Accurate predictions
of the coal temperature in coal spontaneous
combustion (CSC) are important for ensuring coal mine safety. Gas
coal (the Zhaolou coal mine in Shandong Province, China) was used
in this paper. A large CSC experimental device was adopted to obtain
its characteristic temperatures from the macroscopic characteristics
of gas production. A simulated annealing-support vector machine (SA-SVM)
prediction model was proposed to reflect the complex nonlinear mapping
between characteristic gases and the coal temperature. The risk degree
of CSC was estimated in the time domain, and the model was verified
by using in situ data from an actual working face. Furthermore, back-propagation
neural network (BPNN) and single SVM methods were adopted for comparison.
The results showed that the BPNN could not adapt to the small-sample
problem due to overfitting and the output of a single SVM was unstable
due to its strong dependence on the setting of hyperparameters. Through
the SA global optimization process, the optimal combination of hyperparameters
was obtained. Therefore, SA-SVM had higher prediction accuracy, robustness,
and error tolerance rate and better environmental adaptability. These
findings have certain practical significances for eliminating the
hidden danger of CSC in the gob and providing timely warnings about
potential danger.
According to statistics, there are as many as 4000 coal spontaneous combustion accidents every year, causing heavy casualties and property losses. By accurately detecting the change of index gas concentration, it can provide reliable criteria for identifying and warning the early hidden dangers of coal spontaneous combustion. The improvement of sensor precision is the basis of intelligent monitoring. Modification of wireless multi-parameter sensor used in coal mine is of great significance for accurate monitoring of gas concentration in coal mine. In order to modify the wireless multi-parameter sensor, the high and low temperature experiments of the sensor were carried out at -5∼45°C, and the real values of O2 concentration at 15%, 21%, 25% and CO concentration at 175ppm, 250ppm and 375ppm were measured. Measurement data was Non-linear fitting and correction by using SVM (Support Vector Machine), BP neural network and Elastic Network regression method. The experimental results show that the Elastic Network regression compensation reduces the O2 sensor from the original maximum mean absolute percentage error (MAPE) of 18.4% to 0.52%, and the average MAPE decreases from 13.89% to 0.21%. Using the SVM nonlinear compensation, the CO sensor is reduced from the original maximum MAPE of 43.2% to 1.6%, and the average MAPE is reduced from 21.38% to 1.4%. Elastic Network regression compensation excellent achieves nonlinear compensation of O2 sensor. The SVM better realizes the nonlinear compensation of the CO sensor. Through non-linear compensation, wireless multi-parameter sensor can better meet the requirements of coal mine gas concentration monitoring.
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