Prediction Of Cu Research Articles

A Machine Learning Zircon Trace Element Tool to Predict Porphyry Deposit Type and Resource Size

AbstractPorphyry deposits are primarily known for their association with base metals like copper and to some extent molybdenum and gold. Here we present machine learning models, based on zircon composition, that provide quantitative distinction between different deposit types and resource sizes. Using a global zircon compositional database for different porphyry deposits (9,649 samples), we trained several machine learning models. A porphyry deposit type model (PDT model) was developed using XGBoost, which distinguishes between barren, Cu, and Mo bearing deposits. Furthermore, porphyry Cu and Mo reserve models (Porphyry Cu Reserve [PCR] and Porphyry Mo Reserve [PMR] model) were also developed using XGBoost and LightGBM, respectively, to give prediction of resource size in unexplored area. F1‐scores for the models are 0.97, 0.91, and 0.82. The model‐built feature importance and Shapley Additive exPlanations values imply that (EuN/EuN*)/Y, Th/U, Th/U and Ce are important in the PDT model, Ti, T (°C), U, and Hf are important for the PCR model, and Hf, U, Th/U, and EuN/EuN* are important for the PMR model. From a mineral system perspective, the three models imply that water, temperature, and magma evolution are pivotal to the type of deposits that forms. Temperature and magma evolution in particular are important in prediction of Cu and Mo resource size. Application of models to the Wunugetushan deposit gives ore type and resource predictions that are consistent with known deposit occurrence and geochemistry. These findings suggest that machine learning models may not only assist in understanding the main geological processes linked to porphyry mineralization, but also have application in reducing exploration risk.

Multidimensional information fusion and analysis of ultraviolet absorption spectroscopy: Simultaneous detection method for copper and zinc ion concentrations in seawater

Heavy metal pollution poses a significant threat to marine ecological environments and serves as a crucial indicator for seawater quality assessment. The enrichment of heavy metals severely impacts marine ecosystem health and poses threats to human food safety, underscoring the critical importance of monitoring heavy metal levels in marine waters. In this paper, UV absorption spectroscopy was used to collect the spectra of seawater containing different concentrations of Cu and Zn ions, which overcomes the problems of traditional methods that require extensive pre-treatment and lossy samples. To address the characteristics of the data, the Savitzky-Golay (SG) filtering algorithm is used for preprocessing. The acquired spectra are then subjected to further data analysis using the proposed multidimensional information fusion method called SpectraNet Fusion Algorithm (SFA) for UV absorption spectroscopy. The local feature information extraction module and the whole domain information feature extraction module were respectively used to predict the regression of the data in parallel two-channel, and the output results were integrated to form a new feature map, which was further processed by the feature information fusion module, and finally output the prediction results corresponding to the spectra to realize the accurate detection of Cu and Zn heavy metal ions in seawater. The coefficient of determination (R2) of the model training set established by this method reaches 98.00 %, the root mean square error (RMSE) reaches 0.0389, and the mean absolute error (MAE) reaches 0.0243, combined with the experimental design, the detection range was 5 μg·L−1 to 90 μg·L−1,which realizes simultaneous and high-precision prediction of Cu and Zn heavy metal ions in seawater.

Would it be better for partition prediction of heavy metal concentration in soils based on the fusion of XRF and Vis-NIR data?

Heavy metal (HM) contamination in soil necessitates effective methods to diagnose suspected contaminated areas and control rehabilitation processes. The synergistic use of proximal sensors demonstrates significant potential for rapid detection via accurate surveys of soil HM pollution at large scales and high sampling densities, and necessitates the selection of appropriate data mining and modeling methods for early diagnosis of soil pollution. The aim of this study is to evaluate the performance of a subarea model based on geographically partitioned and global models based on high-precision energy dispersive X-ray fluorescence (HD-XRF) and visible near-infrared (vis-NIR) spectra using a random forest model for predicting soil Cu and Pb concentrations. A total of 166 soil samples are acquired from a contaminated plot in Baiyin, Gansu Province, China. The soil samples are subjected to HM analysis and proximal sensor scanning in a laboratory. Vis-NIR spectral data are preprocessed using the Savitzky Golay (SG) and first-order derivative with Savitzky Golay (SGFD) methods. The results show that for predicting Cu and Pb concentrations in soil, the subarea models performs better than the global models in terms of quantitative prediction, based solely on individual HD-XRF data. For the subarea and global models, the R2 values are 0.961 and 0.981, respectively; the RMSE values are 27.8 and 79.6, respectively; and the RPD values are 4.96 and 7.38, respectively. However, making use of the random forest algorithm trained with data fusion obtained from the HD-XRF and vis-NIR sensors, the global model achieves the best predictions for Cu and Pb concentrations via HD-XRF + vis-NIR (SGFD) and HD-XRF + vis-NIR (SG), respectively. The results will provide a new perspective for modeling approaches to rapidly invert HM concentrations based on proximal sensor data fusion within a large scope of the study area.

