配煤比例对可磨性指数的影响

选取7种水分较低的煤,在两种煤可磨性指数相差值ΔHGI的不同的条件下,研究不同配煤比例对配煤HGI的影响。结果发现,对于变质程度较高的两种煤,配煤HGI随配煤比例呈现出良好的线性关系,与ΔHGI关系不明显,并在煤种变质程度相差不大的情况下遵循线性可加性原则。     

混煤可磨性指数(HGI)可加性研究

对比了单煤及两两混煤可磨性指数(HGI)的实测值与计算值,说明同一配比、不同混煤的实测值与计算值不完全相等,存在一定误差。分析了存在偏差的主要原因为:实验本身的系统误差和单煤之间可磨性指数(HGI)不具有线性可加性。最后通过t检验验证可磨性指数(HGI)实测值与计算值之间的差异是否显著。结果表明:同一配比、不同混煤时,大量难磨煤与少量易磨煤组成的混煤可磨性指数(HGI)实测值与计算值具有显著性差异;其它配比条件下,不同混煤实测值与计算值之间差异性不明显。同一混煤、不同配比时,混煤可磨性指数(HGI)实测值与计算值之间误差不全在正常范围内,混煤可磨性指数(HGI)与配比不全具有线性相关性。单煤可磨性指数(HGI)不具有线性可加性。     

低水分澳煤燃尽特性较差关键因素研究

近年来我国燃煤电厂大量进口澳煤,根据煤质特性,澳煤可分为高水分澳煤和低水分澳煤,其中高水分澳煤的煤质指标和燃烧特性与我国神混类煤基本相似,低水分澳煤的煤质指标与我国优混类煤基本相似,但电厂燃用低水分澳煤时存在飞灰含碳量高、锅炉效率下降等问题。为探求低水分澳煤燃尽特性差的关键原因及其判别方法,为低水分澳煤的合理利用或燃煤锅炉优化调整提供指导依据,选取2个典型的澳煤煤样和典型的优混煤煤样,采用煤质指标分析、可磨性分析、热分析、孔隙率分析和马尔文粒径分析等方法,对比研究澳煤与优混煤的煤质指标、燃烧特性、孔隙率和粒径分布等差异。研究结果表明,澳煤的基本煤质指标与国内同类优混差异不大,但氧含量低,说明澳煤的煤化程度高于优混煤;澳煤可磨性系数随氧含量的降低而减小,即随澳煤煤化程度的加深,可磨性变差,因此可通过可磨性系数或氧含量来初步判断澳煤的燃尽特性。采用热分析的燃尽指数C700判断煤的燃尽特性更符合实际应用情况。相同制粉条件下,澳煤的煤粉细度大,粗颗粒煤粉更多,是其难燃尽的根本原因。     

浅析煤制天然气项目气化工艺选择

介绍了新疆准东煤田将军庙矿区煤种的煤质情况,针对准东煤种高水含量、特低硫、高挥发分和低灰熔点,以及可磨性好等特点,对各种煤气化工艺进行了对比,探讨了煤制天然气项目煤气化工艺选择方案。     

新疆煤制天然气项目气化工艺比较

介绍了新疆准东煤田将军庙矿区煤种的煤质情况,针对煤种高水含量、特低硫、高挥发分和低灰熔点,以及可磨性好等特点,对各种煤气化工艺进行了对比,探讨了煤制天然气项目煤气化工艺的选择方案。     

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。     

提高燃煤固硫效果的技术途径分析

选择了几种有代表性的动力煤样,通过试验探讨了Ca/S摩尔比、适当配煤、选煤、燃烧方式及温度、煤种煤质和固硫添加剂等因素对固硫效果的影响,认为固硫效果与煤种煤质有关,煤中的有机硫含量越高、伊利石矿物的含量越高,对固硫越有利;适当的配煤和选煤、增大Ca/S摩尔比、降低燃烧温度及添加适当的添加剂,均利于固硫效果的提高。     

高硫石油焦配煤可磨性规律及粒度分布研究

选取HGI相差较大的两种高硫石油焦B、C和一种淮北煤A按不同比例进行配煤,测定煤样、高硫石油焦样和配煤样品的HGI,采用行星式球磨机将样品（0.63 ～ 1.25 mm）磨制5min,并分析粒度分布.结果表明,在难磨的石油焦中配人一定比例的易磨煤可以提高配煤的HGI;难磨石油焦B与A煤混配后的HGI与配煤比例能够很好地遵循线性可加原则,易磨石油焦C与A煤混配后的HGI与配煤比例线性关系很差;随着配煤中石油焦的比例增加,大颗粒所占的比例增多.     

