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IEEEInternational Workshop on Cellular Neural Networks and 'Ibeir Applications Roceedings
A Cellular Neural Networks Approach to Flame Image Analysis for Combustion Monitoring Bertucco L.('), Fichaa A. (*), N m a r i G. (I), Pagan0 A. (I) (1) Dipartimento Elemim, Elettronicx, e Sistemistico, Universita di Catania 6,95125 Catania, Tel. +95-7382306, Fax +95 330793, Vide A. Ma,
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e-mail:
[email protected],
[email protected] (2! Jstituto di Fisica Tecnica, Universits di Catania Vide A. Dona, 6,95125 - Catania, Tel. +95-7382450, Fax +95 337994,
Abstract - %s p q x r proposes an approach based on Cellular Neural Networks (CAWS) to the analysis offlame images for real time monitoring of comhtion process in a waste incinerator. The use of ChWs analysis is dictaied by the high images sampling rate, which was necessary due to the fasi dynamics of the process in siudy. The dynamical behavior of the descriptors of the images processed by the CAWSwas also studied and ihe results of this analysis are also presented
1. Introduction It is well known that the fulfillment of actual energetic needs is mainly demanded to combustion processes of various kinds, which are prone to produce great amounts of highly polluting emissions (in particular CO and NOJ. The ineoductim in the last years of increasingly stringent regulations on combustion emissions has pointed out the importance of monitoring and controlling combustion processes in order to optimize their performances. One of the most powerful tools for monitoring combustion process is based on the measure of the flame fiont heat release. Many evidences show that the light intensity of the flame eont is proportional to combustion rate and hence to heat release [l-21. In fact, light is emitted by highly unstable chemical intermediate radicals,which can exist only in the reaction h n t . As a consequence, measures of light emitted by flames are the most common way of detecting heat release in combustion processes [3-4]. The heat release rate distribution depends on the flame structure and evolution and determines the distribution of temperature. The heat release measurements and the study of temperature distribution inside combustion chambers is often performed by means of flame image analysis [5-61. In fact,this approach allows to detect the oscillating behavior of the structure of the flame (vortices) and the existence of hot spots, i.e. restricted regions of the flame characterized by high temperatures which may cause the rising of CO and NO, emissions. The main drawback of the classical analysis based on image processing approach, is that it requires large amount of data to be processed. Moreover, due the fast dynamics of combustion systems, flame images analysis has been used for offline studies of combustion but is difficult to apply in real-time monitoring of combustion processes. This paper presents an innovative approach to the analysis of flame images detected on a waste incinerator combustion plant for thermal power production based on Cellular Neural Netwrks (CAWS).In fact, C N N s are particularly able to perform real time image analysis and hence they can be effectively used in monitoring and controllingcombustion process, overcoming the drawbacks previously mentioned.
2. Cellular Neural Networks Approach In the present work a sequence of 6ames detected kom a waste incinerator combustion chamber was analyzed. The incinerator plant is placed in the outskirts of Ferrara and, together with a geothermal plant, provides the 37.5% (corresponding to 45000 GcaVyear) of the whole heat power requested for civil use. The sequence was acquired using a ultrafast videocamera, able to pick up to 800 frames per second, which was operated at a sampling rate of 250 Hz (i.e. 250 frames per second). Each fiame is a gray scale image made of 32x32 pixels. It must be noticed that the sampling fiequency is indeed too high to perform real time flame image analysis using any of the traditional approaches. On the other hand CNNs image prwessing is exbwnely k t , and thmefore it can be particularly useful fb the 0-7803-6344-2/00/$10.00 0 2 0 0 0 IEEE
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in which 250 hmes must be elaboratedeach seccnd Moreover many d i h t opeations application herein can be Pafmed simply modifying a few pawnetersof the cloning tanplates [7-81 of the CNNs; in dher word?., the poposed~~offasgreatflsdbilitymdallowsrealtimeflameimageanalysis The present study was carried out using a CNN software simulator, described in [9]. The operationsm e d on the images are implanented using h eartemplatesand a singlelayer CNN. Figure 1 ( a ) p " t s the image of the flame deteded by the v i d e " In this 6gure the central clear region rep.esentsh e flame bning the fuel emitted by @einjector placed arcamd the m b h e of the bottom of the figure. The
on bc+hsides of the figmare parts of the h a produced by two injectax surramding the central snalla clear one. The first step of the analysis consisted of thresholdig the gray scale images. In order to do this it is necesary to threshold each pixel of the original image. 'Ihis can be simply done by setting to white those pixels characterized by gray levels greater than the threshold and to black all the other. The templates used to perform such an operation are called Threshold templates [IO]. The choice of the threshold is very important as it correspondsto choose a light emission intensity, which plays a specific role in combustion processesmonitoring.
