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.     

用人工神经网络对PZT陶瓷进行性能分析与优化

选取了几种常用的金属氧化物掺杂剂,在均匀实验结构的基础上用人工神经网络方法对掺杂PZT陶瓷的性能进行分析和优化.实验结果表明,掺杂PZT体系的人工神经网络模型要比多重非线形回归模型准确得多,而且以人工神经网络模型为指导对材料进行优化后的性能预测也比较准确,说明人工神经网络在陶瓷这种多组分固溶体材料的性能分析中具有良好的使用前景.     

Application of artificial neural network for prediction of the oxidation behavior of aluminized nano-crystalline nickel

In this paper, the applicability of artificial neural network (ANN) for the prediction of the oxidation kinetics of aluminized coating is presented. For developing the model, a consistent set of experimental data i.e. nanocrystalline Ni samples were aluminized by two steps aluminizing process and oxidized at 800, 900 and 1000 °C for various times are used. The exposure time and temperature of oxidation were used as the inputs of the model and the resulting mass gain of oxidized samples as the output of the model. Multi-layer perceptron neural network structure and back-propagation algorithm are used for the training of the model. After testing many different ANN architectures an optimal structure of the model i.e. 2-5-6-1 is obtained. Comparison of experimental and predicted values using the proposed ANN model shows that there is a good agreement between them with mean relative error less than 1.2%. This shows that the ANN model is an accurate and reliable approach to predict the oxidation behavior of aluminized nanocrystalline coatings.     

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

The main goal of this research is to develop and apply a robust Artificial Neural Networks (ANNs) model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact dryer. Concentrated salt and sugar solutions were used as the osmotic solutions at 27C. Series of experiments were performed at various temperatures of 35C, 40C, and 55C for conduction heat input under vacuum ( −760 mm Hg) condition. Some experiments were also performed in a pure vacuum without heat addition. Dimensionless moisture content (DMC), effective moisture diffusivity, and mass flux were considered as the performance parameters in this study. Results revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87% compared to 5.3% in the salt solution. Furthermore, a significant enhancement of drying performance of about 27% in DMC was observed for both samples at vacuum and 40C compared to pure vacuum drying conditions. Using the experimental data, a robust artificial neural network (ANN) was proposed to describe the osmotic dehydration’s behavior on the drying process. The ANN model outputs are the dimensionless moisture contents (DMC), the diffusivity, and the mass flux. Whereas the ANN inputs were the drying time, the percent of sugar solution, and the percent of salt solution. For the ANN apple’s model, the minimum root mean square error (RMSE) values were 0.0261, 0.0349 and 0.0406, for DMC, diffusivity, and mass flux, respectively. Whereas the best correlation coefficients of the above three parameters’ determination values were 0.9909, 0.9867 and 0.9744, respectively. For the ANN potato’s model, the minimum RMSE values were 0.0124, 0.0140 and 0.0333, for DMC, diffusivity, and mass flux, respectively. And the best correlation coefficients of the parameters’ values were found 0.9969, 0.9968 and 0.9736, respectively. Accordingly, the ANN model’s prediction has a perfect agreement with the experimental dataset, which confirmed the ANN model’s accuracy.     

Simulation of structural features on mechanochemical synthesis of Al2O3–TiB2 nanocomposite by optimized artificial neural network

In this study, structural features of alumina–titanium diboride nanocomposite (Al₂O₃–TiB₂) were simulated from the mixture of titanium dioxide, boric acid and pure aluminum as raw materials via mechanochemical process using the optimized artificial neural network. The phase transformation and structural evolutions during the mechanochemical process were characterized using X-ray powder diffractometry (XRD). For better understanding the refining crystallite size and amorphization phenomena during the milling, XRD data were modeled and simulated by artificial neural network (ANN). An ANN consisting of three layers of neurons was trained using a back-propagation learning rule. Also, the ANN was optimized by Taguchi method. Additionally, the crystallite size, interplanar distance, amorphization degree and lattice strain were compared for the simulated values and experimental results.     

