Artificial neural network modeling in competitive adsorption of phenol and resorcinol from water environment using some carbonaceous adsorbents

This paper illustrates the application of artificial neural network (ANN) for prediction of performances in competitive adsorption of phenol and resorcinol from aqueous solution by conventional and low cost carbonaceous adsorbent materials, such as activated carbon (AC), wood charcoal (WC) and rice husk ash (RHA). The three layer's feed forward neural network with back propagation algorithm in MATLAB environment was used for estimation of removal efficiencies of phenol and resorcinol in bi-solute water environment based on 29 sets of laboratory batch study results. The input parameters used for training of the neural network include amount of adsorbent (g/L), initial concentrations of phenol (mg/L) and resorcinol (mg/L), contact time (h), and pH. The removal efficiencies of phenol and resorcinol were considered as an output of the neural network. The performances of the developed ANN models were also measured using statistical parameters, such as mean error, mean square error, root mean square error, and linear regression. The comparison of the removal efficiencies of pollutants using ANN model and experimental results showed that ANN modeling in competitive adsorption of phenolic compounds reasonably corroborated with the experimental results.     

Thermodynamic properties of lubricant/refrigerant mixtures using statistical mechanics and artificial intelligence

In this research, the volumetric properties of sixteen lubricant/refrigerant mixtures are predicted using the developed statistical mechanical equation of state at a broad range of temperatures, pressures and mole fractions. The equation of state have been examined using corresponding states correlation based on just one input parameter (density at room temperature) as scaling constants. Besides, the artificial neural network (ANN) based on back propagation training with 19 neurons in hidden layer was tested to predict the behavior of binary mixtures of lubricant/refrigerant. The AADs% of a collection of 3961 data points for all binary mixtures using the EOS and the ANN at various temperatures and mole fractions are 0.92% and 0.34%, respectively. Furthermore, the excess molar volume of all binary mixtures calculated from obtained densities of ANN, and the results shown these properties have good harmony with literature.     

Modeling of Magnetic Properties of Nanocrystalline La-doped Barium Hexaferrite

In this paper an artificial neural network (ANN) has been developed to compute the magnetization of the pure and La-doped barium ferrite powders synthesized in ammonium nitrate melt. The input parameters were: the Fe/Ba ratio, La content, sintering temperature, HCl washing and applied magnetic field. A total of 8284 input data set from currently measured 35 different samples with different Fe/Ba ratios, La contents and washed or not washed in HCl were available. These data were used in the training set for the multilayer perceptron (MLP) neural network trained by Levenberg–Marquardt learning algorithm. The hyperbolic tangent and sigmoid transfer functions were used in the hidden layer and output layer, respectively. The correlation coefficients for the magnetization were found to be 0.9999 after the network was trained.     

Giant Magnetoimpedance Effect: Concept and Prediction in Amorphous Materials

Giant magneto impedance (GMI) effect was experimentally measured on as-cast, post-production and coated with chemical technique amorphous wire and ribbon materials consisted of varied chemical composition over a frequency range from 0.1 to 8 MHz under a static magnetic field between ?8 and +8 kA/m. The results show that each amorphous sample has a certain operational frequency for which the GMI effect has maximum magnitude and the other parameters such as annealing and coating have a significant influence on the GMI effect. It is believed that the domain structure and wall mechanism in the material are responsible for this behaviour. A 3-node input layer, 1-node output layer artificial neural network (ANN) model with three hidden layers including 30 neurons and full connectivity between the nodes was developed. A total of 1600 input vectors obtained from varied treated samples was available in the training data set. After the network was trained, better results were obtained from the network formed by the hyperbolic tangent transfer function in the hidden layers, there was a sigmoid transfer function in the output layer and we predicted the GMI. Comparing the predicted values obtained from the ANN model with the experimental data indicates that a well-trained neural network model provides very accurate results.     

吲哚俘精酸酐实现三值偏振全息光学存储器

针对三值光计算机需要直接存储线偏振光束的问题,利用光致各向异性材料吲哚俘精酸酐对偏振态敏感的特性,提出了一种基于吲哚俘精酸酐/PMMA 薄膜的三值偏振全息数字存储方法,并建立了相应的光学存储器模型.该存储器系统采用He-Ne 激光器为记录和读出光源,以双层液晶和偏振器为核心的编码器作为三值数据的输入部件,以双CCD 为核心的解码器作为数据输出器件,采用傅里叶变换全息记录的方法,在吲哚俘精酸酐/PMMA薄膜上实现三值数字光学存储.该存储器系统可望实现直接并行存储用光束的正交线偏振态和无光态表示的三值数字信息,以及以页面为单位的并行寻址和读写操作.     

