Method and system for measuring parameters related to stability of batches of floating material

Abstract

A computer-implemented method (3000) for measuring a thickness e of a batch of material (2005) floating on a melt pool (2006) in an electrical glass or stone melting furnace (1002). The method takes, as input data, a time scale temperature map M [T] set (I3000) of a batch (2005) of material, a range R [Ts] of expected values of a stabilization temperature Ts of the batch (2005), and a threshold of a change in temperature over time. The method provides, as output data, a spatial distribution (O3000) of the thickness of the batch (2005) over its surface.

Description

TECHNICAL FIELD

[0001] The present invention relates to a computer-implemented method and system for monitoring the stability of a material batch floating on a melt pool in an electric glass melting tank. BACKGROUND

[0002] In a conventional electric glass melting furnace, a plurality of electrodes are immersed in a molten glass bath in a predetermined pattern. An electric current is made to flow through the molten glass between the electrodes to heat the glass by Joule effect.

[0003] A material batch, which can include raw materials, cullet and other recycled materials, is continuously or discontinuously supplied on the upper surface of the bath to provide both a source of material and a layer of insulation or crust above it. However, as it is gradually melted to form additional molten glass, the thickness of the layer decreases and the heat loss through the batch from the molten glass body in the furnace increases. Conversely, as additional batch material is distributed over the upper surface of the molten glass, the heat loss through the thicker layer decreases.

[0004] The batch is generally supplied by a movable feeder, conveyor or sprayer in a predetermined pattern to carefully control the amount and maintain a minimum thickness over the upper surface of the bath to reduce heat loss, protect the feeder and avoid spillage from the furnace.

[0005] It is common practice to inspect the interior of a glass melting furnace by means of an infrared optical system arranged within the furnace wall. These systems allow a human observer to inspect the stability of the material batch.

[0006] JPS 5 339 204 A, NIPPON ELECTRON OPTICS LAB 11.04.1978, JPH 0 7216 422 A, NIPPON STEEL CORP 15.08.1995, JP 2010 002 150 A, TAKUMA CO LTD 07.01.2010 and US 2018 231875 A1, HER MAJESTY THE QUEEN IN RIGHT OF CANADA AS REPRESENTED BY THE MINI OF NATURAL RESOURCES [CA] 16.08.2018 describe an inspection system comprising infrared rays arranged in front of a set of observation windows arranged within the furnace wall.

[0007] More complex systems in the prior art are also provided for measuring parameters related to some characteristics of the material batch.

[0008] WO 8002833 Al, OWENS CORNING FIBERGLASS CORP [US], 24.12.1980 describes a system for controlling the level (i.e. thickness) of a batch of material in an electric glass melting furnace. The system comprises an infrared sensor mounted on or adjacent to the feeder for obtaining a non-contact measurement of the outer surface temperature of the batch. The measured temperature is compared to a setpoint temperature and, depending on the empirical relationship between the thickness and temperature of the outer surface of the batch, the level, i.e. thickness, of the batch is increased or decreased by adjusting the feed rate of the feeder.

[0009] US 4 194 077 A, OWENS CORNING FIBERGLASS CORP [US], 18.03.1980 describes a system for controlling the level of a batch of material in an electric glass melting furnace. The system comprises an ultrasonic sensor mounted on the feeder and moving with the feeder over the batch. A non-contact measurement of the level of the batch is obtained and, based thereon, the thickness of the batch can be calculated depending on the relationship between the density of the batch and the molten glass and their levels.

[0010] US 4 409 012 A, OWENS ILLINOIS INC [US], 11.10.1983 describes a method and apparatus for monitoring the surface coverage of a batch of material floating on a melt pool in a glass melting furnace by processing video recordings of the top surface of both the batch and the melt within the furnace. A bimodal distribution of the number of pixels as a function of their grey level is then plotted from the recorded images and, after thresholding to separate the two modes, the relative amounts of batch and melt are estimated by integrating the area covered by each mode for different zones within the furnace.

[0011] JPH 0 656 432 A, NIPPON ELECTRIC GLASS CO, 01.03.1994 describes a method and system for measuring the level of a batch of material floating on a melt pool by processing an image of a portion of the top surface of the batch illuminated by an illumination device. The image is binarized and the barycentric coordinates of the white pixels are calculated. Changes in the barycentric coordinates thus calculated are assumed to reflect changes in the level of the batch due to changes in the amount of light reflected by the illuminated portion of the batch when the level of the batch moves up or down directly above or below.

[0012] WO 0248057 Al, SOFTWARE & TECH GLAS GMBH [DE], 20.06.2002 describes a method for measuring the linear increase of the level of batch material covering the melt pool by processing top surface images of both batch material and melt. The quotient of the number of dark pixels and the number of pixels within each row of the image in a direction transverse to the direction of melt flow is calculated. The evolution of the quotient in the direction of flow is assumed to reflect the level of batch material covering in this direction.

[0013] EP 1 655 570 Al, FRANKE MATTHIAS [DE], 10.05.2006 discloses a computer-implemented method for monitoring an interior region of a glass melting furnace by comparing the positioning of selected objects between infrared images of the interior region acquired at successive points in time. The method can be used for monitoring the motion of different moving objects, such as batches of material floating on the melt pool.

