巴伦诺尔一矿煤发热量的综合预测

通过分析巴伦诺尔一矿煤质得到了17组煤质分析数据,包括Mad,Ad,Hdaf,Qgr,d。利用多元回归分析的原理,建立煤的发热量关于煤中水分和灰分含量的多元回归方程,并通过R检验、F检验、t检验,证明了回归方程的作用显著,即具有实用价值。但是线性回归分析仅能对煤的发热量进行估算,并不能精确预测。因此,采用了支持向量机(SVM)算法对多元线性回归的初步预测结果进行小范围修正,修正结果显示总体预测精度明显提高,这2种方法的结合,效果优于常用方法。     

基于主成分分析的煤元素分析通用预测模型研究

基于大量煤质分析数据,以主成分分析法对煤的发热量和工业分析数据进行预处理,应用三元线性回归和BP网络分析研究主成分与煤的各元素间的关系,进而提出了煤元素分析通用预测模型,并对模型适应性进行了检验;结果表明所建模型具有较强的适应性。     

提高商品煤样分析准确性的浅谈

为了确定煤的各种性质,合理利用煤炭资源,对煤质进行准确分忻,是进行煤贸易或以煤为原料的焦化企业及以煤作为燃料的工业生产所必需的。在生产中要获得准确的煤质分析结果,通常要先对大批量的煤进行采样和制备,获得具有代表性的煤样,然后进行煤质化验。根据煤种的不同,检验人员采用不同的化验项目并提供相应的检验数据。这些检验数据即是判定产品质量的客观依据,因此检验数据必须具备真实性和精确性。在实践中如何提高检验数据精确度是煤质分析工作中必须高度重视的问题。     

煤的工业分析仪器法的影响因素

通过煤的工业分析的组分定义的简单介绍,提出实验条件的影响因素,比较煤的工业分析的仪器法的优缺点,对提高煤质检验的效率进行探讨研究。     

煤的工业分析仪器法的影响因素

通过煤的工业分析的组分定义的简单介绍,提出实验条件的影响因素,比较煤的工业分析的仪器法的优缺点,对提高煤质检验的效率进行探讨研究。     

基于Elman网络的动力配煤煤质预测模型的研究

动力配煤的多种煤质指标与各组分单一煤种的煤质指标之间具有典型的非线性映射特征。Elman回归神经网络是一种典型的动态神经元网络,具有映射动态特征的功能,尤其具有高度的非线性映射能力。笔者利用Elman网络建立了动力配煤煤质预测模型,并以较好的预测效果实现了动力配煤煤质预测,从而证实了该方案的可行性。     

煤的岩相分析在配煤炼焦中的应用

煤岩分析法已成为辨别煤质特别是确认混煤情况的重要手段,它在某些方面弥补了工业分析在检验煤质上的一些不足。文中依据镜质组反射率具有加和性的特点,利用单种煤反射率分布图合成配合煤反射率分布图指导炼焦配煤,可以使焦炭质量达到最优。     

焦炭质量预测模型

结合焦炭质量的影响因素,通过MATLAB环境中regress、robustfit等函数对炼焦配合煤各指标如灰分(A d)、挥发分(V daf)、黏结性指数(G)和胶质层厚度(Y),催化指数(MCI Y)等进行回归分析、比较和总结;用多元线性回归方法建立了配合煤煤质指标预测焦炭质量的预测模型,模型的预测精度符合要求。     

贵州无烟煤灰分与高位发热量相关性研究

煤的燃烧性能与煤中各物质的含量有密切关系。灰分的分析是煤工业分析的重要内容。为了快捷、省时、省力确定煤的发热量,以历年来采集的400组贵州无烟煤煤质数据为基础,通过计算,得到了灰分与发热量的回归方程。通过相关性检验发现:相关系数R为0.999,大于相关系数临界值,表明线性关系特别显著。通过误差检查,确定回归方程式推算误差置信范围为0.16。为了进一步验证回归方程的准确性,用贵州12个主要产煤区煤样的实测值与计算值进行比较,发现最大误差值为0.18,最小误差值为-0.01,远小于GB/T 213—2008《煤的发热量测定方法》规定的再现性临界差。     