Generalized correlation of refrigerant mass flow rate through adiabatic capillary tubes using artificial neural network

A capillary tube is a common expansion device widely used in small-scale refrigeration and air-conditioning systems. Generalized correlation method for refrigerant flow rate through adiabatic capillary tubes is developed by combining dimensional analysis and artificial neural network (ANN). Dimensional analysis is utilized to provide the generalized dimensionless parameters and reduce the number of input parameters, while a three-layer feedforward ANN is served as a universal approximator of the nonlinear multi-input and single-output function. For ANN training and test, measured data for R12, R134a, R22, R290, R407C, R410A, and R600a in the open literature are employed. The trained ANN with just one hidden neuron is good enough for the training data with average and standard deviations of 0.4 and 6.6%, respectively. By comparison, for two test data sets, the trained ANN gives two different results. It is well interpreted by evaluating the outlier with a homogeneous equilibrium model.     

Prediction of hopper discharge rate using combined discrete element method and artificial neural network

An Artificial Neural Network (ANN) was developed to predict the mass discharge rate from conical hoppers. By employing Discrete Element Method (DEM), numerically simulated flow rate data from different internal angles (20°–80°) hoppers were used to train the model. Multi-component particle systems (binary and ternary) were simulated and mass discharge rate was estimated by varying different parameters such as hopper internal angle, bulk density, mean diameter, coefficient of friction (particle-particle and particle-wall) and coefficient of restitution (particle-particle and particle-wall). The training of ANN was accomplished by feed forward back propagation algorithm. For validation of ANN model, the authors carried out 22 experimental tests on different mixtures (having different mean diameter) of spherical glass beads from different angle conical hoppers (60° and 80°). It was found that mass discharge rate predicted by the developed neural network model is in a good agreement with the experimental discharge rate. Percentage error predicted by ANN model was less than ±13%. Furthermore, the developed ANN model was also compared with existing correlations and showed a good agreement.     

Effective parameters modeling in compression of an austenitic stainless steel using artificial neural network

In this study, the prediction of flow stress in 304 stainless steel using artificial neural networks (ANN) has been investigated. Experimental data earlier deduced—by [S. Venugopal et al., Optimization of cold and warm workability in 304 stainless steel using instability maps, Metall. Trans. A 27A (1996) 126–199]—were collected to obtain training and test data. Temperature, strain-rate and strain were used as input layer, while the output was flow stress. The back propagation learning algorithm with three different variants and logistic sigmoid transfer function were used in the network. The results of this investigation shows that the R² values for the test and training data set are about 0.9791 and 0.9871, respectively, and the smallest mean absolute error is 14.235. With these results, we believe that the ANN can be used for prediction of flow stress as an accurate method in 304 stainless steel.     

An Early Stopping-Based Artificial Neural Network Model for Atmospheric Corrosion Prediction of Carbon Steel

The optimization of network topologies to retain the generalization ability by deciding when to stop overtraining an artificial neural network (ANN) is an existing vital challenge in ANN prediction works. The larger the dataset the ANN is trained with, the better generalization the prediction can give. In this paper, a large dataset of atmospheric corrosion data of carbon steel compiled from several resources is used to train and test a multilayer backpropagation ANN model as well as two conventional corrosion prediction models (linear and Klinesmith models). Unlike previous related works, a grid searchbased hyperparameter tuning is performed to develop multiple hyperparameter combinations (network topologies) to train multiple ANNs with mini-batch stochastic gradient descent optimization algorithm to facilitate the training of a large dataset. After that, one selection strategy for the optimal hyperparameter combination is applied by an early stopping method to guarantee the generalization ability of the optimal network model. The correlation coefficients (R) of the ANN model can explain about 80% (more than 75%) of the variance of atmospheric corrosion of carbon steel, and the root mean square errors (RMSE) of three models show that the ANN model gives a better performance than the other two models with acceptable generalization. The influence of input parameters on the output is highlighted by using the fuzzy curve analysis method. The result reveals that TOW, Cl- and SO2 are the most important atmospheric chemical variables, which have a well-known nonlinear relationship with atmospheric corrosion.     

