The use of GA-ANNs in the modelling of compressive strength of cement mortar

In this paper, results of a project aimed at modelling the compressive strength of cement mortar under standard curing conditions are reported. Plant data were collected for 6 months for the chemical and physical properties of the cement that were used in model construction and testing. The training and testing data were separated from the complete original data set by the use of genetic algorithms (GAs). A GA-artificial neural network (ANN) model based on the training data of the cement strength was created. Testing of the model was also done within low average error levels (2.24%). The model was subjected to sensitivity analysis to predict the response of the system to different values of the factors affecting the strength. The plots obtained after sensitivity analysis indicated that increasing the amount of C₃S, SO₃ and surface area led to increased strength within the limits of the model. C₂S decreased the strength whereas C₃A decreased or increased the strength depending on the SO₃ level. Because of the limited data range used for training, the prediction results were good only within the same range. The utility of the model is in the potential ability to control processing parameters to yield the desired strength levels and in providing information regarding the most favourable experimental conditions to obtain maximum compressive strength.     

Fuzzy logic model for the prediction of cement compressive strength

A fuzzy logic prediction model for the 28-day compressive strength of cement mortar under standard curing conditions was created. Data collected from a cement plant were used in the model construction and testing. The input variables of alkali, Blaine, SO₃, and C₃S and the output variable of 28-day cement strength were fuzzified by the use of artificial neural networks (ANNs), and triangular membership functions were employed for the fuzzy subsets. The Mamdani fuzzy rules relating the input variables to the output variable were created by the ANN model and were laid out in the If-Then format. Product (prod) inference operator and the centre of gravity (COG; centroid) defuzzification methods were employed. The prediction of 50 sets of the 28-day cement strength data by the developed fuzzy model was quite satisfactory. The average percentage error levels in the fuzzy model were successfully low (2.69%). The model was compared with the ANN model for its error levels and ease of application. The results indicated that through the application of fuzzy logic algorithm, a more user friendly and more explicit model than the ANNs could be produced within successfully low error margins.     

Nonaqueous synthesis of TiO2 nanorods using inductively coupled plasma

Titanium dioxide (TiO₂) nanorods are widely used in many fields such as self-cleaning surfaces, photocatalytic lithography and pollutant control, owing to their outstanding physical, chemical and optical properties. Traditional methods for synthesizing TiO₂ nanorods are mostly tedious with high cost and tremendous energy consumption. In this work, TiO₂ nanorods with excellent optical, electrochemical, and hydrophilic properties were rapidly synthesized on titanium alloy (TC4) by using inductively coupled plasma (ICP) with strong chemical reactivity and high temperature characteristic. XRD patterns and SEM images confirm the conversion of TC4 into rutile TiO₂ nanorods after irradiated by ICP at 800 W for only one pass, and the nanorods tend to grow longitudinally under prolonged ICP processing. Moreover, the well-developed single-crystalline feature of TiO₂ nanorod is affirmed by TEM test. To reveal the growth mechanism of TiO₂ nanorods, three types of substrates (polished TC4 by electrochemical polishing (ECP), polished TA2 by ECP and oxidized TC4 by anodizing) were used to grow TiO₂ nanorods. However, TiO₂ nanorods with good morphology were only formed on the first type of substrate due to the existence of β phase Ti, which could suppress thermal transmission between grains. In addition, the results of UV–Vis absorption spectrum, electrochemical test, and static water contact angle of the treated TC4 samples show that TiO₂ nanorods synthesized by ICP possess excellent optical, electrochemical, and hydrophilic properties.     

A unified global self-consistent model of a capacitively and inductively coupled plasma etching system

Based on the concept of independent control of ion flux and ion-bombardment energy, a global selfconsistent model was proposed for etching in a high-density plasma reactor. This model takes account of the effect on the plasma behavior of separate rf chuck power in an Inductively Coupled Plasma etching system. Model predictions showed that the chuck power controls the ion bombardment energy but also slightly increases the ion density entering the sheath layer, resulting in an increase in etch rate (or etch yield) with increasing this rf chuck power. The contribution of the capacitive discharge to total ion flux in the ICP etching process is less than about 6% at rf chuck powers lower than 250 W. As a model system, etching of InN was investigated. The etch yield increased monotonically with increasing the rf chuck power, and was substantially affected by the ICP source power and pressure. The ion flux increased monotonically with increasing the source power, while the dc-bias voltage showed the reverse trend.     

