Method and system for measuring parameters related to the stability of a batch of floating material

Abstract

This invention provides an efficient and reliable method for estimating the thickness of the portion of a batch of material floating in a pool of molten material that has reached a steady temperature. [Solution] A computer implementation method (3000) for measuring the thickness e of a batch (2005) of material floating in a pool (2006) of molten material in an electric melting furnace (1002) for glass or stone. The method takes as input data a set (I3000) of a time-scale temperature map M[T] of the batch (2005) and the steady-state temperature T of the batch (2005). s The range of the predicted values ​​R[T] s The method receives the threshold σ of the temperature variation ΔT at time Δt. The method provides as output data the spatial distribution (O3000) of the thickness across the surface of a batch (2005).

Description

[Technical Field]

[0001] The present invention relates to a computer-based method and system for monitoring the stability of batches of material floating in a pool of molten material in an electric melting tank for glass. [Background technology]

[0002] In conventional electric glass melting furnaces, multiple electrodes are immersed in a pool of molten glass in a predetermined pattern. By passing an electric current through the molten glass between the electrodes, the glass is heated by the Joule effect.

[0003] A batch of material may include raw materials, cullet, and other recycled materials. By supplying these batches continuously or discontinuously to the upper surface of the pool, a source of material and a barrier layer or crust formed on top of it are provided. However, as this batch gradually melts and additional molten glass is formed, the thickness of the layer decreases and the heat lost from the molten glass body in the furnace through the batch increases. Conversely, as additional batch material is distributed to the upper surface of the molten glass, the layer thickens and the heat lost through this thicker layer decreases.

[0004] The batch is typically supplied in a predetermined pattern by a mobile feeder, conveyor, or sprinkler, which allows for precise volume control and maintains a minimum thickness on the upper surface of the pool, resulting in reduced heat loss, protection of the feeder, and avoidance of furnace overflow.

[0005] It is common practice to inspect the inside of a glass melting furnace using infrared optical systems positioned within the furnace walls. These systems allow for human observation of the stability of material batches.

[0006] Japanese Patent Publication No. 53-39204 (JEOL Ltd., April 11, 1978), Japanese Patent Publication No. 7-216422 (Nippon Steel Corporation, August 15, 1995), Japanese Patent Publication No. 2010-2150 (Takuma Corporation, January 7, 2010), and U.S. Patent Application Publication No. 2018 / 231875 (Canada [CA], representing Her Majesty the Queen through the Minister of Natural Resources, August 16, 2018) describe an inspection system comprising an infrared unit positioned in front of a viewing window installed inside a furnace wall.

[0007] In this technical field, more advanced systems are also available for measuring parameters related to several characteristics of material batches.

[0008] International Publication No. 80 / 02833 (OWENS CORNING FIBERGLASS CORP [US], December 24, 1980) describes a system for controlling the level, or thickness, of a batch of material in an electric melting furnace for glass. The system includes an infrared sensor installed on or adjacent to the feeder, which measures the temperature of the outer surface of the batch non-contact. The measured temperature is compared to a set temperature, and the feed rate of the feeder is adjusted according to an experimental relationship between the batch thickness and the temperature of the outer surface of the batch, thereby increasing or decreasing the batch level, or thickness.

[0009] U.S. Patent No. 4,194,077 (OWENS CORNING FIBERGLASS CORP [US], March 18, 1980) describes a system for controlling the level of a batch of material in an electric melting furnace for glass. The system includes an ultrasonic sensor, which is mounted on a feeder and moves with the feeder above the batch. A non-contact measurement of the batch level is obtained, and based on this measurement, the batch thickness can be calculated from a relationship between the density of the batch and the molten glass and its level.

[0010] U.S. Patent No. 4,409,012 (OWENS ILLINOIS INC [US], October 11, 1983) describes a method and apparatus for monitoring the surface coating of a batch of material floating in a pool of molten material by processing video recordings of the top surfaces of both the batch and the molten material in a glass melting furnace. Subsequently, a bimodal distribution of pixel counts according to gray level is plotted from the recorded images, and after separating the two modes by thresholding, the relative amounts of the batch and molten material are estimated by integrating the area occupied by each mode across different regions in the furnace.

