WO2003069230A1

WO2003069230A1 - Device and method for controlling a furnace

Info

Publication number: WO2003069230A1
Application number: PCT/FR2003/000348
Authority: WO
Inventors: Fouad Ammouri; Jacques Dugue; Pascal Favier; Christel Champinot
Original assignee: L'air Liquide, Societe Anonyme A Directoire Et Conseil De Surveillance Pour L'etude Et L'exploitation Des Procedes Georges Claude
Priority date: 2002-02-14
Filing date: 2003-02-05
Publication date: 2003-08-21
Also published as: FR2835908A1; FR2835908B1; AU2003222358A1

Abstract

The invention relates to a device for detecting the temperature of a location in an area of a furnace (40) from an image (69-1 - 69-4) of at least said area inside the furnace. The inventive device comprises means (50, 60) for detecting at least three color components in at least one pixel that is associated with said location in said part of the image, means (50, 60) for calculating at least two relations among the three color components when taken two by two, and means (50, 60) for calculating the temperature associated with said pixel according to the at least two relations among the colors.

Description

Device and method for controlling an oven

Technical field and prior art

The present invention relates to the field of image processing of the interior of a furnace, in order to know parameters such as the temperatures of the interior of the furnace, and in particular of its walls or of the surface of a charge placed in the oven.

It also relates to the field of characterization and / or control of zones of temporal fluctuations of a scene. One aspect of the invention relates to a method and a device for characterizing and / or controlling the temporal fluctuations of a scene, implementing an image processing system.

The invention makes it possible in particular to characterize, from a video signal coming from a camera, the zones of temporal fluctuations of a flame or of a scene in an oven.

The invention makes it possible inter alia to distinguish the contours of the flames, but also to distinguish and / or separate the zones of temporal fluctuations from the static zones.

It applies to the characterization and / or control of flames, for example in an oven, in particular an industrial oven, or in any other type of environment. It allows for example the characterization or identification of the outline of a flame.

It also makes it possible to identify the temporal variations of an intensity profile in a scene that is being viewed. The invention can also be applied to other characteristics of industrial ovens.

The control of the thermal state of an industrial furnace is usually carried out by the use of a small number of sensors. The commonly used sensors are gas sampling systems which allow the characterization of the composition of the fumes and / or thermocouples which provide a local temperature measurement of the walls of the furnace or of the load, and / or sensors which carry out a measurement. spectroscopic combustion along an optical axis (called "line-of-sight") for the purpose of monitoring or controlling flames (safety). However, the temperature sensors present in an oven, due to their low number, cannot always provide information on the drift of the flame characteristics over time. Failure to detect a flame that does not conform to its geometry, length or position can result in premature wear of the refractory walls of the oven, degraded quality of the product produced, and the emission of pollutants beyond the limits. environmental standards.

Video cameras are sometimes used on industrial ovens to provide operators with a view of the interior of the oven. The quality of the visual information is nevertheless limited by the highly fluctuating nature of the flame, due to turbulence, and by the subjective nature of the interpretation.

Very recently, commercial computer systems have appeared which offer continuous flame monitoring by analyzing video images. These flame characterization systems, however, do not address the above mentioned problems.

US Patent 5,249,954 describes an analysis by a sensor

CCD, of flame chemiluminescence, in association with a neural network, to correlate different fields of chemiluminescence to the ratio of oxidant / fuel flow rates. Again, this type of device does not provide information on the drifts of the characteristics of the flames over time.

US Pat. No. 5,971,747 describes an automated combustion control system, which implements video cameras, image processing by a neural network, and a fuzzy logic control system. It is combined with other types of sensors, such as photodetectors, temperature sensors, or pressure sensors.

Such a system is very complex and does not solve the problems of wall wear, degraded quality of the product produced, and pollutant emissions linked to the drift of the characteristics of the flames over time.

Furthermore, none of these known techniques is compatible with characterization and / or regulation of a flame, outside of an oven, for example in the open air. Nor are techniques known which can be applied to the characterization and / or control of temporal fluctuations of a scene in an industrial furnace, such as for example the fluctuations on the surface of the load of an furnace or the fluctuations of the state of the walls, or even fluctuations of the flame.

There is also the problem of controlling a monitoring system of a furnace, and of assistance for the proper functioning of such a system. It is indeed important to be able to quickly diagnose a malfunction of the monitoring system itself.

Another problem is that of monitoring several burners or several ovens. There are no known ways to effectively monitor multiple burners.

Statement of the invention

The object of the invention is to provide a method and a device making it possible to follow the characteristics of an oven or the geometric characteristics of a flame, or of a scene in an industrial oven, in order to detect nonconforming operations, by example of a burner, or of a fuel and / or oxidant supply, or of operations which, in the case of an oven, can cause premature wear of the refractory walls of the oven and which, in all cases , can lead to degraded quality of the product produced, and / or to the emission of pollutants beyond the limits of environmental standards.

The invention firstly relates to a device and a method for determining the temperature of a point of a zone in an oven from an image of at least said zone inside the oven, this device and method comprising, respectively:

- programmed means, and a step, for determining at least three color components in at least one pixel associated with said point in said part of the image,

- programmed means, and a step, for calculating at least two ratios among the three color components taken in pairs,

- programmed means, and a step, for calculating the temperature associated with said pixel as a function of these at least two color ratios. The temperature calculation, for example, implements a polynomial whose variables include at least two ratios of the three color components. It is preferably of a degree less than or equal to three. The coefficients and the exponents of this polynomial are for example stored in a memory of the device.

The oven zone can be a portion of the interior wall of the oven, the temperature of which is sought at one of its points.

It can also be a portion of a charge present in the oven, in order to identify the surface temperature of this charge.

Means can also be provided to carry out statistical processing of images of the interior of the oven, for example a sliding statistical processing.

Such a statistical processing can be a calculation of the sliding average of the images of the last n images, or of images of the interior of the oven taken during a sliding time interval.

According to another aspect, the invention also relates to a device and a method for determining the contour of a flame in an oven, from an image of the interior of the oven representing at least part of the flame, this device and this method comprising, respectively:

- programmed means, and a step, for determining a histogram of at least one color component of pixels of the image corresponding to said part of the flame,

- programmed means, and a step, for determining in said histogram a first and a second peak corresponding respectively, on the one hand to the walls of the furnace or to the charge present in the furnace, on the other hand to said part of the flame ,

- programmed means, and a step, for determining the intensity, called threshold intensity, of a point in the histogram located on a perpendicular to a straight line connecting the first and second peaks,

- programmed means, and a step, for determining the contour of the flame by comparison of pixel intensities of at least part of the image with said threshold intensity.

The point of the histogram located on a perpendicular to a line connecting the first and second peaks can be the point located at a maximum distance from said line. Preferably, said histogram is that of the green component of pixels of the image.

The flame contour thus determined can then be identified or located on an image, on means for viewing such an image.

According to yet another aspect, the subject of the invention is a device, and a method, for determining a temporal variation of intensity in a zone of an oven, comprising respectively:

- programmed means or means, and a step, for selecting at least two points or pixels of an image of this area,

means or programmed means, and a step, for determining, for these at least two points or two pixels, and at at least two predefined times, the intensity of at least one of the colors associated with these points or pixels,

- means or programmed means, and a step, for calculating, at said times, the value of a function of the intensity of these points.

Means can also be provided, and a step, for displaying a representation of the temporal evolution of the values of the intensity function calculated at these at least two points.