Predicting Interfacial Loads between the Prosthetic Socket and the Residual Limb for Below-Knee Amputees – A Case Study

Abstract:  In this study, an artificial neural network (ANN) was deployed as a tool to determine the internal loads between the residual limb and prosthetic socket for below-knee amputees. This was achieved by using simulated load data to validate the ANN and captured clinical load data to predict the internal loads at the residual limb–socket interface. Load/pressure was applied to 16 regions of the socket, using loading pads in conjunction with a load applicator, and surface strains were collected using 15 strain gauge rosettes. A super-position program was utilised to generate training and testing patterns from the original load/strain data collected. Using this data, a back-propagation ANN, developed at the University of the West of England, was trained. The input to the trained network was the surface strains and the output the internal loads/pressure. The system was validated and the mean square error (MSE) of the system was found to be 8.8% for 1000 training patterns and 8.9% for 50 testing patterns, which was deemed an acceptable error. Finally, the validated system was used to predict pressure-sensitive/-tolerant regions at the limb–socket interface with great success.     

Artificial neural network optimisation methodology for the estimation of the critical flashover voltage on insulators

To describe an artificial neural network (ANN)methodology in order to estimate the critical flashover voltage on polluted insulators is the objective here. The methodology uses as input variables characteristics of the insulator such as diameter, height, creepage distance, form factor and equivalent salt deposit density, and it estimates the critical flashover voltage based on an ANN. For each ANN training algorithm, an optimisation process is conducted regarding the values of crucial parameters such as the number of neurons and so on using the training set. The success of each algorithm in estimating the critical flashover voltage is measured by the correlation index between the experimental and estimated values for the evaluation set, and finally the ANN with the correlation index closest to 1 is specified. For this ANN and the respective algorithm, the critical flashover voltage of the test set insulators is estimated and the respective confidence intervals are calculated through the re-sampling method.     

Use of artificial neural networks for the accurate prediction of peptide liquid chromatography elution times in proteome analyses

The use of artificial neural networks (ANNs) is described for predicting the reversed-phase liquid chromatography retention times of peptides enzymatically digested from proteome-wide proteins. To enable the accurate comparison of the numerous LC/MS data sets, a genetic algorithm was developed to normalize the peptide retention data into a range (from 0 to 1), improving the peptide elution time reproducibility to approximately 1%. The network developed in this study was based on amino acid residue composition and consists of 20 input nodes, 2 hidden nodes, and 1 output node. A data set of approximately 7000 confidently identified peptides from the microorganism Deinococcus radiodurans was used for the training of the ANN. The ANN was then used to predict the elution times for another set of 5200 peptides tentatively identified by MS/MS from a different microorganism (Shewanella oneidensis). The model was found to predict the elution times of peptides with up to 54 amino acid residues (the longest peptide identified after tryptic digestion of S. oneidensis) with an average accuracy of approximately 3%. This predictive capability was then used to distinguish with high confidence isobar peptides otherwise indistinguishable by accurate mass measurements as well as to uncover peptide misidentifications. Thus, integration of ANN peptide elution time prediction in the proteomic research will increase both the number of protein identifications and their confidence.     

多维线性模型参数估计的神经网络方法

本文提出一种用于多维线性模型（ＡＲ，ＡＲＭＡ）参数估计的神经网络方法和相应的递归预测误差算法。本文在分析多输入、单输出，含一个隐含和多层神经网络的输入输出关系的基础上，提出了首先将理想输出Ｘｉ进行预畸变（Ｆ（Ｘｔ））作为神经网络的训练目标。当神经网络训练完毕后，网络的连接权就是待估计的线性模型参数。本文提出方法的优点是网络结构简单，估计结果准确。仿真模拟结果表明，本文所提出的神经网络方法估计多维线性模型参数是有效的。     

Modular artificial neural network for prediction of petrophysicalproperties from well log data

An application of Kohonen's self-organizing map (SOM), learning-vector quantization (LVQ) algorithms, and commonly used backpropagation neural network (BPNN) to predict petrophysical properties obtained from well-log data are presented. A modular, artificial neural network (ANN) comprising a complex network made up from a number of subnetworks is introduced. In this approach, the SOM algorithm is applied first to classify the well-log data into a predefined number of classes, This gives an indication of the lithology in the well. The classes obtained from SOM are then appended back to the training input logs for the training of supervised LVQ. After training, LVQ can be used to classify any unknown input logs. A set of BPNN that corresponds to different classes is then trained. Once the network is trained, it is then used as the classification and prediction model for subsequent input data. Results obtained from example studies using the proposed method have shown to be fast and accurate as compared to a single BPNN network     

A new Schiff's base ligand immobilized agarose membrane optical sensor for selective monitoring of mercury ion