[0011] Japanese Patent Publication No. 6-56432 (Nippon Electric Glass Co., Ltd., March 1, 1994) describes a method and system for measuring the level of a batch of material floating in a pool of molten material by processing an image of a portion of the top surface of the batch irradiated by an illumination device. The image is binarized and the centroid coordinates of the white pixels are calculated. The changes in the centroid coordinates thus calculated are assumed to reflect the changes in the batch level caused by variations in the amount of light reflected by the illuminated portion of the batch as the batch level rises and falls.

[0012] International Publication No. 02 / 48057 (SOFTWARE & TECH GLAS GMBH [DE], June 20, 2002) describes a method for measuring the linearized increase in the level of batch coverage in a pool of molten material by processing images of the top surfaces of both the batch and the molten material. The quotient of dark pixels to the number of pixels in each line of the image in a direction transverse to the flow direction of the molten material is calculated. The transition of the quotient in the flow direction is assumed to reflect the level of batch coverage in that direction.

[0013] European Patent Application Publication No. 1,655,570 (FRANKE MATTHIAS[DE], May 10, 2006) discloses a computer-based method for monitoring the internal region of a glass melting furnace, which is performed by comparing the position of a selected object between infrared images of the internal region acquired at consecutive points in time. This method allows for monitoring the movement of various moving objects, such as batches of material floating in a pool of molten material.

[0014] International Publication No. 2018 / 104695 (LAND INSTRUMENTS INTERNATIONAL LTD [GB], June 14, 2018) discloses a control unit for identifying batches of material floating in a pool of molten material by a thermal imaging camera configured to acquire thermal images of the top surfaces of both the batch and the molten material. The position of the batch is identified by the low-temperature region of the thermal image. The movement of the batch can be tracked, and its velocity can be calculated by processing thermal images acquired at successive points in time. The control unit may be configured to correct the perspective of the thermal image, so that the parameters obtained therefrom are expressed in real-world coordinates.

[0015] International Publication No. 2022 / 242843 (GLASS SERVICE AS [CZ], November 24, 2022) discloses an improved form of the method described in European Patent Application Publication No. 1,655,570 (FRANKE MATTHIAS [DE], May 10, 2006). The method further includes a deviation correction step to correct deviations in the infrared image due to changes in the field of view of the infrared camera over time. Compared to the method described in European Patent Application Publication No. 1,655,570 (FRANKE MATTHIAS [DE], May 10, 2006), the data obtained from the image processing is more accurate and can determine new parameters such as batch temperature, batch melting rate, batch position, and batch movement pattern and speed. [Overview of the project]

Problems to be Solved by the Invention

[0016] One of the main process parameters for efficiently operating an electric manufacturing furnace for glass or stone is the thickness of the batch material. This is because the batch material directly affects the amount of power to be supplied for uniform melting.

[0017] However, the current method has several drawbacks because local non-uniformities within the batch are not fully considered during measurement and measurement-based calculations. This non-uniformity is particularly what is called a "hot spot" or "volcano", which corresponds to holes inside the batch formed after complete local melting, and / or holes inside the batch formed after sliding occurs inside the batch due to the batch showing granularity or the batch showing inherent instability with material deposited on top of a moving melt pool.

[0018] In a method based on measuring the levels of both the batch and the melt and calculating their difference, it is not possible to accurately model the non-uniform coating of the batch over the melt pool in an electric melting furnace for glass or stone. In fact, inside an electric melting furnace for glass or stone, like the Earth's plate tectonics, parts of the batch may separate from each other under the convective effects occurring in the melt pool. Different from the case of a fuel-fired melting furnace for glass or stone, this phenomenon can cause the thickness of the batch to vary significantly across the surface of the melt pool. For example, in a fuel-fired melting furnace, the thickness of the batch is expected to decrease as it moves towards the forehearth. However, in an electric melting furnace, the thickness of the batch may remain relatively stable at the forehearth, or the thickness of the batch may decrease in a direction other than the melt flow direction.

