in the CSTR effluent.     

Application of artificial neural network method to predict the breakage properties of PGE bearing chromite ore

In the present investigation, systematic grinding experiments were conducted in a laboratory ball mill to determine the breakage properties of low-grade PGE bearing chromite ore. The population balance modeling technique was used to study the breakage parameters such as primary breakage distribution (B_{i, j}) and the specific rates of breakage (S_i). The breakage and selection function values were determined for six feed sizes. The results stated that the breakage follows the first-order grinding kinetics for all the feed sizes. It was observed that the coarser feed sizes exhibit higher selection function values than the finer feed size. Further, an artificial neural network was used to predict breakage characteristics of low-grade PGE bearing chromite ore. The predicted results obtained from the neural network modeling were close to the experimental results with a correlation of determination R² = 0.99 for both product size and selection function.     

小波包能量谱和BP神经网络在波纹管压浆超声检测中的应用

针对小波分析在信号处理的局限性,将小波包分析和反向传播(Back Propagation,BP)神经网络相结合,提出一种基于小波包能量谱和BP神经网络的波纹管压浆超声检测方法。采用超声检测方法接收波纹管模型的回波信号,以小波包分解后各子频带的能量作为检测特征,当波纹管内部出现脱落时,检测特征会发生变化,最后将特征输入BP神经网络中进行分类识别。试验结果表明,该方法能够理想地实现波纹管内部缺陷的诊断,可为波纹管超声检测提供一定的技术支持。     

Degradation of 4-nitrophenol (4-NP) using ZnO nanoparticles supported on zeolites and modeling of experimental results by artificial neural networks

In this paper, we report the synthesis of ZnO, ZnO/HZSM-5, ZnO/HY and ZnO/Clin by a poly acrylamide pyrolysis method for the first time. The presences of carbon network/cages in the poly acrylamide gel can effectively prevent particle agglomeration. The catalytic activity of all specimens was tested by carrying out the 4-nitrophenol degradation, used as a “probe” reaction, in the aqueous medium under ambient visible light. The prepared samples were characterized by X-ray diffraction (XRD), specific surface area (BET) and porosity determination, scanning electron microscopy (SEM) coupled with energy dispersive X-ray analysis (EDX), visible-ultraviolet diffuse reflectance spectroscopy (DRS) and Fourier transform infrared spectroscopy (FT-IR), to evaluate particle structure, size distribution and composition. The results revealed that among the catalysts, ZnO/HZSM-5 showed higher percentage of adsorption than others. The time required for complete mineralization of 4-NP under ambient visible light over ZnO/HZSM-5 was 75 min. The higher activity of ZnO/HZSM-5 is mainly due to fine dispersion of ZnO and hydrophobicity of the support. An artificial neural networks (ANNs) model was developed to predict the performance of catalytic degradation process over synthesized catalysts based on experimental data. A comparison between the predicted results of the designed ANN model and experimental data was also conducted.     

Liquid density prediction of five different classes of refrigerant systems (HCFCs,HFCs, HFEs,PFAs and PFAAs) using the artificial neural network-group contribution method

In this work, the densities of 48 refrigerant systems from 5 different categories including hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs), hydrofluoroethers (HFEs), perfluoroalkanes (PFAs), and perfluoroalkylalkanes (PFAAs) have been studied using a combined method that includes an artificial neural network (ANN) and a simple group contribution method (GCM). A total of 3825 data points of liquid density at several temperatures and pressures have been used to train, validate and test the model. This study shows that the ANN-GCM model represents an excellent alternative to estimate the density of different refrigerant systems with a good accuracy. The average absolute deviations for train, validation, and test sets are 0.18, 0.26, and 0.28, respectively. A comparison between our results and those obtained from some previous methods shows that as well as generality, this model can predict the density of different refrigerants in a better accord with experimental data up to high temperature, high pressure (HTHP) conditions.     

An exploratory study of the impact of information communication technology (ICT) or computer mediated communication (CMC) on the level of violence and access to service among intimate partner violence (IPV) survivors in Canada

This paper presents exploratory research on the use of information communication technologies (ICTs) or computer mediated technologies (CMCs) (i.e., cell phones and the internet) among immigrant women who are intimate partner violence survivors (IPV) in Canada. The discussion begins with a presentation of initial data examining the impact of such technology on the level and extent of violence experienced by IPV survivors, and on their ability to access appropriate services. Furthermore, an assessment of whether this form of technology aided in the development of a prevention or safety plan, is explored. The data is based on non-random sample surveys of immigrant women IPV survivors with Canada. While the limited scholarship on ICT or CMC usage indicates that there is a digital divide and that various socio-demographic factors do play a role in utilizing the technology, our data does not display a black and white or any streamlined pattern with regards to the digital divide and sociodemographics factors. An examination of the participants' various sociodemographics indicates that the digital divide within this population is not influenced by access or knowledge to the technology but by other factors often not discussed in the intersectionality models. An intersectional model of race and immigration status along with the existing literature on intimate partner violence among immigrant women especially issues of isolation and social networking inform this paper.     

Ba(OH)2 Equilibria in the System Ba-O-H-F,With Application to the Formation of Ba2YCu3O6.5 + x From BaF2-Precursors

The ex situ process for fabricating Ba₂YCu₃O_{6.5 + x} superconducting tapes from BaF₂- based precursors involves a hydration/oxidation reaction at ≈730 °C to 750 °C generally written as: (2BaF2+Y+3Cu)(amorphous)+(2H2O+2.25O2)(g)→Ba2YCu3O6.5+x(s)+4HF(g).However, microscopic observations of partially processed films suggest the presence of a transient liquid phase during conversion. Alternatively, the conversion reaction can be rewritten as the sum of several intermediate steps, including the formation of a barium hydroxide liquid: (BaF2)(amorphous)+2H2O(g)→Ba(OH)2(liq)+2HF(g).To evaluate the possibility of a hydroxide liquid conversion step, thermodynamic calculations on the stability of Ba(OH)₂(liq) have been completed from 500 °C to 900 °C at 0.1 MPa p_total. Based on currently available data, the calculated phase diagrams suggest that a viable hydroxide reaction path exists in the higher part of this temperature range. The calculations indicate that Ba(OH)₂(liq) may be stable at log p_H2O (Pa) values from ≈4 to 5, provided log p_HF (Pa) values can be maintained below 0 to −1. Limited experimental confirmation is provided by results of an experiment on BaF₂(s) at 815 °C, 0.1 MPa p_H2O, in which essentially all F at the surface was replaced by O. It is therefore possible that processing routes exist for producing Ba₂YCu₃O_{6.5 + x} based on the presence of a Ba(OH)₂ liquid, which might have an effect on conversion rates and texturing in the superconducting film.