The subject of the invention is also a device, and a method, for determining a temporal variation of intensity in a zone of an oven, comprising respectively:

- means, or programmed means, and a step, for determining, for these at least two points or two pixels, and at at least two predefined times, the intensity of at least one of the colors associated with these points or pixels ,

- Means, and a step, for displaying a representation of the temporal evolution of the values of the intensity determined at these at least two points.

The display can for example take the form of a graph with a timescale, a graph on which the intensities of the points or of the pixels are represented. Another scale of the graph may correspond to a spatial representation of the points or the pixels or to a distribution of the points or the pixels with respect to a given spatial origin.

Means, and a step, can be provided for selecting a set of one-dimensional points in said image, comprising for example at least a portion of line or curve.

It is also possible to select, with these means, a set of two-dimensional points in said image, and / or at least two subsets of one-dimensional points in said set.

According to the invention, a “sliding” statistical processing of images of flames or images of a scene in an oven, obtained for example using cameras, is carried out using image processing. , said processing eliminating rapid fluctuations in the content of the images. The images are taken during a sliding time interval, the duration of which may be variable, in particular due to the greater or lesser speed with which the statistical processing is carried out.

According to another aspect, the invention also relates to a method for processing images of a flame or a scene in an oven, characterized in that, after having acquired n images of flame or of the scene:

(a) at least one statistical processing of the last n images is carried out

(b) a new image is acquired when the processing (a) is finished

(c) step (a) is repeated. The acquired images are stored for statistical processing, the latter taking into account only the last n images acquired or recorded or stacked. The acquisition of a new image involves the elimination, from the memory or of the stack in which the images are memorized, of the most anciently acquired image Preferably, the statistical processing is carried out on a series of images such than :

1. the delay between the last image processed (the present instant) and the oldest is between 5 and 1000 seconds, preferably between 20 and 200 seconds, 2. and / or the number of images contained in the series and participating in the calculation of a statistical result is greater than 5, preferably between 25 and 1000.

The statistical processing carried out can be a calculation of variance of the images over time.

It can also be a sliding average calculation of the images of the flame or of the scene taken during the sliding time interval.

It can also be a treatment making it possible to obtain, from each image, an image of its instantaneous fluctuations, or even a treatment making it possible to obtain the spatial envelope of the fluctuations of the flame or of the scene.

It is also possible to select the points of the image obtained whose intensities are above a certain threshold. This method can, in addition, include a step of extracting the contour of the spatial envelope from the flame fluctuations or from the scene.

Likewise, it may also include a step of determining a rectangle which contains or which encompasses the outline of the flame or zones of temporal fluctuation of the scene, and / or a calculation of the center of gravity and / or the area and / or perimeter of this outline.

An image processing method according to the invention can be combined with a step of regulating a physical parameter of a flame, or of a combustion or of an oven in which the combustion or the scene takes place, or one or more burners.

More precisely, we can distinguish between the parameters to be regulated (geometric characteristics of the flame) and the parameters on which we act.

The invention also relates to a device for implementing a method according to the invention, in particular as described above.

Thus, the invention also relates to a device for characterizing images of a flame or a scene in an oven, comprising means for carrying out statistical processing of the images. As explained above, such a treatment makes it possible to eliminate the rapid fluctuations of the flames or of the scene. The invention also relates to a device for processing images of a flame or a scene in an oven, comprising:

- means for memorizing n images of flame or of the scene, acquired chronologically, - means for carrying out at least one statistical processing of the n last images,

- Means for storing an additional image, when the statistical processing is finished.

The invention also relates to a device and a method for controlling a control system of an oven, this control system and method comprising, respectively:

- sensor means, and a step, for supplying or measuring at least one physical item of information inside the furnace, - means, and one step, for processing said measured physical item, to supply processed physical item of information,

means, and a step, for controlling at least one parameter of the oven, as a function of said physical information processed, said device comprising means, and said method comprising a step, for sending, from the sensor means and / or the processing means and / or the control means, at remote computer means, of parameters relating to the operation of said sensor means and / or said processing means and / or said control means and / or part of the physical information processed. Thus, a check of the proper functioning of the control system or of the correct progress of the removal of the physical information and / or of the means or of the processing or control step can be carried out remotely, therefore quickly.

Said measured physical information may include at least one physical parameter measured inside the oven, for example a temperature measured inside the oven and / or at the outlet of the oven, and / or a pressure measured inside the oven. and / or leaving the oven, and / or a concentration of a gaseous component in the atmosphere of the oven and / or leaving the oven. Said measured physical information can also include at least one image of the interior of the oven. According to a particular embodiment, the sensor means and / or the processing means and / or the control means comprise software means.

Said operating parameters of said sensor means and / or of said processing means and / or of said control means can then comprise parameters of these software means, for example messages relating to the progress of the execution of said software means.

According to yet another aspect of the invention, it also relates to a device for controlling the operation of an oven, this device comprising:

- sensor means for capturing or supplying or measuring at least one piece of physical information from inside the oven,

means for processing said measured physical information, to provide processed physical information,

- means, remote from said processing means, for controlling at least one parameter of the oven, as a function of said physical information processed.

According to the invention, a method for controlling the operation of an oven comprises:

- a step for capturing or providing or measuring at least one piece of physical information from inside the oven,

- a step of processing said measured physical information, to provide processed physical information, - a step of transmitting said processed physical information to means, remote from said processing means, for controlling at least one parameter of the oven, depending of said physical information processed.

This device and this method make it possible to control different burners or different ovens, the means for processing the physical information being distinct from means for controlling remote from these processing means.

BRIEF DESCRIPTION OF THE FIGURES The characteristics and advantages of the invention will appear more clearly in the light of the description which follows. This description relates to exemplary embodiments, given by way of non-limiting explanation, with reference to the appended drawings in which:

FIGS. 1A, 1B and 2 represent temporal diagrams of image acquisition according to the invention, FIG. 3 is a flowchart representing an aspect of a statistical processing according to the invention,

- Figures 4, 5 and 6 respectively representing an instantaneous image of a flame, a sliding average of several instantaneous images and an instantaneous flame envelope, - Figures 7, 8, 10, 11 represent various images obtained by treatments of '' images according to the invention,

FIGS. 9 and 12 are histograms of the intensity levels of an image,

FIG. 13 represents display means with an image and a graph displayed simultaneously,

FIG. 14 represents a graph of intensity evolution, over time for an area of an image,

FIG. 15 represents the selection, in an image, of a two-dimensional area and of sub-sets of one-dimensional points in this area,

FIGS. 16 and 18 are schematic representations of devices according to the invention,

- Figure 17 shows various components of a computer system, - Figures 19 and 20 show other embodiments of a device according to the invention.

Detailed description of embodiments of the invention

The invention uses instant images of the interior of a furnace, or else images resulting from a statistical processing, for example a "sliding" statistical processing applied to instant images.

Such a sliding statistical processing is applied to n successive images as illustrated in FIG. 1A. A sliding interval of duration Tg is defined, in which n images lmg (i), i = 1 n are recorded and memorized, at times 1, 2 n. These n images are then processed, according to a statistical treatment such as one of those described below.

At time n + 1, a new lmg (n + 1) image is stored, and the statistical processing is applied to the lmg (2) Img (n + 1) images. The lmg (1) image is deleted from the memory or stack of images to be processed.

The n images are located in an interval of duration Tg, which moves with a step of duration δt, predetermined.

However, as illustrated in FIG. 1B, if, at time n + 1, the statistical processing of the n images is not completed, the image acquisition lmg (n + 1) does not take place.

