Efficient diagnosis of emissions from combustion processes plays a key role in their control, an essential part of the overall effort to mitigate the increasing greenhouse effect. In industrial furnaces, a set of sensors (CO\(_{\mathrm{x}}\), SO\(_{\mathrm{x}}\), NO\(_{\mathrm{x}}\)) at the exhaust is used to monitor pollutant rates, thus providing the necessary information for control purposes. In the case of natural gas furnaces, measurements of O\(_{\mathrm{2}}\) and CO\(_{\mathrm{2}}\) contents are used to check the condition of the combustion process. In this work, we propose a method to estimate the O\(_{\mathrm{2}}\) and CO\(_{\mathrm{2}}\) contents at the exhaust of a natural gas prototype furnace from images of flames grabbed by a charge-coupled device (CCD) camera. Feature vectors obtained from computer processing of the grabbed images are used as input data to identify auto-regressive moving average (ARMAX) “black box” models having CO\(_{\mathrm{2}}\) content as output. Estimates of O\(_{\mathrm{2}}\) content by a Kalman filter running a preliminary ARMAX model help the overall performance of the method. Results show that the flame dynamics identified model is capable of yielding statistically significant estimates of both O\(_{\mathrm{2}}\) and CO\(_{\mathrm{2}}\) composition in the flue gas up to 10 s before the arrival of actual O\(_{\mathrm{2}}\) measurements. This outcome suggests that the inclusion of the proposed method in the closed-loop control strategy of similar combustion processes might be advantageous.

燃烧过程中排放物的有效诊断在控制过程中起着关键作用，这是减轻温室效应日益增加的总体努力的重要组成部分。在工业炉中，一组传感器（CO \（_ {\ mathrm {x}} \），SO \（_ {\ mathrm {x}} \），NO \（_ {\ mathrm {x}} \\））处的排气口用于监测污染物的发生率，从而为控制目的提供必要的信息。对于天然气炉，使用O \（_ {\ mathrm {2}} \）和CO \（_ {\ mathrm {2}} \）含量的测量来检查燃烧过程的条件。在这项工作中，我们提出了一种方法来估计O \（_ {\ mathrm {2}} \）和CO \（_ {\ mathrm {2}} \）电荷耦合器件（CCD）摄像机捕获的火焰图像中的天然气原型炉排气中的气体含量。从捕获图像的计算机处理获得的特征向量用作输入数据，以识别具有CO \（_ {\ mathrm {2}} \）内容作为输出的自回归移动平均值（ARMAX）“黑匣子”模型。运行初步的ARMAX模型的卡尔曼滤波器对O \（_ {\ mathrm {2}} \）含量的估计有助于该方法的整体性能。结果表明，火焰动力学识别模型能够对烟道气中的O \（_ {\ mathrm {2}} \）和CO \（_ {\ mathrm {2}} \）组成进行统计上显着的估计。到实际O \（_ {\ mathrm {2}} \）之前的10 s测量。该结果表明，将所提出的方法包括在类似燃烧过程的闭环控制策略中可能是有利的。