Prediction of Cu(II) retention, sorption mechanism and biochar effect in tropical soil

The sorption behavior of potentially toxic metals in soils influences their spread to other environmental compartments and their bioavailability to life forms. The present study evaluated the sorption of Cu(II) ions in two representative soils from Yala and Tindinyo regions along River Yala in Lake Victoria basin, Kenya. The sorption experiments were carried out in single component solution systems using the batch technique. The equilibrium data was modeled using Henry’s isotherm model and four two-parameter isotherm models. Freundlich and bi-phasic Henry’s isotherm models best described the data, while both negative constants for Langmuir and Elovich necessitated rejection of the models. The y-intercept values for Henry’s monophasic fitting were negative, but the corresponding soil solution values of 0.43 μg/mL and 0.45 μg/mL for the Yala and Tindinyo soils, respectively, were taken as the background Cu(II) solution levels. We propose that in cases where the y-intercepts are negative, the bi-phasic Henry’s model can be used to predict a soil’s critical retention limit for a given pollutant, while the corresponding soil solution amount gives the permissible level in the soil solution. Using bi-phasic Henry’s model, the critical Cu(II) retention limits were determined as 300.11 μg/g and 301.48 μg/g for Yala and Tindinyo soils, respectively. The corresponding soil solution amounts were 3.47 μg/mL and 3.36 μg/mL for Yala and Tindinyo soils, respectively. The background and equilibrium soil solutions were below and above the WHO limit of 2 μg/mL for drinking water, respectively, while the sorbed amount was above the WHO limit of 36 μg/g soil. These findings indicate that a contaminant in soil may not pose any toxicity risk unless its concentration exceeds the critical retention limit, and that a pollutant’s toxicity is dependent on the soil solution amount rather than the total amount in soil. Dissolved organic carbon was more dominant than clay fractions in influencing the degree of sorption in both soils. Surface complexation between Cu(II) ions and carboxylic functional groups is hypothesized as a plausible sorption mechanism. Immobilization of Cu(II) in soil was significantly increased by amendment with fish-scales-derived biochar which, therefore, has promising potential application in mitigating environmental contamination by metals.

(Invited) Metallizations for Advanced Interconnects and Challenges for Future Nodes

