Arias Luis, Sbarbaro Daniel, Torres Sergio
Department of Electrical Engineering, University of Concepcion, Casilla 160 C, Concepcion, Chile.
Appl Opt. 2012 Sep 1;51(25):6111-6. doi: 10.1364/AO.51.006111.
In this paper, a novel automated algorithm to estimate and remove the continuous baseline from measured flame spectra is proposed. The algorithm estimates the continuous background based on previous information obtained from a learning database of continuous flame spectra. Then, the discontinuous flame emission is calculated by subtracting the estimated continuous baseline from the measured spectrum. The key issue subtending the learning database is that the continuous flame emissions are predominant in the sooty regions, in absence of discontinuous radiation. The proposed algorithm was tested using natural gas and bio-oil flames spectra at different combustion conditions, and the goodness-of-fit coefficient (GFC) quality metric was used to quantify the performance in the estimation process. Additionally, the commonly used first derivative method (FDM) for baseline removing was applied to the same testing spectra in order to compare and to evaluate the proposed technique. The achieved results show that the proposed method is a very attractive tool for designing advanced combustion monitoring strategies of discontinuous emissions.
本文提出了一种新颖的自动算法，用于估计和去除测量火焰光谱中的连续基线。该算法基于从连续火焰光谱学习数据库中获得的先前信息来估计连续背景。然后，通过从测量光谱中减去估计的连续基线来计算不连续的火焰发射。学习数据库的关键问题在于，在无间断辐射的情况下，连续火焰发射在烟灰区域占主导地位。所提出的算法在不同燃烧条件下使用天然气和生物油火焰光谱进行了测试，并使用拟合优度系数（GFC）质量指标来量化估计过程中的性能。此外，将常用的用于去除基线的一阶导数法（FDM）应用于相同的测试光谱，以便比较和评估所提出的技术。所取得的结果表明，所提出的方法是设计不连续排放的先进燃烧监测策略的一种非常有吸引力的工具。
