Ciência da Computação
URI permanente desta comunidadehttps://repositorio.fei.edu.br/handle/FEI/342
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2 resultados
Resultados da Pesquisa
- A new approach based on computer vision and non-linear Kalman filtering to monitor the nebulization quality of oil flames(2013-09-15) FLEURY, A. T.; TRIGO, F. C.; MARTINS, F. P. R.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 ofthe 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 correlate d. Given the economical importance of keeping this parameter under control, a laborator ial vertical furnace was devised with the purpose of carrying out experiments to prototype acomputer vision system capable of estimati ng VFR values through the examination of test charact eristic vectors based on geometric properties of the grey level histogram of instantaneous flame images. Firstly, atraining set composed of feature vectors from all the images collected during experiments with a priori known VFR values are properly organized and analgorithm 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 tobe state-vectors inarandom-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 onsmall training data sets, indicates that the proposed approach could beapplied to synthesize a real-time algorithm for evaluating the nebulization quality of combustion processes in petroleum refinery furnaces that use oil flamesasthe heating source. © 2013 Elsevier Ltd. All rights reserved.
- Identification of the state-space dynamics of oil flames through computer vision and modal techniques(2015-04-01) SILVA, R. P.; FLEURY, A. T.; MARTINS F. P. R.; PONGE-FERREIRA, W. J. A.; TRIGO, F. C.© 2014 Elsevier Ltd.In industrial oil furnaces, unstable flames can lead to potentially dangerous conditions. For this reason, elaborate control systems are used to monitor the various parameters of the process that could become the source of such problems. A current trend in research is the one that seeks to apply artificial intelligence techniques to efficiently identify a priory anomalous behavior of the flames, so as to help improving the time response of the automatic control. In system dynamics theory, it is common sense that an accurate modeling of the process under study directly affects the performance of the controlling apparatus. Unfortunately, due to the complexity of the process, physical models of flame propagation are still not as much faithful as they should to be used for control purposes. On the other hand, could the complex dynamics of flame propagation be described in terms of an identified assumed model, one would come up with a tool for the improvement of the control strategy. In this work, a new approach based on Operational Modal Analysis (OMA) tools is used to identify four degree-of-freedom second order state-space models of oil flame dynamics in a prototype furnace. Grabbed images of a CCD camera, after being processed through a computer vision method, provide sets of characteristic vectors which, then, serve as input data to an identification OMA algorithm based on the Ibrahim Time Domain Method. Models of unstable and stable flames are built and validated through spectral analysis of the reconstructed time-domain characteristic vectors. The truthfulness of the validation scheme was then confirmed by a quantitative modal assurance criterion modified to suit the current application. On the grounds of the results obtained, it is possible to assert that the proposed approach for the description of flame dynamics can likely predict the occurrence of unstable conditions, thus becoming another tool that might be used in an automated control system.