Modeling of thermal conductivity and density of alumina/silica in water hybrid nanocolloid by the application of Artificial Neural Networks

In this research work, the thermal conductivity and density of alumina/silica(Al_2O_3/SiO_2) in water hybrid nanofluids at different temperatures and volume concentrations have been modeled using the artificial neural networks(ANN). The nanocolloid involved in the study was synthesized by the two-step method and characterized by XRD, TEM, SEM–EDX and zeta potential analysis. The properties of the synthesized nanofluid were measured at various volume concentrations(0.05%, 0.1% and 0.2%) and temperatures(20 to 60 °C). Established on the observational data and ANN, the optimum neural structure was suggested for predicting the thermal conductivity and density of the hybrid nanofluid as a function of temperature and solid volume concentrations. The results indicate that a neural network with 2 hidden layers and 10 neurons have the lowest error and a highest fitting coefficient o thermal conductivity, whereas in the case of density, the structure with 1 hidden layer consisting of 4 neurons proved to be the optimal structure.     

Artificial neural network modeling for predicting melt‐blowing processing

An artificial neural network (ANN) model is established for predicting the fiber diameter of melt‐blown nonwoven fabrics from the processing parameters. An attempt is made to study the effect of the number of the hidden layers and the hidden layer neurons to minimize the prediction error. The artificial neural network with three hidden layers (5, 2, and 3 neurons in the first, second, and third hidden layer, respectively) yields the minimum prediction error, and thus, is determined as the preferred network. The square of correlation coefficient of measured and predicted fiber diameters shows the good performance of the model. Using the established ANN model, computer simulations of the effects of the processing parameter on the fiber diameter are carried out. The results show great prospects for this research in the field of computer‐assisted design of melt‐blowing technology. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4275–4280, 2006     

Artificial neural network modeling for predicting melt blowing processing

An artificial neural network model is established for predicting the fiber diameter of melt blown nonwoven fabrics from the processing parameters. An attempt is made to study the effect of the number of hidden layers and hidden layer neurons to minimize the prediction error. The artificial neural network with three hidden layers (5, 2, and 3 neurons in the first, second, and third hidden layer, respectively) yields the minimum prediction error and thus is determined as the preferred network. The square of the correlation coefficient of measured and predicted fiber diameters shows the good performance of the model. Using the established ANN model, computer simulations of the effects of the processing parameters on the fiber diameter are carried out. The results show great promise for this research in the field of computer assisted design of melt blowing technology. © 2005 Wiley Periodicals, Inc. J Appl Polym Sci 99: 424–429, 2006     

Thermal conductivity prediction of foods by Neural Network and Fuzzy (ANFIS) modeling techniques

A neuro-fuzzy modeling technique was used to predict the effective of thermal conductivity of various fruits and vegetables. A total of 676 data point was used to develop the neuro-fuzzy model considering the inputs as the fraction of water content, temperature and apparent porosity of food materials. The complexity of the data set which incorporates wide ranges of temperature (including those below freezing points) made it difficult for the data to be predicted by normal analytical and conventional models. However the adaptive neuro-fuzzy model (ANFIS) was able to predict conductivity values which closely matched the experimental values by providing lowest mean square error compared to multivariable regression and conventional artificial neural network (ANN) models. This method also alleviates the problem of determining the hidden structure of the neural network layer by trial and error.     

AN ARTIFICIAL NEURAL NETWORK APPROACH TO CAPILLARY RISE IN POROUS MEDIA

An artificial neural network (ANN) was used to analyze the capillary rise in porous media. Wetting experiments were performed with 15 liquids and 15 different powders. The liquids covered a wide range of surface tension (15.45-71.99 mJ/m²) and viscosity (0.25-21 mPa.s). The powders also provided an acceptable range of particle size (0.012-45 μm) and surface free energy (25.5-62.2 mJ/m²). An artificial neural network was employed to predict the time of capillary rise for a known given height. The network's inputs were density, surface tension, and viscosity for the liquids and particle size, bulk density, packing density, and surface free energy for the powders. Two statistical parameters, the product moment correlation coefficient (r²) and the performance factor (PF/3), were used to correlate the actual experimentally obtained times of capillary rise to: (i) their equivalent values as predicted by a designed and trained artificial neural network; and (ii) their corresponding values as calculated by the Lucas-Washburn equation as well as the equivalent values as calculated by its various other modified versions. It must be noted that for a perfect correlation r² = 1 and PF/3 = 0. The results showed that only the present ANN approach was able to predict with superior accuracy (i.e., r² = 0.92, PF/3 = 51) the time of capillary rise. The Lucas-Washburn calculations gave the worst correlations (r² = 0.15, PF/3 = 1002). Furthermore, some of the modifications of this equation as proposed by different workers did not seem to conspicuously improve the relationships, giving a range of inferior correlations between the calculated and experimentally determined times of capillary rise (i.e., r² = 0.27 to 0.48, PF/3 = 112 to 285).     

