Prediction and optimization of hydrogen yield and energy conversion efficiency in a non-catalytic filtration combustion reactor for jet A and butanol fuels

Hydrogen production is one of main subjects in fuel cells. The traditional method of synthesis gas production is based on fuel reforming using catalysts. The main problem of these methods is sensitivity and fast degradation of catalysts especially when fuels with high sulfur content are used. A new technique for hydrogen production is fuel-reforming using non-catalytic filtration combustion in porous media reactors. Various experimental works have been carried out to increase hydrogen production under different operating conditions such as inlet fuel velocity and equivalence ratio. First, we investigated the ability of adaptive neuro fuzzy inference system (ANFIS) for predicting the filtration combustion characteristics. Four distinct ANFIS models were developed for estimating the hydrogen yield and energy conversion efficiency for fuels of jet A and butanol. Eight different membership functions of dsigmf, gauss2mf, gaussmf, gbellmf, pimf, psigmf, trapmf and trimf were tested for training of the ANFIS networks. The results showed that the RMSE of the best developed ANFIS models for estimating of the hydrogen yield of jet fuel, hydrogen yield of butanol, conversion efficiency of jet fuel and conversion efficiency of butanol were 1.399, 1.213, 0.508 and 2.191, respectively. Moreover the R² values of 0.998, 0.998, 0.999 and 0.999 were obtained for predicting the above mentioned variables, respectively. In the second step, a novel algorithm based on imperialist competitive algorithm (ICA) was used for optimization of hydrogen yield and energy efficiency. The maximum value of hydrogen yield and energy efficiency was 50.46% and 67.88% for jet A and 47.27% and 96.93% for butanol, respectively. The results showed that the imperialist competitive algorithm is an efficient and powerful algorithm to optimize combustion processes.     

CO2 capture by benzene-based hypercrosslinked polymer adsorbent: Artificial neural network and response surface methodology

In this research, porous benzene-based hypercrosslinked polymeric adsorbents with different morphological properties were synthesized through Friedel–Crafts alkylation reaction. The resulting samples were applied for CO₂ capture at different operational conditions. Two modelling approaches, including artificial neural network (radial basis function [RBF] and multi layer perceptron [MLP]) and response surface methodology (RSM), were employed to investigate the effect of independent parameters on adsorption capacity. A semi-empirical quadratic model for adsorption capacity was presented based on RSM-central composite design technique. Additionally, the optimal structure of RBF was determined with 200 neurons, and the optimal structure of MLP was determined with three hidden layers and 10, 8, and 7 neurons. The modelling results demonstrate the better prediction of MLP and RBF approaches than the RSM method with correlation coefficient values of 0.999, 0.989, and 0.931, respectively. Finally, process optimization was carried out using RSM optimization module and the optimized values of synthesis time, crosslinker ratio (formaldehyde dimethyl acetal [FDA]/benzene), adsorption time, pressure, and temperature were obtained at 10.11 h, 1, 220 s, 9 bar, and 55°C, respectively. The optimum value of CO₂ uptake capacity was obtained around 167 (mg/g).     

Aerodynamic and diluent effects on the emission of nitrogen oxides from hydrocarbon diffusion flames

An experimental study of the quantities of oxides of nitrogen produced in hydrocarbon gas jet flames in concentric and cross-flow air streams is presented. It is seen that the structure of flames has a strong influence on the quantities of NO and NO₂ generated in diffusion flames. Increases in jet velocity result in the increase of NO and NO₂ emitted from flames in concentric air streams. However, air stream velocity has opposite effects on the yields of NO and NO₂ from flames in cross-flows. Additions of diluent to jet and air streams decrease NO and NO₂ in both concentric and cross-flow air streams. The implications of scaling these results to fullsize flare stack flames have been discussed.     

Jet fires: An experimental study of the main geometrical features of the flame in subsonic and sonic regimes

Although jet fires are usually smaller than other fires, they may lead to a destructive chain of events that can increase the scale of an accident. Therefore, their size should be predicted for accurate risk assessment. In the literature, most of the proposals for estimating jet fire size concern small jet fires (up to 2.5 m in length) or subsonic flames. In this study, experiments on relatively large propane jet fires in still air were performed. Vertical turbulent diffusion flames up to 10 m in length, with sonic and subsonic mass flow rates, were obtained using six different orifice exit diameters. The experiments were filmed with video and thermographic cameras and the resulting visible and infrared images were used to determine flame length and lift‐off distance. Expressions for estimating jet length as a function of several variables (mass flow rate, orifice exit diameter, Froude and Reynolds numbers) are also proposed. © 2008 American Institute of Chemical Engineers AIChE J, 2009     

甲烷/富氧射流扩散火焰NOx的排放特性

对甲烷/富氧同轴射流扩散火焰燃烧条件下氧化剂流速对NOx排放的影响进行了实验研究. 通过对火焰径向温度分布、火焰形态以及喷嘴出口附近扩散燃烧的流场的观测,分析了不同条件下NOx的生成特性. 结果显示,在保持氧化剂流量不变的条件下,NOx排放指数EINOx随氧化剂流速的增加而减小,在保持氧浓度及过量空气系数不变的条件下,小火焰有利于保持较低的EINOx.     