[0014] WO 2018 / 104695 Al, LAND INSTRUMENTS INTERNATIONAL LTD [GB], 14.06.2018 discloses a control unit for identifying batches of material floating on a melt pool from a thermal imaging camera configured to acquire thermal images of the top surface of both batch material and melt. The positioning of the batch material is identified by low temperature zones of the thermal images. The movement of the batch material can be tracked and its velocity is calculated by processing thermal images acquired at successive points in time. The control unit can be configured to correct the perspective of the thermal images, so that parameters derived therefrom are in real world coordinates.

[0015] WO 2022 / 242843 Al, GLASS SERVICE A S [CZ], 24.11.2022 discloses an improvement of the method described in EP 1 655 570 Al, FRANKE MATTHIAS [DE], 10.05.2006. The method further comprises a bias compensation step to correct for biases within the infrared images due to changes in the infrared camera view over time. Compared to the method described in EP 1 655 570 Al, FRANKE MATTHIAS [DE], 10.05.2006, the data derived from the image processing is more accurate and allows to determine new parameters such as the temperature of the batch material, the batch material melting rate, the batch material positioning and the batch material migration pattern and velocity. SUMMARY

[0016] TECHNICAL PROBLEM Among other things, one key process parameter for the efficient operation of an electric glass or stone manufacturing furnace is the thickness of the batch material, as it directly influences the amount of power to be supplied for homogeneous melting.

[0017] However, the current method suffers from several drawbacks, due to the fact that local inhomogeneities within the batch (especially so-called "hot spots" or "volcanoes" and holes corresponding to the formation of holes after complete local melting within the batch) and / or due to the granular nature of the batch and its inherent instability (as material that has been slidingly moved within the batch and has been deposited on the moving melt pool) are not well taken into account in the measurements or the calculations based thereon.

[0018] For a method based on the measurement of the level of both the batch and the melt and the calculation of the difference thereof, they cannot accurately model the inhomogeneous covering of the batch above the melt pool in an electric glass or stone melting furnace. Indeed, within an electric glass or stone melting furnace, parts of the batch can drift away from each other under the effect of convection effects occurring within the melt pool, like the tectonic plates on Earth. In contrast to a fuel glass or stone melting furnace, this phenomenon can lead to strong variations of the thickness of the batch above the whole melting pool surface. For example, whereas in a fuel melting furnace the thickness of the batch is expected to decrease as it moves towards the forehearth, in an electric melting furnace the thickness of the batch can remain relatively stable at the forehearth or decrease in a direction other than the direction of the melt flow.

[0019] On the other hand, for the same reasons, a method for estimating the thickness of the batch relying only on the measurement of the level of the batch and assumptions about the relationship between the density of the batch and the melt and their levels suffers from the same flaws. Indeed, the assumption that an inhomogeneous batch should exhibit the same density everywhere can lead to overestimation, unless a correction function is applied to take into account local variations. However, such a correction can not be easy to apply, as it can require a prior modeling of the evolution over time of the batch properties and dynamics.

[0020] Moreover, as a pressure drop can occur between the melting and refining sections in an electric glass or stone melting furnace, the levels of the molten glass within these two sections can not be equal. Using the level in the refining section as a representative to estimate the level of the molten glass under the batch of material in the melting section, as can currently be done by the prior art, can then lead to an erroneous estimation thereof and, consequently, the thickness of the batch determined from this estimation can be erroneous.

[0021] On the other hand, the methods based on image processing of both the batch and the melt should be able to provide results that are more representative of the batch heterogeneity. Since the images are a direct acquisition of the batch along with its "hot spots" or "volcanoes", they should better measure the actual surface coverage of the batch over the melt bath. However, although it is mentioned that the thickness of the batch can be derived from the image processing, the current methods remain silent on how this is actually done. In particular, they do not provide any details or examples of the calculations or image transformations performed to get accurate values.

[0022] Finally, past experience in optimizing the energy consumption of electric furnaces shows that the portion of the batch that has reached its local steady temperature, i.e. is in thermal equilibrium, has a direct impact on the power to be supplied for an efficient melting. Therefore, monitoring in real time the amount of batch at the thermal equilibrium should help overcome the problems of under-heating and over-heating that the furnace operators can often encounter when setting up an electric furnace. However, the current methods do not provide such information on the state of the batch in the furnace.

[0023] Therefore, there is still a need for an efficient and reliable method to estimate the thickness of the portion of the batch of material floating on the melt bath that has reached its steady temperature. Ideally, this method should also be able to provide the variation and / or the spatial distribution of the thickness over time above the melt bath. In addition, it should also be versatile enough to provide values of other key parameters related to the stability of the batch over time, such as the spatial distribution of the "hot spots" or "volcanoes" and their movement over time.

[0024] Solution to the technical problem In a first aspect of the application, a computer-implemented method for measuring the thickness of a batch of material floating on a melt bath in a glass melting furnace as described in claim 1 is provided, the dependent claims being advantageous embodiments.

[0025] In a second aspect of the application, a data processing device, a computer program and a computer readable medium to implement the method of the first aspect are provided.

[0026] In a third aspect of the application, a process for measuring the thickness of a batch of material floating on a melt bath is provided.

[0027] In a fourth aspect of the application, a system for measuring the thickness of a batch of material floating on a melt bath is provided.