Use of Artificial Neural Network and Image Analysis to Predict Physical Properties of Osmotically Dehydrated Pumpkin

The objectives of this research were to predict, using neural networks, the color intensity (ΔE), percentage of shrinkage as well as the Heywood shape factor, which is the representative of deformation, of osmotically dehydrated and air dried pumpkin pieces. Several osmotic solutions were used including 50% (w/w) sorbitol solution, 50% (w/w) glucose solution, and 50% (w/w) sucrose solution. Optimum artificial neural network (ANN) models were developed based on one to two hidden layers and 10–20 neurons per hidden layer. The ANN models were then tested against an independent data set. The measured values of the color intensity, percentage of shrinkage, and the Heywood shape factor were predicted with R² > 0.90 in all cases, except when all the drying methods were combined in one data set.     

Prediction of the Characteristics of a New Sonochemical Reactor Using an Expert Model

Artificial neural network (ANN) models were developed to estimate the characteristics of a novel high-frequency sonoreactor. Mean residence time and ultrasound dissipated power were considered to evaluate the performance of this sonoreactor. These parameters were calculated from predicted tracer concentration and temperature variation by ANN models. The best network configurations were determined as two layers network including 10 and 12 neurons in hidden layer for the concentration and temperature prediction, respectively. For both networks Levenberg–Marquardt was the best training algorithm with average absolute relative error (AARE) below than 2%. The estimated characteristics were in good agreements with experimental data. Also, sensitivity analyses were done to find the relative importance of each input variable in networks training.     

ESTIMATION OF TRANSPORT PROPERTIES OF LIQUIDS

Based on the theory of intermolecular forces and the Hildebrand relation, a generalized equation in terms of refractive index is proposed to estimate viscosity, thermal conductivity and diffusion coefficients of liquid non-polar compounds at various temperatures. Analytical equations are provided to estimate transport properties of n-alkanes in terms of molecular weight. Average deviations for all three properties of various hydrocarbons from C⁵ to C²⁰ are within 1 % for a large number of data points. The proposed equation also predicts transport properties of some polar compounds with a reasonable degree of accuracy.     

NEURAL NETWORK MODELING OF TRANSITION METAL - ZEOLITE EXHAUST CATALYSTS

Recent research in automobile exhaust catalysts has addressed the substitution of platinum-group metals Pt, Pd and Rh by metals such as Cu, Co, Ag, Zn, Mn and Sr exchanged or impregnated on zeolites, TiO₂ or Zro₂ carriers. These catalysts have the potential of becoming good alternatives to the commercial three-way catalysts to convert pollutant hydrocarbons (HC), carbon monoxide (CO) and nitrogen oxides (NO_x). This paper describes a technique based on neural networks, to correlate the catalyst synthesis variables and resulting exhaust conversion. The optimum catalyst composition and operating conditions for a specified exhaust conversion are determined

A back-propagation algorithm was used to train the feed-forward neural network consisting of two hidden layers with 45 and 60 neurons in the first and second hidden layers respectively, with optimum values of the learning factor and momentum gain coefficient. The effects of the operating and compositional parameters on NO_x conversion by Cu-ZSM-5 were found. The optimum conversion was predicted for Si/Al atom ratio in the range 30-35, Cu-loading (in Cu-ZSM-5) of 1·1 - 1.2% of the zeolite weight, and an operating temperature of 650-675  K. The rare-earth metals (Ce, Cs and La) that act as promoters for three-way catalysts did not have a considerable effect on the exhaust conversion. The conversion increased by at least 10% when Co is used as a co-cation in Cu-ZSM-5.     

