Method for predicting gas emission quantity of driving face based on big data analysis

• Main Authors: CAO TENGFEI, WANG CHUANBING, BI BO, CHEN BENLIANG, GUO ZHONGKAI, BANG EUN-JAE, TONG QIANXIAN, ZHOU TAO, FENG ANXIANG, CHENG HAIYAN, YANG WEI, YANG YANG, YE CHUNHUI
• Format: Patent
• Language: Chinese; English
• Published: 02.02.2024
• Subjects: CALCULATING; COMPUTING; COUNTING; ELECTRIC DIGITAL DATA PROCESSING; INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIRCHEMICAL OR PHYSICAL PROPERTIES; MEASURING; PHYSICS; TESTING

The invention discloses a big data analysis-based driving working face gas emission quantity prediction method, which comprises the following steps of: constructing a drill hole in a driving working face, obtaining a coal seam sample, and quickly determining a large number of coal seam analysis data samples by using coal seam gas analysis data determination equipment, so that the prediction accuracy of the working face emission quantity is improved. The equipment has a coal seam analysis parameter outlier analysis method, so that the measurement error in the test process can be corrected, and gas early warning can be carried out when a coal seam gas abnormal region is found. According to the method, the waveform and wavelength of the gas emission quantity fluctuation are fitted based on the working face gas monitoring data through the ARIMA algorithm, and the amplitude of the gas emission quantity fluctuation is corrected based on the factors such as the gas content, the analysis rate, the coal seam structure