[0028] Advantages of the application A first outstanding advantage of the present invention is that the thickness of any zone of the material batch at thermal equilibrium is calculated precisely and accurately. Over- or under-estimation of the thickness is avoided, since the present invention does not rely on the measurement of the level of the batch and / or of the melt, or on a relationship between their densities. Variations in thickness above the batch from local inhomogeneities or convection effects within the melt pool can also be better modelled.

[0029] A second advantage of the present invention is that it can provide the variation and / or the spatial distribution of the thickness of the batch above the melt pool over time. It is thus advantageously possible to achieve real-time monitoring of the thickness of the batch during its entire life cycle within the electric furnace.

[0030] A third advantage, as a result of the first, is that the present invention provides more reliable and more representative information about the state of the batch in terms of thickness, so that the power supplied to the electrodes of the electric furnace can be better adjusted for carrying out an efficient melting, thus saving energy and costs. In this context, the information provided as output data by the present invention can advantageously be fed as input data to a control loop, for example to a feedback controller, for adjusting in real time the power supplied to the electrodes of the furnace.

[0031] A fourth advantage is that, in particular embodiments, the present invention can further provide key parameters related to the stability of the batch over time, such as the spatial distribution of "hot spots" or "volcanoes", their movement, and the velocity vector field of the batch. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a schematic representation of an example of a glass or stone fibre manufacturing line.

[0033] Figure 2 is a schematic cross-section of an example of an electric glass or stone melting furnace.

[0034] Figure 3 is a dataflow graph of a computer-implemented method according to the first aspect of the present invention.

[0035] Figure 4 is a physical dataflow graph of a processing data system to implement the method according to the first aspect of the present invention. DETAILED DESCRIPTION

[0036] REFERENCE Figure 1The manufacturing line 1000 of glass or stone fibers by internal centrifugation method can generally comprise a silo 1001 for storing raw materials 1001a (e.g. mineral compounds and / or cullet), an electric glass or stone melting furnace 1002 for melting the raw materials 1001a conveyed from the silo 1001 by means of a conveyor 1003, and one or more fiberizing tools 1005a, 1005b, 1005c fed with molten glass or stone 1006 through a forehearth 1004. The forehearth is generally designed as an open or closed channel provided with an opening directly above each fiberizing tool 1005a, 1005b, 1005c to feed the molten glass or stone 1006 to each of the fiberizing tools.

[0037] With reference to Figure 2 , Figure 2 According to US 4 194 077 A, OWENS CORNING FIBERGLASS CORP [US], 18.03.1980, the electric glass or stone melting furnace 1002 comprises a refractory wall 2001 and a refractory floor 2002 forming a tank 2003 divided into a melting section 2003a and a refining section 2003b by a dividing wall 2001a. The two sections 2003a, 2003b are in communication with each other through a mouth 2004 at the bottom of the dividing wall 2001a at the level of the floor 2002.

[0038] The raw materials 1001a are conveyed into the melting section 2003a, forming a batch 2005 which floats on the surface of a melt bath 2006 and is progressively melted by a series of immersed electrodes 2007. Alternatively or complementarily, the series of electrodes can comprise an immersed throw-in as described in FR 2 599 734 A1, SAINT GOBAIN RECH [FR] 11.12.1987. When convection occurs within the melt bath 2006, the melt flows from the melting section 2003a to the refining section 2003b connected to the forehearth or feeder through the mouth 2004. One or two immersed electrodes 2008 can be located at the level of the floor 2002 on one or each side of the mouth 2004.

[0039] With reference to Figure 2 and Figure 3 In a first aspect of the invention, a computer-implemented method 3000 is provided for measuring the thickness e of a batch 2005 of material floating on a melt bath 2006 in an electric glass or stone melting furnace 1002; wherein said method takes as input data the time-scaled temperature atlas I3000, M[T] of the material batch 2005, the range R[Ts] of expected values of the stabilization temperature Ts of the batch 2005 and the temperature variation of the threshold over time ; wherein said method provides as output data the spatial distribution O3000 of the thickness of the batch 2005 over its surface 2005a; wherein said method 3000 comprises the following steps: (a) selecting 3001, within each temperature map M[T] of the provided atlas I3000, a zone for which the temperature is within the range R[Ts] of expected values of the stabilization temperature Ts of the batch 2005; (b) computing 3002 the temperature variation within the same detected zone between successive temperature maps in a given time range ; (c) selecting 3003 the zones of step (b) for which the variation of the temperature over time is lower than the threshold provided as input; ; (d) for each point of the temperature maps within each of the zones selected at step (c), computing 3004 the thickness E of the batch 2005, wherein said thickness E is computed by applying on said each point a function E(Ts) defining the relationship between thickness and stabilization temperature Ts, and wherein said function E(Ts) is based on simulated or experimental heat flow transfer, or on an empirical model.

[0040] In the context of the invention, a "temperature map" should be interpreted in its ordinary definition in the field of cartography, e.g. representation or distribution of temperature over a scaled or non-scaled surface. In practice, it can be interpreted as a collection of temperature data, where each temperature data is linked to a localization over the surface of the material batch 2005. These localizations can be coordinates in pixel or length units.

[0041] In the context of the invention, a temperature atlas should be understood as a collection of temperature maps, the number of which can be arbitrary, or depending on technical limitations or settings of the means used to acquire them, e.g. cameras.

