Combustion processes in industrial furnaces pose challenging difficulties when one attempts to model flame. Chemical and physical phenomena interact and affect flame behavior in such a complex way that several different approaches have been under development to tackle the modeling problem. Flame instabilities could potentially harm safety operation of the furnace. Ultimate goal of flame control is to develop means to actuate over the flame state in order to avoid dangerous situations. A critical step towards this objective is to come up with a reliable model for the flame, so that control theory could be applied to automatically maintain flame state within safe standards. This work proposes a methodology to model the dynamics of flames through computer vision to acquire and process flame images in order to extract image features evolution in time. Then, a dynamic model is identified through random decrement algorithm and Ibrahim time domain method, which are operational modal analysis techniques, together with a random contribution. Flames from seven different combustion conditions were modeled by such methodology, and their estimated values were compared with experimental data for validation. Results show that estimated and experimental data possess a high degree of correlation, thus confirming the viability of the proposed dynamic model of flames.

当人们尝试对火焰进行建模时，工业炉中的燃烧过程会带来挑战。化学和物理现象以一种复杂的方式相互作用并影响火焰行为，因此正在开发几种不同的方法来解决建模问题。火焰不稳定可能会危害炉子的安全运行。火焰控制的最终目标是开发一种在火焰状态下致动以避免危险情况的方法。实现该目标的关键步骤是提出一个可靠的火焰模型，以便可以将控制理论应用于自动将火焰状态保持在安全标准范围内。这项工作提出了一种通过计算机视觉对火焰动力学建模的方法，以获取和处理火焰图像，以便及时提取图像特征。然后，通过随机递减算法和易卜拉欣时域方法（它们是操作模态分析技术），以及随机贡献，来确定动态模型。通过这种方法对来自七个不同燃烧条件的火焰进行建模，并将其估计值与实验数据进行比较以进行验证。结果表明，估计和实验数据具有高度相关性，从而证实了所提出的火焰动态模型的可行性。并将其估计值与实验数据进行比较以进行验证。结果表明，估计和实验数据具有高度相关性，从而证实了所提出的火焰动态模型的可行性。并将其估计值与实验数据进行比较以进行验证。结果表明，估计和实验数据具有高度相关性，从而证实了所提出的火焰动态模型的可行性。