[0019] On the other hand, for a method of estimating the batch thickness based only on the assumption that, for the same reason, the level of the batch is measured and there is a relationship between the density of the batch and the molten glass and that level, there are similar drawbacks. In reality, assuming that a non-uniform batch exhibits the same density everywhere, there is a risk of overestimation unless a correction function for considering local variations is applied. However, such a correction may not be easy because it may be necessary to model in advance the characteristics and time-dependent changes in the dynamics of the batch.

[0020] Furthermore, in an electric melting furnace for glass or stone, a pressure drop may occur between the melting section and the fining section, so the levels of the molten glass in these two parts may not be equal. If the level of the fining section is used as a substitute indicator for estimating the level of the molten glass existing below the batch of material in the melting section, as is sometimes done in the current art, the estimation may be incorrect. Therefore, the batch thickness obtained based on this estimation may also be inaccurate.

[0021] In contrast, a method based on the processing of images of both the batch and the melt is considered to be able to provide results that better represent the non-uniformity of the batch. Since the image directly captures the batch together with its "hot spots" or "volcanoes", it is considered that the actual surface coverage of the batch with respect to the melt pool is better measured. However, although it is said that the batch thickness can be derived by image processing, the current method does not mention specifically how to proceed. In particular, no details or examples of the calculations or image conversions performed to obtain accurate values are provided.

[0022] Finally, past experience in optimizing the energy consumption of electric furnaces has shown that the portion of a batch that has reached a local steady temperature, i.e., the portion in thermal equilibrium, directly affects the amount of power that needs to be supplied to efficiently melt the metal. Therefore, to overcome the problems of underheating and overheating that furnace operators often face when setting up electric furnaces, it would be helpful to monitor the amount of batch in thermal equilibrium in real time. However, current methods cannot provide such information about the state of the batch inside the furnace.

[0023] Therefore, there is still a need for an efficient and reliable method to estimate the thickness of the portion of a batch of material floating in a pool of molten material that has reached a steady temperature. Ideally, such a method should also be able to provide the temporal variation and / or spatial distribution of the thickness above the pool of molten material. Furthermore, it is desirable that such a method be versatile enough to provide values ​​for other important parameters related to the temporal stability of the batch, such as the spatial distribution and temporal movement of "hot spots" or "volcanoes." [Means for solving the problem]

[0024] In a first aspect of the present invention, a computer-implemented method for measuring the thickness of a batch of material floating in a pool of molten material in a glass melting furnace as described in claim 1 is provided, and the dependent claims are advantageous embodiments.

[0025] A second aspect of the present invention provides a data processing device, a computer program, and a computer-readable medium for carrying out the method of the first aspect.

[0026] A third aspect of the present invention provides a process for measuring the thickness of a batch of material floating in a pool of molten material.

[0027] A fourth aspect of the present invention provides a system for measuring the thickness of a batch of material floating in a pool of molten material. [Effects of the Invention]

[0028] A first notable advantage of the present invention is that the thickness of any region of a batch of material in thermal equilibrium can be calculated precisely and accurately. Since the present invention does not depend on measurements of the batch and / or molten levels or the relationship between their densities, overestimation or underestimation of thickness is avoided. Variations in the overall batch thickness due to local heterogeneity and convection effects within the molten pool are also better modeled.

[0029] A second advantage of the present invention is that it can provide temporal variation and / or spatial distribution of batch thickness above the pool of molten material. Therefore, advantageously, the present invention can be implemented for real-time monitoring of batch thickness throughout the entire lifecycle of a batch in an electric furnace.

[0030] A third advantage resulting from the first advantage is that the present invention provides more reliable and representative information regarding the batch thickness state, allowing for better adjustment of the power supplied to the electrodes of the electric furnace toward efficient melting, thereby reducing energy consumption and costs. In this context, the information provided as output data by the present invention can, advantageously, be supplied as input data to a control loop, such as a feedback control device, thereby enabling real-time adjustment of the power supplied to the furnace electrodes.