[0042] In the context of the present invention, a time-scaled temperature map set is a collection of temperature maps taken over a time period, i.e. a time scale. This time period can be arbitrarily fixed depending on the component, e.g. the camera used for taking, and / or the time within which the temperature maps are considered to be taken. For example, in the case of monitoring, it can also be a continuous, i.e. endless, time period. The frequency at which the temperature maps are taken can be a matter of choice depending on the technical limitations of the component, e.g. the camera, used for taking them and the time scale within which the batch is known or expected to evolve. As a rule of thumb, the frequency should be set such that the evolution, e.g. movement, of a particular feature, e.g. hot and / or cold spot, can be observed on successively taken temperature maps.

[0043] In the context of the present invention, the expression "steady temperature" applied to the batch shall be interpreted as corresponding to the temperature of the thermal equilibrium of the batch 2005, i.e. when the heat flow from the melt pool below the batch is balanced by the heat flow from the furnace 1002 within the inner region 2009 above the batch 2005. At thermal equilibrium, i.e. when the batch has reached its steady temperature Ts, the temperature of the batch 2005 should stabilize or vary very slightly over time.

[0044] The heat equation of the batch can be expressed by the following equation (1): .

[0045] wherein, is the density of the batch, cp is the specific heat capacity of the batch, e is the thickness of the batch, T is the temperature of the batch 2005, t is the time, is the thermal conductivity of the batch 2005, is the Stefan-Boltzmann constant, h is the convective heat transfer coefficient of the batch 2005, is the thermal emissivity coefficient of the batch 2005, Tf is the temperature within the inner region 2009 of the furnace 1002 above the batch 2005, Tmelt is the temperature of the melt pool 2006, and T is the temperature of the batch 2005.

[0046] In the zone of the furnace 1002 where the fresh, unheated batch is supplied by the feeder, conveyor or sparger 1003 onto the melt pool 2006, the batch 2005 can be colder than in the more distant zones of the furnace 1002. Over time, the batch is progressively heated and its temperature increases to reach a steady temperature.

[0047] On the contrary, in the proximity of "hot spots" or "volcanoes", the temperature of the batch 2005 can increase and become higher than its steady temperature, because of the local imbalance between the heat flow coming from the melt pool 2006 below the batch 2005 and the heat flow coming from the inner region of the furnace 1002 above the batch 2005.

[0048] Between these extremes, for example far from the feed zone and far from hot spots or volcanoes, the batch 2005 is expected to be in thermal equilibrium and has reached its steady temperature.

[0049] In addition to the above mentioned problems regarding the prior art, the present application provides a smart solution to this problem.

[0050] In step (a), a region of the temperature map is selected within a range R[Ts] of expected values of the steady temperature provided as input. This operation allows to identify the regions of the batch 2005 that are likely to be affected by being in thermal equilibrium, i.e. those for which they have the highest probability to reach their steady temperature.

[0051] The range R[Ts] of expected values of the steady temperature can be determined empirically and / or by processing the temperature map provided as input.

[0052] In an advantageous embodiment, the range R[Ts] of expected values of the steady temperature Ts of the batch 2005 can be a range of values experimentally determined on similar batches of material in similar melting conditions. For example, said values can come from the history of punctual and / or monitored measurements of the temperature in specific regions of the batch surface collected by operators and / or automatic sensors (e.g. infrared sensors), from previous production trials or activities carried out in a specific electric glass or stone melting furnace 1002. A prominent advantage of this approach is that the range R[Ts] of expected values of the steady temperature can better represent the thermal behavior of both the electric furnace and the composition of the batch under consideration.

[0053] Surprisingly, it has been found in the context of the present application that a range R[Ts] of expected values of the steady temperature between 20°C and 200°C, preferably between 70°C and 140°C, can be suitable for most cases of electric glass or stone melting furnaces.

[0054] Alternatively or complementarily, the range R[Ts] of expected values of the stabilized temperature Ts of the batch 2005 can be a range of defined values between the two modes of a bimodal distribution calculated from one or more of the provided temperature maps of the set I 3000. Calculating a bimodal distribution of the temperature map can allow to discriminate between two extreme values, the coldest zone of the batch, for example close to the feeder, and the hottest zone, for example a hot spot or a volcano. Fixing a range of stabilized temperatures between these extreme values can be interpreted as a filter to select the zones having the highest probability to be in thermal equilibrium. Methods such as those described in US 4 409 012 A, OWENS ILLINOIS INC [US], 11.10.1983 can be adapted to the temperature map to calculate a bimodal distribution.

[0055] In step (c), the zones for which the variation of the temperature over time is lower than a threshold provided as input are selected among the zones selected at step (b). If the zones of the batch 2005 reach their stabilized temperature, their temperature should remain stable or slightly varying over time, i.e., . Therefore, this operation allows to identify the zones of the batch which are effectively in thermal equilibrium, i.e. those which have effectively reached their stabilized temperature Ts. In other words, step (c) further refines the selection of the zones in thermal equilibrium launched at step (a).

[0056] Surprisingly, in the context of the present invention, it has been found that a threshold of 1 °C / min can be low for most cases of electric glass or stone melting furnaces. Therefore, in a preferred embodiment, the threshold of variation of the temperature over time of 5 °C / min, preferably 2 °C / min, more preferably 1 °C / min.