If the statistical processing of the lmg (1) images, ..., Img (n) is finished at time n + 2, the lmg image (n + 2) is recorded and a statistical processing is applied to the n lmg images (2) Img (n-1), lmg (n), lmg (n + 2).

The sliding time interval is then variable (Tg1 ≠ T'g2).

This is reflected in the flowchart in Figure 3.

The last n images are processed statistically (step 10). It is then checked whether the statistical processing has ended (step 12). The acquisition of a new image (step 14) takes place only if the last n previously acquired or stored images have been processed.

According to yet another aspect, it is sometimes desirable to impose a minimum time interval between the acquisition of two consecutive images. This may be the case if the computer performs the calculations quickly relative to the desired time window.

Preferably, the calculations are done by recurrence: when a new image is captured, the algorithm does not need to recalculate all the images in the stack. Just take into account (add) the contribution of the new image, and remove the contribution of the oldest image that will be overwritten.

In certain cases, for example in the case of oscillatory combustion, it may be advantageous to select images in phase with the combustion cycle. This is the case in FIG. 2, where the images are recorded at times t1, t2, t3, t4, corresponding for example to determined phases of a combustion process, for example to a determined state of opening of a fuel or oxidant supply valve.

In the case of FIG. 2, the upper part represents the time evolution of the flow rate Q of a fuel injected into a burner. This evolution is periodic (here: sinusoidal) and the image acquisition takes place only when the flow rate Q is at a predetermined phase of its cycle. Other examples may relate to any other type of variation or periodic phenomenon of an oscillatory combustion, the image acquisition being synchronized with this variation or this periodic phenomenon or being carried out only for a predetermined phase of this variation or periodic phenomenon.

Again, it is preferable to wait for the end of the statistical calculation performed on the last n images before acquiring a new image, as already explained above in connection with FIGS. 1B and 3. The statistical processing implemented according to the invention is slippery, which means that it is performed on a stack of n slippery images: it is therefore not frozen over time. When a new image is captured (for example: image n + 1 in Figure 1A, or image n + 2 in Figure 1B), it overwrites the oldest image (image 1) in the stack. Thus, the memory allocation for the algorithm always corresponds to the number of images of the stack on which the statistical processing is applied.

The number n of images is chosen by the user when the calculation is initialized. A number greater than 5, for example between 5 or 10 and

1000, for example equal to 20 or 25 or between 25 and 1000, or even between 20 or 25 and 200, or even greater than 50, makes it possible to obtain a sufficient smoothing effect of the video noise on all of the n images . All fluctuations in the image are considered noise to the eye. Therefore, the fluctuations of the flame or the scene viewed in an oven, as well as the background of the image, will also be smoothed.

The time interval Tg used to select the images is preferably between 10 s and 1000 s. A duration of approximately 10 s is well suited to the nature of the turbulence which may appear in a flame or in an oven, the turbulence having durations of less than 10 s. The upper limit of the interval (approximately 1000 s, or more than ten minutes) is chosen so that the information resulting from a statistical processing of images acquired during this period still has meaning for the 'observer, in relation to the combustion process in progress, or does not arrive to him with too much delay compared to this process.

It follows from the explanations given above in connection with FIGS. 1B and 3 that this time interval Tg can be variable. Thus, in Figure 1 B: T'g ₂ is different from Tgi. In general, the length of this interval is a function of the number n of images to be acquired for statistical processing, as well as the speed of calculation of the computer which performs the statistical processing. A user can define, before any statistical calculation is carried out, an (approximate) duration for Tg. However, an actual acquisition does not take place over a duration strictly equal to Tg, the acquisition duration being able to be greater or slightly higher when a statistical calculation is not completed at the time when a new acquisition should be made.

FIG. 4 represents an example of an instant image Img. We can see the walls 20 of the oven and the burner 22, with its different fuel injection and oxidant orifices.

A first example of statistical processing on n images is a variance calculation on these n images. An image is therefore obtained, each pixel or zone of which is the result of the variance calculation for the corresponding pixel or zone on all the images. High intensity values in this image correspond to the areas where the intensity fluctuations are high, and low values correspond to the areas where the fluctuations are small.

Variance is defined as the difference between the average image of a sequence of images and a snapshot. For a stack of n images, the variance (denoted V) is expressed for example by the formula:

with x the average image of the stack, and x the instantaneous image. The Variance tends towards zero if the images are all identical, that is to say if there is no movement from one image to another. Indeed, there is then no difference between the averaged image and a snapshot. In the present invention, the variance informs about the fluctuations of the flame.

The standard deviation image is defined as follows in the article "Average Centerline Temperature of a Buoyant Pool Fire Obtained by Image processing of video recordings", L. Audouin, G. Kolb, et al (1995), Laboratoire de combustion et de detonics (University of Poitiers):

where I is the gray level of each pixel for each snapshot. The variance is the square of the standard deviation.

A second example of statistical processing on n images, which can be applied in the context of the present invention, is a sliding average processing (or "running average" in English).

The sliding average calculation is used for the algorithms of the envelope of the flame fluctuations or of a scene in an oven and can also be used for the calculations of color ratios. It is for example an operation of summation of matrices, point by point, carried out for example on images on a Y∑ scale (384 * 288 pixels).

The mathematical expression of the algorithm is as follows:

- 1 + 1 is the instant of acquisition of image i + 1 - 1 is the instant of acquisition of image i

- x _{i +} ι represents the image i + 1, newly acquired

- X _! represents image 1, that is to say the oldest image of the stack; this image will be overwritten by the new image that will enter the stack.

This algorithm is recurrent, in the sense that it does not require recalculating the average from all the initial images; it only requires the addition of the quantity (Xj ₊ rXj) / N after each new acquisition. Figure 5 shows an average over 60 images. This figure shows the static areas 20 of the image, which are the refractory walls. Reference 22 designates the burner output and reference 24 the smoothed image of the flame. Typically, on an image taken in an industrial furnace (for example: image of a flame or of a bath in an furnace), the static zones are the refractory walls and the fluctuating zones are the turbulent flames and / or, possibly, the load (glass bath, molten metal ...). The sliding average processing gives a smoothing effect on the information and therefore tends to make the areas of fluctuation of the flame or of the scene viewed disappear.

The result image obtained is of good quality, independently of the processing time, of the noise due to the video acquisition electronics. This result image is used to more easily determine the threshold to be chosen for the following calculations. Indeed, one can determine an intensity value or the maximum intensity value on the walls (zones 20 of FIG. 5). The threshold is then selected at this wall intensity value. A third example of processing or processing algorithm makes it possible to follow the fluctuations of the flame image by image.

This example makes it possible in fact to obtain an intermediate result for subsequent calculations. According to this processing, the image of the moving average Avg is calculated and is then subtracted from each of the instant images of the stack. Then, the absolute value image of each of the result images is binarized according to a threshold chosen by the user.

Finally, it is checked whether the statistical processing is finished. If not, it is completed by returning to the previous steps. If so, a new image is acquired.

The image of rank 1 is then overwritten or eliminated from the stack and replaced by the image of rank 2. Similarly, the image of rank i + 1 replaces the image of rank i, and this for all i between 1 and N. The new average is calculated on the new n last images thus obtained. Subtracting the average from each snapshot removes the static envelope from the flame. Only its instantaneous fluctuations remain. A simple mathematical expression of the algorithm is as follows: Bin (Abs (lm (i) - Avg), threshold)

Or:

- Im (i) is the instantaneous image taken at time i,

- Avg is the average over the last n images,

- Abs is the absolute value calculation operation, - Bin is the binarization operation carried out with respect to an intensity threshold chosen by the user (the pixels of Abs (lm (i) -Avg) of which l intensity exceeds, or is equal to, this value is set to 1, those below are set to 0).