[0031] A fourth advantage is that, in certain embodiments, the present invention can further provide important parameters related to the time-dependent stability of the batch, such as the spatial distribution of “hot spots” or “volcanoes,” their motion, and the velocity vector field of the batch. [Brief explanation of the drawing]

[0032] [Figure 1] This is a schematic diagram of an example of a manufacturing line for glass fiber or stone fiber.

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

[0034] [Figure 3] This is a data flow diagram of a computer implementation method according to a first aspect of the present invention.

[0035] [Figure 4] This is a physical data flow diagram of a processing data system for carrying out a method according to the first aspect of the present invention. [Modes for carrying out the invention]

[0036] Referring to Figure 1, the internal centrifugal production line 1000 of glass or stone fibers may generally comprise a silo 1001 for storing raw materials 1001a, such as inorganic compounds and / or cullet; an electric melting furnace 1002 for glass or stone for melting the raw materials 1001a transported from the silo 1001 by a conveyor 1003; and one or more fiberization tools 1005a, 1005b, 1005c to which molten glass or molten stone 1006 is supplied through a pre-furnace 1004. The pre-furnace is typically designed as an open or closed channel with openings directly above each fiberization tool 1005a, 1005b, 1005c to which molten glass or molten stone 1006 is supplied.

[0037] Referring to Figure 2, which is an adaptation of U.S. Patent No. 4,194,077 (OWENS CORNING FIBERGLASS CORP [US], March 18, 1980), an electric melting furnace 1002 for glass or stone comprises a refractory wall section 2001 and a refractory floor section 2002, which together form a tank 2003, which is divided by a partition wall 2001a into a melting section 2003a and a clarification section 2003b. These two sections 2003a and 2003b are connected to each other by an opening 2004 located at the bottom of the partition wall 2001a at the level of the floor section 2002.

[0038] The raw material 1001a is transported to the melting section 2003a, forming a batch 2005 floating on the surface of the pool 2006, which is gradually melted by a series of immersion electrodes 2007. Alternatively, or complementaryly, the series of electrodes may include immersion plunging, as described in French Patent Application Publication No. 2,599,734 (SAINT GOBAIN RECH [FR], December 11, 1987). When convection occurs in the pool 2006 of molten material, the molten material flows from the melting section 2003a through the opening 2004 to the clarification section 2003b. This clarification section 2003b is connected to a pre-furnace or feeder. One or two immersion electrodes 2008 can be positioned at the level of the floor section 2002 on one or both sides of the opening 2004.

[0039] Referring to Figures 2 and 3, a first aspect of the present invention provides a computer-implemented method 3000 for measuring the thickness e of a batch 2005 of material floating in a pool 2006 of molten material in an electric melting furnace 1002 for glass or stone, This method takes as input data a set I3000 of time-scale temperature maps M[T] of batch 2005 of the material, and the steady-state temperature T of batch 2005. s The range of the predicted values ​​R[T] s ] and a threshold σ for the temperature variation ΔT over time Δt are received; This method provides, as output data, the spatial distribution of thickness O3000 across the surface 2005a of batch 2005; This method 3000 is, (a) Within each temperature map M[T] of the provided set I3000, the temperature is the steady-state temperature T of batch 2005. s The range of the predicted values ​​R[T] s Step 3001 involves selecting the region located within ]; (b) Step 3002 of calculating the temperature variation ΔT between multiple temperature maps that are continuous over a given time range Δt within the same detection region; (c) Step 3003 selects from the region in step (b) the region in which the temperature variation ΔT at time Δt is below the threshold σ provided as input; (d) Step 3004, for each point of the multiple temperature maps within each region selected in step (c), calculate the thickness E of batch 2005, wherein the thickness E is calculated as a function E(T) for each point. s The calculation is performed by applying the function E(T s ) In this case, thickness and steady temperature T s The relationship is defined, and the function E(T s ) shall be based on heat flow transfer obtained through simulation or experiment, or on an empirical model, as in step 3004; Includes.

[0040] In the context of this invention, "temperature map" should be interpreted as the general definition in the field of cartography, for example, as a representation or distribution of temperature on a scaled or unscaled surface. In practice, this temperature map can be interpreted as a collection of temperature data, where each temperature data is associated with a location on the surface of batch 2005 of material. These locations may be coordinates in pixels or length units.