乙醇和二甲醚微射流火焰燃烧特性研究

采用实验及数值计算研究了乙醇和二甲醚微圆管射流火焰燃烧特性。通过实验观察到不同燃料流速下乙醇和二甲醚火焰都具有四种典型的火焰形态；使用平面激光诱导荧光测试系统获得了微射流火焰的OH基元分布，实验结果表明在较高流速下稳定燃烧的乙醇火焰比二甲醚火焰直径小，且略高于二甲醚火焰；采用考虑详细化学反应机理的数值计算对乙醇和二甲醚火焰进行了数值模拟，计算结果与实验现象吻合较好；利用一维非预混对冲火焰计算进一步研究了这两种燃料的化学反应路径，分析结果表明乙醇和二甲醚火焰的中间产物有显著差异，两种燃料化学反应特性的差异导致了不同的微火焰结构。     

Hydrothermal flame impingement experiments. Combustion chamber design and impingement temperature profiles

The current work presents the hydrothermal flame impingement experiments conducted for the design of a hydrothermal spallation drilling nozzle. The products of hydrothermal flames of mixtures of ethanol, water and oxygen were injected as free jets in a high pressure water bath. The nozzle design was based on ideas stemming from underwater welding and cutting of metal sheets. Water entrainment in the flame-jets and the heat transfer capabilities of flames injected from various nozzles have been analyzed by measuring their impingement temperature profiles on a flat stainless steel plate. It was found that the thermal-to-kinetic energy ratio of the jet has a direct influence on the entrainment of water in it. Furthermore, the cooling water of the combustion chamber was injected in various angles to the axis of the jet resulting to different entrainment rates. It was found that higher water injection angles reduced the rate of entrainment. Finally, it was indicated that at certain operational points of the jet, its trans-critical properties had an important influence on the impingement temperatures.     

Experimental and artificial neural network modeling study on soot formation in premixed hydrocarbon flames

The formation of soot in premixed flames of methane, ethane, propane, and butane was studied at three different equivalence ratios. Soot particle sizes, number densities, and volume fractions were determined using classical light scattering measurement techniques. The experimental data revealed that the soot properties were sensitive to the fuel type and combustion parameter equivalence ratio. Increase in equivalence ratio increased the amount of soot formed for each fuel. In addition, methane flames showed larger particle diameters at higher distances above the burner surface and propane, ethane, and butane flames came after the methane flames, respectively. Three-layer, feed-forward type artificial neural networks having seven input neurons, one output neuron, and five hidden neurons for soot particle diameter predictions and seven hidden neurons for volume fraction predictions were used to model the soot properties. The network could not be trained and tested with sufficient accuracy to predict the number density due to a large data range and greater uncertainty in determination of this parameter. The number of complete data set used in the model was 156. There was a good agreement between the experimental and predicted values, and neural networks performed better when predicting output parameters (i.e. soot particle diameters and volume fractions) within the limits of the training data.     

Growth characteristics of polycyclic aromatic hydrocarbons in dimethyl ether diffusion flame

The growth characteristics of polycyclic aromatic hydrocarbons (PAHs) in laminar dimethyl ether (DME) diffusion flame were investigated experimentally, and we assumed that the growth of PAHs within the flame was predominantly due to methyl addition/cyclization (MAC) mechanism. Methane and propane laminar diffusion flames were also investigated for comparison, and their PAHs growth characteristics had been explained by reactions concerning acetylene and propargyl radical. Laser-induced fluorescence (LIF) and laser-induced incandescence (LII) techniques were used to measure the relative concentration of soot and PAHs, respectively. Two-dimensional images of the OH-LIF, PAHs-LIF, and LII from soot were measured in the test flames. Furthermore, to investigate the growth characteristics of the PAHs in the flames, the fluorescence spectra of the PAHs were measured at several heights in the flames, using a spectrograph. The molecular size of the PAHs was estimated based on an emission wavelength region of the PAHs-LIF that varied along with the PAH size. The results show that although the PAHs were widely distributed within the unburned region similar to that of the methane and propane flames, the intensity and detection region of LII were much smaller than that of the methane and propane flames. The PAHs-LIF spectra indicated that the growth of the PAHs within the DME flame was much slower than the methane and propane flames, and thus a large number of small PAHs were discharged into the OH region distributed around the outer edge of the flame.     

