Gaussian process regression based optimal design of combustion systems using flame images

With the advanced methods of digital image processing and optical sensing, it is possible to have continuous imaging carried out on-line in combustion processes. In this paper, a method that extracts characteristics from the flame images is presented to immediately predict the outlet content of the flue gas. First, from the large number of flame image data, principal component analysis is used to discover the principal components or combinational variables, which describe the important trends and variations in the operation data. Then stochastic modeling of the combustion process is done by a Gaussian process with the aim to capture the stochastic nature of the flame associated with the oxygen content. The designed oxygen combustion content considers the uncertainty presented in the combustion. A reference image can be designed for the actual combustion process to provide an easy and straightforward maintenance of the combustion process.