[0041] In the context of the present invention, a set of temperature maps should be understood as a collection of temperature maps, the number of temperature maps may be arbitrary or depend on the means of acquiring the temperature maps, such as the technical constraints or settings of the camera.

[0042] In the context of the present invention, a set of time-scale temperature maps is a set of temperature maps acquired over a certain period, i.e., a time scale. This period can be arbitrarily set according to the means used for acquisition, e.g., a camera, and / or according to the time when the temperature map is considered to be acquired. Also, this period can be a continuous, i.e., endless, period as in the case of monitoring, for example. The frequency at which the temperature maps are acquired can be a matter of choice according to the means used for acquiring the temperature maps, e.g., the technical constraints of a camera, and the time scale in which the batch is known or predicted to change. As a rule of thumb, the frequency needs to be set so that certain features, e.g., the transition of hot and / or cold spots, e.g., movement, can be observed in the continuously acquired temperature maps.

[0043] In the context of the present invention, the expression "steady temperature" applied to a batch should be interpreted as the temperature corresponding to the thermal equilibrium of batch 2005, i.e., the temperature when the heat flow from the melt pool existing below the batch and the heat flow from furnace 1002 within the internal region 2009 of furnace 1002 existing above batch 2005 are in equilibrium. When in thermal equilibrium, i.e., when the batch has reached its steady temperature T s the temperature of batch 2005 should be stable over time or change only very slightly over time.

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

Equation

[0045] Here, ρ is the density of the batch, c p is the specific heat capacity of the batch, e is the thickness of the batch, T is the temperature of batch 2005, t is time, λ is the thermal conductivity of batch 2005, σ is the Stefan-Boltzmann constant, h is the convective heat transfer coefficient of batch 2005, ε is the thermal emissivity of batch 2005, Tf This is the temperature within the internal region 2009 of furnace 1002, which is located above batch 2005, and T melt T is the temperature of the molten pool 2006, and T is the temperature of batch 2005.

[0046] In the region of furnace 1002 where fresh, unheated batches are supplied to the molten pool 2006 by a feeder, conveyor, or sprinkler 1003, the batches 2005 may be cooler than those in more distant parts of furnace 1002. Over time, the batches are gradually heated, and their temperature rises until it reaches a steady-state temperature.

[0047] Conversely, in the vicinity of a "hot spot" or "volcano" region, the heat flow from the molten pool 2006 located below batch 2005 and the heat flow from the internal region of furnace 1002 located above batch 2005 are not locally in equilibrium, which can cause the temperature of batch 2005 to rise and exceed its steady-state temperature.

[0048] Between these extremes, for example, in areas away from the supply zone, hotspot, or volcano, batch 2005 is expected to be in thermal equilibrium and have reached its steady temperature.

[0049] In addition to the problems of the prior art described above, the present invention provides an ingenious solution to this problem.

[0050] In step (a), the range R[T] of the predicted steady-state temperature provided as input is selected from the region of the temperature map. s Items within the specified range are selected. This operation allows us to identify the region of batch 2005 that is likely to be in thermal equilibrium, i.e., the region that is most likely to have reached its steady temperature.

[0051] Range of predicted steady-state temperature R[T s This can be determined empirically and / or by processing the temperature map provided as input.

[0052] In a favorable embodiment, the steady-state temperature T of batch 2005 s The range of the predicted values ​​R[T] s R[T] can be a range of values ​​experimentally determined for batches of similar materials under similar melting conditions. For example, these values ​​may originate from a history of single and / or monitoring measurements of temperature in a specific area of ​​the batch surface, acquired by operator and / or automated sensors, such as infrared sensors, from previous manufacturing tests or processes carried out in an electric melting furnace 1002 for a particular glass or stone material. A notable advantage of this approach is the range of predicted steady-state temperatures R[T] s The advantage is that it can better represent the thermal behavior of both the electric furnace and the composition of the batch under consideration.

