新河烟煤地下气化模型

<P>LIANG Jie; WU Meiyunting; LI Wenjun; ZHAO Ming</P>

您当前的位置：

首页 >

文章列表页 >

新河烟煤地下气化模型

更新时间：2025-12-10

- 新河烟煤地下气化模型
- CIESC Journal Issue 12, (2006)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Online First：25 December 2006，
  
  Published：25 December 2006
- 稿件说明：
移动端阅览
LIANG Jie, WU Meiyunting, LI Wenjun, et al. 新河烟煤地下气化模型[J]. CIESC Journal, 2006, (12).
DOI：

LIANG Jie, WU Meiyunting, LI Wenjun, et al. 新河烟煤地下气化模型[J]. CIESC Journal, 2006, (12).
DOI：

摘要

为了探索烟煤地下气化过程的基本规律，为新河“煤炭地下气化发电示范工程”制定合理的工艺参数，测定了新河烟煤反应活性，并进行了富氧-水蒸气地下气化模型实验;研究了不同工艺条件下，出口煤气有效组分含量、热值的变化规律.实验结果表明，气化初期因煤层中含水，纯氧直接气化，可获得合格的煤气;在保持汽氧比在1.5∶1～2∶1之间时，新河烟煤采用富氧-水蒸气正向供风、辅助孔供风和反向供风连续气化可获得有效气体组分在70%、热值在10 MJ·m-3左右的煤气;新河烟煤的产气率平均为1950 m3·t-1，煤层气化率可达到74.6%.

Abstract

Based on the model test of “rich-oxygen-steam” underground coal gasification (UCG) of Xinhe coal

the basic regularities of the UCG process was studied and the reasonable parameters were established for a power generation project by using the UCG technology.The proximate analysis

ultimate analysis and reactivity test of Xinhe coal were made.From the tests

the change of effective gas composition and heat value under different technical conditions was investigated.The result showed that a normal fuel gas can be obtained by blasting pure oxygen at the beginning of the test because there was some water in the coal bed.In the“rich-oxygen-steam”process

when the ratio of steam to oxygen was kept between 1.5∶1 and 2∶1

a fuel gas(effective composition about 70%

heat value about 10 MJ·m-3)could be achieved.The average productivity of Xinhe coal was 1950 m3·t-1 and the gasification rate of the coal seam was 74.6%.

关键词

Keywords

references

Views

559

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Performance study of gas-liquid vortex separators under low cross-sectional gas velocity

Zn-doped PdO/ZrO2 with rich oxygen vacancies for the synthesis of diphenyl carbonate from CO2 and phenol

Physics-informed spatio-temporal graph convolutional network for fermentation processes

Prediction model for COD in petrochemical wastewater influent based on temporal-spatial dual-stream convolutional neural network

Research progress of ionic liquids in separation of aromatics/alkanes and olefins/alkanes

Related Author

No data

Related Institution

No data

⁰