[0057] Since the efficiency and accuracy of the present invention for measuring the thickness E of the batch 2005 relies on the effective and precise selection of the zones of the batch in thermal equilibrium, the combination of steps (a) to (c) should be considered as one of the core features of the method 3000 according to the present invention and therefore as a salient difference with the practices described in the prior art.

[0058] In step (d), the thickness E of the batch 2005 is calculated for the zones selected at step (c) according to a function defining the relationship between the thickness E and the stabilized temperature Ts. There is no unique function. Any adapted function, empirical or theoretical, either one, which can be able to link the thickness of the batch to its local stabilized temperature can be used. ​​​​

[0059] As already emphasized, one advantage of the present application is to allow an efficient and accurate selection of the zones of the batch 2005 that are in thermal equilibrium, i.e. that have locally reached their steady temperature. Therefore, if the thermal properties of the batch can be determined, the thickness of the batch can be advantageously derived from the aforementioned thermal equilibrium equation (1).

[0060] Therefore, in a particular embodiment, the method can further take as input data the temperature T f , inside the internal zone of the furnace 1002 above the batch 2005, melt where the function E(Ts) is a heat flow transfer function provided by the following equation (2): where is the thermal conductivity of the batch 2005, is the Stefan-Boltzmann constant, h is the convective heat transfer coefficient of the batch 2005, is the thermal emissivity coefficient of the batch 2005, Tf is the temperature inside the internal zone of the furnace above the batch 2005, Tmelt is the temperature of the melt pool 2006, and Ts is the steady temperature of the batch 2005 for which the thickness E is to be computed.

[0061] Knowing the convective effects inside the internal zone 2009 of the furnace 1002, the chemical composition of the batch 2005 and its bulk density due to the fact that the batch is generally supplied as a granular material, the parameters , h and can be theoretically computed. However, as already emphasized, the bulk density, the particle distribution and also the chemical composition of the batch can locally vary within its thickness and over its entire coverage during the lifetime of the batch 2005 in the furnace, depending on the temperature gradients and the convective movements that occur within the melt pool 2006, which locally determine the melting state and the melting rate of the batch 2005. As a direct consequence, the theoretically computed parameters can not well represent the local properties of the batch 2005 and can lead to a determined inaccuracy in the determination of the local thickness within the batch. Despite these drawbacks, the theoretically computed parameters can still be relevant as an approximation of the batch, the thermal and physical properties of which are not expected to vary excessively during its lifetime in the furnace.

[0062] Alternatively or complementarily, the parameters , h and can be experimentally determined by performing measurements on similar batches evolving under similar melting conditions. For example, experiments can be implemented in pilot electric furnaces to simulate the behavior in large-scale production furnaces and to compute the parameters from in-situ and / or ex-situ measurements of the thermal and physical properties of the batch.​ , h and The parameters thus determined can then better represent the real behavior of the batch in the electric furnace of the manufacturing line.

[0063] However, the time scale and the rate of change of the manufacturing line can not always be compatible with those of the experiments implemented in the laboratory or pilot furnace framework. Still further, when applied to large-scale electric furnaces, scaling effects can occur, such that the results calculated and / or measured in the laboratory or pilot can not work efficiently. It can thus be more valuable to rely on an empirical function for defining the relationship between the thickness and the steady temperature of the batch.

[0064] In this context, in a preferred embodiment, the function E(Ts) can be an empirical model provided by the following equation (3): where T1 and T2 are two experimentally measured steady temperatures of the batch 2005, e1 and e2 are two experimentally measured thicknesses of the batch 2005, corresponding to the temperatures T1 and T2, respectively, and Ts is the steady temperature of the batch 2005 for which the thickness E is to be calculated.

[0065] Surprisingly, it was found that the above equation (3) is a simple, elegant and robust way of calculating the thickness of the batch 2005 corresponding to a selected zone of the temperature map, at a high level of accuracy, without relying on a priori knowledge of any physical and / or thermal properties of the batch. Moreover, the two temperatures T1 and T2 and the two thicknesses e1 and e2 can be experimentally measured in a laboratory scale or pilot furnace and / or in an industrial furnace. In the latter case, the results can be obtained that best represent what actually happens in an industrial furnace. As a further result of not relying on a priori knowledge of any physical and / or thermal properties of the batch 2005, the equation (3) can be easily implemented industrially.

[0066] The method according to the present application can be adapted to provide other key parameters of the stability of the batch over time, such as the spatial distribution of the "hot spots" or "volcanoes" and their movement over time.

[0067] In a particular embodiment, the method can further comprise a step (e) of detecting a zone within each temperature map of the provided set I3000 by applying an object detection function to said temperature map, wherein the object detection function is configured to process the zones having a temperature equal to or exceeding a threshold value ; wherein the method further provides as output data the spatial distribution of the detected zones over time.

[0068] In order to detect the "hot spots" or "volcanoes", the threshold value The temperature at which the "hot spots" or "volcanoes" are expected or observed to occur within the layer of batch material 2005 can be fixed. This temperature typically varies depending on several parameters such as the melting temperature of the batch material, the temperature gradient, and the convective movements within the melt bath 2006, the power provided to the furnace 1002, the temperature within the interior region of the furnace above the batch material 2005.

[0069] Threshold value It can be determined experimentally by means of an internal inspection of the furnace 1002 with the help of temperature sensors, for example thermocouples, infrared sensors. It can also be determined automatically by calculating a statistical distribution within the temperature map and determining a cut-off temperature from this distribution, above which temperatures correspond to "hot spots" or "volcanoes".

