Assessing performance of low grade and upgraded coals in different utilization technologies based on advanced characterization techniques

基于先进表征技术评估低品位煤和改质煤在不同利用技术中的性能

• 批准号: RGPIN-2014-04816
• 负责人: Gupta, Rajender
• 金额: $ 2.11万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=611262
• 关键词: Assessing; performance; low; grade; upgraded

The demand for coal worldwide is expected to increase significantly over next two decades at least. This will eventually result in increased utilization of low-grade coals. These low-grade coals have one or more problematic properties such as high inorganic content or high moisture or high sulfur or high alkalis or all of these. Furthermore, coal is an extremely heterogeneous material. No two coal particles are same in a given coal sample. Even a single coal particle consists of different macerals and minerals. Assessing ash related depositional problems such as slagging or fouling based on bulk ash composition or assessing un-burnt carbon in fly-ash based on single combustion kinetics is bound to be inadequate. Different property sets may influence the performance in fluidized bed, circulating fluidized bed and entrained flow combustors or gasifiers. Currently, techniques such as Computer Controlled Scanning Electron Microscope (CCSEM) can provide the detailed information such as size and type of minerals in coal. Similarly, the full phase reflectogram or grain-by-grain reflectivity distribution analysis of coal can provide the heterogeneous organic nature of individual coal particle such as coal maceral distribution. These advanced analytical techniques for describing coal particles in detail are not only important in assessing the performance of the coals before and after upgrading, but also in assessing the upgrading potentials based on coal mineral association providing mineral liberation data for these coals. There are a number of technologies to upgrade these coals. Some of these techniques may remove selectively some minerals (pyrites and other heavy minerals) and reduce the melting temperatures of ash and may result in reduced suitability in slagging gasifier. One important issue in these techniques is that these provide a two dimensional information (from surface analysis). For example, the information on coal-mineral association from 2D information is not accurate and needs to be translated to 3D information. The utilization of this detailed information on the heterogeneous nature of coal for assessing the performance of coal in different technologies such as combustion, gasification, coke-making and upgrading of coal is, therefore, not trivial. There are some coal quality models for performance prediction of coal on slagging/ fouling/ erosion/ corrosion available mostly based on empirical correlations and bulk coal properties. The mechanistic coal model, taking into account the heterogeneous nature of coal, developed in this discovery project will predict the performance more reliably in coal fired combustors and gasifiers using low-grade coals, upgraded coals, other solid fuels and their blends. The current proposal will eventually develop coal quality models and strategies for better performance prediction for utilization technologies of coal based on these advanced characterizing techniques for characterizing heterogeneous nature of coal, and other advanced bulk analytical tools such as XRD, FTIR, Chemical Fractionation, Thermo-Mechanical Analysis, and other suits of specialized experiments (like drop tube furnace and thermo-gravimetric analysis) and mechanistic models. The current discovery proposal would be assessing potential of upgrading the low grade coals and assessing the performance of these coals based on the heterogeneous nature of coal described by these advanced characterization techniques. These advanced characterization technologies will be applied to other solid fuels such pet-coke and biomass. This discovery proposal would have an impact on almost all coal related applications.

至少在未来二十年，全球煤炭需求预计将大幅增加。这最终将导致低品位煤炭的利用率增加。这些低品位煤具有一种或多种有问题的特性，例如高无机含量或高水分或高硫或高碱或所有这些。此外，煤是一种极其异质的材料。在给定的煤样中没有两个煤颗粒是相同的。即使单个煤颗粒也由不同的显微组分和矿物质组成。评估与灰分相关的沉积问题，例如基于散装灰分成分的结渣或结垢，或基于单一燃烧动力学评估飞灰中未燃烧的碳，注定是不够的。不同的属性组可能会影响流化床、循环流化床和气流床燃烧器或气化器的性能。目前，计算机控制扫描电子显微镜（CCSEM）等技术可以提供煤中矿物的尺寸和类型等详细信息。类似地，煤的全相反射图或逐粒反射率分布分析可以提供单个煤颗粒的非均质有机性质，例如煤显微分布。 这些用于详细描述煤颗粒的先进分析技术不仅对于评估提质前后煤炭的性能很重要，而且对于根据提供这些煤的矿物释放数据的煤矿物组合来评估提质潜力也很重要。有多种技术可以升级这些煤炭。其中一些技术可能会选择性地去除一些矿物（黄铁矿和其他重矿物）并降低灰分的熔化温度，并可能导致造渣气化炉的适用性降低。 这些技术的一个重要问题是它们提供了二维信息（来自表面分析）。例如，2D信息中的煤矿关联信息并不准确，需要转换为3D信息。因此，利用有关煤炭异质性的详细信息来评估煤炭在燃烧、气化、炼焦和煤炭提质等不同技术中的性能并非易事。有一些煤炭质量模型可用于预测煤炭在结渣/结垢/侵蚀/腐蚀方面的性能，这些模型主要基于经验相关性和散装煤炭特性。该发现项目中开发的机械煤模型考虑到了煤炭的非均质性，将更可靠地预测使用低品位煤、提质煤、其他固体燃料及其混合物的燃煤燃烧器和气化器的性能。 目前的提案最终将开发煤炭质量模型和策略，以更好地预测煤炭利用技术的性能，这些技术基于这些用于表征煤炭非均质性质的先进表征技术，以及其他先进的批量分析工具，如 XRD、FTIR、化学分馏、热分析等。机械分析，以及其他专业实验套件（例如落管炉和热重分析）和机械模型。当前的发现提案将评估低品位煤炭升级的潜力，并根据这些先进表征技术描述的煤炭的非均质性质评估这些煤炭的性能。这些先进的表征技术将应用于其他固体燃料，例如石油焦和生物质。这一发现提案将对几乎所有与煤炭相关的应用产生影响。