Monitoring and Control in Underground Coal Gasification: Current Research Status and Future Perspective

Clicks: 266
ID: 53951
2019
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Steady

Ranked #98 of 275 articles by views in sustainability

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 275 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Abstract
By igniting in the coal seam and injecting gas agent, underground coal gasification (UCG) causes coal to undergo thermochemical reactions in situ and, thus, to be gasified into syngas for power generation, hydrogen production, and storage. Compared with traditional mining technology, UCG has the potential sustainable advantages in energy, environment, and the economy. The paper reviewed the development of UCG projects around the world and points out that UCG faces difficulties in the field of monitoring and control in UCG. It is expounded for the current research status of monitoring and control in UCG, and clarified that monitoring and control in UCG is not perfect, remaining in the stage of exploration. To improve the problem of low coal gasification rate and gas production, and then to make full use of the potential sustainable advantages, the paper offers a perception platform of a UCG monitoring system based on the Internet-of-Things (IoT) and an optimal control model for UCG based on deep learning, and has an outlook on breakthrough directions of the key technologies related to the package structure design for moisture-proof and thermal insulation, antenna design, the strategy for energy management optimization, feature extraction and classification design for the network model, network structure design, network learning augmentation, and the control of the network model, respectively.
Reference Key
xiao2019monitoringsustainability Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Xiao, Yuteng;Yin, Jihang;Hu, Yifan;Wang, Junzhe;Yin, Hongsheng;Qi, Honggang;
Journal sustainability
Year 2019
DOI
DOI not found
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.