An example of an image obtained (also called: instantaneous flame envelope) is shown in FIG. 6.

This image helps to detect punctually abnormal states of the flame (drift or variation of its geometry).

This third algorithm makes it possible to highlight the zones where the light intensity fluctuates over time. It also removes areas where the light intensity is constant, and highlights the edges of the flame.

A fourth and a fifth algorithm make it possible to detect the envelope of the fluctuations of the flame.

The fourth algorithm implements the third processing example above. It uses the moving average as a reference image to subtract the background noise from the resulting image. For each image in the sequence, the absolute value of the difference between the image and the averaged image of the series is calculated. This result image is then binarized with respect to an arbitrary threshold, then averaged with the other images in the series.

It is then checked whether the statistical processing has ended. If not, it is completed by returning to the previous steps. If so, a new image is acquired. The image of rank 1 is then overwritten or eliminated from the stack and replaced by the image of rank 2. Similarly, the image i + 1 replaces the image i, and this for any rank i between 1 and n. The new average is calculated. The expression of the algorithm for calculating the flame envelope can be summarized by the following expression:

Flame envelope 1 = Avg (Bin (Abs (lmg (i) - Avg), threshold))

Or :

- Avg is the running average, as already explained above (second example of treatment).

- Bin is the binarization operation according to the threshold defined by the user (the pixels of Abs (lmg (i) -Avg) whose intensity exceeds, or is equal to, this value is set to 1, those which are below are set to 0),

- Im (i) represents each instant image in the image stack.

The choice of the threshold depends on the gray level values of the starting image (average image of the n last images). These values are used to separate the background noise from the foreground flame. The threshold therefore makes it possible to identify the zones of fluctuations whose amplitudes exceed this value. This user-defined threshold eliminates all low intensity fluctuations caused by video noise on static areas. Thus, only the areas of intensity fluctuations greater than the threshold are visible on the result image.

An example of an image obtained by this fourth algorithm is given in FIG. 7.

The fifth algorithm consists in calculating the absolute value of the difference between a snapshot and the one that precedes it in the stack. Subtracting two consecutive images highlights what has changed between the two moments. The resulting image is transformed into a binary image according to an arbitrary threshold. It is then averaged (on a sliding average) with the other images in the stack.

It is then checked whether the statistical processing has ended. If not, it is completed by returning to the previous steps. If so, a new image is acquired. The image of rank 1 is then overwritten or eliminated from the stack and replaced by the image of rank 2. Similarly, the image i + 1 replaces the image i, for any value of i. The new average is calculated.

The fifth algorithm is expressed mathematically as follows:

Flame shell 2 = Avg (Bin (Abs (lmg (i) - Img (M)), threshold)) Or :

- Avg is the rolling average,

- Bin is the binarization operation according to an arbitrary threshold (the pixels of Abs (lmg (i) -lmg (i-1)) whose intensity exceeds, or is equal to, this value is set to 1, those which are below are set to 0),

- Im (i) represents each instant image of the acquisition stack,

- Im (i) and lm (i-1) are two successive images of the acquisition stack.

Figure 8 shows the result of a "flame envelope 2" algorithm. The images in FIGS. 7 and 8 are examples obtained by executing the algorithms on stacks of 60 images. The static walls of the oven have, in both cases, disappeared from the picture. They are black and the flame outline is smoothed.

For the fourth algorithm, the resulting image is no longer sensitive to the most rapid variations of the flame.

The difference between the fourth and fifth algorithms is that the fourth is influenced by the contribution of the last n images of the stack while the fifth is a more instant result.

By subtracting the average, the fourth algorithm highlights the constant part of the flame (in black at the heart of the flame in Figure 7). Therefore, the fluctuating edges of the flame are also better highlighted in this result image. The information of the fifth algorithm is more instantaneous.

A sixth example of calculation or algorithm is a calculation of "instant intermittence" prior to the seventh algorithm. Such a calculation, applied to the initial images, depends on another threshold chosen by the user of the software. Each pixel exceeding the chosen value is set to 1 (white on the image) and the pixels below the predetermined threshold are set to 0 (black on the image). This algorithm is written mathematically as follows:

Instant intermittence = Bin (lm (i), threshold) An instant intermittence image is a binary image which informs about the probability of exceeding a threshold set by the user (p = 0% = black or p = 100% = white). Consequently, the chosen threshold determines the presence of flame in the image. The binary image, whose pixel values are 0 or 1, will be white at the flame location and black if no pixel exceeds the threshold value.

The histogram of the grayscale values of a flame image can help the user to choose the threshold. The threshold value is generally located above the maximum intensity of the walls. Figure 9 shows the typical histogram of an image taken in an oven.

One can carry out (seventh example of calculation or algorithm) a calculation of "average intermittence", which is the sliding average in time of instantaneous intermittences. The pixels of the image therefore vary from 0 to 255, and their gray levels (or intensities) represent the probability of exceeding the chosen threshold, during the time interval of the sliding window.

The image of the probability of exceeding a luminosity threshold greater than the luminosity of the walls therefore makes it possible to show the presence and effective position of the flame in the image as shown in FIG. 10.

The mathematical expression of this algorithm is as follows: Avg (Bin (lm (i)), threshold), Where: - Avg is the sliding average,

- Bin is the binarization operation of the instantaneous image Im (î) according to the arbitrary threshold: a pixel of lm (i) is set to 0 (or to 1) if its intensity is lower (or higher) than the threshold.

The use of a threshold according to one of the third to seventh algorithms is advantageous for erasing the image noise and the brightness of the walls and extracting the flame contour. This type of algorithm has certain limits. Indeed, one can thus characterize the flame only if it is brighter than the bottom. However, this is not always the case on industrial sites, in particular due to the low luminosity of certain flames compared to the radiation from the refractory walls. The image of the result is dependent on the choice of the threshold, but this is compensated for by the systematic aspect of this dependence which allows a comparison of the images results over time.

The fourth, fifth and seventh algorithms implement the AVG (BIN (, threshold)) function, the argument being, respectively, Abs (Img (i) -Avg) (fourth algorithm), Abs (lmg (i) -lmg (i-1)) (fifth algorithm) and Abs (img (i)) (seventh algorithm).

Consequently, according to another definition of the invention, a statistical processing implemented comprises: the calculation of a binary image, with respect to a threshold, from an argument image, a pixel of this argument image being set to 0 (or 1) by the binarization operation, if its intensity is less (or greater than or equal) to the threshold,

- a calculation of the average of the binary images thus obtained. The argument image can be for example one of the three types of images indicated above Abs (lmg (i) -Avg), Abs (lmg (i) -lmg (i-1)) or Abs (lmg ( i)).

Beyond the simple use as display of an image easier to interpret by an operator, the envelope of the fluctuations of the flame, obtained by one of the fourth and fifth algorithms or the images of instantaneous or average intermittence ( sixth and seventh algorithms), can (can) be used to extract quantitative geometric parameters such as perimeter, area, flame length. It is possible to apply an object detection process (here the flame) by image processing, for example by contour extraction, or even "contour segmentation".