The dimensional scaling of the back-end of line (BeOL)interconnects is a significant challenge for the deposition and fill of conductive metals in narrow lines and small vias that are needed to connect the semiconductor devices. Especially at the lower and the smaller interconnect levels, the scaling of the copper (Cu) dual damascene is becoming the limiting factor due to the increase in the resistance-capacitance delay. The increase in RC delay results in a degradation of the chip performance. So, while the scaling in the logic device landscape leads to a continuous improvement of the device performance and an increased transistor density, the Cu wiring in the interconnects systems tend to perform worse when scaling down the dimensions. This paper addresses methodologies to continue the scaling of the BEOL interconnects down to small CD’s like 12nm. For this, process and materials innovation are the key to reduce the interconnect area and its resistance.[1] Examples that will be discussed include Cu hybrid metallization and the use of new conductor materials or integration methodologies like metal patterning.In being the workhorse for building multilevel interconnects, the first desired direction is to push and extend the conventional Cu dual damascene metallization to small dimensions. However, extending Cu is not only challenging from a metal fill point of view, but also from the resistance as well as reliability point of view. The ideal metal that could replace the conventional Cu should have a low electrical resistance in scaled dimensions, have a good thermal conductivity, is resistant against oxidation and possesses a high melting point.[2] This melting is a good measure for the ease of electromigration due to metal diffusion where a high melting point would allow for a reliable operation without the need for a barrier material to prevent it to diffuse. This brings Ru, Mo and W in the picture as interesting material to replace Cu in the vias, and potential later in the lines as well. Figure 1 (left) shows the tabulated via and line resistance predictions for Cu and alternative metals to Cu like Co, Ru, Mo and W (method described elsewhere [3]). The red color coding is used to indicate too high resistance values, where green indicates the desired target resistance. The resistance benefit for the use of Ru, Mo or W compared to Cu is clearly visible in the table. An efficient way to introduce a new alternative metal in the Cu interconnect metallization without being too disruptive is using a selective metal deposition for the vias landing on the exposed bottom metal (Fig.1 middle). After the vias are filled using a selective metal-on-metal deposition with a barrierless metal like Ru or W [4,5], the remainder of the structures can be filled using the conventional Cu metallization scheme. This process is called a Cu hybrid metallization scheme. Filling the vias before the Cu line metallization, improves the process window and yield for the Cu gapfill. Challenges for the selective deposition of metals in vias will be discussed. The XTEM in Figure 1 (right) shows a successful example of the metal prefill in a via hole with bottom CD of 14nm. The via is nicely filled with the metal while the top lines in the dielectric that are not connected to vias do not show any non-selective deposition.Even though the vias are becoming more and more critical in the signal routing on a system-on-chip level, the resistance penalty for the Cu lines is unacceptable at small CDs as can be seen in the table in Fig.1. But eventually, the Cu electromigration will set the limit because at 10nm CD copper lines are not expected to meet electromigration requirements anymore [6]. This is then an inflection point to also replace the lines with alternative metals like Ru, Mo or more exotic conductors like binary metals. For these metals, the challenges in the line fill, processing, and integration will be discussed which may lead to the introduction of the so-called semi-damascene module [7] instead of using the dual damascene methodology.[1] J. Clarke et al, IEEE VLSI 2014, p. 176[2] D. Gall et al, J. Appl. Phys. 2016, 119, p.085101[3] I. Ciofi et al, IEEE transactions on Electron Devices 2017, 64 (5), p.2306 [5] M.H. van der Veen et al, Proc. of the IITC 2021, S7-2 [4] M. van der Veen et al, Proc. of the IITC 2020, p.16 [6] K. Croes et al, IEDM 2018, p 5.3.1[7] Zs. Tőkei et al, IEDM 2020, p 32.2.2 Figure 1

Prediction of Undrained Shear Strength by the GMDH-Type Neural Network Using SPT-Value and Soil Physical Properties.

This study presents a novel method for predicting the undrained shear strength (cu) using artificial intelligence technology. The cu value is critical in geotechnical applications and difficult to directly determine without laboratory tests. The group method of data handling (GMDH)-type neural network (NN) was utilized for the prediction of cu. The GMDH-type NN models were designed with various combinations of input parameters. In the prediction, the effective stress (σv’), standard penetration test result (NSPT), liquid limit (LL), plastic limit (PL), and plasticity index (PI) were used as input parameters in the design of the prediction models. In addition, the GMDH-type NN models were compared with the most commonly used method (i.e., linear regression) and other regression models such as random forest (RF) and support vector regression (SVR) models as comparative methods. In order to evaluate each model, the correlation coefficient (R2), mean absolute error (MAE), and root mean square error (RMSE) were calculated for different input parameter combinations. The most effective model, the GMDH-type NN with input parameters (e.g., σv’, NSPT, LL, PL, PI), had a higher correlation coefficient (R2 = 0.83) and lower error rates (MAE = 14.64 and RMSE = 22.74) than other methods used in the prediction of cu value. Furthermore, the impact of input variables on the model output was investigated using the SHAP (SHApley Additive ExPlanations) technique based on the extreme gradient boosting (XGBoost) ensemble learning algorithm. The results demonstrated that using the GMDH-type NN is an efficient method in obtaining a new empirical mathematical model to provide a reliable prediction of the undrained shear strength of soils.