高炉喷吹用煤的可磨性研究

通过实验测试高炉喷吹用混合煤及其组成中的单种煤的可磨性，并分析单种煤的可磨性、配比等对混合煤可磨性的影响。研究单种煤的可磨性和单种煤的配比对混合煤可磨性的双重影响，对混合煤的组成煤种和单种煤的配比进行调整得到可磨性适宜的混合煤，进而降低混合煤制粉成本。     

基于数据驱动建模的气化炉配煤成本优化研究

为实现配煤煤质和配煤成本的最优化匹配,以配煤成本为优化目标,气化炉产气量为主要约束,采用数学建模的方式,研究了鄂尔多斯地区煤化工装置气化炉多煤种的用煤和配煤优化问题。根据现场数据,构建了基于数据驱动的气化炉煤质对产量影响的非线性过程模型,通过聚类分析,得到各煤质指标的约束条件;在满足约束条件的情况下,构建煤种与煤质之间的线性模型;通过帕累托遗传算法,在气化炉产气量满足约束条件的同时,给出了成本最低的多煤种配煤策略,并用MATLAB对气化炉配煤优化系统进行可视化组态界面设计。     

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.     

An investigation of the grindability of two torrefied energy crops

The process of torrefaction alters the physical properties of biomass, reducing its fibrous tenacious nature. This could allow increased rates of co-milling and therefore co-firing in coal fired power stations, which in turn would enable a reduction in the amount of coal used and an increase in the use of sustainable fuels, without the need for additional plant. This paper presents an experimental investigation of the pulverisation behaviour of two torrefied energy crops, namely: willow and Miscanthus. A multifactorial method approach was adopted to investigate the three process parameters of temperature, residence time and particle size, producing fuels treated using four different torrefaction conditions. The untreated and torrefied fuels were subjected to standard fuel analysis techniques including ultimate analysis, proximate analysis and calorific value determination. The grindability of these fuels was then determined using a laboratory ball mill and by adapting the Hardgrove Grindability Index (HGI) test for hard coals. After grinding, two sets of results were obtained. Firstly a determination similar to the HGI test was made, measuring the proportion of sample passing through a 75 μm sieve and plotting this on a calibrated HGI chart determined using four standard reference coals of known HGI values. Secondly the particle size distributions of the entire ground sample were measured and compared with the four standard reference coals. The standard fuel tests revealed that temperature was the most significant parameter in terms of mass loss, changes in elemental composition and energy content increase. The first grindability test results found that the untreated fuels and fuels treated at low temperatures showed very poor grindability behaviour. However, more severe torrefaction conditions caused the fuels to exhibit similar pulverisation properties as coals with low HGI values. Miscanthus was found to have a higher HGI value than willow. On examining the particle size distributions it was found that the particle size distributions of torrefied Miscanthus differed significantly from the untreated biomass and had comparable profiles to those of the standard reference coals with which they had similar HGI values. However, only the torrefied willow produced at the most severe conditions investigated exhibited this behaviour, and the HGI of torrefied willow was not generally a reliable indicator of grindability performance for this energy crop. Overall it was concluded that torrefied biomass can be successfully pulverised and that torrefied Miscanthus was easier to grind than torrefied willow.     

A correlation between composition and explosibility index for coal dust

A composition index for coal and an explosibility index for coal dust are defined. The first is based on the proximate and ultimate analysis of different types of coals, ranging from lignites to anthracites. The second index depends on the explosibility parameters of coal dust determined by means of laboratory tests, such as the Hartmann Tube and the Godbert-Greenwald Furnace. Through a canonical correlation analysis, three regions of danger for coal dust are established; these represent safe coals, dangerous coals and very dangerous coals. The theoretical percentage of incombustible dust required to make the coal dust inert is determined.     

Proximate analysis based multiple regression models for higher heating value estimation of low rank coals

In this paper, multiple nonlinear regression models for estimation of higher heating value of coals are developed using proximate analysis data obtained generally from the low rank coal samples as-received basis. In this modeling study, three main model structures depended on the number of proximate analysis parameters, which are named the independent variables, such as moisture, ash, volatile matter and fixed carbon, are firstly categorized. Secondly, sub-model structures with different arrangements of the independent variables are considered. Each sub-model structure is analyzed with a number of model equations in order to find the best fitting model using multiple nonlinear regression method. Based on the results of nonlinear regression analysis, the best model for each sub-structure is determined. Among them, the models giving highest correlation for three main structures are selected. Although the selected all three models predicts HHV rather accurately, the model involving four independent variables provides the most accurate estimation of HHV. Additionally, when the chosen model with four independent variables and a literature model are tested with extra proximate analysis data, it is seen that that the developed model in this study can give more accurate prediction of HHV of coals. It can be concluded that the developed model is effective tool for HHV estimation of low rank coals.     