According to an example of extracting contours, an image is first binarized with respect to a gray level chosen by the user. For example, each pixel of the image is assigned the value 1 or 0 depending on whether it is considered to be part of the flame, or not. In a second step, the image is expanded by one pixel wider. Then the original image is subtracted from the expanded image by one pixel. The result of this subtraction is one or more continuous contour (s), one pixel thick. Finally, this or these contours are superimposed on the result image (for example "flame envelope 1" or "flame envelope 2"), by adding the two images.

Figure 11 shows an example of contour extraction with a rectangle enclosing the flame. The coordinates of the enclosing rectangle (Xmj _n , x _max , y _min , y _max ) and / or one or more other parameters such as its center of gravity, or the area of the contour or its perimeter can be calculated and displayed. The results are sent to files or memory areas to allow archiving and monitoring of the characteristics of the flame over time.

This analysis gives a good number of quantitative geometric parameters. They can be dynamically linked to advanced control systems such as neural networks. They can thus be used as additional inputs for online flame control.

The monitoring of these flame contour parameters can be advantageously used to maintain an optimum adjustment of one or more type (s) of parameters of an oven and / or of combustion and / or of one or more burners, for example one or more of the following parameters:

1. The pressure of the spray fluid. In the case of a burner operating with liquid fuel, the flame envelope parameters, and in particular the position of the flame root, can be used to regulate the spraying conditions, and in particular the flow rate and / or pressure. Too low a pressure usually results in a flame that is too long, with a flame root farther from the injector.

2. The degree of flame staging.

For burners that divert part of the fuel or the oxidant to a secondary injector, the flame outline can be used to regulate the degree of staging (therefore the proportion of fuel or oxidant to be directed towards the secondary injector) and optimize the length and volume of the flame. We can seek in particular to avoid situations where the flame is too close to the thermal load (glass bath, metallurgical products) or the refractory walls. Regulation of the degree of staging can also be used to minimize pollutant emissions.

3. Fuel and oxidant flow rates.

The fuel and oxidant flow rates, as well as the oxidant / fuel flow rate ratio can be used to maintain a correct flame envelope. Indeed, a flow report oxidant / fuel lower than the stoichiometric ratio generally results in an excessively long flame, and a total flow (oxidant + fuel) too low compared to the nominal power of the burner can cause an elevation of the flame towards the roof of an oven.

4. The power and the momentum of the neighboring burners.

If several burners are used, the image processing system can be used to simultaneously follow the envelopes of several flames, and will make it possible to diagnose or identify undesirable interactions between the flames of neighboring burners. The information from the flame envelopes can be used to optimize the position, the injection modes and the momentum of the fluids (mass flow and velocity of the fluid) of each burner so as to avoid these undesirable interactions (the momentum of a fluid is equal to the product of the mass flow by the speed of this fluid). These conditions concern in particular glass furnaces as well as certain metallurgical furnaces (reheating furnaces).

5. The fraction of waste introduced into the burner. In the case of combustions where waste is co-incinerated with conventional fuels, the image processing system may be used to control the fraction of waste co-incinerated to a shape and / or to a flame root position. This could for example be the case with cement kilns, where it is desirable to maximize the fraction of energy supplied by waste, while respecting satisfactory combustion characteristics (stable flame, flame root immediately downstream of the burner).

6. A fraction of total oxidant introduced by the burner. In the case of burners which combine oxidants of different oxygen concentrations (for example air and oxygen, recycled fumes and oxygen, etc.), the flame contour can be used to regulate the ratio between the two oxidants so as to maintain the flame length in an acceptable range. Indeed, the increase in the overall oxygen content in the oxidant generally results in a shortening of the length of the flame. 7. The oven pressure.

The presence of air inlets near a burner can have significant consequences on the direction and shape of the flame. The information on the flame envelope, possibly in combination with that from other sensors, can therefore be controlled by a parameter which controls the air inlets from a furnace. This parameter could be the position of a valve in the flue gas exhaust to act on the pressure inside the oven. It is also possible to act on the air inlets by a maintenance action aimed at improving the seal around the burner. Variations in the position of the flame envelope can indeed be a sign of the presence of parasitic air inlets in the oven.

8. The frequency of oscillation of the oxidant and fuel supplies of a burner.

In the case of the use of a valve allowing an oscillatory combustion, the acquisition of images can be synchronized in phase with the valve, and the image analysis can allow a statistical treatment on the flame or the envelope of the flame for different phases of the oscillations of the fuel / oxidant mixture. Image processing according to the invention makes it possible to verify that, for each phase of the oscillation cycle, the flame envelope maintains acceptable characteristics. Control by video analysis, for example in combination with other temperature and smoke composition sensors, allows the frequency and / or amplitude of the oscillations to be optimized so as to minimize the emission of pollutants while keeping a flame envelope compatible with the process.

Each raw or instant video image results from the combination of 3 colors or 3 channels R (red), V (green) and B (blue).

According to the invention, it is possible to link, in at least one pixel of the image, the temperature of the object in this pixel and the color components

(R, G and B) in this pixel. It is preferable to relate the temperature to the color ratios

B / R, G / R and B / G in order to free oneself from the knowledge of the emissivity of the object. The temperature of each pixel of the image can be linked to the 3 color ratios according to the following polynomial function:

where (ngr, nbr, nbg) denote the degrees of the polynomials and (a, -, b _h ci) denote the coefficients of these polynomials.

Preferably, and in order to limit the calculation times, each of the degrees ngr, nbr, nbg is less than or equal to 3. 0 The color ratios can be average over time or else the instantaneous values of these ratios can also be used directly .

The coefficients a, -, b _h and c, - can be determined as follows. 5 Thermocouples are installed on an interior wall of an oven. For each value of temperature T, an image is taken, from which we deduce the components R, G, and B.

The number of measurements is sufficient to be able to solve the system of equations which results from the use of equation 0 (1) for each of the measurement temperatures, the unknowns of this system being the coefficients a, -, b „ and c, -.

The monitoring of the color ratios or of the temperature calculated from these ratios can be advantageously used to monitor or control the oven by visualizing for example the intensity levels of these 5 ratios, and / or the temperature gradients on the walls. from the oven or on the load.

A rapid pre-treatment can be carried out using a single color ratio in a polynomial to an unknown, of degree ngr, nbr or nbg depending on the ratio chosen. This polynomial is for example of degree 0 two or three, but it can also be of higher degree.

It is therefore possible to make a preliminary identification of certain zones or of a zone of the image or of the interior of the oven, for example the hottest zones or for which the temperature is between two given limits or is lower or higher. at a given limit 5. Then, a more precise calculation can be implemented in this or these zone (s) previously identified (s), which limits the calculation times.

The calculation carried out according to formula (1) makes it possible to identify, for example on an instant image, the hottest areas of the walls of the oven.

For example, depending on the setting of the burners or the type of burner, hot spots may appear on the walls of the oven, or at the root or at the end of the flame. Identifying or locating these hot spots allows you to draw a conclusion on the setting of the burners, and to modify the appropriate parameters of this setting.

It is also possible to identify the zones whose points or pixels have one or more color ratios located above or below a threshold of the selected color ratio (s) or have a temperature (determined according to (1)) above or below a threshold.

The location of hot spots or certain temperature zones can for example make it possible to decide on an action on the burner in order to obtain the hottest zone where the process requires it.

The determination of the temperature at two points allows that of the temperature gradient between these points.