A highly selective optical sensor was developed for the Hg(2+) determination by chemical immobilization of 2-[(2-sulfanylphenyl)ethanimidoyl]phenol (L), on an agarose membrane. Spectrophotometric studies of complex formation between the Schiff's base ligand L and Hg(2+), Sr(2+), Mn(2+), Cu(2+), Al(3+), Cd(2+), Zn(2+), Co(2+) and Ag(+) metal ions in methanol solution indicated a substantially larger stability constant for the mercury ion complex. Consequently, the Schiff's base L was used as an appropriate ionophore for the preparation of a selective Hg(2+) optical sensor, by its immobilization on a transparent agarose film. A distinct color change, from yellow to green-blue, was observed by contacting the sensing membrane with Hg(2+) ions at pH 4.5. The effects of pH, ionophore concentration, ionic strength and reaction time on the immobilization of L were studied. A linear relationship was observed between the membrane absorbance at 650 nm and Hg(2+) concentrations in a range from 1×10(-2) to 1×10(-5) mol L(-1) with a detection limit (3σ) of 1×10(-6) mol L(-1). No significant interference from 100 times concentrations of a number of potentially interfering ions was detected for the mercury ion determination. The optical sensor was successfully applied to the determination of mercury in amalgam alloy and spiked water samples.     

Double Glow Plasma Surface Alloying Process Modeling Using Artificial Neural Networks

A model is developed for predicting the correlation between processing parameters and the technical target of double glow by applying artificial neural network (ANN). The input parameters of the neural network (NN) are source voltage, workpiece voltage, working pressure and distance between source electrode and workpiece. The output of the NN model is three important technical targets, namely the gross element content, the thickness of surface alloying layer and the absorpticm rate (the ratio of the mass loss of source materials to the increasing mass of workpiece) in the processing of double glow plasma surface alloying. The processing parameters and technical target are then used as a training set for an artificial neural network. The model is based on multiplayer feedforward neural network. A very good performance of the neural network is achieved and the calculated results are in good agreement with the experimental ones.     

基于人工神经网络的NiZn铁氧体结构不敏感性能的预测模型

为了系统研究配方对铁氧体电磁性能的影响,制备了一系列Mn2 、Ge4 和Si4 替代的NiZn铁氧体材料,建立了铁氧体配方与结构不敏感性能之间的人工神经网络预测模型.利用所建立的模型研究了ZnO对NiZn铁氧体3个结构不敏感性能居里温度、磁饱和强度及介电常数的影响规律,以及多个组分的交互作用.结果表明:模型的预测结果与实验结果吻合良好,二者的相对误差较小.ZnO含量的增加会导致铁氧体居里温度下降,但会提高饱和磁化强度和介电常数.NiO和ZnO的交互作用对铁氧体的结构不敏感性能影响明显.利用模型得到的铁氧体性能-成分等值线图对寻找最佳配方有较高参考价值.     

ANN Model to Predict Fracture Characteristics of High Strength and Ultra High Strength Concrete Beams

This paper presents fracture mechanics based Artificial Neural Network (ANN) model to predict the fracture characteristics of high strength and ultra high strength concrete beams. Fracture characteristics include fracture energy (Gf), critical stress intensity factor (KIC) and critical crack tip opening displacement (CTODc). Failure load of the beam (Pmax) is also predicated by using ANN model. Characterization of mix and testing of beams of high strength and ultra strength concrete have been described. Methodologies for evaluation of fracture energy, critical stress intensity factor and critical crack tip opening displacement have been outlined. Back-propagation training technique has been employed for updating the weights of each layer based on the error in the network output. Levenberg- Marquardt algorithm has been used for feed-forward back-propagation. Four ANN models have been developed by using MATLAB software for training and prediction of fracture parameters and failure load. ANN has been trained with about 70% of the total 87 data sets and tested with about 30% of the total data sets. It is observed from the studies that the predicted values of Pmax, Gf, failure load, KIc and CTODc are in good agreement with those of the experimental values.     

Predicting the Fatigue Life of Different Composite Materials Using Artificial Neural Networks

Artificial Neural Networks (ANN) have been recently used in modeling the mechanical behavior of fiber-reinforced composite materials including fatigue behavior. The use of ANN in predicting fatigue failure in composites would be of great value if one could predict the failure of materials other than those used for training the network. This would allow developers of new materials to estimate in advance the fatigue properties of their material. In this work, experimental fatigue data obtained for certain fiber-reinforced composite materials is used to predict the cyclic behavior of a composite made of a different material. The effect of the neural network architecture and the training function used were also investigated. In general, ANN provided accurate fatigue life prediction for materials not used in training the network when compared to experimentally measured results.