Prediction of coal grindability based on petrography,proximate and ultimate analysis using multiple regression and artificial neural network models

The effects of proximate and ultimate analysis, maceral content, and coal rank (R_max) for a wide range of Kentucky coal samples from calorific value of 4320 to 14960 (BTU/lb) (10.05 to 34.80 MJ/kg) on Hardgrove Grindability Index (HGI) have been investigated by multivariable regression and artificial neural network methods (ANN). The stepwise least square mathematical method shows that the relationship between (a) Moisture, ash, volatile matter, and total sulfur; (b) ln (total sulfur), hydrogen, ash, ln ((oxygen + nitrogen)/carbon) and moisture; (c) ln (exinite), semifusinite, micrinite, macrinite, resinite, and R_max input sets with HGI in linear condition can achieve the correlation coefficients (R²) of 0.77, 0.75, and 0.81, respectively. The ANN, which adequately recognized the characteristics of the coal samples, can predict HGI with correlation coefficients of 0.89, 0.89 and 0.95 respectively in testing process. It was determined that ln (exinite), semifusinite, micrinite, macrinite, resinite, and R_max can be used as the best predictor for the estimation of HGI on multivariable regression (R² = 0.81) and also artificial neural network methods (R² = 0.95). The ANN based prediction method, as used in this paper, can be further employed as a reliable and accurate method, in the hardgrove grindability index prediction.     

A comparison of the results obtained from grinding in a stirred media mill lignite coal samples treated with microwave and untreated samples

Various studies have been carried out on the effect of microwave-treatment on grinding different types of coal. However, the effect of microwave treatment on grinding coal samples −3.35 mm in size which can be considered to be fine is still under investigation. The purpose of this paper is to make contributions to these studies conducted. In the study, lignite coal samples with pyritic sulphur and 25% structural moisture were crushed below −3.35 mm particle size using jaw and cone crushers and then classified into three different mono size groups by Russel sieve. For a complete removal of the structural moisture from the lignite coal, a microwave application with 600 W needs approximately 35% more energy consumption than that with 850 W. The untreated coal samples and the ones treated with microwave at 850 W were ground for 5, 15, 30, 60, 120 s in a stirred media mill. The breakage rates of microwave-treated coal increased and accordingly the ground products of microwave-treated coal yielded finer particles than −106 μm as compared to untreated coals. The untreated and microwave-treated feed coals of −3350 μm and −1180 μm particle sizes were ground for 2 min in the stirred media mill. It was found that the increases in the rate of weight percentages for −106 μm particle size fraction after 2 min of grinding of untreated and microwave-treated feed coals of −3350 μm and −1180 μm were found to be 15.81% and 2.69%, respectively. Moreover, Hardgrove Index (HGI) test results of lignite coal showed that the HGI index value increased by approximately 23% after microwave treatment with 850 W.     

Particle size reduction of anthracite coals during devolatilization in a thermobalance reactor

The effects of devolatilization temperature (750-900 °C), coal size (2-12 mm) and coal properties (carbon content, Hardgrove index (HGI), pore volume) of anthracite coals on the primary fragmentation and particle size reduction during devolatilization have been determined in a thermobalance reactor. The fragmentation index increases with increasing devolatilization temperature and particle size. The fragmentation index is also influenced by coal properties, such as carbon content, HGI, pore volume, etc. Thus, the reduction ratio of particle size before and after devolatilization increases with increasing devolatilization temperature and particle size.     

Maximum solid concentrations of coal water slurries predicted by neural network models

The nonlinear back-propagation (BP) neural network models were developed to predict the maximum solid concentration of coal water slurry (CWS) which is a substitute for oil fuel, based on physicochemical properties of 37 typical Chinese coals. The Levenberg-Marquardt algorithm was used to train five BP neural network models with different input factors. The data pretreatment method, learning rate and hidden neuron number were optimized by training models. It is found that the Hardgrove grindability index (HGI), moisture and coalification degree of parent coal are 3 indispensable factors for the prediction of CWS maximum solid concentration. Each BP neural network model gives a more accurate prediction result than the traditional polynomial regression equation. The BP neural network model with 3 input factors of HGI, moisture and oxygen/carbon ratio gives the smallest mean absolute error of 0.40%, which is much lower than that of 1.15% given by the traditional polynomial regression equation.     

青海焦煤煤质的特殊性研究

分析了青海焦煤的工业分析指标、镜质组反射率分布指标、焦炭机械强度、焦炭热性能,研究了青海焦煤的成焦显微结构,发现青海焦煤参与配煤炼焦,使焦炭质量劣化,不起焦煤作用的原因是其特殊成因造成其特殊的成焦结构,从而揭示出炼焦煤的工业质量指标及煤岩指标在评价炼焦煤质量上有一定的局限性,而单种煤成焦的显微结构(光学组成)能真正揭示炼焦煤的本质。