Monitoring combustion process with the vision diagnostic system

Combustion process is complex, nonlinear and non-stationary therefore analyzing is difficult. One method of diagnosis the combustion process is to use flame as the source of information. By analyzing the image of the flame can get information about the process almost without any delay. This is particularly important in the case of fuel combustion characterized by a high variability of physico-chemical properties. Current regulations require continuous increase in the proportion of biomass in order to obtain electricity. Co-combustion of biomass and coal is the easiest way to use it but it is technologically difficult process due to the different characteristics of the components of the mixture combusted. Measurements were made at the position of the camera perpendicular to the axis of the flame for different variants of power. After the initial analysis of images of the co-pulverized coal and biomass were determined sequence of images of stable and unstable combustion. Image classification method used to determine the state of the process. Article presented methods for image classification with naive Bayesian classifier and K nearest neighbors and support vector machine with non-lineral kernel function.