High accuracy prediction of Cu(II) cations removal from synthetic aqueous solutions with ANNs using normal and modified biosorbent

ABSTRACT This study aims to accurately predict Cu(II) removal efficiencies from synthetic aqueous solutions with Artificial Neural Networks (ANNs) using normal and modified Abies bornmulleriana cone. The predicted values of Cu(II) removal efficiencies for normal and modified Abies bornmulleriana cone were determined using a single ANNs model structure. In the present investigation, the neural network’s input parameters are biosorption time, biosorbate concentration, biosorbent amount, pH, particle size, agitation rate, and temperature. The removal efficiencies of the Cu (II) ions were selected as the experimental responses. The optimal network design is (7:7-9-2:2). Sensitivity analysis was employed to estimate the relative contribution of the input parameters to the ANN’s performance. The most critical parameter affecting the removal efficiencies of the Cu(II) was defined as biosorption time. The ANNs model for the normal and the modified Abies bornmulleriana cone had determination coefficients of (0.95, 0.98), standard deviation ratios of (0.22, 0.14), root mean square errors of (27.89, 26.02), and mean absolute errors of (2.21, 2.06), respectively. The outcomes from the network signify that the ANNs model could predict high accuracy Cu (II) removal efficiencies for both biosorbents.

EPR Spectroscopy of Cu(II) Complexes: Prediction of g-Tensors Using Double-Hybrid Density Functional Theory

Computational electron paramagnetic resonance (EPR) spectroscopy is an important field of applied quantum chemistry that contributes greatly to connecting spectroscopic observations with the fundamental description of electronic structure for open-shell molecules. However, not all EPR parameters can be predicted accurately and reliably for all chemical systems. Among transition metal ions, Cu(II) centers in inorganic chemistry and biology, and their associated EPR properties such as hyperfine coupling and g-tensors, pose exceptional difficulties for all levels of quantum chemistry. In the present work, we approach the problem of Cu(II) g-tensor calculations using double-hybrid density functional theory (DHDFT). Using a reference set of 18 structurally and spectroscopically characterized Cu(II) complexes, we evaluate a wide range of modern double-hybrid density functionals (DHDFs) that have not been applied previously to this problem. Our results suggest that the current generation of DHDFs consistently and systematically outperform other computational approaches. The B2GP-PLYP and PBE0-DH functionals are singled out as the best DHDFs on average for the prediction of Cu(II) g-tensors. The performance of the different functionals is discussed and suggestions are made for practical applications and future methodological developments.

Comparison of Density Functional and Correlated Wave Function Methods for the Prediction of Cu(II) Hyperfine Coupling Constants.

The reliable prediction of Cu(II) hyperfine coupling constants remains a challenge for quantum chemistry. Until recently only density functional theory (DFT) could target this property for systems of realistic size. However, wave function based methods become increasingly applicable. In the present work, we define a large set of Cu(II) complexes with experimentally known hyperfine coupling constants and use it to investigate the performance of modern quantum chemical methods for the prediction of this challenging spectroscopic parameter. DFT methods are evaluated against orbital‐optimized second‐order Møller‐Plesset (OO‐MP2) theory and coupled cluster calculations including singles and doubles excitations, driven by the domain‐based local pair natural orbital approach (DLPNO‐CCSD). Special attention is paid to the definition of a basis set that converges adequately toward the basis set limit for the given property for all methods considered in this study, and a specifically optimized basis set is proposed for this purpose. The results suggest that wave function based methods can supplant but do not outcompete DFT for the calculation of Cu(II) hyperfine coupling constants. Mainstream hybrid functionals such as B3PW91 remain on average the best choice.

Neural network prediction of bioleaching of metals from waste computer printed circuit boards using Levenberg‐Marquardt algorithm

AbstractThe applicability of artificial neural network (ANN) to predict the bioleaching of metals using from computer printed circuit boards (CPCB) and the influence of process parameters were studied. The influence of process parameters initial pH (1.6‐2.4), pulp density (2%‐13%), and the initial volume of Inoculum (5%‐25%) were investigated on the rate of bioleaching of metals from CPCB. Network inputs were fed as initial pH, pulp density, and inoculum volume and with the extraction of Cu, Ag, and Au as output. The ANN was developed using the Levenberg‐Marquardt algorithm and trained for modeling and prediction. The most fitting architectures for Cu, Ag, and Au were [4‐5‐5‐2‐1], [4‐7‐5‐2‐1], [4‐7‐1‐1‐1] trained with Levenberg‐Marquardt algorithm, respectively. The R values were observed to be 0.996, 0.997, and 0.993 for Cu, Ag, and Au extraction predictions, respectively. The genetic algorithm model defined by ANN was used to achieve maximum extraction rates for Cu, Au, and Ag. The predicted data showed that there is a great capability of using ANN for the prediction of Cu, Ag, and Au extraction from CPCB through bioleaching process. Hence, the ANN model can be used to control the operational conditions for improved metals extraction through bioleaching.

