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Study on CO concentration measurement of TDLAS based on baseline nonlinear improvement

机译:基于基线非线性改进的TDLAS CO浓度测量研究

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CO is one of the main air pollution gases. The monitoring of CO concentration is very important for air pollution control. At present, TDLAS has been widely used in air environment monitoring. However, in the measurement process, the baseline fitting error will lead to a large error in CO concentration measurement. Therefore, this paper proposes to adjust the input signal of the signal generator iteratively and modify the baseline nonlinearity to improve the baseline fitting error, so as to effectively reduce the CO concentration measurement error. First of all, by comparing the real baseline with the polynomial fitting baseline of different orders, the polynomial fitting error is 33% ~ 56%, which proves that the non-linearity of baseline will lead to large concentration measurement error. Secondly, by modifying the baseline nonlinearity, polynomial fitting is used to measure the baseline concentration, the error is reduced to 6.2%, and the measurement accuracy is improved obviously. This method improves the polynomial fitting baseline error caused by baseline nonlinearity, improves the accuracy of CO concentration measurement, and provides technical support for CO monitoring in air pollution.
机译:一氧化碳是主要的空气污染气体之一。一氧化碳浓度的监测对于控制空气污染非常重要。目前,TDLAS已被广泛用于空气环境监测。但是,在测量过程中,基线拟合误差将导致CO浓度测量中的较大误差。因此,本文提出对信号发生器的输入信号进行迭代调整,并修改基线非线性,以改善基线拟合误差,从而有效降低CO浓度测量误差。首先,通过将实际基线与不同阶次的多项式拟合基线进行比较,可知多项式拟合误差为33%〜56%,证明了基线的非线性会导致较大的浓度测量误差。其次,通过修改基线非线性度,采用多项式拟合法测量基线浓度,误差降低到6.2%,测量精度明显提高。该方法改善了由基线非线性引起的多项式拟合基线误差,提高了CO浓度测量的准确性,为空气污染中CO的监测提供了技术支持。

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