Tian, H.; Gao, F.; Meng, Y.; Jia, X.; Yu, R.; Wang, Z.; Liu, Z. Multi-Frequency Microwave Sensing System with Frequency Selection Method for Pulverized Coal Concentration. Preprints2024, 2024102176. https://doi.org/10.20944/preprints202410.2176.v1
APA Style
Tian, H., Gao, F., Meng, Y., Jia, X., Yu, R., Wang, Z., & Liu, Z. (2024). Multi-Frequency Microwave Sensing System with Frequency Selection Method for Pulverized Coal Concentration. Preprints. https://doi.org/10.20944/preprints202410.2176.v1
Chicago/Turabian Style
Tian, H., Zhan Wang and Zicheng Liu. 2024 "Multi-Frequency Microwave Sensing System with Frequency Selection Method for Pulverized Coal Concentration" Preprints. https://doi.org/10.20944/preprints202410.2176.v1
Abstract
Accurate measurement of pulverized coal concentration (PCC) is crucial for optimizing the production efficiency and safety of coal-fired power plants. Traditional microwave attenuation methods typically rely on a single frequency for analysis while neglecting valuable information in the frequency domain, making them susceptible to the varying sensitivity of the signal at different frequencies. To address this issue, we propose an innovative frequency selection method based on principal component analysis (PCA) and orthogonal matching pursuit (OMP) algorithms and implement a multi-frequency microwave sensing system for PCC measurement. This method transcends the constraints of single-frequency analysis by employing a developed hardware system to control multiple working frequencies and signal paths. It measures insertion loss data across the sensor cross-section at various frequencies and utilizes PCA to reduce the dimensionality of the high-dimensional full-path insertion loss data. Subsequently, the OMP algorithm is applied to select the optimal frequency signal combination based on the contribution rates of the eigenvectors, enhancing measurement accuracy through multi-dimensional fusion. Experimental results demonstrate that the multi-frequency microwave sensing system effectively extracts features from high-dimensional PCC samples and selects the optimal frequency combination. Filed experiments conducted on five coal mills show that, within a common PCC range of 0–0.5 kg/kg, the system achieves a minimum mean absolute error (MAE) of 1.41% and a correlation coefficient of 0.85. These results indicate that the system can quantitatively predict PCC and promptly detect PCC fluctuations, highlighting its immediacy and reliability.
Keywords
pulverized coal concentration; frequency selection; microwave sensor; switch matrix; principal component analysis (PCA); orthogonal matching pursuit (OMP)
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
