Digital twin based intelligent control system on gas extraction from boreholes and experimental research

Abstract

Intelligent gas extraction in mines is a critical enabler for the realization of smart mine development. As an essential component of this process, the intelligent control of borehole gas extraction relies on real-time monitoring data from numerous extraction parameters, integrated with next-generation information technologies, to achieve objectives such as intelligent deployment of negative pressure, control of inefficient boreholes, and evaluate extraction effectiveness. This paper constructs an intelligent control system for borehole gas extraction based on a digital twin “Four-Dimensional” framework, enabling bidirectional mapping between physical entities and virtual digital twins through the integration of physical entities (PE) as foundational carriers, virtual entities (VE) as three-dimensional models, digital twin data (DD) as the control core, and services (SS) and connectivity (CN) as the methodological framework. Key technologies include developing a control model for the gas flow process from coal seam to borehole, processing multi-source extraction data using data fusion methods, constructing virtual twins with SolidWorks, and designing control schemes based on Model Predictive Control (MPC) algorithm. In order to verify the rationality and feasibility of the system, a pilot study on the digital twin for intelligent control of borehole gas extraction was carried out. The results show that the concentration of gas extraction after control rises significantly, and the gas extraction concentration of borehole 1# reaches the optimal range when the valve is opened to 75 %, while that of borehole 2# reaches the maximum concentration of the adjustable range when the valve is opened to 70 %, which meets the control target.