Synthesis of ferromagnetic nanopowders from iron pentacarbonyl in capacitively coupled RF plasma

Ferromagnetic nanopowders (NP), synthesized with magnetically decoupled cores, may represent interesting raw materials for their further conversion into nanostructured magnetic bulk materials, with properties suitable for their utilization in low and high-frequency applications. Capacitively coupled RF discharges at a pressure of 100-250 Pa, were used for the synthesis of binary iron/carbon NP from iron pentacarbonyl (Fe(CO)₅) vapours, diluted with argon. The properties of the obtained NP products were investigated by TEM, XRD, TGA, FTIR and magnetization measurements. Diamagnetic NP, with ≈4% iron content, were obtained at lower pressures (100-130 Pa), whereas increasing the pressure to 250 Pa resulted in the synthesis of ≈40% iron content NP that demonstrated ferromagnetic properties (M_s ≈ 50 emu/gr). The first type of these NP is formed of carbon/iron rods, being 10-50 nm long and several nm wide, covered by graphitic layers and integrated into an amorphous carbon matrix. The second NP type, ferromagnetic NP, consists of isolated hexagonal shaped 20-60 nm crystals of Fe₇C₃ or Fe₂C₃ carbides, again embedded in an amorphous carbon matrix. Factors for favoring the formation of the ferromagnetic carbide NP at the higher pressure are discussed. These factors include: the lower temperatures presumed in the reaction zone, the shifting of the chemical equilibrium from non-bound Fe and its oxides towards the state of carburization, and the decreasing rate of removal of the Fe oxidation products towards the reactor walls.     

人工神经网络在锂铝硅超低膨胀透明微晶玻璃热处理研究中的应用

人工神经网络在锂铝硅超低膨胀透明微晶玻璃热处理研究中的应用陈建华，孙方明（盐城工业专科学校２２４００３）（华东理工大学２００２３７）ＡｒｔｉｆｉｃｉａｌＮｅｕｒａｌＮｅｔｗｏｒｋｓＡｐｐｌｉｅｄｔｏＳｔｕｄｙｏｆＨｅａｔＴｒｅａｔｍｅｎｔｏｆＬｉｔｈ...     

Artificial neural network based modelling of head and power characteristics of pump-mixer

Present work involves detailed experimentation on pump-mixer, and processing of experimental data to model its head and power characteristics using multi-variable least square based empirical correlations and artificial neural network (ANN). The latter modelling technique is shown to be superior. Trained and frozen ANN model has been used to generate head and power characteristics. Wherever possible, these characteristics have been qualitatively compared with the observations reported earlier. Also, an attempt has been made to physically explain these characteristics in order to demonstrate that the ANN model successfully captures the physics of the system.     

Moisture Content Prediction in the Switchgrass (Panicum virgatum) Drying Process Using Artificial Neural Networks

This article proposes two artificial neural network (ANN)-based models to characterize the switchgrass drying process: The first one models processes with constant air temperature and relative humidity and the second one models processes with variable air conditions and rainfall. The two ANN-based models proposed estimated the moisture content (MC) as a function of temperature, relative humidity, previous MC, time, and precipitation information. The first ANN-based model describes MC evolution data more accurately than six mathematical empirical equations typically proposed in the literature. The second ANN-based model estimated the MC with a correlation coefficient greater than 98.8%.     

Surface Tension Estimation of Binary Mixtures of Organic Compounds Using Artificial Neural Networks

The surface tension of binary mixtures at different temperatures and compositions is required in much scientific and technological research. Therefore, having an exact correlation between surface tension and easily accessible physical properties is essential. In this work, the sensitivity of the surface tension to some physical properties was studied by using artificial neural networks (ANNs) to find the most effective ones. Furthermore, ANNs were used to estimate the surface tension of binary systems as a function of the most effective physical properties including critical pressure, reduced temperature, acentric factor, and molar density. The experimental data, collected for training and verifying the networks, include various materials such as alkanes, alkenes, aromatics, alcohols, organic acids, as well as chlorine, iodine, sulfur, nitrogen, and fluorine-containing compounds in the composition ranges from 0 to 100 mole percent, and temperatures between 116 K and 393 K. The average absolute relative deviation (AARD) of the most accurate network, obtained for all 2038 data points regarding 83 binary mixtures, is 1.75%.     

Artificial neural network simulation of rare earths solvent extraction equilibrium data

Separation of high purity rare earth elements from their mixed oxides, obtained from monazite or xenotime, requires multiple stages of separation by circuits incorporating one or more solvents. The separation factors being small, a large number of counter-current stages become necessary. Process development, analysis, optimization and control of rare earths are a complex task. Computer simulation provides useful tools in this area. Application of artificial neural networks (ANN) for simulation of equilibrium data in solvent extraction of rare earths is described in this paper. The back propagation ANN model has been used. The input neurons correspond to the system state variables such as equilibrium concentration and acidity. The partitioning of the metal ion into the two immiscible phases involved in solvent extraction is measured in terms of distribution ratio D. The model predicts the D value under varying process conditions. Comparison of ANN with conventional models shows that ANN is superior. The average absolute error for ANN model is one-fourth that of the conventional models. The approach has been used, in conjunction with a process simulation model, successfully for industrial process development involving production of high purity neodymium.     

