RESUMEN
To discuss the inhibitory effect of micrometer scale coal dust explosion pressure, three types of explosion suppressants are selected for mixed explosion suppression. The results indicate that the coal dust explosion process includes three stages: accelerated and decelerated energy release, as well as energy dissipation. When using explosive suppressants, K2CO3 has the greatest inhibitory effect on coal dust explosion, followed by KCl, and CaCO3 has the smallest effect. The K2O, K2O2, and KOH generated by the thermal decomposition of K2CO3 can also block the heat transfer of coal dust, playing a good role in suppressing explosions. The explosion suppression effect of mixing CaCO3 and K2CO3 is better than that of mixing CaCO3 and KCl, and is worse than the explosion suppression effect of using K2CO3 alone. The synergistic effect of KCl and K2CO3 mixed explosion suppression makes the suppression effect better than using K2CO3 alone. This is because KCl generates K2O during pyrolysis, promoting the dynamic equilibrium of K2CO3 explosion suppression process. This makes mixed explosion suppression more worthy of attention and adoption when considering purchase costs.
RESUMEN
Sulphur dioxide is one of the most common air pollutants, forming acid rain and other harmful substances in the atmosphere, which can further damage our ecosystem and cause respiratory diseases in humans. Therefore, it is essential to monitor the concentration of sulphur dioxide produced in industrial processes in real-time to predict the concentration of sulphur dioxide emissions in the next few hours or days and to control them in advance. To address this problem, we propose an AR-LSTM analytical forecasting model based on ARIMA and LSTM. Based on the sensor's time series data set, we preprocess the data set and then carry out the modeling and analysis work. We analyze and predict the proposed analysis and prediction model in two data sets and conduct comparative experiments with other comparison models based on the three evaluation indicators of R2, RMSE and MAE. The results demonstrated the effectiveness of the AR-LSTM analytical prediction model; Finally, a forecasting exercise was carried out for emissions in the coming weeks using our proposed AR-LSTM analytical forecasting model.