长焰煤、贫瘦煤实施动力配煤的可行性分析

介绍了河南义马煤业集团长焰煤、贫瘦煤的煤质特性以及动力配煤技术要求和煤质指标的计算方法;通过计算不同煤种、不同比例配煤的煤质特性参数,表明配煤后达到了节煤降耗与减轻结焦的目的,并可获得较好的经济效益。     

在线煤质分析仪ECA在神东洗选中心的应用

介绍了煤质在线分析的重要性,以及美国ThennoFisherScientific公司在线煤质分析仪ECA的工作原理,并以神华神东洗选中心保德选煤厂的实际应用为例,深入分析了ECA的使用效果。通过随机抽取若干ECA在线监测的煤质数据,以及对应时间段内人工化验的煤质数据,通过线性回归研究二者之间的关联性。结果显示,ECA在线监测数据与人工采样化验数据具有非常好的线性关系,在生产中具有较强的指导意义。借助于ECA的在线监测,保德选煤厂可以随时调整分选参数,确保商品煤各项指标稳定、合格,使用中效果较好,发挥了较为理想的参考价值。ECA的应用有效地克服了实际生产中煤质化验数据不及时、数据分析不准确、代表性不强等对生产的负面影响,实时提供数据,为生产提供可靠的参考,克服了生产参数制定的盲目性和经验性。     

Formulas for calculating the calorific value of coal and coal chars: Development,tests, and uses

A new formula for calculating the calorific value of coal from its ultimate analysis has been obtained by regression analysis of a data bank consisting of data on 775 samples of U.S. coals of all ranks. It yielded zero average difference between observed and calculated values and a standard deviation of 129 Btu/lb (300 kJ/kg). Neither average difference nor standard deviation varied much with rank of the coal. The Dulong, Mott-Spooner, Boie, and Grummel-Davies formulae were also tested with the data but gave substantially poorer results. The new formula has been substantiated with data from other laboratories and with data on chars. The distribution of variance, attributable to variability of mineral matter, variability of coal organic matter not related to rank, and variance of laboratory determinations was estimated. Use of the formula to monitor performance of a laboratory is illustrated, as is modification to obtain maximum precision in heat balance calculations on a coal conversion process.     

Prediction of grindability with multivariable regression and neural network in Chinese coal

Grindability index of coal is usually determined by Hardgrove Grindability Index (HGI). The correlation between the proximate analysis of Chinese coal and HGI was studied. It was found from statistical analysis that, the higher the moisture and the volatile matter content in coal, the less the HGI will be. On the contrary, the higher the ash and the fixed carbon content in coal, the higher the HGI will be. But the correlation between proximate analysis and HGI in coals is nonlinear. The prediction equation of HGI reported in literature, which is based on proximate analysis of coal and linear regression method, is not correct for coals in China. In this paper, the generalized regression neural network (GRNN) method was used to predict the HGI. A higher precision in the prediction result was obtained through such new method. By this method, the HGI can be estimated indirectly from the proximate analysis of coal when the HGI measurement equipment is not available.     

现代煤化工设计煤质确定方法的探讨

设计煤质数据是现代煤化工项目中重要的设计基础,直接影响到项目能否稳定运行。本文根据神华煤化工项目的建设经验,提出了原料煤设计煤质确定的程序方法。目前煤化工项目常用的煤气化技术包括:干粉气流床气化技术、水煤浆气化技术、固定床碎煤加压气化技术及流化床气化技术。根据不同气化技术对煤质的不同要求,归纳出了在煤化工项目确定设计煤质过程中所需的煤质分析项目。在煤样的采集与分析工作过程中,采集的煤样具有代表性是保证获得典型煤质数据的前提。文中根据项目配套的煤源矿井所处的生产阶段不同,提出了不同的煤样采集方案包括采样地点、煤样类型、采样质量、煤样粒度及采样方式等。同时对后续煤样制备与分析及最终数据校核工作进行了探讨。     