Modeling and optimization of photocatalytic/photoassisted-electro-Fenton like degradation of phenol using a neural network coupled with genetic algorithm

Oxidation of phenol in aqueous media using supported TiO₂ nanoparticles coupled with photoelectro-Fenton-like process using Mn²⁺ cations as catalyst of electro-Fenton reaction was studied. The influence of the basic operational parameters such as initial pH of the solution, applied current, initial Mn²⁺ concentration, initial phenol concentration and kind of ultraviolet (UV) light on the oxidizing efficiency of phenol was studied. An artificial neural network (ANN) model was coupled with genetic algorithm to predict phenol oxidizing efficiency and to find an optimal condition for maximum phenol removal. The findings indicated that ANN provided reasonable predictive performance (R² = 0.949).     

Constrained optimization of combustion in a simulated coal-fired boiler using artificial neural network model and information analysis

Combustion in a boiler is too complex to be analytically described with mathematical models. To meet the needs of operation optimization, on-site experiments guided by the statistical optimization methods are often necessary to achieve the optimum operating conditions. This study proposes a new constrained optimization procedure using artificial neural networks as models for target processes. Information analysis based on random search, fuzzy c-mean clustering, and minimization of information free energy is performed iteratively in the procedure to suggest the location of future experiments, which can greatly reduce the number of experiments needed. The effectiveness of the proposed procedure in searching optima is demonstrated by three case studies: (1) a bench-mark problem, namely minimization of the modified Himmelblau function under a circle constraint; (2) both minimization of NO_x and CO emissions and maximization of thermal efficiency for a simulated combustion process of a boiler; (3) maximization of thermal efficiency within NO_x and CO emission limits for the same combustion process. The simulated combustion process is based on a commercial software package CHEMKIN, where 78 chemical species and 467 chemical reactions related to the combustion mechanism are incorporated and a plug-flow model and a load-correlated temperature distribution for the combustion tunnel of a boiler are used.     

Application of Near-Infrared Spectroscopy for Evaluation of Drying Stress on Lumber Surface: A Comparison of Artificial Neural Networks and Partial Least Squares Regression

This study aimed to examine the feasibility of evaluating the stress level at the surface of lumber during drying using near-infrared (NIR) spectroscopy combined with artificial neural networks (ANNs). Sugi (Cryptomeria japonica D. Don) lumber with an initial moisture content ranging from 41.1 to 85.8% was dried using a commercial drying schedule. An ANN model for predicting surface-released strain (SRS) was developed based on NIR spectra collected from the lumber during drying. The predictive ability of the ANN model was compared with a partial least squares (PLS) regression model.

The ANN model showed good correlation between laboratory-measured SRS and predicted SRS with an R ² of 0.79, a root mean square error of prediction (RMSEP) of 0.0009, and a ratio of performance to deviation (RPD) of 1.81. The PLS regression model gave a lower R ² of 0.69, a higher RMSEP of 0.0010, and a lower RPD of 1.38 than the ANN model, suggesting that the predictive performance of the ANN model was superior to the PLS regression model. The SRS evolution during drying as predicted by the models showed a similar trend to the laboratory-measured one. The predicted elapsed times to reach maximum tensile SRS and stress reversal roughly coincided with the laboratory-measured times. These results suggest that NIR spectroscopy combined with multivariate analysis has the potential to predict the drying stress level on the lumber surface and the critical periods during drying, such as the points of maximum tensile stress and stress reversal.     

Thermal plasma synthesis of Si/SiC nanoparticles from silicon and activated carbon powders

In this study, Si/SiC nanocomposites were synthesized by non-transferred arc thermal plasma processing of micron-sized SiC powder. First, micron-sized SiC was synthesized by solid-state method where waste silicon (Si) and activated carbon (C) powder were used as precursor materials. The effect of Si/C mole ratio and solid-state synthesis temperature on structural and phase formation of SiC was investigated. Diffraction pattern confirmed the formation of SiC at 1300 °C. High C content was required for the synthesis of pure SiC as Si remained unreacted when Si/C mole ratio was below 1/1.5. Highly agglomerated micron-size (0.6–10 µm) SiC particles were formed after solid-state synthesis. Thermal plasma processing of solid-state synthesized micron-sized SiC resulted into the formation of uniformly dispersed (20–60 nm) Si/SiC nanoparticles. It was proposed that Si/SiC nanocomposites were formed due to partial decomposition of SiC during high temperature plasma processing. The formation of Si/SiC nanoparticles from micron-sized SiC was resulted from dissociation of grains from their grain boundary during plasma processing.     