Characterization of Rice Hull Ash and Its Performance in Turbidity Removal From Water

This study characterizes the locally obtained samples of rice hull ash and investigates its performance on turbidity removal from water. Four samples of this material were studied, namely, unwashed parboiled rice hull ash (UPRHA), washed parboiled rice hull ash (WPRHA), unwashed unparboiled rice hull ash (UUPRHA), and washed unparboiled rice hull ash (WUPRHA). Scanning electron microscopy (SEM), x-ray diffractometer (XRD), and Fourier infrared spectroscopy (FTIR) were carried out to characterize these samples. A filtration process was carried out to investigate the effectiveness of the rice hull ash medium in removing water turbidity. The XRD results showed the silica, which is present in the ashes, to be cristobalite, quartz, and tridymite. The silica contents of the UUPRHA and WUPRHA were observed to be 77.10% and 98.24%, respectively, while those of UPRHA and WPRHA were 79.07% and 94.97%, respectively. The SEM images showed agglomeration of ash particles after the ashes were washed. The washed RHA samples showed improved pH, a good percentage of turbidity removal (<5 NTU) from water sample. Washing RHA with distilled water increased the efficiency of RHA in turbidity removal from water and regulated water pH to an acceptable range.     

基于子结构和遗传神经网络的递推模型修正方法

根据实际动力响应对结构有限元模型进行修正,是实现损伤识别和健康监测的必要前提。针对基于神经网络的模型修正方法的不足,选用均匀设计法构造样本从而有效减少所需样本数量,而且计算效率高。采用遗传算法优化神经网络权值,提高了运算速度。基于上述研究,提出了基于子结构和神经网络的递推模型修正方法。该方法将结构分解成多层次的子结构,选取适当的损伤因素逐步实现逐级的修正。应用该方法对一网壳结构进行了模型修正,修正中首先采用固有频率作为损伤因素,结果表明遗传算法明显地提高了神经网络的计算速度,最后的递推修正效果令人满意;其次提出了采用小波包频带能量作为损伤因素的修正方法,该方法同样准确有效,并且不再依赖传统的模态分析技术,更为实用便捷。     

基于神经网络预测ORC系统的最佳蒸发温度

有机朗肯循环系统(ORC)的蒸发温度是决定系统净发电量的关键参数。采用热力学的方法建立数值模型,计算了不同热源温度、冷凝温度及蒸发器夹点温差下的最佳蒸发温度。以此为样本,对神经网络模型进行训练,研究神经网络对ORC系统最佳蒸发温度的预测效果。结果表明,训练速率为0.4、隐层神经元数目为5、训练函数为“trainlm”时,神经网络的预测精度最高。采用两种方式对神经网络的预测结果进行验证,具体为:(1)以9:1比例划分训练集和验证集,(2)系统输入参数取值范围内随机生成100组数据作为验证集。两种验证方式的结果均显示,神经网络对ORC蒸发温度的预测值与数值模拟值较为接近,误差均在2%范围内,表明神经网络模型可以较好的预测ORC最佳蒸发温度,可以为ORC系统的运行参数优化提供参考。     

Evaluation of characteristic deuterium distributions of ephedrines and methamphetamines by NMR spectroscopy for drug profiling

We have established a method for quantitative analysis of the deuterium contents (D/H) at the phenyl, methine, benzyl, N-methyl and methyl groups of l-ephedrine/HCl, d-pseudoephedrine/HCl and methamphetamine/HCl by 2H NMR spectroscopy. Comparison of the 5 position-specific D/H values of l-ephedrine/HCl and d-pseudoephedrine/HCl prepared by three methods (chemical synthesis, semichemical synthesis, and biosynthesis) showed that chemically synthesized ephedrines and semisynthetic ephedrines have highly specific distributions of deuterium at the methine position and at the benzyl position, compared with the other positions. The classification of several methamphetamine samples seized in Japan in terms of the D/H values at these two positions clearly showed that the methamphetamine samples had been synthesized from ephedrines extracted from Ephedra plants or semisynthetic ephedrines but not from synthetic ephedrine. This isotope ratio analysis method should be useful to trace the origins of seized methamphetamine in Southeast Asia.     