At a given instant, the temperature gradient between the coldest zones and the warmest zones of the furnace can be useful for controlling the melting of the charge. This is the case in particular in certain furnaces, such as glass furnaces, where the charge is mainly heated by the radiation of the refractories.

Thus the thermal profile of the furnace can then be better understood and controlled for better melting of the load. Another application of the invention relates to the wear of the refractory walls.

In fact, over time, the wear of refractories changes; in particular, a temperature that is too high as well as a temperature that is too low promotes this wear. Monitor, in high or low temperature zones, the evolution over time of color ratios and / or temperature calculated from these reports can extend the life of the oven by acting on the burners in a corrective manner or by programming the change of the wall before complete wear, the latter can lead to unexpected production stoppages. Another application of the invention relates to the evolution of the charge present in the furnace.

The visualization of the color ratios, or the temperature calculated from these ratios, can advantageously make it possible to qualify the process by providing information on the temperature profile of the surface of the charge, at a given instant or over time.

Likewise, this visualization and / or this calculation can (ven) t allow highlighting areas of unfounded generators of defects in different types of fusion processes, or of reactions of the charge with the gaseous atmosphere close to the area. This treatment making it possible to obtain the temperature can be applied to raw or instantaneous images, or else to images resulting from a statistical treatment, and in particular a sliding statistical treatment as described above.

Preferably, a temperature measurement is carried out on an image resulting from a sliding average processing, which is also already explained above.

The information relating to the temperature at a given point or in a given zone of the furnace or of the load can allow a control of one or more parameters of the burner, such as those described above (1-8): pressure of the spraying fluid and / or degree of flame staging and / or fuel and oxidant flow rates and / or power and momentum of movement of the neighboring burners and / or fraction of the waste introduced into the burner and / or fraction d total oxidant introduced by the burner into the oven, and / or pressure in the oven and / or frequency of oscillation of the oscillations.

According to another aspect of the invention, an image (instantaneous or resulting from a statistical processing) of the interior of the oven can be used to detect the contour of the flames. Examples of image processing have already been given above, which make it possible to detect a flame contour. The invention proposes here another method making it possible to identify, more quickly and easily automated, a flame contour. To this end, it is possible to use a triangulation method applied to the histogram of the pixel intensities of the image of the oven representing the flames.

In the case of a color image, there are 3 histograms corresponding to the 3 color components of the image: red, green and blue. We can also use the histogram of the global intensities of each pixel. Only one color, usually green (channel G), can be selected. We can also use the other two colors (blue channel B and / or red channel R).

Each histogram is represented in the form of a graph, with an abscissa axis corresponding for example to the intensity of the pixels and the ordinate axis to the number of pixels having this intensity.

As illustrated in Figure 12, two maximums appear in the histogram.

The highest maximum, therefore representing a large number of pixels in the image, has a low intensity. This maximum corresponds to the walls and the load surrounding the flames.

Indeed, the surface occupied by the walls and the charge in the image is often greater than that of the flames.

The second maximum, lower, has a greater intensity (therefore an abscissa) than the first. It corresponds to the flames in the image.

The two maximums are connected with a straight line. A perpendicular to this line is determined. It presents an intersection with the curve of the histogram. Preferably, the perpendicular which passes through the point of the histogram located furthest from the line connecting the two maximums is used. It is this point which is represented on figure 12.

The abscissa of the point of intersection between this perpendicular and the histogram constitutes the intensity threshold used for the automatic detection of the contour of the flames. The image pixel intensities can be compared to this threshold.

All the points or pixels of intensity higher than this threshold are inside the contour of the flame. This outline can be viewed on viewing or display means.

This contour detection method allows, in cases where the power of the burners installed in an oven often varies, to quickly change the intensity threshold used for detecting the flame contour in order to adapt it to the change in intensity bright flames.

So we can :

- apply this method to detect the threshold s of the contour of a flame, at a first instant t,

- apply it again to t ', t'> t, to detect the threshold s 'to t', - if, at t ', the threshold value s' is different from s, or if the difference between s 'and s is greater than a predetermined value, a flame contour corresponding to s' is determined and possibly displayed.

This method can be automated, since a maximum search program or algorithm can identify the maximums in each histogram, and the line connecting them can also be calculated automatically.

We then have successively:

- selection of an image of the flame whose contours are to be determined, - determination of the histogram corresponding to this image,

- determination of the peaks or maximums of the histogram,

- determination of the line connecting the peaks or the maximums,

- determination of a perpendicular to this straight line,

- determination of the point of intersection of this perpendicular and the histogram, the threshold chosen being the abscissa of this points.

- determination, and possibly display or graphic or visual identification, of the contour or of the interior of the flame, as a function of this threshold value.

This threshold determination method can be applied to a snapshot or raw image, but can also be used in conjunction with a statistical treatment, for example one or the other of the sliding statistical treatments described above.

According to yet another aspect of the invention, an image of the interior of the oven can be used to quickly determine or view or identify a change over time of an intensity in an oven area.

To this end, an operator has an image of the interior of the furnace. This image can be a snapshot image or a processed image, for example according to one of the statistical treatments exposed above. Using any suitable means, the operator can select a line or a curve or an area or in any case at least two points or pixels of the displayed image.

In FIG. 13 is represented the image of a flame in an oven, as well as two end points A, B of a segment AB between which all the pixels are selected.

The user uses for example the cursor of his computer or a stylus to directly select the desired set of points or pixels on the screen.

A time interval dt is also determined or selected.

At each instant nxdt (n natural integer) is determined the intensity profile along the line or in the selected area.

Thus, in Figure 14 is shown a graph with the time on the abscissa, and, on the ordinate, the distance from the origin of the line or the selected area (for example the distance on segment A, B between a point current and point A chosen as origin).

The intensity is represented, for example by a color according to a predefined color scale.

So each vertical in the graph represents at a given moment the intensity profile along the line or the area.

This method is useful for quickly detecting a change in intensity over time along a line or in an area. In fact, we represent both the temporal evolution of the intensity and a spatial distribution of this intensity corresponding to the lines or points or zones chosen. The graph of FIG. 14 can be displayed on a control screen 69, for example simultaneously with the image of the flame or of the interior of the oven as illustrated in FIG. 13. In this figure are also represented the points selected by the operator, which define the line along which the intensity profile is determined.

A similar representation makes it possible to represent not the intensity of each pixel, but any function of this intensity, and in particular the temperature of each pixel, calculated for example according to the method described above using the polynomial (1), or according to one of the variants of this process described above. The distribution of the temperature is then made according to a temporal component and a spatial component as in FIG. 14.

More generally, one can represent in two dimensions, including a time dimension and a space dimension, a function of the intensity of several pixels located on lines, straight or curved, in a given area.

Thus, as illustrated in FIG. 15, one can select 4 points C, D, E, F on the image of a flame, and a set of segments S1, S2, ... Sn in the zone defined by the four points C - F. These segments are spread in a direction G. At each instant nxdt is calculated a function f of the set of pixels located on each segment Si, for example the average of these pixels. It is also possible to use other functions of the pixels such as the variance or another statistical function, or else an average temperature resulting from the calculation of the temperature (for example according to equation (1)) in each of the pixels.

A graph identical to that of FIG. 14 can thus be represented, the points of each vertical line of this graph representing, at the same instant, the result of the calculation of the function f for the different segments S1, ... Sn. For example, take the distance from a point on the line G as the ordinate axis, for example point H (Figure 15).