Fig. I (a) Original Image (b) Threshold Image
In fact, the light intensity of the flame &ant is proportional to combustion rate and hence to heat release [1-21, In other words, measure of light intensity can be used to localize the core of the flame. The cure is the region of the flame reaching high temperature levels in which combustion mainly occurs; the flame front separates this typical of combustion vortices. This structure is region by the exhausted gas and assume the " r e characterized by different temperature levels depending on the progressive mixing of the burning gas with the exhausted gas. Therefore, thresholdig the images can be used either to determine the cure of the flame or to detect hot spots, i.e. regions characterized by temperatures higher than a specified value. In both cases it is neceSSary to opportunely choose the temperature threshold and the correspondinggray scale level. In this work the threshold was chosen to allow the analysis of the structure of vortices, basiig on empirical considerations obtaiied fiom the observation of several sequences. Figure 1 @) reports the image obtaiied thresholding the one of Fig. 1 (a) and indeed well describes the vortex produced by the central injector. Nevertheless, the image obtained applying only the threshold operator is in some way affected by the interference of the flames due to the l a t a l injectors. Moreover, the prem~ceof isolated white pixels is in general due to very fast local combustion phenomena and can therefore be regarded as noise. Both the problems were easily solved applying another typical C N N s operator, which consists in performing the logic AND (ImagesAND,)of N successive threshold fiames (Images Th,,,i=1,2, ... N) as shown in Fig. 2 (a). The output of this operator is the image resulting &om the intersection of the se4 of white pixels of the N images (Fig. 2 The application of the logic AND,@formed by using AND templates [lo]), to the case in study can be considered as a sort of image firrer acting both on the pixels spatial distribution and on the temporal evolution of the flame image. Thafore, the choice of the number of h e s N must be done considering both these actions. In the present study it was sufficient to apply the logic AND to four images (Step = 4).
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Fig. 2 (a) Sequence:AND Images ConstnrctionwithStep=3 (b) AND Pixel
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The images obtained applying the threshold and the logic And operator were used to describe the evolution of vortexes inside the combustion chamber. Fig. 3 (a) shows six gray scale images, which were selected by sampling a sequence of 90 fiames and hence correspond to a time period of 0.28 seconds. Fig. 3 @) reports the images obtained applying the threshold and the logic AND to the previous sequence.
Fig. 3 (a) Original images (b) Threshold + logic AND images Fig. 3, reports a sequence that well represents the genaal behavior of several other sequences cohsidered during this study. This sequence evidences the ability of the C N N s in describing the dynamical evolution of a vortex. Time evolution of this structure is charactaid by the mergingprocess [5], which consists of the periodical formation of a great scale vortex fiom the fusion of two small scale vortexes. This behavior cannot be revealed by the analysis of gray scale images but is evident in those processed by the CNN. 'Ihe analysis of vortex evolution is extremely important to reach a full insight of combustion phenomena In fact, the mechanism of vortex shedding and the m t e " between vortexes govern pressure oscillations and heat release fluctuations related to CO and N9, emissions and to vibratory phenomena that can be harmful for the combustion chamber. In the next section an analysis of the complex dynamics characterizing vortex behaviors is addressed.
