宝钢焦炭质量预测模型Ⅱ. 焦炭质量预测模型的建立和应用

在总结国内外焦炭质量预测模型的基础上，采用改进的GMDH方法，以大量SCO（Simulated Coke Oven)炉炼焦试验数据为基础，建立了适用于宝钢的SCO炉焦炭质量预测模型，再校正到生产焦炉。实践表明，该模型能很好地预测大生产焦炭质量，指导宝钢炼焦配煤生产。预测平均偏差DI15^150为0．16％，CRI 0．65％，CSR 0．82％。     

宝钢焦炭质量预测模型Ⅰ.影响焦炭热性质的因素

根据19种单种煤和64个配煤方案在模拟焦炉（SCO炉）上的试验，研究了原料煤性质对焦炭热性质的影响。结果：表明，煤中的无机组分对焦炭的热性质影响也较大，矿物质催化指数MCI与焦炭的热性质存在较好的线性关系。配合煤的煤质指标中Vd,Ad,Std具有较好的加和性，G,IgMF在一定范围内具有加和性，Y值，奥亚膨胀度（全膨胀a b)，的实测值高于其加和值，此外成型煤的配入对焦炭的热性质改善效果明显。     

基于改进BP神经网络的煤催化气化预测模型研究

采用改进的三层BP神经网络建立了煤催化气化反应失重率、气化初始温度和最大气化速率所对应温度的预测模型。结果表明,采用改进BP神经网络模型在此研究中可达到较高的精度,其最大预测误差分别为5.18% 、5.65% 、2.33%,明显小于归回公式的预测误差。
     

厌氧胶的组成、结构和固化引发机理研究进展

厌氧胶是一种单组分无溶剂的胶粘剂,当有氧气存在时为液态,而当氧气被排除后即迅速成为固态。厌氧胶在机械、电子等领域有着广泛的应用。介绍厌氧胶的组成、结构和固化引发机理的研究进展。     

基于布谷鸟算法与BP神经网络的煤灰变形温度预测

以120种煤样为数据基础,采用布谷鸟算法(CS)优化BP(Back Propagation)神经网络,建立了CSBP模型对单煤、煤掺添加剂和配煤等3类样本的煤灰变形温度(DT)样本进行预测。模型以煤灰化学成分及其组合参数等13个变量作为输入量,以变形温度(DT)作为输出量。CSBP模型预测结果与BP神经网络模型预测结果进行对比发现,无论是单煤、煤掺添加剂还是配煤,CSBP模型较BP模型对煤灰变形温度(DT)的预测都更加精准,平均相对误差分别达到了3.11%、4.08%和4.22%。另外,对比3类样本预测结果发现,无论是CSBP模型还是BP模型,相比单煤预测而言,煤掺添加剂及配煤的预测误差都有明显的增加。     

二(2—乙基己基)亚砜萃取钯(Ⅱ)的机理及萃合物的组成和结构

考察了低酸度下二(2—乙基己基)亚砜(DEHSO)萃取钯(Ⅱ)的机理。研究了H~+、Cl~-和温度对分配比的影响。萃取剂浓度较高时([DEHSO](o):[Pd~(2+)]>2:1)反应为 萃取剂浓度较低时,反应为 用斜率法、Asmus法、饱和法、固态萃合物的组成分析及红外图谱确定了萃合物的组成和结构。     

基于苯磺酸和2，2'-联吡啶双配体的锰配合物的结构及其催化Mannich反应性能和机理

由溶剂热法合成了 2 个锰的超分子配合物[Mn₂(2,2''-bipy)₄(H₂O)Cl₃](L₁)·6H₂O (1)和[Mn(2,2''-bipy)₂(H₂O)Cl](L₂)·3H₂O (2)(L₁^-=对甲基苯磺酸根,L₂^-=间硝基苯磺酸根,2,2''-bipy=2,2''-联吡啶),并用单晶X射线衍射、红外光谱、热重分析和氮气吸附-脱附测试对其进行了表征。以Mannich反应为探针,研究了2种配合物的催化性能,并通过对比2种配合物的扫描电镜和粉末X射线衍射表征结果,分析了配合物结构对其催化性能的影响。最后通过密度泛函理论预测了配合物的活性位点,利用X射线光电子能谱证明了活性位点的活化作用,进而阐述了配合物催化Mannich反应的机理。     