Other lines or set of points can be selected, which are not necessarily line segments such as the segments Si. These are for example portions of curves, or combinations of segments and portions of curves. In practice, a program allows an operator: - define a time step,

- perform the selection of an area in the image, as well as the selection of portions of this area on which a calculation will be made at each instant, as a function of at least two pixels of these portions, - define or to select (for example in a drop-down menu, the various functions being previously memorized) a function of the pixels to be calculated at each instant.

The calculations are then performed, and the verticals in Figure 14 can be progressively displayed, depending on the course of the calculation.

Finally, the operator has a simultaneous display of all the calculated intensities, distributed in time and space with respect to an origin.

This method allows an operator to quickly make a decision on the operating conditions of the burner, and in particular on a possible variation of one or more of the parameters 1-8 already mentioned above.

FIG. 16 shows an example of a device for implementing the invention in an industrial oven 40. This example is given for viewing a flame. It also applies to the observation of a charge in an oven, or of the walls of an oven.

A burner 42 is shown diagrammatically, as is a flame 44. Image acquisition means, such as one or more camera (s) 46, make it possible to acquire images of the flame 44. These images are processed by an image scanning device or card 48.

Video cameras used in industrial ovens can operate in the visible, ultraviolet or infrared. To increase the contrast between the flame and the refractory walls, these cameras can be equipped with an interferometric filter (in the ultraviolet: filter centered around 310 nm to highlight the emission of the OH radical; in the visible: filter centered around 431 nm for the CH radical, or 516 nm for the C2 radical, or 589 nm for the sodium emission; the bandwidth of the filters is between 10 and 20 nm). The digitized images are transmitted to computer means 50, essentially comprising a central unit 60, display and display means 69, and control peripherals such as a keyboard 72 and a mouse 61. Other selection means of a zone or of a field of a page displayed on the screen 69 can also be used, for example any means allowing a selection to be made by touch on the screen.

In the case of oscillatory combustion, an additional datum is introduced into the computer system 50: it is a digital signal representative of the oscillatory periodic signal.

As illustrated in FIG. 17, the central unit 60 itself comprises a microprocessor 62, a set 64 of ROM and RAM memory, a hard disk 66, which also has an information storage function, all of these elements being coupled to a bus 68. The screen 69 makes it possible to view one or more of the raw images (before treatment) or of the images obtained after treatment. This processing can be a statistical processing or according to one of the other methods described above. In FIG. 12, the screen 69 is represented with an instantaneous image 69-1, an average intermittency image 69-2, a flame envelope image 69-3 and a contour image 69-4. The instructions for implementing one or more of treatments according to the invention are stored in the means 64, 66 for storing the computer system.

It can be a statistical treatment, and / or a treatment for determining temperatures; and / or processing for determining a threshold; and / or processing for the graphic representation of the intensity profile as a function of time. All of these treatments have been described above.

Means, for example a menu and a cursor moved with the mouse, allow a user to select the treatment to be performed. He can also choose to carry out several of these treatments in parallel.

Identical or the same type of means can also offer the user the possibility of choosing the number n of images to be acquired to carry out a sliding statistical processing, and / or the duration, exact or approximate, of a sliding time interval. Identical or same type means can also offer the possibility of selecting one or more threshold values, for the implementation of one or the other of the statistical processing algorithms described above. Identical or same type means can also offer the possibility of selecting one or more argument images with a view to statistical processing implementing the AVG (BIN (, threshold)) function mentioned above.

Identical or same type means allow an operator to select the number of color channels to be retained (B and / or R and or B) for processing for calculating the temperature, or even to select the pitch temporal to calculate successive, threshold values (according to a method as described above in connection with FIG. 12), or to select the temporal step and the points or pixels or the zones in an image to obtain a spatiotemporal distribution of the intensities of the corresponding points or pixels (according to a method as described above in conjunction with FIG. 14).

The raw images acquired using the camera 46 and the digitization card 48 are stored in a memory area of the central processing unit 60. A set or a stack of the last n images can also be stored in this memory area. acquired, or images acquired during the duration of the selected sliding interval.

Can also be stored in a memory area the n last images obtained by sliding average Avg, or a stack of these n last average images, or other stacks of images which evolve during the acquisition (for example the stack results of Abs (lm (i) -lm (i-1)), or temperature maps of a given area, or histograms such as that of Figure 12 or graphical representations such as that of Figure 14. The display process can also indicate to the operator, the light intensity and / or the temperature and / or the time evolution of the intensity profile corresponding to a portion or an area of a displayed image. on screen 69.

This function is implemented by means of selecting a portion or an area of the image, for example using the cursor, and by means of display, on the image, for example in a field determined this, the intensity and / or the temperature and / or the time evolution of the intensity profile of the selected area.

The user can then set a threshold value in relation to such information, for example by selecting a specific field on the screen.

In contour display mode (figure 11 and image 69-4 in figure 16), the quantitative values of the coordinates of the contour frame are also displayed, and possibly the calculated values such as the center of gravity, and / or the contour area and / or the perimeter of this contour.

The instructions of the programs for implementing a method according to the invention are stored in a memory area of the computer system 50. There is therefore a device or means specially programmed for implementing one of the methods described above. above. These instructions are for example installed from a medium which can be read by this system, and on which they are recorded. Such a medium can be for example a hard disk, a ROM read-only memory, a compact optical disk, a dynamic random access memory DRAM or any other type of RAM memory, a magnetic or optical storage element, registers or other volatile memories. and / or non-volatile.

The device can be used to view instant images or images resulting from a processing such as one of those described above. This information is already very useful for monitoring and understanding a combustion.

From this information, an operator can optionally act on oven or burner (s) control parameters (for example power, and / or stoichiometric ratio, etc.) in order to control one or more parameters characterizing the position and geometry of the flame or flames (in the case of several burners).

It can also be one of the parameters 1 to 8 already mentioned above.

As illustrated in FIG. 16, the device can also comprise means 52 for regulating parameters, for example one or more of the parameters 1 to 8 mentioned above. This regulation can be carried out, for example, from an analysis of the images obtained by statistical processing, for example an analysis implementing neural processing and / or control by fuzzy logic. It can also be carried out on the basis of an analysis of temperatures or the time evolution of the intensity or temperature profile. The control 52 then makes it possible to regulate, for example, the opening of a fuel or oxidant supply valve.

According to another example of use of a device according to the invention, the images obtained by digitization can be stored on a video cassette 74 (see FIG. 18) which can then be played by a video recorder 76. After digitization, the images can be viewed on a computer system 50 as already described above. A combustion or flame analysis can thus be performed offline, in the laboratory.

As already indicated above, each raw video image results from the combination of 3 colors or 3 channels R (red), V (green) and B (blue). It may be advantageous, in certain cases, to retain only one channel. For example, in some cases, the R channel is highly saturated, the B channel has a low contribution and the V channel is best "balanced". Only the V channel is then selected. For each type of image (instantaneous or obtained by statistical processing), the device can therefore include means for selecting a display of the images in only one of the colors R, G, B, or in two of these colors. These means (for example a menu in which the user selects one or more fields with a cursor) also make it possible to select, for each given type of image, a representation of the ratio of two of these colors in the image.

The images are coded on 8 bits (therefore on 256 intensity levels).

In the case of the above algorithms for which the Avg function is applied to a binarized image, each pixel is averaged with the corresponding pixels of the other images. The result is, for each pixel, an intensity value between 0 and 1, which is then converted back to full scale (on 256 intensity levels) by multiplication by 255. All the statistical image processing indicated in this description and which involve the choice of a threshold are, because of this threshold, arbitrary or biased. But this arbitrary character is constant over time, it is the same for all the n images of the sliding interval Tg or for all the last n images.