Prediction of thermodynamic properties of polyvinylpyrrolidone solutions by artificial neural networks coupled with genetic algorithm

In the present study, the artificial neural networks coupled with the genetic algorithm (ANN–GA) models were used to predict the thermodynamic properties of polyvinylpyrrolidone (PVP) solutions in water and ethanol at various temperatures, mass fractions, and molecular weights of polymer. The genetic algorithm (GA) was used to find the best weights and biases of the network and improve the performance of ANNs. The proposed model was composed of three input variables including the temperature of the solution, the mass fraction, and molecular weight of the polymer. Density, viscosity, and surface tension of PVP solutions with various molecular weights (10,000, 25,000, and 40,000) in water and ethanol have been measured in the temperature range 20–55°C and various mass fractions of polymer. The ANN–GA models were trained by the experimental datasets and the prediction of density, surface tension, and viscosity of PVP solutions was performed using these models. The predicted values were compared with the experimental ones and the mean absolute relative error was less than 0.5% for the density and surface tension and about 3% for the viscosity of solutions.     

Artificial neural network modelling of supercritical fluid CO2 extraction of polyunsaturated fatty acids from common carp (Cyprinus carpio L.) viscera

Common carp viscera, obtained from Tikveš Lake in Macedonia, was investigated as a possible source of polyunsaturated (PUFA) fatty acids. Supercritical fluid CO₂ extraction (SFE-CO₂) was employed for extraction of investigated bioactive components. The GC-FID analysis on the total extract obtained by supercritical fluid CO₂ extraction confirmed the assumption of presence of these bioactive components. A three layer artificial neural network was created for prediction and modelling of the extraction yield of polyunsaturated fatty acids from lyophilized viscera matrixes. Operating values of pressure, temperature, mass flow of CO₂ and extraction time were defined as input vectors to the ANN where PUFA extraction yield was considered as an output vector. Created ANN model provided adequate fitting of experimental data, with a correlation coefficient of 0.9968 for the entire data set. RSM-3D method was employed for mathematical modelling of the ANN output values as a function of operating variables and their interactions.     

Comparison of Response Surface Methodology and Artificial Neural Network for Modeling Xylose‐to‐Xylitol Bioconversion

Previous experimental data of xylose‐to‐xylitol bioconversion by Debaryomyces hansenii carried out according to a 3³ full factorial design were used to model this process by two different artificial neural network (ANN) training methods. Models obtained for four responses were compared with those of response surface methodology (RSM). ANN models were shown to be superior to RSM in the predictive capacity, whereas the latter showed better performance in the generalization capability step. RSM with optimization using a genetic algorithm was revealed as a whole to be the best modeling option, which suggests that the comparative performances of RSM and ANN may be a highly problem‐specific issue.     

Artificial Neural Networks for Aging Simulation of Electrolysis Stacks

This work shows the application of artificial neural networks in terms of modeling and simulating the aging process and the degradation of proton exchange membrane water electrolysis stacks. It includes the training process based on extracted measurement data, the evaluation, and the extrapolation of the network. The fundamentals of the utilized artificial neural network and the training algorithm are clarified. Next, the principle degradation effects are presented as well as the methodology of the underlying measurements. The resulting degradation of the electrolysis stack for different operation conditions is shown.     

改进型EMD-Elman神经网络在铁水硅含量预测中的应用

针对高炉炼铁过程的多尺度和动态特征,建立了基于经验模态分解(empirical mode decomposition, EMD)和Elman神经网络的铁水硅含量预测模型。该模型先采用EMD将硅含量序列分解成有限个、相对平稳的本征模函数(intrinsic mode function, IMF)和剩余分量;然后,分别对每个IMF和剩余分量建立Elman神经网络子模型;为了进一步提高预测精度,将子模型的结果进行加权融合,并利用粒子群算法进行权值的寻优,最终获得硅含量的预测结果。将该模型用于某钢厂铁水硅含量的预报,实验结果证实了该方法的有效性。     

Drying Kinetics Prediction of Solid Waste Using Semi‐Empirical and Artificial Neural Network Models

The drying process of organic solid waste is investigated, based on an experimental study involving its drying kinetics. The experiments were conducted in a thin‐layer fixed‐bed dryer under various operational conditions. The problem of selecting the best fit for solid waste moisture content as a function of time is addressed as well, using artificial neural network (ANN) models and four well‐known drying kinetics correlations commonly applied to biological materials. According to the statistical analysis employed, the simulations showed good results for the ANN, and the Overhults model provided optimum agreement with experimental data among all other models evaluated. Empirical correlations between the Overhults model parameters and the drying operational conditions using nonlinear regression techniques were determined.