2,4,6-三(二甲胺基甲基)苯酚有机改性蒙脱土

以2,4,6-三(二甲胺基甲基)苯酚(DMP-30)作为插层剂, 采用阳离子交换法对钠基蒙脱土进行了有机改性; 研究了DMP -30与盐酸的摩尔比对蒙脱土有机改性效果的影响; 采用SEM、 TEM、 IR和XRD等对样品的微观形貌和结构进行了表征。结果表明: DMP-30与盐酸的摩尔比对蒙脱土有机改性具有明显的影响, 在合适的摩尔比(1 ∶ 2)时, DMP-30分子插入到蒙脱土片层间, 片层间距由1.34nm 增加至1.81nm。对改性后蒙脱土与环氧基体的复合效果进行了研究, 结果显示, 复合材料中蒙脱土的d₀₀₁衍射峰消失, 表明有机改性蒙脱土在环氧树脂基体中得到了充分剥离。      

Artificial neural networks as alternative tool for minimizing error predictions in manufacturing ultradeformable nanoliposome formulations

This work was aimed at determining the feasibility of artificial neural networks (ANN) by implementing backpropagation algorithms with default settings to generate better predictive models than multiple linear regression (MLR) analysis. The study was hypothesized on timolol-loaded liposomes. As tutorial data for ANN, causal factors were used, which were fed into the computer program. The number of training cycles has been identified in order to optimize the performance of the ANN. The optimization was performed by minimizing the error between the predicted and real response values in the training step. The results showed that training was stopped at 10?000 training cycles with 80% of the pattern values, because at this point the ANN generalizes better. Minimum validation error was achieved at 12 hidden neurons in a single layer. MLR has great prediction ability, with errors between predicted and real values lower than 1% in some of the parameters evaluated. Thus, the performance of this model was compared to that of the MLR using a factorial design. Optimal formulations were identified by minimizing the distance among measured and theoretical parameters, by estimating the prediction errors. Results indicate that the ANN shows much better predictive ability than the MLR model. These findings demonstrate the increased efficiency of the combination of ANN and design of experiments, compared to the conventional MLR modeling techniques.     

神经网络预测爆炸密实引起的地表沉降

爆炸密实法用于地基处理已有很长的历史,用此方法处理时所引起的地表沉降是衡量其处理效果的一个重要指标,许多学者提出了引起地表沉降量的统计预测模型。本文尝试建立了预测爆炸密实引起地表沉降的神经网络模型,由于用来训练网络的原始样本太少,难以训练出一个具有高精度的预测模型,为此采用了遗传算法与神经网络相结合的方法,得到了一个只有小样本输入却能得到高精度输出的神经网络模型。对检验样本的预测结果表明,所建立的神经网络模型具有很强的预测能力,其预测精度比已有的统计预测模型高。     

Using GA–ANN algorithm to optimize soft magnetic properties of nanocrystalline mechanically alloyed Fe–Si powders

In this investigation a theoretical model based on artificial neural network (ANN) and genetic algorithm (GA) has been developed to optimize the magnetic softness in nanocrystalline Fe–Si powders prepared by mechanical alloying (MA). The ANN model was used to correlate the milling time, chemical composition, milling speed, and ball to powders ratio (BPR) to coercivity and crystallite size of nanocrystalline Fe–Si powders. The GA–ANN combined algorithm was incorporated to find the optimal conditions for achieving the minimum coercivity. By comparing the predicted values with the experimental data it is demonstrated that the combined GA–ANN algorithm is a useful, efficient and strong method to find the optimal milling conditions and chemical composition for producing nanocrystalline Fe–Si powders with minimum coercivity.     

