Characterization of Ashes from Co-Firing Biochar with Coal under Pulverized-Fuel Conditions

IF 4.3 Q2 ENGINEERING, CHEMICAL
Xixia Chen, Xiangpeng Gao and Hongwei Wu*, 
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引用次数: 2

Abstract

This contribution presents results on the systematic characterization of the ashes from the co-combustion of biochar or its corresponding raw biomass and coal under pulverized-fuel conditions. A mallee bark (75–90 μm) was subjected to fast pyrolysis at 500 °C to prepare a biochar. The bark and the biochar were then co-fired with a Collie coal of identical size fraction in a laboratory-scale drop-tube furnace at 1400 °C in air, with biomass/biochar shares of 5, 20, and 40% expressed based on lower heating values. The produced ashes were collected using a cyclone and systematically characterized. The results demonstrate that the morphology of the ashes from the bark and the biochar is of irregular shape, whereas the coal ash particles are round. The ash particles follow a unimodal distribution, with an area-equivalent mode diameter of ∼5–12 μm, except for the ash from the bark combustion that also shows two larger peaks at ∼65 and ∼95 μm. The compositions of the ashes from the bark and the biochar are similar, both rich in Ca and Mg, whereas the coal ash contains dominantly Si, Al, Fe, and Ca. Under identical co-firing ratios, replacing the bark with the biochar results in higher contents of Mg and Ca in the ashes because of the enrichment of these elements in the biochar. The major minerals identified in the coal ash include mullite, quartz, and hematite, and those in the bark ash and the biochar ash are portlandite, magnesite, calcite, and lime. Up to ∼56% of Na, ∼41% of K, ∼56% of Mg, and ∼69% of Ca in the ashes can be recycled via water leaching, with negligible environmental concerns. These data are important in developing suitable strategies for the utilization and management of ashes derived from the co-combustion of biochar (or biomass) and coal.

Abstract Image

煤粉条件下生物炭与煤共烧灰烬的表征
这篇文章介绍了在粉状燃料条件下生物炭或其相应的原料生物质和煤共燃烧产生的灰烬的系统表征结果。以75 ~ 90 μm的木屑皮为原料,在500℃下快速热解制备生物炭。然后,将树皮和生物炭与相同大小分数的柯利煤在实验室规模的降管炉中在1400°C空气中共烧,生物质/生物炭的份额分别为5%、20%和40%,基于较低的热值表示。产生的灰烬使用旋风收集和系统地表征。结果表明:树皮灰分和生物炭灰分呈不规则形态,而粉煤灰颗粒呈圆形;除了树皮燃烧产生的灰分在~ 65和~ 95 μm处也有两个较大的峰外,灰分颗粒遵循单峰分布,其面积等效模态直径为~ 5-12 μm。树皮和生物炭灰烬的成分相似,都富含Ca和Mg,而煤灰主要含有Si、Al、Fe和Ca。在相同的共烧比下,用生物炭代替树皮,由于生物炭中Mg和Ca元素的富集,灰烬中Mg和Ca的含量更高。在煤灰中发现的主要矿物有莫来石、石英和赤铁矿,在树皮灰和生物炭灰中发现的主要矿物有波特兰矿、菱镁矿、方解石和石灰。灰烬中高达~ 56%的Na、~ 41%的K、~ 56%的Mg和~ 69%的Ca可以通过水浸回收,对环境的影响可以忽略不计。这些数据对于制定利用和管理生物炭(或生物质)和煤共燃烧产生的灰烬的适当战略很重要。
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来源期刊
ACS Engineering Au
ACS Engineering Au 化学工程技术-
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期刊介绍: )ACS Engineering Au is an open access journal that reports significant advances in chemical engineering applied chemistry and energy covering fundamentals processes and products. The journal's broad scope includes experimental theoretical mathematical computational chemical and physical research from academic and industrial settings. Short letters comprehensive articles reviews and perspectives are welcome on topics that include:Fundamental research in such areas as thermodynamics transport phenomena (flow mixing mass & heat transfer) chemical reaction kinetics and engineering catalysis separations interfacial phenomena and materialsProcess design development and intensification (e.g. process technologies for chemicals and materials synthesis and design methods process intensification multiphase reactors scale-up systems analysis process control data correlation schemes modeling machine learning Artificial Intelligence)Product research and development involving chemical and engineering aspects (e.g. catalysts plastics elastomers fibers adhesives coatings paper membranes lubricants ceramics aerosols fluidic devices intensified process equipment)Energy and fuels (e.g. pre-treatment processing and utilization of renewable energy resources; processing and utilization of fuels; properties and structure or molecular composition of both raw fuels and refined products; fuel cells hydrogen batteries; photochemical fuel and energy production; decarbonization; electrification; microwave; cavitation)Measurement techniques computational models and data on thermo-physical thermodynamic and transport properties of materials and phase equilibrium behaviorNew methods models and tools (e.g. real-time data analytics multi-scale models physics informed machine learning models machine learning enhanced physics-based models soft sensors high-performance computing)
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