基于可调谐激光吸收光谱技术的预混火焰重建算法比较

Comparison of premixed flame reconstruction algorithms based on tunable laser absorption spectroscopy

燃煤锅炉发电会产生大量的污染气体,本文基于可调谐激光吸收光谱技术,结合层析成像技术,对预混火焰的温度和浓度进行重建,通过这些参数来调节锅炉运行的工况,以此来减少污染气体的排放和提高能源利用率。经过谱线分析,文章选择在7148.8~7151cm^(-1)附近选取2条适合高温重建H_(2)O的吸收谱线(7149.058cm^(-1)和7150.4716cm^(-1)),通过采取自适应迭代算法、BP-神经网络算法和卷积神经网络算法,对不同的温度场和浓度场进行模拟重建。研究发现,卷积神经网络算法在重建精度和稳定性方面都优于其他两种算法。为了探究误差对于重建结果的影响,通过添加随机误差发现,误差对于卷积神经网络算法影响较小,温度和浓度重建精度高。为了验证卷积神经网络算法的可行性,文章选取了不同的燃烧工况进行了重建对比。研究表明,卷积神经网络算法重建图像趋于平缓,与实际燃烧情况更加符合,该研究也证明了卷积神经网络算法在燃烧场重建方面的优势和可行性。

Power generation from coal-fired boilers generates a large amount of polluting gases.This is based on tunable laser absorption spectroscopy combined with chromatographic imaging to reconstruct the temperature and concentration of premixed flames,and use these parameters to regulate the operating conditions of boilers as a means to reduce polluting gas emissions and improve energy efficiency.After the spectral analysis,two absorption spectral lines(7149.058cm^(-1)and 7150.4716cm^(-1))near 7148.8~7151cm^(-1)were selected as suitable for high temperature reconstruction of H_(2)O,and different temperature and concentration fields were simulated and reconstructed by adopting adaptive iterative algorithm,BP-neural network algorithm and convolutional neural network algorithm.It was found that the convolutional neural network algorithm outperformed the other two algorithms in terms of reconstruction accuracy and stability.To investigate the effect of error on the reconstruction results,it was found that the error had less effect on the convolutional neural network algorithm by adding random errors,and the temperature and concentration reconstructions were highly accurate.In order to verify the feasibility of the convolutional neural network algorithm,different combustion conditions were selected for reconstruction comparison.The study shows that the reconstructed images of the convolutional neural network algorithm tend to be flatter and more consistent with the actual combustion conditions.The study also demonstrates the advantages and feasibility of the convolutional neural network algorithm in the reconstruction of combustion fields.