Effect of Operating Conditions on PV Performance of PVA Membranes: Experimental and Neural Network Modeling

Poly (vinyl alcohol) membranes were prepared by in-situ crosslinking of poly(vinyl alcohol) with glutaraldehyde as crosslinking agent and hydrochloric acid as catalyst and used for dehydration of IPA mixtures. Effects of feed composition, operating temperature, vacuum pressure, and Reynolds number on the permeation performance of the membranes were evaluated. Eighty-nine experimental data was applied to investigate ANN modeling. A multi layered feedforward neural network was applied to model the PV membranes. Two major training algorithms and optimum number of neurons and layers were investigated. As a result, Bayesian regularization successfully predicted experimental data. Different network structures were optimized, using multi object genetic optimization algorithm. The results concluded that the network with structure composing two hidden layers performs better than the other with one hidden layer, and also there is an excellent compatibility between the experimental data and the predicted values of optimum network structure (4:3:2:2). Furthermore, the optimum network was applied to predict extrapolation data and the results showed that this network can extrapolate data as well as interpolating.     

十二醇-癸酸-纳米粒子复合相变材料传热性能

针对有机相变材料热导率低的共性,以质量比为58.47:41.53的脂肪烃类低共熔有机物十二醇(DA)-癸酸(CA)为基液,添加纳米粒子MWNT、Cu、Al₂O₃及分散剂SDBS制备出纳米复合相变材料,从纳米粒子种类、添加浓度及超声时间方面研究其对复合有机相变材料热物性的影响。实验发现MWNT、Cu、Al₂O₃的添加都可以不同程度上提高DA-CA的热导率。当超声时间为50min、纳米粒子浓度均为0.1g·L^-1时3种纳米复合材料的热导率大小依次是:MWNT>Al₂O₃>Cu。最优例:超声分散时间90min,DA-CA+MWNT(0.1g·L^-1)+SDBS(0.2g·L^-1)的热导率最大,为0.3602W·m^-1·K^-1,相较DA-CA提高了20.5%,在不影响基液热物性的基础上具有良好的热稳定性。     

微藻/核桃壳混合热解需热量及制备芳烃研究

利用热重分析仪（TG）、气相色谱-质谱联用仪（GC/MS）与固定床反应器,考察了微藻、核桃壳及混合物主要热解阶段的需热量特性,以及混合比、温度、催化剂种类对二者混合热解制备芳烃的影响规律。结果表明：在不同升温速率下,核桃壳热解在180～270℃和380～485℃处存在两个吸热峰,整体表现为吸热效应（378.56～596.45 kJ·kg^-1）;微藻热解在280～450℃处存在一个放热峰,整体表现为放热效应（-814.76～-1191.52 kJ·kg^-1）。微藻/核桃壳热解呈现较低的放热效应（-99.05～-158.04 kJ·kg^-1）,表明二者混合热解可以实现一定程度的热量耦合。微藻/核桃壳热解在制备芳烃上表现出明显的协同效应,且芳烃相对含量在600℃、混合比1：1下达到最大值,为20.51%;加入Cu/HZSM-5可进一步提高混合热解的芳烃相对含量,达到35.74%。为微藻与核桃壳的高值化利用提供了新思路。     

Intelligent Prediction of CO2 Capture in Propyl Amine Methyl Imidazole Alanine Ionic Liquid: An Artificial Neural Network Model

The objective of this paper is to create a new artificial neural network (ANN) model to predict solubility of CO₂ in a new structure of task specific ionic liquids called propyl amine methyl imidazole alanine [pamim][Ala]. Equilibrium data of CO₂ solubility were measured at the temperatures of 25, 40, and 60°C and the pressures up to 50 bar. For the purpose of performance comparison, the two most common types of ANNs, multilayer perceptron (MLP) network and radial basis function (RBF) network were used. Water content, ionic liquid content, temperature, and pressure set as input variables to ANN, while CO₂ capture rate assigned as output. Based upon optimization process, MLP neural network with 14 neurons in the hidden layer, log-sigmoid transfer function in the hidden layer and linear transfer function in the output layer, exhibited much better performance in prediction task than RBF neural network with the same neuron numbers in the hidden layer. Results obtained demonstrated that there is a very little difference between the estimated results of ANN approach and experimental data of CO₂ capture rate for the training, validation, and test data sets. Furthermore, Henry’s law constants were obtained by fitting the equilibrium data.     

