| SONG Li-mei,GUO Su-qing,YANG Yan-gang,GUO Qing-hua,WANG Hong-yi,XIONG Hui.Quantitative analysis of multicomponent mud logging gas based on infrared spectra[J].Optoelectronics Letters,2019,15(4):312-316 |
| Quantitative analysis of multicomponent mud logging gas based on infrared spectra |
| Author Name | Affiliation | | SONG Li-mei | Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China | | GUO Su-qing | Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China | | YANG Yan-gang | School of Mechanical Engineering, Tianjin University of Technology and Education, Tianjin 300222, China | | GUO Qing-hua | School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, Wollongong | | WANG Hong-yi | Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China | | XIONG Hui | Key Laboratory of Advanced Electrical Engineering and Energy Technology, Tianjin Polytechnic University, Tianjin 300387, China |
|
| Abstract: |
| This work deals with quantitative analysis of multicomponent mud logging gas based on infrared spectra. An accurate analysis method is proposed by combining a genetic algorithm (GA) and a radial basis function neural network (RBFNN). The GA is used to screen the infrared spectrum of the mixed gas, while the selected spectral region is used as the input of the RBFNN to establish a calibration model to quantitatively analyze the components of logging gas. The analysis results demonstrate that the proposed GA-RBFNN performs better than FS-RBFNN and ES-RBFNN, and our proposed method is feasible. |
| Hits: 705 |
| Download times: 0 |
|
| View Full Text Download reader |
|
|
|
