In:
Sustainability, MDPI AG, Vol. 14, No. 15 ( 2022-07-28), p. 9247-
Abstract:
For a long time, the serious mismatch between negative pressure and drainage parameters of underground gas drainage has been the main reason for the standing engineering problems in coal mines, such as low gas drainage concentration, fast decay, and low-utilization rate. Aiming at these problems, an innovative method by adding micro-frequency conversion drainage pumps and electronically controlled valves at the key nodes of the conventional pipe network system of gas drainage and the joint quantitative regulation of underground regulation facilities and surface drainage pumps based on the intrinsic correlation between the drainage parameters and negative pressure is proposed in this paper to solve the difficulty of how to regulate increasing pressure or resistance in the on-site gas-drainage system and to realize energy matching in the whole drainage system on demand. For this method, the study further defines the safety and efficiency criteria of gas drainage, proposes the adaptive control strategy of gas-drainage parameters, and establishes the adaptive control model based on particle swarm optimization. The model took the safety and efficiency criteria of gas drainage as the constraint conditions and the maximum gas-drainage flow or concentration as the objective function to adaptively adjust the operating conditions of drainage pumps, micro-frequency conversion drainage pumps, and electric control valves to realize the adaptive regulation of gas-drainage parameters. Finally, based on the adaptive control strategy and model of gas-drainage parameters, the numerical simulation research was carried out through Comsol with Matlab. The results show that the gas-drainage concentration and high-concentration drainage period can be increased many times, and the adaptive drainage parameters of valves and micro pumps can be adjusted intelligently, which provides a theoretical basis for the intelligent field implementation of gas.
Type of Medium:
Online Resource
ISSN:
2071-1050
Language:
English
Publisher:
MDPI AG
Publication Date:
2022
detail.hit.zdb_id:
2518383-7
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