[0070] Since "hot spots" or "volcanoes" mainly correspond to holes within the batch material 2005 through which the melt bath 2006 can rise or be seen, and the melt bath 2006 often shows the highest temperatures, the threshold value may be fixed such that it represents a relatively small difference to the highest temperature detected in the temperature map.

[0071] The regions of the temperature map detected by the object detection function according to the above embodiments can provide a spatial distribution of the "hot spots" or "volcanoes" above the layer of batch material 2005. Other indicators can also be derived, such as their number, their density, i.e. the number per surface area, their distribution in size.

[0072] Spot detection function and threshold value It can be implemented by different image processing algorithms. Thus, in a particular embodiment, the object detection function can be chosen among the Otsu threshold function, the Laplacian of a Gaussian function, the determinant of a Hessian function, the Gaussian difference method and the watershed-based gray level spot detection function.

[0073] The prior art provides different implementations of these algorithms, for example the python scikit-image package.

[0074] Above the surface of the melt bath 2006, the batch material 2005 can not follow a strictly linear path from the feeder 1003 to the dividing wall 2001a separating the melting portion 2003a and the refining portion 2003b of the furnace 1002 due to the convective movements occurring within it. Instead, portions of the batch material 2005 can deviate from the global flow direction of the melt bath 2006 to the refining portion 2003b and can follow more complex curvilinear paths. In this context, the travel of the batch material 2005 can be better represented by a velocity vector field pointing in several directions but averaging to the flow direction of the melt bath 2006.

[0075] In order to better adjust the furnace parameters, such as the supply rate of batch material, the power distribution between electrodes 2007, 2008, it can be valuable to monitor the velocity vector field of the batch material 2005. In this scope, in a complementary embodiment, the method 3000 can further comprise a step (f) of calculating the velocity of the zones detected at step (e) by calculating the displacement of said zones over time in the time scale of the time scale temperature atlas. For example, by comparing successive temperature maps in which the same zone is detected, the displacement of said detected zones can be calculated and their velocity by dividing the calculated displacement by the time interval between successive temperature maps. This operation can be repeated for the whole time scale temperature atlas.

[0076] In a second aspect of the disclosure, there is provided a data processing system 4000 comprising means 4001 for carrying out the method 4000 according to any one of the embodiments of the first aspect of the application. There is also provided a computer program I 4001 comprising instructions which, when executed by a computer, cause the computer to carry out the method 4000 according to any one of the embodiments of the first aspect of the application. Figure 4

[0077] The data processing system 4000 comprises means 4001 for carrying out the method according to any one of the embodiments of the first aspect of the application. An example of means 4001 can be a device capable of being instructed to automatically carry out a sequence of arithmetic or logical operations to perform a task or action. Such a device, also called a computer, can comprise one or more central processing units (CPU) and at least a controller device adapted to perform those operations.

[0078] It can further comprise other electronic components such as input / output interfaces 4003, non-volatile or volatile storage devices 4002 and buses, which are communication systems for data transfer between components inside the computer or between computers. One of the input / output devices can be a user interface for human-machine interaction, for example a graphical user interface to display human understandable information.

[0079] As the computation can require a large amount of computing power to process a large amount of data, the data processing system can advantageously comprise one or more graphic processing units (GPU), whose parallel structure makes them more efficient than CPUs, especially for image processing.

[0080] The computer program I 4001 can be written in any kind of programming language, compiled or interpreted, to implement the steps of the method according to any embodiment of the first aspect of the application. The computer program I 4001 can be part of a software solution, i.e. of a set of executable instructions, code, scripts, etc. and / or of a database.​

[0081] In a particular embodiment, a computer readable storage or medium 4002 comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of the embodiments of the first aspect of the application can also be provided.

[0082] The computer readable storage 4002 can preferably be a non-volatile non-transitory storage or memory, such as a hard disk drive or a solid state drive. The computer readable storage can be a removable storage medium or a non-removable storage medium as part of the computer.

[0083] Alternatively, the computer readable storage can be a volatile memory inside a removable medium.

[0084] The computer readable storage 4002 can be part of a computer used as a server, the executable instructions can be downloaded from this server and, when they are executed by the computer, cause the computer to carry out the method according to any one of the embodiments described herein.

[0085] Alternatively, the program I 4001 can be implemented in a distributed computing environment, for example in cloud computing. The instructions can be executed on a server to which a client computer can be connected and provide encoded data as input to the method. Once the data has been processed, the output can be downloaded and decoded onto the client computer, or sent directly for example as instructions. This kind of implementation can be advantageous as it can be implemented in a distributed computing environment such as a cloud computing solution.

[0086] In a third aspect of the application, a process for measuring the thickness of a batch 2005 of material 1001a floating on a melt pool 2006 in an electric glass or stone melting furnace 1002 is provided, wherein the process comprises the steps of: - acquiring a time-scale temperature map M[T] set I 3000 of the surface of the batch 2005 of material 1001a within the interior region 2009 of the melting furnace 1002.

[0087] - implementing the method 3000 according to any one of the embodiments of the second aspect of the application by means of a data processing device 4000, wherein the acquired time-scale temperature map M[T] set I 3000 is provided as input data to the method 3000.