[0053] Surprisingly, in the context of the present invention, the range R[T] of the predicted steady-state temperature is... s It was found that a temperature range of 20°C to 200°C, preferably 70°C to 140°C, is suitable for most cases of electric melting furnaces for glass or stone.

[0054] Alternatively, or complementaryly, the steady-state temperature T of batch 2005. s The range of the predicted values ​​R[T] s ] can be a defined range of values ​​between two modes of a bimodal distribution calculated from one or more of the temperature maps in the provided set I3000. By calculating the bimodal distribution of the temperature map, it may be possible to distinguish between two extremes. These two extremes are the coldest region of the batch, e.g., the region near the feeder, and the hottest region, e.g., a hot spot or volcano. Fixing the steady-state temperature range between these extremes can be considered a type of filter for selecting the region most likely to be in thermal equilibrium. The bimodal distribution can be calculated by fitting the temperature map to a method such as that described in U.S. Patent No. 4,409,012 (OWENS ILLINOIS INC [US], October 11, 1983).

[0055] In step (c), from the regions selected in step (b), those whose temperature fluctuation ΔT over time Δt is below the threshold σ provided as input are selected. If a region of batch 2005 has reached its steady temperature, its temperature should remain stable over time or change only slightly; that is, ΔT / Δt ≈ 0. Therefore, this operation selects the regions of the batch that are actually in thermal equilibrium, i.e., those that actually have a steady temperature T s It becomes possible to identify those that have reached this state. In other words, the selection of regions in thermal equilibrium was initiated in step (a), but this selection is further narrowed down in step (c).

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

[0057] The efficiency and accuracy of this invention for measuring the thickness E of batch 2005 depend on effectively and accurately selecting a region of the batch that is in thermal equilibrium. Therefore, the combination of steps (a) to (c) should be considered one of the main features of method 3000 according to the present invention, and thus a significant difference from the methods described in the art.

[0058] In step (d), for the region selected in step (c), the thickness E of batch 2005 is determined by the thickness E and the steady-state temperature T s It is calculated according to a function that defines the relationship with [the other variable]. There is no specific function; any fitted empirical or theoretical function that can map the batch thickness to its local steady temperature can be used.

[0059] As already emphasized, one of the advantages of the present invention is that it is possible to effectively and accurately select regions of batch 2005 that are in thermal equilibrium, that is, those that have locally reached their steady temperature. Therefore, advantageously, if the thermal properties of the batch can be determined, the thickness of the batch can be derived from the thermal equilibrium equation (1) mentioned above.

[0060] Therefore, in a particular embodiment, the method takes as input data the temperature T in the internal region of the furnace 1002 located above batch 2005. f , the temperature of the molten pool 2006 T melt You can receive even more; Here, the function E(T s ) is the heat transfer function provided by equation (2) below:

number

[0061] Here, λ is the thermal conductivity of batch 2005, σ is the Stefan-Boltzmann constant, h is the convective heat transfer coefficient of batch 2005, ε is the thermal emissivity of batch 2005, and T f This is the temperature within the internal region of the furnace located above batch 2005, and T melt This is the temperature of the molten pool 2006, and T s This is the steady-state temperature of batch 2005, which is the subject of the calculation of thickness E.

[0062] The parameters λ, h, and ε can be theoretically calculated by understanding the convection effect within the internal region 2009 of the furnace 1002, the chemical composition of batch 2005, and, if the batch is supplied as granular material overall, its bulk density. However, as already emphasized, during the lifetime of batch 2005 in the furnace, the bulk density, particle size distribution, and even the chemical composition of the batch may change locally within its thickness and within its entire coating, depending on the temperature gradient and convection motion occurring within the pool of molten material 2006, which locally determines the melting state and melting rate of batch 2005. One direct consequence of this is that theoretically calculated parameters may not accurately represent the local characteristics of batch 2005, potentially leading to inaccuracies in determining the local thickness within the batch. Despite these drawbacks, theoretically calculated parameters can still be useful as approximate surrogates for batches whose thermal and physical properties are not expected to change excessively during their lifetime in the furnace.

