A new approach based on computer vision and non-linear Kalman filtering to monitor the nebulization quality of oil flames

Abstract

The nebulization quality of oil flames, an important characteristic exhibited by combustion processes of petroleum refinery furnaces, is mostly affected by variations on the values of the vapor flow rate (VFR). Expressive visual changes in the flame patterns and decay of the combustion efficiency are observed when the process is tuned by diminishing the VFR. Such behavior is supported by experimental evidence showing that too low values of VFR and solid particulate material rate increase are strongly correlated. Given the economical importance of keeping this parameter under control, a laboratorial vertical furnace was devised with the purpose of carrying out experiments to prototype a computer vision system capable of estimating VFR values through the examination of test characteristic vectors based on geometric properties of the grey level histogram of instantaneous flame images. Firstly, a training set composed of feature vectors from all the images collected during experiments with a priori known VFR values are properly organized and an algorithm is applied to this data in order to generate a fuzzy measurement vector whose components represent membership degrees to the ‘high nebulization quality’ fuzzy set. Fuzzy classification vectors from images with unknown a priori VFR values are, then, assumed to be state-vectors in a random-walk model, and a non-linear Tikhonov regularized Kalman filter is applied to estimate the state and the corresponding nebulization quality. The successful validation of the output data, even based on small training data sets, indicates that the proposed approach could be applied to synthesize a real-time algorithm for evaluating the nebulization quality of combustion processes in petroleum refinery furnaces that use oil flames as the heating source.