[0088] To implement this process, in a fourth aspect of the application, with reference to Figure 2 , a system for measuring the thickness of a batch 2005 of material 1001a floating on a melt pool 2006 in an electric glass or stone melting furnace 1002 is provided, wherein the system comprises: - the acquisition device 2010 is configured to acquire a time-scaled temperature map M[T] set I 3000 of the surface of the batch 2005 of material 1001a within the internal region 2009 of the furnace 1002; - the data processing device 4000 according to any one of the embodiments of the second aspect of the application, wherein the data processing device 4000 and the acquisition device 2010 are wired or wirelessly connected to each other for transferring data.

[0089] In a preferred embodiment, the acquisition device 2010 can comprise an infrared camera configured to acquire the time-scaled infrared map M[T] set I 3000, and the infrared camera, or the data processing device 4000 comprises means for converting the time-scaled infrared map set into the time-scaled temperature map set I 3000.

[0090] Examples of acquisition devices comprising an infrared camera adapted to acquire infrared maps in a glass or stone material melting furnace are described in the state of the art, for example, JPS 5 339 204 A, NIPPON ELECTRON OPTICS LAB 11.04.1978, JPH 0 7216 422 A, NIPPON STEEL CORP 15.08.1995, JP 2010 002150 A, TAKUMA CO LTD 07.01.2010 and US 2018 231875 A1, HER MAJESTY THE QUEEN IN RIGHT OF CANADA AS REPRESENTED BY THE MINI OF NATURAL RESOURCES [CA] 16.08.2018.

[0091] An example of an infrared camera can be the OPTRIS® PI 400 infrared camera commercialized by the company OPTRIS® infrared sensing. This camera has an optical resolution of 382 x 288 pixels, and an IR FPA 25 pm x 25 pm sensor in the spectral range 7.5 pm - 13 pm. The detected temperature range is 0 - 250 °C or 150 °C - 900 °C.

[0092] The acquisition device 2010 can be provided with a heat and dust shield in the form of a protective window and / or a gas shield located in front of the target. In a preferred embodiment, the acquisition device can be an infrared camera, in front of which is placed a horn-shaped corolla adapted to be placed in front of an opening in the wall 2001 of the electric glass or stone melting furnace 1002. The corolla can comprise injection nozzles on its periphery to inject an inert gas, such as nitrogen, towards the target, so as to clear any dust and discharge heat from the furnace. Between the corolla and the camera lens, an unremovable infrared transparent window can also isolate the camera from the furnace atmosphere.

[0093] The means for converting the infrared map into a temperature map rely on an extrapolation from blackbody or greybody radiation. They are widely described in the prior art, for example in the technical specification IEC 62492-1 TS: Industrial process control devices - Radiation thermometers - Part 1 : Technical data for radiation thermometers, and in the standard test method ASTM-E1256 Standard Test Method for Radiation Thermometers (Single Waveband Type).

[0094] Depending on the position of the acquisition device 2010 relative to the surface of the batch 2005 of material 1001a, the temperature or infrared map can be subject to perspective distortion. In this context, in a particular embodiment, the data processing device 4000 can further comprise means for correcting the perspective view of the acquisition device by applying a perspective or homography transformation function on each temperature map of the set I 3000 of time-scaled temperature maps M[T].

[0095] Examples of perspective or homography transformation functions are provided in the OPENCV python library. They can also be implemented as part of the computer-implemented method 3000 according to the first aspect of the application.

[0096] The application can be applied, in all its aspects, but in no way limited to, numerous processes for manufacturing glass products, such as glass wool, rock or stone wool, textile glass yarn, flat or hollow glass.

[0097] Patent documents JPS 5 339 204 A, NIPPON ELECTRON OPTICS LAB 11.04.1978.

[0098] US 4 194 077 A, OWENS CORNING FIBERGLASS CORP [US], 18.03.1980.

[0099] WO 8002833 A1, OWENS CORNING FIBERGLASS CORP [US], 24.12.1980.

[0100] US 4 409 012 A, OWENS ILLINOIS INC [US], 11.10.1983.

[0101] JPH 0 656 432 A, NIPPON ELECTRIC GLASS CO, 01.03.1994.

[0102] JPH 0 7216 422 A, NIPPON STEEL CORP 15.08.1995.

[0103] WO 0248057 A1, SOFTWARE & TECH GLAS GMBH [DE], 20.06.2002.

[0104] EP 1 655 570 A1, FRANKE MATTHIAS [DE], 10.05.2006.

[0105] JP 2010 002 150 A, TAKUMA CO LTD 07.01.2010.

[0106] WO 2018 / 104695 A1, LAND INSTRUMENTS INTERNATIONAL LTD [GB], 14.06.2018.

[0107] US 2018 231875 A1, HER MAJESTY THE QUEEN IN RIGHT OF CANADA AS REPRESENTED BY THE MINI OF NATURAL RESOURCES [CA] 16.08.2018.

[0108] WO 2022 / 242843 A1, GLASS SERVICE A S [CZ], 24.11.2022.

[0109] Non-Patent Literature Technical Specification IEC 62492-1 TS: Industrial process control devices - Radiation thermometers - Part 1 : Technical data for radiation thermometers.

[0110] ASTM-E1256 Standard Test Methods for Radiation Thermometers (Single Waveband Type).