[0063] Alternatively, or complementarily, the parameters λ, h, and ε can be determined experimentally by conducting measurements on similar batches that vary under similar melting conditions. For example, by conducting experiments in a pilot electric furnace, the behavior in a large-scale production furnace can be simulated, and parameters λ, h, and ε can be obtained calculated from on-site and / or off-site measurements of the thermal and physical properties of the batch. The parameters thus obtained may better represent the actual behavior of the batch in the electric furnace of the production line.

[0064] However, the time scale and rate of change in a production line do not always align with those of experiments that can be conducted within the framework of a laboratory or pilot furnace. Furthermore, scaling effects may occur, meaning that calculations and / or measurements performed in a laboratory or pilot may not function efficiently when applied to a large-scale electric furnace. Therefore, it may be more useful to rely on empirical functions when defining the relationship between batch thickness and steady-state temperature.

[0065] In this context, a preferred embodiment is the function E(T s ) may be the empirical model provided by the following equation (3):

number

[0066] Here, T1 and T2 are two steady temperatures measured by the experiment in batch 2005, and e1 and e2 are two thicknesses measured by the experiment in batch 2005 corresponding to temperatures T1 and T2, respectively. s This is the steady-state temperature of batch 2005, which is the subject of the calculation of thickness E.

[0067] Surprisingly, equation (3) above proved to be a concise, sophisticated, and robust method for calculating with high accuracy the thickness of batch 2005 corresponding to a selected region of the temperature map, without relying on any known information about the physical and / or thermal properties of the batch. Furthermore, the two temperatures T1 and T2, and the two thicknesses e1 and e2 can be measured experimentally on a laboratory scale, in a pilot furnace, and / or an industrial furnace. When measured in an industrial furnace, the results best represent what is actually happening in the industrial furnace. As a further consequence of not relying on known information about the physical and / or thermal properties of batch 2005, equation (3) can be easily implemented industrially.

[0068] The method according to the present invention may be adapted to provide other important parameters for batch stability over time, such as the spatial distribution of "hot spots" or "volcanoes" and their movement over time.

[0069] In a particular embodiment, the method may further include step (e) detecting regions within each of the provided set I3000 temperature maps by applying an object detection function to the temperature map, wherein the object detection function is configured to process regions having a temperature equal to or greater than a threshold θ; Here, the method further provides the spatial distribution of the detected region over time as output data.

[0070] To detect a "hot spot" or "volcano," a threshold θ can be fixed at the temperature at which the occurrence of the aforementioned "hot spot" or "volcano" is expected or observed within the batch 2005 layer. This temperature typically varies depending on several parameters, such as the melting temperature of the batch, the temperature gradient, the convection motion within the pool of molten material 2006, the power supplied to the furnace 1002, and the temperature within the internal region of the furnace above the batch 2005.

[0071] The threshold θ can be determined experimentally through internal inspection of the furnace 1002 using temperature sensors such as thermocouples or infrared sensors. Alternatively, the threshold θ can be automatically determined by calculating the statistical distribution within the temperature map and finding the cutoff temperature that exceeds the temperature corresponding to a "hot spot" or "volcano" from this distribution.

[0072] "Hot spots" or "volcanoes" are primarily holes within batch 2005, through which the molten pool 2006 rises and can be visually observed, often exhibiting the highest temperature. Therefore, the threshold θ can be fixed such that the difference between it and the highest temperature detected on the temperature map is relatively small.

[0073] According to the above embodiment, the regions of the temperature map detected by the object detection function can provide the spatial distribution of "hot spots" or "volcanoes" in the layer of batch 2005. Other indicators that can be derived include their number, density (i.e., number per unit area), and size distribution.

[0074] The blob detection function and threshold θ can be introduced by different image processing algorithms. Therefore, in certain embodiments, the object detection function can be selected from Otsu's thresholding function, the Laplacian of a Gaussian function, the Hessian determinant, the Gaussian difference, and a watershed-based gray-level blob detection function.

[0075] In this technical field, various implementations of these algorithms are available, such as the scikit-image package in Python.