Image processing according to the invention is much lighter and more demanding in terms of computing capacity than the system described in US Pat. No. 5,971,747, where neural processing is applied to each image. According to the present invention, a sliding statistical processing is applied to the images, and a neuronal processing as described in US Pat. No. 5,971,747 is not necessary. Such neural processing only intervenes in a possible regulatory loop, such as loop 52 described above (FIG. 16).

The invention applies to the visualization and control of flames or combustion in an oven, or to the control of the walls of the oven or of the state of a charge in the oven, but also in any type of other environment. industrial, including outdoor.

The invention and the treatments described also make it possible to characterize fluctuations in a scene in an oven, for example of a charge present in the oven (clods floating on the surface of a glass bath, line showing the presence limit unfounded material in a melting furnace, spatial envelope of the billet trajectory in metallurgical furnaces, etc.). Any of the algorithms or methods described above can then be applied, with the same advantages as what has been described for the case of a flame or walls. In particular, it is possible to apply a contour extraction function to the fluctuating zone of the charge in the furnace, to deduce therefrom geometric parameters such as those already mentioned above (perimeter of the contour, and / or center of gravity, and / or contour area), or the temperatures in this zone and possibly regulate the bath (its temperature or its feed under load) or the trajectory of the billets.

The invention makes it possible to view, in an oven, any element of a fluctuating nature over time or of a luminosity different from the luminosity of the environment.

Another aspect of the invention will be described in conjunction with Figures 19 and 20. In FIG. 16, the means 50 made it possible to carry out a man-machine interface, image processing, but also to send control instructions, by means of the means 52, to burn 42. Certain installations use several burners .

This is the case, for example, of a single oven in which several flames are formed. This is also the case, obviously, of a set of several ovens each comprising one or more burners.

In such a case, it may be advantageous to dissociate the image acquisition and processing means from the control means of the burner (s).

This solution is represented in FIG. 19, or the reference 50 designates, as in FIG. 16, digital means making it possible to acquire and process the images of the interior of the oven. The information resulting from the treatments carried out is sent to digital means 150, separated from the means 50, which will make it possible to act, by means of the means 52, on the supply and the parameters of the corresponding burner, or burners.

Thus, means 50 can be associated with each oven, to allow the acquisition and processing of the images of the interior of each oven, all these means 50 being connected to a single central system 150 which controls all of the power supplies and burners of all ovens.

The means 150 can be geographically distant from the means 50, the communication between these different means being ensured for example by a network link.

Whether the means of data acquisition and processing, and the means of controlling the operating parameters of the burners, are separate (as in Figure 19), or not (as in Figure 16), all of these means can also be connected to a remote server 110. The link between these different elements can be of the local network type, like the LAN link 112.

To this end, an exchange protocol can be used between the means 50, 150 and the server 110, based on the OPC technology (abbreviated expression of “OLE for process control”). In FIG. 19, the exchange interfaces are represented, on the side of the means 50 and 150, by the references 114 and 116 and, on the side of the server 110, by the numerical references 115 and 117.

It is possible to use dedicated software (PC Anywhere, or LapLink for example), in combination with a communication port to which a modem (PSTN, ISDN, ADSL, or other) can be connected depending on the type of access that the end user can provide.

According to another embodiment, a display of HTML pages is integrated into the image processing software. The information is then broadcast by the server 110.

A terminal 120 provides secure access to the data hosted on the server 110.

A terminal 122, remote from the server 110, can be in connection with this terminal 120, for example by an Internet link, or Intranet, or Extranet, or WAN, or LAN, or any other type of link.

This architecture makes it possible to send to the server 110 information coming from the means 50 and / or 150 and relating to the operation of the software, and in particular of the image processing software, which are installed there. It is also possible to send to the server 110 other information, such as the value of thresholds deduced from the images observed, or profiles of evolution of temperature or intensity as a function of time.

This information may be displayed by display means associated with the means 120. A defect in the operation of the software or of the means of acquisition and / or processing of the images, and / or in the software or the means of controlling the, or of, burner (s) can thus be detected and analyzed by a remote operator, using means 120 or means 122. These means 122 can be a service of assistance to a user of means 50 and 150 of analysis and control the operation of the ovens they have.

The server 110 can also host means or software 124 for image processing. This assistance process can work for all types of processing implemented by the user. It can be an image processing according to what has already been described above.

It can also be a processing of physical parameters and / or of other data, such as for example pressure or temperature or concentration data measured directly in the oven or at its outlet. In this case, an example of data processing is processing by a neural network, itself implemented by means, such as the means 50 of FIG. 16, and available to the user. These means are, in turn, connected to a server 110 in the manner described above.

In general, it is therefore possible to have, on the one hand, a surveillance system comprising:

sensor means for measuring at least one piece of physical information from inside the oven,

- Control means 150, 52 of at least one parameter of the oven, as a function of said physical information, and, on the other hand, remote computer means such as the server 110. Processing means 50 can make it possible to process the physical information measured or captured, the control means then acting on said processed physical information. The processing can be an image processing according to one of the methods described above. To the remote means 110 are sent, from the sensor means and / or the processing means and / or the control means, parameters relating to the operation of said sensor means and / or said processing means and / or said control means and / or part of the physical information processed. The sensor means and / or the processing means and / or the control means may comprise software means, and the parameters sent to the means 110 may then include parameters relating to these software means or to their operation. These may for example be messages relating to the progress of the execution of said software means. The parameters sent to said remote computer means 110 can be sent directly by the sensor or processing or control means and / or on request from the remote computer means 110. FIG. 20 is a functional representation of the means 50 made available to the user. 'user.

The reference 130 designates data exchange means, which implement:

- a material layer 136,

an operating system 138, communication means 140,

- a modem 142,

- security means 144,

- Means 146 defining a data exchange protocol (Web, or FTP, or SMTP / POP3, or OPC client / server).

A database 132 hosts user data

(images, all information from image processing). Means 133 (for example XML or XHTML software means or

HTML) allow you to create web pages containing data such as images from this database.

Reference 134 designates the processing and acquisition means.

The assembly is connected to a network link 112.

Claims

1. Device for determining the temperature of a point in an area in an oven (40) from an image (69-1-69-4) of at least said area inside the oven, comprising :

- means (50, 60) for determining at least three color components in at least one pixel associated with said point in said part of the image,

- means (50, 60) for calculating at least two ratios among the three color components taken two by two,

- Means (50, 60) for calculating the temperature associated with said pixel as a function of these at least two color ratios.

2. Device according to claim 1, wherein said calculation implements a polynomial whose variables include at least two ratios of the three color components.

3. Device according to claim 2, said polynomial being of degree less than or equal to three.

4. Device according to one of claims 1 to 3, the furnace area being a portion of the interior wall of the furnace (40).

5. Device according to one of claims 1 to 3, the furnace area being a portion of a load present in the furnace.

6. Device according to one of claims 1 to 5, comprising means (50, 60) for determining the temperature gradient between two points, from the temperature at these two points.

7. Device according to one of the preceding claims, further comprising means (50, 60) for performing statistical processing of images of the interior of the oven.

8. Device according to claim 7, said images being taken during a sliding time interval, and the statistical processing being a sliding statistical processing.

9. Device according to claim 8, the statistical processing being a sliding average calculation of the images of the last n images, or of images of the interior of the furnace taken during said time interval.