基于苯磺酸和2，2′-联吡啶双配体的锰配合物的结构及其催化Mannich反应性能和机理

由溶剂热法合成了 2 个锰的超分子配合物[Mn₂(2,2′-bipy)₄(H₂O)Cl₃](L₁)·6H₂O (1)和[Mn(2,2′-bipy)₂(H₂O)Cl](L₂)·3H₂O (2)(L₁^-=对甲基苯磺酸根,L₂^-=间硝基苯磺酸根,2,2′-bipy=2,2′-联吡啶),并用单晶X射线衍射、红外光谱、热重分析和氮气吸附-脱附测试对其进行了表征。以Mannich反应为探针,研究了2种配合物的催化性能,并通过对比2种配合物的扫描电镜和粉末X射线衍射表征结果,分析了配合物结构对其催化性能的影响。最后通过密度泛函理论预测了配合物的活性位点,利用X射线光电子能谱证明了活性位点的活化作用,进而阐述了配合物催化Mannich反应的机理。     

配煤对高熔点煤灰熔融特性影响的研究

采用灰熔点较低的襄阳煤和灰熔点较高的晋城无烟煤组成的混合煤样,利用XRF、SEM、DSC、XRD、三元相图等分析方法,探究了襄阳煤对晋城无烟煤煤灰熔融温度的影响。结果表明,配煤能有效降低高熔点煤灰的熔融温度,当襄阳煤的加入量小于24%时,混合煤灰熔融温度显著降低;襄阳煤的加入量在24%-40%时,混合煤灰熔融温度变化平缓且流动温度低于1 400℃。混合煤灰中的成分在1 000-1 200℃发生一系列的化学反应,主要包括SiO_2与Al_2O_3结合产生高熔点物质莫来石以及Fe_2O_3、CaO与莫来石反应转化形成铁尖晶石、钙长石等新物质,由此造成了煤灰熔融温度的变化。基于BP神经网络对实验数据建立预测模型,其预测效果优于前人总结的经验公式,平均准确度高于99%。利用热力学软件HSC 5.0分析了CaO、Fe_2O_3对降低煤灰熔融温度的影响,分析表明,CaO对莫来石的转化作用优于Fe_2O_3。     

Predicting coal ash fusion temperature based on its chemical composition using ACO-BP neural network

Coal ash fusion temperature is important to boiler designers and operators of power plants. Fusion temperature is determined by the chemical composition of coal ash, however, their relationships are not precisely known. A novel neural network, ACO-BP neural network, is used to model coal ash fusion temperature based on its chemical composition. Ant colony optimization (ACO) is an ecological system algorithm, which draws its inspiration from the foraging behavior of real ants. A three-layer network is designed with 10 hidden nodes. The oxide contents consist of the inputs of the network and the fusion temperature is the output. Data on 80 typical Chinese coal ash samples were used for training and testing. Results show that ACO-BP neural network can obtain better performance compared with empirical formulas and BP neural network. The well-trained neural network can be used as a useful tool to predict coal ash fusion temperature according to the oxide contents of the coal ash.     

固定床渣油加氢脱金属废催化剂上焦炭结构和组成沿床层变化研究

针对取自中石油某装置不同床层轴向位置的工业固定床渣油加氢脱金属废催化剂,采用元素分析、热重分析、X射线光电子能谱、傅里叶变换红外光谱和核磁共振碳谱等,研究了催化剂上沉积焦炭的结构组成特征参数。结果表明,不同轴向位置废催化剂上的焦炭具有某些共同特征,如相同的碳类型和官能团等,但其结构和组成各不相同。模拟建立了基于各种表征结果的焦炭结构组成模型,并利用可计算核磁共振波谱化学位移和预测核磁共振谱图的gNMR软件对所建立焦炭模型的精确性进行了验证,表明模型与实验结果具有很好的一致性。     

气化中煤灰熔点和黏度预测模型

根据煤灰中硅铝含量及硅铝比对煤灰进行分类研究,构建了灰熔融点和黏度与组分关系的优化模型,并对宽组分范围的煤灰熔点、黏度关系进行探讨。获得了更加精确的灰熔点预测模型,全液相温度模型预测误差为±40℃,实验值和预测值的标准误差为25℃。采用修正的Urbain模型和Roscoe模型相结合,模型预测值和实验值吻合较好,低黏度下对数黏度的预测值和实验值误差为±0.1;高黏度下黏度的预测值和实验值误差为±0.2。结果表明,基于煤灰组分分类的拟合结果优于涵盖宽组分的模型。     

Experimental investigation on wetting mechanism for coal dust with different metamorphic degree based on infrared spectrum and 13C-NMR