Long-term prediction of nonlinear hydrodynamics in bubble columns by using artificial neural networks

An artificial neural network (ANN) model was proposed for the long-term prediction of nonlinear dynamics underlying holdup fluctuations in bubble columns with three different diameters of 200, 400 and 800 mm. Local holdup fluctuations were measured with an optical probe in the bubble columns. The superficial gas velocity was varied in the range of 33–90 mm/s. The time intervals between successive bubbles were extracted from the time series of holdup fluctuations to represent hydrodynamic behaviors in the system and used as training and validation data sets. The effect of data preprocessing as well as the numbers of nodes in input and hidden layers on the ANN training behavior was systematically investigated. The prediction capability of the ANN was evaluated in terms of time-averaged characteristics, power spectra and Lyapunov exponents. It was observed that: the ANN model, which was trained with experimental time series and gas velocity, can be used for the long-term prediction of dynamic characteristics in bubble columns by using random data as the initial input. The results indicate that the trained ANN models have the potential of modeling nonlinear hydrodynamic behaviors in bubble columns.     

W2C/AC催化快速热解松木磨木木质素

以活性炭（AC）为载体制备了不同钨负载量的W₂C/AC催化剂,将其和松木磨木木质素（MWL）机械混合后进行Py-GC/MS（快速热解-气相色谱/质谱联用）实验,考察了钨负载量、催化剂/MWL比例对产物分布的影响,并通过外标法对主要产物（芳烃类和酚类）的真实产率进行了定量分析。结果表明,W₂C/AC催化剂可有效促进木质素的热解解聚生成单酚类产物,并对酚类产物具有脱羰基、脱甲氧基、脱羟基以及加氢的效果,从而促进稳定的酚类产物（不含羰基、甲氧基和不饱和碳碳双键）和芳烃类产物的生成。在4种W₂C/AC催化剂中,10%-W₂C/AC的催化效果最佳,在催化剂/MWL比例为5时热解产物总产率达到最大值,此时芳烃类和酚类产物的总产率由无催化剂时的21.2 mg·g^-1和151.0 mg·g^-1增加至102.1 mg·g^-1和191.1 mg·g^-1。     

Thermal Conductivity of Multiwalled Carbon Nanotubes-Kapok Seed Oil-Based Nanofluid

The synthesis of a nanofluid from multiwalled carbon nanotubes (MWCNTs) and Kapok seed oil by a one-step method is reported. The nanofluid showed excellent stability of nanoparticle dispersion in the base fluid. Furthermore, this study deals with the prediction of the thermal conductivity of the MWCNTs-kapok seed oil nanofluid. To improve the prediction of the thermal conductivity of the nanofluid, the artificial neural network (ANN) computing approach was used with different algorithms including the back-propagation, Levenberg-Marquardt, and genetic algorithm (GA). Finally, the ANN-GA model is recommended for the prediction of thermal conductivity with higher accuracy.     

Comparison between the artificial neural network,SAFT and PRSV approach in obtaining the solubility of solid aromatic compounds in supercritical carbon dioxide

The aim of this study is to model the solubilities of solid aromatic compounds in supercritical carbon dioxide (SCCO₂) using feed-forward artificial neural network (ANN). Temperature, pressure, critical properties and acentric factor of each solute have been used as independent variables of ANN model. The parameters of multi-layer perceptron (MLP) network have been adjusted by back propagation learning algorithm using experimental data which have been collected from various literatures. In order to find the optimal topology of the MLP, different networks were trained and examined and the network with minimum absolute average relative deviation percent (AARD%), mean square error (MSE) and suitable regression coefficient (R²) has been selected as an optimal configuration. By this procedure a single hidden layer network composed of nineteen hidden neurons has been found as an optimal topology. Sensitivity error analyses confirmed that the optimal ANN can predict experimental data with an excellent agreement (AARD% = 4.99, MSE = 7.08 × 10⁻⁷ and R² = 0.99699). Capability of the proposed ANN model has compared with those published results which have obtained by SAFT combined with eight different mixing rules (one, two and three parameters mixing rules) and PRSV equation of state (EOS). The best presented overall AARD% for SAFT approach with one, two and three parameters mixing rules are 16.15, 12.32% and 7.65%, respectively while PRSV EOS showed AARD% of 21.10%. The results emphasize that the proposed ANN model can predict the solubilities of solid aromatic compounds in SCCO₂ more accurate than SAFT and PRSV EOS.     

