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

Yuteng Xiao, Jihang Yin, Yifan Hu, Junzhe Wang, Hongsheng Yin and Honggang Qi
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Yuteng Xiao: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Jihang Yin: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Yifan Hu: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Junzhe Wang: Bell Honors School, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
Hongsheng Yin: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou 221116, China
Honggang Qi: School of Computer and Control Engineering, University of Chinese Academy of Sciences, Beijing 101408, China

Sustainability, 2019, vol. 11, issue 1, 1-14

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.

Keywords: underground coal gasification; monitoring and control; Internet of Things; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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