Deriving lifetime predictions for wire bonds at high temperatures

We report a method to predict wire bond lifetimes. Wire bond resistance increase due to thermal stress can be modelled by the Arrhenius equation, permitting prediction of wire bond reliability in a product prior to qualification if sufficient experimental data is collected and statistically analysed. Activation energies for various experiment “legs” are determined by stressing a number of wire bonds at 3 different temperatures, then, using survival analysis techniques for accelerated failure time models, extrapolating results to use conditions. Example lifetime predictions for Cu and PCC wire bonds on Al pads are shown to illustrate the method, using a small number of test dies that incorporate four-wire resistance measurements of wire bond pairs, with two ball bond sizes and two different wire types, stressed at precise temperatures with in-situ resistance monitoring in the University of Waterloo's “mini-oven” reliability test systems. These results show that typical Cu wire bonds on Al bond pads can be highly reliable up to 163 °C without encapsulation or 155 °C with encapsulation, with estimated lifetimes of 12,000 h with <1 ppm failure rate, assuming a failure condition of 10% resistance change.

Biosorption of copper(II) ions by flax meal: Empirical modeling and process optimization by response surface methodology (RSM) and artificial neural network (ANN) simulation

Abstract In the present study, application of waste flax meal was investigated for the removal of copper(II) ions from aqueous solution. The effect of operating parameters such as metal ions concentration (20–200 ppm), biosorbent dosage (1–10 g/L) and solution pH (2–5) was modeled by both response surface methodology (RSM) and artificial neural network (ANN). This study compares central composite design (CCD), Box–Behnken design (BBD) and full factorial design (FFD) utility for modeling and optimization by response surface methodology. The best statistical predictability and accuracy resulted from CCD ( R 2 = 0.997, MSE = 0.34). Maximum biosorption efficiency expressed as the sorption capacity, which was found to be 34.4 mg/g, at initial Cu 2+ concentration of 200 ppm, biosorbent dosage of 1 g/L and initial solution pH of 5. The precision of the equation obtained by RSM was confirmed by the analysis of variance and calculation of correlation coefficient relating the predicted and the experimental values of biosorption efficiency. A feed-forward neural network with a topology optimized by response surface methodology was applied successfully for prediction of biosorption performance for the removal of Cu 2+ ions by waste flax meal. The number of hidden neurons, the number of epochs, the adaptive value and the training goal were chosen for optimization. The multilayer perceptron with three neurons in one input layer, twenty two neurons in one hidden layer and one neuron in one output layer were required to build the model. The neural network turned out to be more accurate than RSM model in the prediction of Cu 2+ biosorption by flax meal. The novelty of this paper is application of response surface methodology in order to optimize artificial neural network topology. The research on modeling biosorption by RSM and ANN has been highly developed and new waste material flax meal as potential biosorbent has been proposed.

Impact of Maternal Prenatal Risk Factors On Oxytocin Methylation, and On Occurrence of Conduct Disorders During Adolescence

Youth with high callous-unemotional traits (CU) are at risk for early-onset and persistent conduct problems. Research suggests that there may be different developmental pathways to CU (genetic/constitutional vs environmental), and that the absence or presence of co-occurring internalizing problems is a key marker. However, it is unclear whether such a distinction is valid. Intermediate phenotypes such as DNA methylation, an epigenetic modification regulating gene expression, may help to clarify etiological pathways. This is the first study to examine prospective inter-relationships between environmental risk (prenatal/postnatal) and DNA methylation (birth, age 7 and 9) in the prediction of CU (age 13), for youth low vs high in internalizing problems. We focused on DNA methylation in the vicinity of the oxytocin receptor (OXTR) gene as it has been previously implicated in CU. Participants were 84 youth with early-onset and persistent conduct problems drawn from the Avon Longitudinal Study of Parents and Children. For youth with low internalizing problems (46%), we found that (i) OXTR methylation at birth associated with higher CU (age 13) as well as decreased experience of victimization during childhood (birth–age 9), (ii) higher prenatal parental risks (maternal psychopathology, criminal behaviors, substance use) associated with higher OXTR methylation at birth and (iii) OXTR methylation levels were more stable across time (birth–age 9). In contrast, for youth with high internalizing problems, CU were associated with prenatal risks of an interpersonal nature (that is, intimate partner violence, family conflict) but not OXTR methylation. Findings support the existence of distinct developmental pathways to CU.