开滦动力煤选煤厂洗选炼焦精煤方案探讨

介绍了开滦动力煤选煤厂的主要工艺流程,通过对原煤资料的分析提出洗选炼焦精煤的方案,对主要选煤环节进行了能力校核,并对经济效果进行了评价,论述了在当前煤炭市场下利用现有流程洗选十二级炼焦精煤的可行性。     

Quality fluctuations of coking coal

The quality fluctuations of coal from different sources is assessed, in terms of technical analysis, petrographic analysis, and plastometric data. Considerable fluctuations are seen in the moisture, ash, and sulfur content, the thickness of the plastic layer, the vitrinite content, and the sum of lean components. To obtain stable coal quality, more consistent properties of individual coal ranks are required, as well as more effective blending.     

Application of near infrared diffuse reflectance spectroscopy for on-line measurement of coal properties

The applicability of the Multi-wavelength Near-infrared sensor to analyze coal properties such as proximate analysis (moisture, ash, volatile matter, fixed carbon), ultimate analysis (carbon, hydrogen, nitrogen, oxygen, sulfur) and heating value is discussed. The most useful wavelengths (1,680, 1,942, 2,100, 2,180, 2,300 nm) for determining coal properties concentration were chosen by analyzing the NIR spectrum according to coal properties. Absorbances at the characteristic wavelength obtained from 128 mixed coal samples, which are using at a conventional thermal power plant, were correlated to the coal properties by using multiple regression analysis. The accuracy of coal analysis was examined by calculating the RMSEC (%), RMSEP (%), comparing the error with ASTM/ISO tolerance and performing paired Student’s T-test. The result of on-line coal analysis for all moisture, volatile matter, fixed carbon, carbon, hydrogen and heating value is not different from that of ASTM/ISO traditional methods at 90% confidence level. The technology appears suitable for the determination of several coal prorperties. If calibrated periodically, this on-line analysis of coal properties is helpful to efficiently operate a coal fired power plant.     

Low-temperature oxidation of Victorian brown coal

A study has been made of low-temperature oxidation of Victorian brown coal at 35 °C and oxygen pressure of 0.1 M Pa and regression analysis of the experimental results shows that the reaction can best be described by the continuous reaction model. The reaction is subject to reaction product inhibition, apparently caused by product adsorption. Progressive conversion is obtained through a series of cycles consisting of a reaction step followed by evacuation of the coal. The rate equation developed from the continuous reaction model describes satisfactorily the progress of the reaction in each of these cycles. A study of earlier findings on the nature of Victorian brown coal oxidation products indicates that the product water may be significant in this reaction inhibition.     

Monitoring the sulfur content of coal

Information regarding the sulfur content of the coal is required not only in metallurgy but also when burning coal as a fuel. The traditional method of determining the sulfur content of coal is unsuitable for large-scale monitoring of processes because it is laborious and not sufficiently reliable. Nuclear-physics methods are free of those problems. However, satisfactory precision of the results is only possible with relatively constant composition of the coal or when the matrix effect due to fluctuations in the composition is taken into account. In the present work, the composition of coal is studied by correlation and regression analysis of the ash composition in coal from different deposits. The correlation coefficients between the main ash components (Al₂O₃, SiO₂, SO₃, CaO, and Fe₂O₃) are calculated. For the example, of coal from Kazakhstan and the Minusinsk Basin, statistically significant correlations are established between the contents of sulfur and calcium in the coal; the correlation coefficient is 0.75–0.93. No significant and consistent correlations are found between other ash components. These findings permit optimal development of nuclear-physics methods of monitoring the sulfur content of coal—specifically, in improving the precision of the analysis by taking account of the matrix effect due to redistribution of the mineral components.