3. Analysis of Flame Dynamics The dynamical behavior of the vortex was studied using the sequences processed according to the approach discussed in the previous section and consisting of the application of the logic AND operator to the threshold images. To this aim, for each of the images of the sequences the cenirul moments, described in [l I], were calculated. This step was necesary to reduce the study of the dynamical evolution of the h e to traditional time series analysis. Therefore, seven invariant moments, i.e. image descriprors,were calculated per each image 7 3 7 25
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The time spies obtained following this approach were affected by noisy components and were filtered. Wmelet Analysis was applied as it allows to preserve local features existing in the experimental time series whereas traditional filters based on the cut-off of undesired fresuencies are not [121. A comparison of the noisy time saies describing the first invariant moment and the corresponding filtered time series is presented in Fig. 4. As the Fig. 4 evidences, the filtered time series well reproduced the original one and satisfactory performed the reduction of noisy components. The dynamics of the central moments was represented in a phase space obtained applying the Reconstruction Method underpinned on Takns ’ Embedding Theorem [ 131. Figure 5 (a)-(r) reports the 2-Drepresentation of the attractors of six of the central moments. I
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(4 lel fl Figure 5 Atiractor of sir (1-6) invariani moments offlame images: the structure is typical of a n-scroll. A detailed analysis of these plots is not in the aims of this work but it must be pointed out that the system dynamics is described by a n-scroll, which is a typical chaotic attractcx. This consideration is very important as it allows to characterize the vortex as a process dominated by the existence of chaos.
4. Conclusions In this paper a Cellular Neural Networks based approach to flame image analysis was proposed to study the combustion process occurring in an incinerator. The proposed methodology represents a valid solution to the problems deriving !?om the onerous processing neceSSary to analyze considerable amount of data in real time. Indeed, the image sampling rate necesary to adequately monitor the combustion process is very high and real time analysis of such sequences cannot be addressed using traditional image processing techniques. The results show that the combustion process strongly depends on the vorticous structure of the flame and on its evolution. Moreover, they evidence that the behavior of such strudures could be adequately monitored by means of CNNs. Finally, the dynamics governing the dynamical evolution of the vortex was studied by means of phase space representations of image descriptors. This analysis pointed out the existence of chaos in the system in study.
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5. References [l] John R R and Summafield U,“Studies of the Mechanism of Flame Stabilization by a Spectral Intensity Method”, Jet Propulsion, 25,535,1995. [2] John R R and Summafield M, ‘TBect of Turbulence on Radiation Intensity from Propane-Air Flames”, Jet Propulsion, 27,169,1957. [3] h4cManus IC. R, Vandhrger U., Bowman C. T. “Combustor Performance Enhancement through D u d Shear Layer Excitation”, Corobustion a d Flame, 82, pp.75-92,1990 [4] Padmanabhan K. T., Bowman C. T., Powell J. D., “An Adaptive Optimal Combustion Conlrol Strategy”, Combustion and Flame, 100, pp. 101-110,1995. [SI Schadow K. C. and Gutmark E., “Combustion Instability Related to Vortex Shedding in Dump Combustors and Their Passive Control”, Progress in Energy Combustion Science, 1992,18, pp. 117-132,1992. [6] Pomsot T., Veynante D., Bourienne F., Candel S., Esposito E. Surget J., “Initiation and Suppression of Combustion Instabilities by Active Conlrol”, 22th Symp. (hem.) on Combustion, The Combustion Institute, pp. 1363-1370,1988. [7] Chua L. 0. and Yang L., “Cellular Neural Network Theory”, IEEE Transactions Circuits and Systems Vol. 35, NO. 10, pp. 1257-1272, October 1988. [8] Chua L. 0. and Yang L., “Cellular Neural Networks: Applications”, IEEE Transactions on Circuits and Systems Vol. 35, NO. IO, pp. 1273-1290, October 1988. [9] Bertucco L. and N& G., “A Multi-Layer Cellular Neural Network Simulator for Image Processing Applications”, Roceediings of the Third International ICSC Symposia on Intelligent Industrial Automation I N 9 9 and Soft Computing SOC0’99, pp. 147-151, Genova, Italy, June 1999,. [lo] Roska T., K6k L, Nemes L., zantrdy A. and Szolgay P. CSL CNN Software Library (Templates and Algorithms) Vers. 7.3, Hungarian Academy of Sciences, Budapest (Hungary), August, 1999. [ l l ] G d e z R C. and Woods R E. “Digital Image F’rocessing”, 2nd edn. Addison-Wesley Publishing Company, pp.514-519, 1993. [12] R K. Young,“Wavelet Theory and Its Applications, Kluwer Academic Publishers”, Dordrecht, 1993, [I31 Takens F., “Lecture Notes in Mathanatics, Dynamical System and Turbulence”, D. A. Rand & L. S. Young, Springer, New York, 1981.
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