Respirable coal dust accounts for heart and respiratory diseases of coal miners such as asthma, pneumoconiosis, and black lung disease. What is more, coal dust explosion seriously affects coal mine safety production and coal miners' life safety. Generally, dust suppressants are commonly applied in coal mines. However, current dust suppressants are not working effectively. To develop a better dust suppressant, we attempt to explore the factors affecting the wettability of coal dust under different metamorphic levels from the essence of coal dust wetting mechanism in this paper. Specifically, we use Fourier transform infrared (FTIR) spectroscopy and nuclear magnetic resonance (NMR) to reveal the microstructure of coal from two aspects of functional group and carbon skeleton structure and obtain the micro information of the surface functional group types, quantity, and carbon structure of coal with different degrees of metamorphism, as well as the change rule of functional group of coal sample with coal rank and the law of carbon increase and deoxidization of coal metamorphism. After that, we acquired the structural parameters of coal by the NMR experiments and fitted the quantitative mathematical relationship between the microstructure parameters and wettability of coal through SPSS and ORIGIN software. Finally, this paper constructs an evaluation model for the influencing factors of coal dust wettability, explains the influence degree of different coal dust structure on coal dust wettability, improves the coal dust wettability mechanism, and provides more quantitative research ideas and methods for the control of coal dust.     

神东2-2煤镜质组大分子结构模型13C-NMR谱的构建与修正

利用浮沉离心法得到了神东原煤的镜质组（SDV）,并对其进行了固体¹3C-CP/MAS NMR测试和元素分析。由¹3C-NMR实验得到了SDV中碳原子的结构分布特征和12种结构参数。结果表明,在SDV的结构中芳香碳原子的存在形式主要是缩合程度为2的萘,其余则是以苯环和含杂原子的芳香环。利用结构参数和元素分析的结果构建了SDV的大分子结构模型, 并运用¹3C-NMR预测软件ACD/CNMR predictor计算了SDV大分子结构模型的¹3C 化学位移。根据计算结果对SDV的大分子结构模型进行了修正,获得了能和实验谱图吻合较好的大分子结构模型。     

基于机器人制样和激光诱导击穿光谱分析技术的选煤厂检测系统无人化

目前选煤厂煤质检测过程自动化程度低,采样与制样技术需要大量的人工参与。检测技术的落后直接影响数据的准确性从而对整个选煤厂生产过程控制与产品质量控制产生重要影响,直接关系选煤厂的经济效益。面对产业升级的需要,将先进的自动采制样技术引进煤炭生产经营企业是当务之急。机器人采制样系统包含:机械化采样与初制样技术、机器人精细化制样技术、煤炭快速检测技术、自动快速浮沉技术,为选煤厂提供了一种全自动采制检的新技术方案。     

K~+、Ca~(2+)和Fe~(3+)对和丰煤热解产物分布、结构及品质的影响

将K~+、Ca~(2+)、Fe~(3+)的硝酸盐用于处理脱灰和丰煤样(K-DC、Ca-DC和Fe-DC),在热重分析仪中考察了处理煤样的失重特征和气体逸出规律。结果表明,处理煤样的总失重率减少,CO_2和H_2的浓度较原煤(DC)的高。通过固定床研究了处理煤样热解过程中产物的分配规律,采用元素分析、FT-IR、模拟蒸馏和GC-MS等分析了半焦的结构特征和焦油的组成与品质。结果表明,与DC相比,处理煤样的半焦和气体产率增加,焦油产率降低,相应半焦的不饱和度和缩合程度降低。在各金属组分的作用下,焦油中的轻质组分分率增加,其中,Fe~(3+)的作用最为显著,其值增加了22.4%。GCMS结果表明,长链烷烃含量高达70%,是焦油组分重的主要原因,K和Fe组分可促进其分解。     

Comprehensive method based on model free method and IKP method for evaluating kinetic parameters of solid state reactions

This article presents, firstly, a short review of methods for evaluating kinetic parameters of solid state reactions and a critical analysis of the isoconversional principle of model free methods. It shows theoretically that the activation energy for complex reactions is not only a function of the reaction degree but also of heating programs, and points out that any method that attempts to extract the dependences of activation energy on conversion degree without considering the dependences of heating programs is problematic. Then an analysis is given of the invariant kinetic parameters (IKP) method and recommends an incremental version of it. Based on the incremental IKP method and model free method, a comprehensive method is proposed that predicts the degree of the dependences of activation energy on heating programs, selects reliable values of activation energy and extracts the values of variable pre‐exponential factor. This comprehensive method is tested using both simulation data and experimental data, the results of which show it can not only give reliable values of kinetic parameters but also be helpful in explaining inconsistencies of kinetic results in solid state reactions. © 2012 Wiley Periodicals, Inc.