Using soil-specific partition coefficients to improve accuracy of the new South African guideline for contaminated land

Contaminated land in South Africa is regulated through the National Environmental Management Waste Act (Act 59 of 2008) (NEMWA) and the National Norms and Standards for the Remediation of Contaminated Land and Soil Quality (NSCLA) (GN R.331 of 2014). These standards were obtained from the Framework for the Management of Contaminated Land. A soil screening value (SSV1) for the protection of groundwater resources is proposed which is based on a 2-phase (stage) equilibrium partitioning and dilution model which includes a dilution factor and partitioning coefficient (Kd), converting the water quality guideline to a total soil screening value. The appropriateness of the screening values has been questioned because of the uncertainties surrounding the Kd values used by the Framework. This paper investigates the Kd values of Cu, Pb, and V for selected South African diagnostic soil horizons to evaluate the reliability of the current Kd values used by the Framework during Phase 1 screening. The Kd values of Cu for the 10 horizons ranged between 13 and 19 044 l∙kg -1 , all exceeding the value of 10 l∙kg -1 provided by the Framework. For Pb the values ranged from 25 to >252 294 l∙kg -1 as compared to the Framework’s 100 l∙kg -1 . Similarly, the Kd value of 200 l∙kg -1 for V recommended by the Framework is higher than the measured Kd value of 15 to 173 l∙kg -1 for all 10 diagnostic horizons. This study demonstrated that the observed wide Kd value ranges for each element were related to the variation in basic soil properties such as soil pH, organic carbon, clay, Fe, and Al content. Therefore, the Kd values for Cu, Pb, and V currently used by the Framework are not representative of typical South African diagnostic soil horizons. Linear regression models were developed for the prediction of Cu, Pb, and V Kd values from measured soil properties, which could be used to generate soil-specific Kd values. Keywords : Kd value, contaminated land, NEMWA, screening value, risk, soil

Environmental risk, Oxytocin Receptor Gene (OXTR) methylation and youth callous-unemotional traits: a 13-year longitudinal study.

Youth with high callous-unemotional traits (CU) are at risk for early-onset and persistent conduct problems. Research suggests that there may be different developmental pathways to CU (genetic/constitutional vs environmental), and that the absence or presence of co-occurring internalizing problems is a key marker. However, it is unclear whether such a distinction is valid. Intermediate phenotypes such as DNA methylation, an epigenetic modification regulating gene expression, may help to clarify aetiological pathways. This is the first study to examine prospective inter-relationships between environmental risk (prenatal/postnatal) and DNA methylation (birth, age 7, age 9) in the prediction of CU (age 13), for youth low vs. high in internalizing problems. We focused on DNA methylation in the vicinity of the oxytocin receptor (OXTR) gene as it has been previously implicated in CU. Participants were 84 youth with early-onset and persistent conduct problems drawn from the Avon Longitudinal Study of Parents and Children. For youth with low internalizing problems (46%), we found that: (i) OXTR methylation at birth associated with higher CU (age 13) as well as decreased experience of victimization during childhood (birth – age 9), (ii) higher prenatal parental risks (maternal psychopathology, criminal behaviors, substance use) associated with higher OXTR methylation at birth, and (iii) OXTR methylation levels were more stable across time (birth – age 9). In contrast, for youth with high internalizing problems, CU was associated with prenatal risks of an interpersonal nature (i.e., intimate partner violence, family conflict) but not OXTR methylation. Findings support the existence of distinct developmental pathways to CU.

Artificial Neural Network prediction of Cu–Al2O3 composite properties prepared by powder metallurgy method

Artificial Neural Networks (ANNs) are excellent tools for prediction of complex processes that have many variables and complex interactions. In the present study, the properties of copper based composite prepared from sintering of mechanically alloyed powders, were predicted using Artificial Neural Network (ANN) approach. In order to prepare copper based composites, copper powder with four different amounts of Al2O3 reinforcement (1, 1.5, 2, 2.5wt%) were mechanically alloyed and the consolidated compacts of prepared powders were sintered in five different temperatures of 725–925°C at seven several sintering times of 15–180min. Hardness and electrical conductivity measurements were performed to evaluate the properties of these composites. Then, for modeling and prediction of hardness and electrical conductivity, a multi layer perceptron back propagation feed forward neural network was constructed to evaluate and compare the experimental calculated data to predicted values. It was found that, in a given sintering temperature of 875°C, the electrical conductivity increases as the sintering time increases and the amount of Al2O3 reinforcement decreases. Also, increasing of reinforcement amount and sintering time in a given sintering temperature of 875°C leads to a decrease in hardness. Furthermore, electrical conductivity and hardness of specimens have shown a consistency with predicted results of ANN. These trained values had an average error of 3% and 5% for electrical conductivity and hardness values, respectively.