Claims

1. A computer-implemented method (3000) for measuring the thickness e of a batch of material (2005) floating on a melt pool (2006) in an electric glass or stone melting furnace (1002); wherein said method takes as input data a time-scaled temperature map (I3000), M[T] of the material batch (2005), a range R[Ts] of expected values of the stabilization temperature Ts of the batch (2005), and a threshold value of the change of the temperature over time of the temperature over time over time. wherein the method provides as output data the spatial distribution (O3000) of the thickness of the batch (2005) over its surface; wherein the method (3000) comprises the steps of: (a) selecting (3001) within each temperature map M[T] of the provided set I3000 a region for which the temperature is within a range R[Ts] of expected values of the stable temperature Ts of the batch (2005); (b) calculating (3002) a temperature change within the same detected region between successive temperature maps in a given time range ;​ (c) Select the region of step (b) in (3003), where the temperature changes over time. Changes Below the threshold provided as input ; (d) for each point of the temperature map within each of the regions selected at step (c), computing (3004) the thickness E of the batch 2005, wherein the thickness E is computed by applying on said each point a function E(Ts) defining the relation between thickness and stable temperature Ts, and wherein the function E(Ts) is based on simulated or experimental heat flow transfer, or on an empirical model.

2. The method (3000) according to claim 1, wherein the method further increases the temperature T in the internal region of the furnace 1002 above the batch (2005). f Temperature T of the melt pool (2006) melt Take as input data; wherein the function E(Ts) is a heat flow transfer function provided by the following equation wherein is the thermal conductivity of the batch 2005, is the Stefan-Boltzmann constant, h is the convective heat transfer coefficient of the batch 2005, is the thermal emissivity coefficient of the batch 2005, T f is the temperature in the interior region of the furnace above the batch 2005, T melt is the temperature of the melt bath 2006, and Ts is the steady temperature of the batch 2005 for which the thickness E is to be calculated.

3. The method (3000) according to claim 1, wherein the function E(Ts) is an empirical model provided by the following equation: where T1 and T2 are two experimentally measured stable temperatures of the batch 2005, e1 and e2 are two experimentally measured thicknesses of the batch 2005 corresponding to the temperatures T1 and T2, respectively, and Ts is the stable temperature of the batch 2005 for which the thickness E is to be calculated.

4. The method (3000) according to any one of claims 1 to 2, wherein the threshold value for the change of the temperature over time is 5 °C / min, preferably 2 °C / min, more preferably 1 °C / min.​​​ 5. The method (3000) according to any one of claims 1 to 4, wherein the range R[Ts] of expected values of the stable temperature Ts of the batch 2005 is a range of values determined on experiments on similar batches of material under similar melting conditions, or is a range of defined values between two modes of a bimodal distribution computed from one or more temperature maps of the provided set (I3000).

6. The method (3000) according to any one of claims 1 to 5, wherein the method further comprises a step (e) of detecting a region within each temperature map of the provided set I3000 by applying an object detection function to the temperature map, wherein the object detection function is configured to process regions with a temperature equal to or exceeding a threshold value . wherein the method further provides as output data the spatial distribution of the detected regions over time.

7. The method (3000) according to claim 6, wherein the method comprises a step (f) of computing the velocity of the regions detected at step (e) by computing the displacement of said regions over time in a time scale of the set of time scale temperature maps.

8. The method (3000) according to any one of claims 6 to 7, wherein the object detection function is selected among an Otsu threshold function, a Laplacian of a Gaussian function, a determinant of a Hessian function, a Gaussian difference method and a watershed-based gray level blob detection function.

9. A data processing device (4000) comprising means for carrying out the method according to any one of claims 1 to 8.

10. A computer program (I4001) comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 8.

11. A computer readable medium (4002) comprising instructions which, when executed by a computer, cause the computer to carry out the method according to any one of claims 1 to 8.

12. A process for measuring the thickness of a batch of material (2005) of a material (1001a) floating on a melt pool (2006) in an electric glass or stone melting furnace (1002), wherein the process comprises the steps of: - a set of time-scale temperature maps M[T] (13000) of the surface of the batch (2005) of material (1001a) within the internal area (2009) of the melting furnace (1002) is acquired; - the method (3000) according to any one of claims 1 to 8 is implemented by means of a data processing device (4000), wherein the acquired set of time-scale temperature maps M[T] (I3000) is provided as input data to the method (3000).

13. A system for measuring the thickness of a batch (2005) of material (1001a) floating on a melt bath (2006) in an electric glass or stone melting furnace (1002), wherein the system comprises: - an acquisition device (2010) configured to acquire a set of time-scale temperature maps M[T] (I3000) of the surface of the batch (2005) of material (1001a) within the internal area (2009) of the melting furnace (1002); - a data processing device (4000) according to any one of claims 1 to 8, wherein the data processing device (4000) and the acquisition device (2010) are wired or wirelessly connected to each other for transferring data.

14. The system according to claim 13, wherein the acquisition device (2010) comprises an infrared camera configured to acquire a set of time-scale infrared maps M[T], and the infrared camera, or the data processing device (4000) comprises means for converting the set of time-scale infrared maps into the set of time-scale temperature maps I3000.

15. The system according to any one of claims 13 to 14, wherein the data processing device (4000) further comprises means for correcting the perspective view of the acquisition device by applying a perspective or homography transformation function on each temperature map of the set of time-scale temperature maps M[T] (I3000).

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