平板狭缝间C1~C4烷烃/空气预混射流火焰的燃烧特性

对平行平板狭缝间C₁~C₄烷烃预混射流火焰进行了实验研究,考察了壁面温度、狭缝间距、当量比、燃料种类等对火焰形态和稳定性的影响,并利用高速相机获得了狭缝间的火焰图像。结果表明,随着狭缝间距的减小,火焰经历了稳定、脉动和熄火3个阶段。其中,火焰脉动发生在大于熄火间距的狭小范围内,其脉动频率随着壁面温度的升高而增加。对同一种燃料,当预混气当量比和壁面温度保持恒定时,火焰的脉动频率在脉动发生的区域内保持不变。对比C₁~C₄烷烃预混火焰的脉动频率及脉动火焰持续距离范围,发现甲烷预混火焰的均最小,而其他3种燃料则比较接近。     

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.     

4种智能算法在相平衡数据拟合中的应用

针对Marquardt-Levenberg法应用于多元物系相平衡数据拟合时,模型参数剧增,初值难以设定的难题,将4种智能算法,即遗传算法、神经网络,退火算法及粒子群算法,应用于相平衡数据的拟合。以正丙醇（1）+乙腈（2）二元物系汽液相平衡数据的Wilson拟合和甲醇（1）+乙腈（2）+1-乙基-3-甲基咪唑四氟硼酸盐（3）三元物系汽液相平衡数据的NRTL拟合为例,系统讨论了4种算法在应用时的主要影响因素,并将所得结果进行了分析和比较。结果表明遗传算法和粒子群算法可以较好地解决初值难设的问题,并且给出了每种算法的适用范围和使用建议。     

Removal of Acid Red G dye from aqueous solutions by adsorption to MCM-41-layered double hydroxides composite

MCM-41 supported layered double hydroxides (LDH) composite materials (ML) were synthesized and studied for removal of Acid Red G (ARG), an anionic dye, with the adsorption method. ML was prepared using in situ synthesis procedure for the low supersaturation coprecipitation method, and ML10 and ML20 presented promising application towards ARG dye adsorption capacity in industrial wastewater. Powder samples were characterized by Xray diffraction, FTIR spectroscopy, scanning electron microscopy and energy dispersive spectrometry. The effects of different reaction time, initial solution pH and temperature on the dye adsorption capacity were investigated. Adsorption process was well described by pseudo-second-order kinetic model and Langmuir isotherm model. The fitting curves showed that ML10 and ML20 had higher adsorption rates and maintained a certain theoretical saturated adsorption capacity (92.19807mg/g and 96.41947mg/g, respectively) compared with LDH.     

Experimental evaluation and modeling of liquid jet penetration to estimate droplet size in a three-phase riser reactor

In this work,the effects of injecting an evaporating liquid jet into solid–gas flow are experimentally investigated.A new model(SHED model) and a supplementary model(spray model) have also been proposed to investigate some flow-field characteristics in three-phase fluidized bed with the mean relative error 4.3% between model and measured results.Some experiments were conducted to study the influences of flow-field parameters such as liquid volumetric flow rate,injection velocity,jet angle and gas superficial velocity as well as solid mass flux on the jet penetration depth(JPD).In addition,independent variables were experimentally employed to propose two empirical correlations for JPD by using multiple regression method and spray cone angle(SCA) by using dimensional analysis technique.The mean relative errors between the JPD and SCA correlations versus experimental data were 7.5% and 3.9%,respectively.In addition,in order to identify the variable effect,a parametric study was carried out.Applying the proposed model can avoid direct use of expensive devices to measure JPD and to predict droplet size.     

Experimental and numerical study of fluid dynamic parameters in a jetting fluidized bed of a binary mixture

The solid circulation pattern, the voidage profile, and the jet penetration height have been investigated experimentally and computationally in a cold-flow model of jetting fluidized beds (JFBs) of a binary mixture in this paper. This rectangular two-dimensional bed is 0.30 m wide and 2.05 m high with a central jet and a conical distributor, which roughly stands for the ash-agglomerating fluidized-bed coal gasifier. A video camera and coloured particle tracer method were employed to explore the fluid dynamics in the bed. In terms of the average physical properties of binary mixtures, a hydrodynamic model describing the gas-solid flow characteristics in a jetting bed is resolved by using a modified Semi-Implicit Method for Pressure-Linked Equation (SIMPLE) algorithm. This paper focuses on three features of the fluid dynamics—solid circulation pattern, voidage profile, and jet penetration height. The solid circulation pattern is composed of three regions: the jetting region, the bubble street, and the annular region. Above the central nozzle the time-averaged isoporosity contours are almost elliptic, while near the walls of the bed, the voidage in high solid concentration region is approximately equal to that at the minimum fluidization state. The jet penetration height increases with increasing jet gas velocity and with decreasing average particle diameter. The increase in weight percentage of the lighter component in the binary system reveals that reduction of average density causes the enlargement of jet penetration height. The simulated results show good agreement with the experimental data.     