Characteristics of translucent alumina produced by slip casting method using gypsum mold

Translucent Al₂O₃ ceramics were successfully produced by slip casting using a gypsum mold, provided that CaSO₄ impurities, which had penetrated into the green bodies from the gypsum mold, were removed by the wash of HCl aqueous solution. Some of the calcined Al₂O₃ compacts were washed with HCl aqueous solution before sintering the compacts and the others were not washed with HCl aqueous solution. The relative densities of the sintered Al₂O₃ ceramics with HCl treatment were higher than those of the untreated samples. Grains in the HCl-treated samples, which sintered at 1350°C, grew homogeneously with about 1 m in diameter. When the sintering temperature was higher, the grains grew homogeneously. The sintered Al₂O₃ ceramics with the HCl treatment were translucent. The transmittance value increased from 0 to 12% with increasing wavelength from 300 to 900 nm. The Al₂O₃ ceramics with the HCl treatment did not have the transmittance when the solid contents of slurry were low. The transmittance was influenced by the solid contents of slurry. On the other hand, grains in the HCl-untreated samples, which sintered at 1350°C, grew heterogeneously with the range from 0.2 to 2 m. The Al₂O₃ ceramics did not have the transmittance.     

Machine Learning-Based Predictions on the Self-Heating Characteristics of Nanocomposites with Hybrid Fillers

A machine learning-based prediction of the self-heating characteristics and the negative temperature coefficient (NTC) effect detection of nanocomposites incorporating carbon nanotube (CNT) and carbon fiber (CF) is proposed. The CNT content was fixed at 4.0 wt.%, and CFs having three different lengths (0.1, 3 and 6 mm) at dosage of 1.0 wt.% were added to fabricate the specimens. The self-heating properties of the specimens were evaluated via self-heating tests. Based on the experiment results, two types of artificial neural network (ANN) models were constructed to predict the surface temperature and electrical resistance, and to detect a severe NTC effect. The present predictions were compared with experimental values to verify the applicability of the proposed ANN models. The ANN model for data prediction was able to predict the surface temperature and electrical resistance closely, with corresponding R-squared value of 0.91 and 0.97, respectively. The ANN model for data detection could detect the severe NTC effect occurred in the nanocomposites under the self-heating condition, as evidenced by the accuracy and sensitivity values exceeding 0.7 in all criteria.     

Modeling of rotary vane compressor applying artificial neural network

The thermal modeling of rotary vane compressor (RVC) was performed in this paper by applying Artificial Neural Network (ANN) method. In the first step, appropriate tests were designed and experimental data were collected during steady state operating condition of RVC in the experimental setup. Then parameters including refrigerant suction temperature and pressure, compressor rotating speed as well as refrigerant discharge pressure were adjusted.With these input values, the operating output parameters such as refrigerant mass flow rate and refrigerant discharge temperature were measured. In the second step, the experimental results were used to train ANN model for predicting RVC operating parameters such as refrigerant mass flow rate and compressor power consumption. These predicted operating parameters by ANN model agreed well with the experimental values with correlation coefficient in the range of 0.962-0.998, mean relative errors in the range of 2.79-7.36% as well as root mean square error (RMSE) 10.59 kg h⁻¹ and 12 K for refrigerant mass flow rate and refrigerant discharge temperature, respectively. Results showed closer predictions with experimental results for ANN model in comparison with nolinear regression model.     