Numerical modelling, prediction of Cu–W nano powder composite in dry sliding wear condition using response surface methodology

High-energy mechanical milling was used to reduce the size and mix Cu and W powders. Extruded cylindrical preforms with theoretical density of 93% were prepared using a die and punch assembly with the help of sintering in an electric muffle furnace from 750 to 850°C, and subsequently furnace cooled. Scanning Electron Microscope, Transmission Electron Microscope, Particle Size Analyser, X-ray Diffraction and Energy Dispersive Spectrum were used for characterisation. The experiments were conducted by using a dry sliding wear tester at room condition. Response surface methodology five level central composite approach design was used to minimise the number of experimental conditions and develop mathematical models between the key process parameters namely weight percentage of tungsten, sintering temperature, load and sliding distance to predict the abrasive response of specific wear rate and coefficient of friction. Analysis of variance was used to validate the developed model. The developed mathematical model is effectively used to predict the specific wear rate and coefficient of friction at 99% confidence level.

Recovery and grade accurate prediction of pilot plant flotation column concentrate: Neural network and statistical techniques

In this study, the metallurgical performance (grade and recovery) forecasting of pilot plant flotation column using Artificial Neural Networks (ANN) and Multivariate Non-Linear Regression (MNLR) models is investigated. Modeling is performed based on 90 datasets at different operating conditions. The values of chemical reagents dosage, froth height, air and wash water flow rates, gas holdup and Cu, Mo grades in the rougher feed and flotation column feed, column tail and final concentrate streams are used to the simulation by means of NN and MNLR. The model validation analysis demonstrates the capability of both models to predict Cu and Mo grades and recoveries for a wide range of operating conditions in pilot flotation columns. It must be noted that ANN approach offers superior predictive capability over statistical method. It was also found that the error in prediction of metallurgical performance using the NN model was less than the error of the regression model. The best network is proposed with multi-layer perceptron (MLP) model, sigmoid activation function and Levenberg–Marquardt learning rule with 8-12-8-2 and 8-9-12-2 architectures, in order to estimate metallurgical performance of Cu and Mo respectively in flotation column. The results of this study indicate that a back-propagation neural network model with Root Mean Square Errors (RMSE) of 0.68 and 0.02 for prediction of Cu and Mo grades and 0.48 and 1.16 for prediction of Cu and Mo recoveries respectively has a better performance than the statistical method.

Size-Dependent Elastic Modulus and Vibration Frequency of Nanocrystals

The elastic properties and the vibration characterization are important for the stability of materials and devices, especially for nanomaterials with potential and broad application. Nanomaterials show different properties from the corresponding bulk materials; the valid theoretical model about the size effect of the elastic modulus and the vibration frequency is significant to guide the application of nanomaterials. In this paper, a unified analytical model about the size-dependent elastic modulus and vibration frequency of nanocrystalline metals, ceramics and semiconductors is established based on the inherent lattice strain and the binding energy change of nanocrystals compared with the bulk crystals, and the intrinsic correlation between the elasticity and the vibration properties is discussed. The theoretical predictions for Cu, Ag, Si thin films, nanoparticles, andTiO2nanoparticles agree with the experimental results, the computational simulations, and the other theoretical models.

Large Observed v 2 as a Signature for Deconfinement

We present a new plot for representing R AA(ø) data that emphasizes the strong correlation between high-p η suppression and its elliptic anisotropy. We demonstrate that existing models cannot reproduce the centrality dependence of this correlation. Modification of a geometric energy loss model to include thermal absorption and stimulated emission can match the trend of the data, but requires dN g/dy values inconsistent with the observed multiplicity. By including a small, outward-normal directed surface impulse opposing energy loss, \(\Delta p_ \bot \hat n\), one can account for the centrality dependence of the observed Au+Au elliptic quench pattern. We also present predictions for Cu+Cu reactions.