Prediction of the gas solubility in polymers by a radial basis function neural network based on chaotic self‐adaptive particle swarm optimization and a clustering method

A novel model based on a radial basis function neural network (RBF NN), chaos theory, self‐adaptive particle swarm optimization (PSO), and a clustering method is proposed to predict the gas solubility in polymers; this model is hereafter called CSPSO‐C RBF NN. To develop the CSPSO‐C RBF NN, the conventional PSO was modified with chaos theory and a self‐adaptive inertia weight factor to overcome its premature convergence problem. The classical k‐means clustering method was used to tune the hidden centers and radial basis function spreads, and the modified PSO algorithm was used to optimize the RBF NN connection weights. Then, the CSPSO‐C RBF NN was used to investigate the solubility of N₂ in polystyrene (PS) and CO₂ in PS, polypropylene, poly(butylene succinate), and poly(butylene succinate‐co‐adipate). The results obtained in this study indicate that the CSPSO‐C RBF NN was an effective method for predicting the gas solubility in polymers. In addition, compared with conventional RBF NN and PSO neural network, the CSPSO‐C RBF NN showed better performance. The values of the average relative deviation, squared correlation coefficient, and standard deviation were 0.1282, 0.9970, and 0.0115, respectively. The statistical data demonstrated that the CSPSO‐C RBF NN had excellent prediction capabilities with a high accuracy and a good correlation between the predicted values and the experimental data. © 2013 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 130: 3825–3832, 2013     

The stability of turbulent hydrogen jet flames with carbon dioxide and propane addition

In the study the lift-off, blow-out and blow-off stability limits of hydrogen/propane flames and hydrogen/carbon dioxide flames were tested in three different mixing arrangements. The first was to premix hydrogen with carbon dioxide or propane to form a jet flame. The second was to add the gas as an annular jet around the hydrogen flame. The third was to inject into the centre of the hydrogen flame. Propane and carbon dioxide have the same density but create very different chemical kinetic changes when added to hydrogen flames. The results showed that when premixed with hydrogen, propane is more effective in flame lift-off and blow-out. The analysis of kinetic mechanisms revealed that the propane is the dominating fuel in determining the burning rate of the hydrogen/propane while carbon dioxide mainly acted to dilute the hydrogen/CO₂ mixture. Comparing the three mixing arrangements, the experiments showed that hydrogen flame can be effectively lifted or blown out when gases were in annular flow around the hydrogen flame. The isothermal mixing process of the co-flow configuration was discussed.     

基于改进的RBF网络和自适应小波图像增强的ECT两相流测量系统(英文)

Electrical capacitance tomography(ECT) is a non-invasive imaging technique that aims at visualizing the cross-sectional permittivity distribution and phase distribution of solid/gas two-phase flow based on the measured capacitance.To solve the nonlinear and ill-posed inverse problem:image reconstruction of ECT system,this paper proposed a new image reconstruction method based on improved radial basis function(RBF) neural network combined with adaptive wavelet image enhancement.Firstly,an improved RBF network was applied to establish the mapping model between the reconstruction image pixels and the capacitance values measured.Then,for better image quality,adaptive wavelet image enhancement technique was emphatically analyzed and studied,which belongs to a space-frequency analysis method and is suitable for image feature-enhanced.Through multi-level wavelet decomposition,edge points of the image produced from RBF network can be determined based on the neighborhood property of each sub-band;noise distribution in the space-frequency domain can be estimated based on statistical characteristics;after that a self-adaptive edge enhancement gain can be constructed.Finally,the image is reconstructed with adjusting wavelet coefficients.In this paper,a 12-electrode ECT system and a pneumatic conveying platform were built up to verify this image reconstruction algorithm.Experimental results demonstrated that adaptive wavelet image enhancement technique effectively implemented edge detection and image enhancement,and the improved RBF network and adaptive wavelet image enhancement hybrid algorithm greatly improved the quality of reconstructed image of solid/gas two-phase flow [pulverized coal(PC)/air].     

Formation of Jets by Shaped Charges with Metal Powder Liners

The characteristics of shaped charges with metal powder liners for perforating borehole pipes are described. The liners were manufactured using powder metallurgy technology. Two types of powder liners were made: a homogeneous one made of copper powder and a heavy one made of copper and tungsten powder blend. Laminar liners consisting of two layers made from powders of different densities were also examined. X‐ray pulse technique was applied to research the process of jet stream formation. Radiograms revealed the discontinuous (discrete) structure of jets formed from powder liners. The corresponding computer simulations of jet formation are presented.