Quantification of ternary mixtures of heavy metal cations from metallochromic absorbance spectra using neural network inversion

A new method based on artificial neural networks (ANN) for the processing of spectrophotometric data is proposed and illustrated on the example of the simultaneous quantification of ternary mixtures of zinc, cadmium, and mercury cations in aqueous solutions. Three types of commercially available metallochromic indicators were used as a simple model setup to create spectral data analogous to those normally received from an optical sensor array. In conventional ANN training methods for chemical sensors based on spectrophotometric data, a calibration is established by mathematically correlating the measured optical signal as network input with the concentration of the calibration sample as network output. In several situations, however, especially when dealing with mixed sample solutions, the relationship between a measured absorption spectrum and the corresponding ion concentrations is ambiguous, resulting in an "ill-posed problem". On the other hand, if the training direction is reversed by correlating known sample concentrations with measured optical signals, the relationship becomes reasonable for the ANN to obtain its structure. The proposed model illustrated in this paper is based on a more reasonable direct mapping and estimation by artificial neural network inversion (ANNI). In the training step, sample mixtures of known concentrations are optically measured to construct networks correlating the input data (ion concentrations) and the output data (absorption spectra). In the estimation step, the ion concentrations of unknown samples are estimated using the constructed ANN. The measured spectra of the unknown samples are fed to the output layer, and the appropriate input concentrations are determined by ANNI. When training the ANN system with 143 ternary mixtures of Zn2+, Cd2+, and Hg2+ in a concentration range from 1 to 100 microM, root-mean-square errors of prediction (RMSEP) of 0.45 (Zn2+), 0.96 (Cd2+), and 0.32 microM (Hg2+) were observed for the estimation of concentrations in 30 test samples, using the ANNI procedure. This newly proposed model, which involves the construction of an ANN based on direct mapping and estimation by ANNI, opens up one way to overcome the limitations of nonselective sensors, allowing the use of more easily accessible semiselective receptors to realize smart chemical sensing systems.     

Artificial neural network modelling to predict hot deformation behaviour of as HIPed FGH4169 superalloy

Abstract

The hot deformation behaviour of as HIPed FGH4169 superalloy was studied by single stroke compression test on MMS-200 test machine at the temperatures of 950–1050°C and the strain rates of 0·004–10 s^?1. Based on the experimental results, a back-propagation artificial neural network model and constitutive equation method were established to predict the flow stress of FGH4169 superalloy. The predictability of two different models was compared. The correlation coefficients of experimental and predicted flow stress with the trained BP ANN model and constitutive equation were 0·9995 and 0·9808 respectively. The average root mean square error (RMSE) values of the trained ANN model and constitutive equation are 0·39 and 2·21 MPa respectively. And the average absolute relative error (AARE) values of the trained ANN model and constitutive equation are 1·79 and 7·47% respectively. The results showed that the ANN model is an effective tool to predict the flow stress in comparison with constitutive equation.     

Prediction of permeability coefficients of compounds through caco-2 cell monolayer using artificial neural network analysis

Artificial neural network (ANN) analysis was used to predict the permeability of selected compounds through Caco-2 cell monolayers. Previously reported models, which were shown to be useful in the prediction of permeability values, use many structural parameters. More complex equations have also been proposed using both linear and non-linear relationships, including ANN analysis and various structural parameters. But proposed models still need to be developed using different neuron patterns for more precise predictions and a better understanding of which factors affect the permeation. To develop a simple and useful model or method for easy prediction is also a general need. Permeability coefficients (log k_p) were obtained from various literature sources. Some structural parameters were calculated using computer programs. Multiple linear regression analysis (MLRA) was used to predict Caco-2 cell permeability for the set of 50 compounds (r² = 0.403). A successful ANN model was developed, and the ANN produced log kp values that correlated well with the experimental ones (r² = 0.952). The permeability of a compound, famotidine, which has not previously been studied, through the Caco-2 cell monolayer was investigated, and its permeability coefficient determined. It was then possible to compare the experimental data with that predicted using the trained ANN with previously determined Caco-2 cell permeability values and structural parameters of compounds. The model was also tested using literature values. The developed and described ANN model in this publication does not require any experimental parameters; it could potentially provide useful and precise prediction of permeability for new drugs or other penetrants.