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| Pressure Compensation of Industrial Ambient Gases and Their Prediction Based on Infrared Spectroscopy |
| TIAN Fu-chao1, 2, 3, ZHANG Hai-long1, 2, 3, SU Jia-hao1, 2, 3*, LIANG Yun-tao1, 2, 3, WANG Lin1, 2, 3, WANG Ze-wen1, 2, 3 |
1. Graduate School, China Coal Research Institute, Beijing 100013, China
2. State Key Laboratory of Coal Mine Safety Technology, China Coal Technology and Engineering Group Shenyang Research Institute, Shenfu Demonstration Zone 113122, China
3. Department of Safety Engineering and Engineering, School of Emergency Management and Safety Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
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Abstract Infrared spectroscopy is one of the important means of quantitative analysis of industrial environmental gases. Still, the current infrared gas detector's measurement accuracy is greatly affected by ambient pressure changes, resulting in the detection data deviating from the actual gas concentration under different pressure conditions. To improve the accuracy of the infrared gas sensor, this paper chooses a pressure compensation algorithm combining the Whale Optimization Algorithm (WOA) and Wavelet Neural Network (WNN). It combines it with Long Short-Term Memory (LSTM). Memory (LSTM) to predict the compensated data. By building an experimental platform for gas pressure compensation in industrial environments, using a high-precision gas dispenser to configure 100~900 ppm standard CO gas, and conducting hundreds of repetitive experiments in the range of 80~120 kPa, it is found that the measured value of the CO gas sensor is less than the concentration of the standard gas under negative pressure conditions, and more than the concentration of the standard gas under positive pressure conditions, and the absolute error is linearly correlated with the pressure change, with the highest absolute error of 0.5 ppm. A linear relationship was found, with an absolute error of up to 86 ppm. The sensor data was used to reduce the error using a wavelet neural network, and the initial compensated CO error was reduced to 26 ppm. Still, the individual data error was large due to poor parameter portability. After further optimizing the parameters of the wavelet neural network using the whale optimization algorithm, the compensation effect was significantly improved. The difference between the sensor measurement and the true value was kept within 0.004%, and the data were stable. The root mean square error (RMSE) between the predicted and actual values is less than 0.1, and the mean absolute error (MAE) is less than 0.020. The experimental results show that the WOA-WNN-LSTM algorithm can effectively improve the measurement accuracy of the infrared gas sensors and successfully eliminate the influence of ambient pressure on the results, providing a more reliable and accurate measurement of the gases in industrial environments. It provides a more reliable and accurate solution for gas detection in industrial environments.
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Received: 2024-06-24
Accepted: 2024-10-18
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Corresponding Authors:
SU Jia-hao
E-mail: 15670880885@163.com
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