Comparison on Thermal Conductivity and Permeability of Granular and Consolidated Activated Carbon for Refrigeration

This paper focuses on the development of three types of activated carbon (AC) adsorbents, i.e. granular AC, consolidated AC with chemical binder, and consolidated AC with expanded natural graphite (ENG). Their thermal conductivity was investigated with the steady-state heat source method and the permeability was tested with nitrogen as the gas source. Results show that the thermal conductivity of granular AC with different sizes almost maintains a constant at 0.36 W·(m·K)^-1, while the value modestly increases to 0.40 W·(m·K)^-1 for the consolidated AC with chemical binder. The consolidated AC with ENG at the density of 600 kg·m^-3 shows the best heat transfer performance and their thermal conductivity vary from 2.08 W·(m·K)^-1 to 2.61 W·(m·K)^-1 according to its fraction of AC. However, the granular AC and consolidated AC with chemical binder show the better permeability performance than consolidated AC with ENG binder whose permeability changes from 6.98×10^-13 m² to 5.16×10^-11 m² and the maximum occurs when the content of AC reaches 71.4% (by mass). According to the different thermal properties, the refrigeration application of three types of adsorbents is analyzed.     

Temperature programmed desorption of toluene, p-xylene, mesitylene and naphthalene on mesoporous high silica MCM-41 for characterizing its surface properties and measuring heats of adsorption

Temperature programmed desorption (TPD) of aromatic hydrocarbons (viz. toluene, p-xylene, mesitylene and naphthalene) on mesoporous high silica MCM-41 from 323 to 673 K at different linear heating rates (5, 10, 15 and 25 K min⁻¹) has been investigated. TPD of toluene at different adsorbate loading (0.5 to 16.3 μmol g⁻¹) has also been investigated. All the TPD curves have a single asymmetric peak. The heats of adsorption of aromatic hydrocarbons on the mesoporous material, estimated from the TPD peak maximum temperatures measured at different heating rates, was found to occur in the following order: toluene<p-xyleneH_a increases with the decrease in the ionization potential of the hydrocarbons. The adsorption results from weak interactions between the π-electrons of aromatic hydrocarbons and the terminal poorly acidic silanol (Si–OH) groups. In the case of toluene TPD, the TPD peak maximum temperature increases with decreasing the adsorbate loading, indicating the presence of site energy distribution on the high silica MCM-41.     

硬脂醇/膨胀石墨复合相变材料的制备及储热性能

相变储热技术是解决热量在时空上分配不平衡问题的有效手段之一,研制高性能的复合相变材料（phase change material, PCM）成为当前研究者关注的重点。硬脂醇（stearyl alcohol, SAL）等有机PCM目前主要存在热导率偏低以及循环稳定性较差等问题而限制了实际应用。以SAL作为PCM,膨胀石墨（expanded graphite, EG）为高导热多孔基质,采用吸附定形工艺制备了16种SAL/EG复合PCMs[EG含量为7%、14%、21%、28%（质量）;样品密度为700kg/m³、800kg/m³、900kg/m³、1000kg/m³]。对复合PCMs样品的微观结构、储热能力、导热性能、循环稳定性及充放热性能进行研究与分析。结果表明：SAL完全填充于EG的多孔网络。当样品密度为900kg/m³,EG质量分数为28%的水平热导率最高,其值为28.58W/(m ? K),相比于纯SAL[0.38W/(m ? K)]提高了74倍,该值大约是相对应垂直热导率[5.99W/(m ? K)]的4.8倍。另外在构建的充放热性能试验台上研究了样品中心位置的储/放热性能,结果显示样品密度为900kg/m³,EG质量分数为28%的样品充放热速率最大,固-液潜热吸热和放热阶段所经历的时间分别为53min和20min。与此同时验证了样品的导热性能和熔化-凝固特性,说明SAL/EG复合PCMs具有稳定可靠的储/放热性能。