[0076] Due to convection currents occurring within the molten pool 2006, batch 2005 may not follow a strictly linear path on the surface of the molten pool 2006 from feeder 1003 to the partition wall 2001a separating the molten section 2003a from the clarification section 2003b of the furnace 1002. Instead, portions of batch 2005 may deviate from the overall flow direction toward the clarification section 2003b in the molten pool 2006 and follow a more curvilinear and complex path. In this context, the movement of batch 2005 can be better represented by a velocity vector field that is oriented in multiple directions but, on average, oriented toward the flow direction of the molten pool 2006.

[0077] Monitoring the velocity vector field of batch 2005 may be beneficial to better adjust furnace parameters, such as the batch feed rate and the power distribution between electrodes 2007 and 2008. In this range, in complementary embodiments, method 3000 may further include step (f) of calculating the velocity of the region detected in step (e) by calculating the displacement of the region over time on a time scale of a set of time-scale temperature maps. For example, the displacement of the detected region can be calculated by comparing a series of consecutive temperature maps in which the same region is detected, and the velocity can be calculated by dividing the calculated displacement by the time interval between the series of temperature maps. This operation can be repeated for the entire set of time-scale temperature maps.

[0078] A second aspect of this disclosure, with reference to Figure 4, is provided, which includes means 4001 for carrying out a method 4000 according to any embodiment of the first aspect of the present invention. A computer program I4001 including instructions is also provided, which, when executed by a computer, causes the computer to carry out a method 4000 according to any embodiment of the first aspect of the present invention.

[0079] The data processing system 4000 includes means 4001 for carrying out a method according to any embodiment of the first aspect of the present invention. An example of means 4001 is a device capable of being instructed to automatically perform a sequence of arithmetic or logical operations in order to perform a task or action. The device is also called a computer and may comprise one or more central processing units (CPUs) and at least one control unit configured to perform these operations.

[0080] The device may further include other electronic components such as an input / output interface 4003, a non-volatile or volatile storage device 4002, and a bus which is a communication system for data transfer between components within or between computers. One of the input / output devices may be a user interface for human-machine interaction, for example, a graphical user interface for displaying human-readable information.

[0081] Since calculations sometimes require high computing power to process large amounts of data, data processing systems, especially in image processing, can be advantageously equipped with one or more image processing units (GPUs) that are more efficient than CPUs due to their parallel architecture.

[0082] Computer program I4001 can be written in any type of programming language, whether compiled or interpreted, for carrying out the steps of the method according to any embodiment of the first aspect of the present invention. Computer program I4001 may be part of a software solution, i.e., part of a collection of executable instructions, code, scripts, etc., and / or databases.

[0083] In certain embodiments, a computer-readable storage device or medium 4002 can also be provided, which includes instructions, the instructions causing a computer to perform a method according to any embodiment of the first aspect of the present invention when executed by a computer.

[0084] The computer-readable storage device 4002 may preferably be a non-volatile, non-temporary storage device or memory, such as a hard disk drive or a solid-state drive. The computer-readable storage device may be a removable storage medium or a non-removable storage medium as part of a computer.

[0085] Alternatively, the computer-readable storage device may be volatile memory located inside a removable medium.

[0086] The computer-readable storage device 4002 may be part of a computer used as a server, and can download executable instructions from the server, which, when executed by the computer, cause the computer to carry out any of the methods described herein.

[0087] Alternatively, program I4001 can be implemented in a distributed computing environment, such as cloud computing. Instructions can be executed on a server, and client computers can be connected to this server, providing encoded data as input to the method. After data processing, the output can be downloaded to the client computer for decoding, or sent directly as instructions, for example. This type of implementation can be advantageous because it is feasible in distributed computing environments such as cloud computing solutions.

[0088] A third aspect of the present invention provides a process for measuring the thickness of a batch 2005 of material 1001a floating in a pool 2006 of molten material in an electric melting furnace 1002 for glass or stone, the process being: - Steps include obtaining a set of time-scale temperature maps M[T] of the surface of batch 2005 of material 1001a within the internal region 2009 of the melting furnace 1002; - A step of carrying out a method 3000 according to any embodiment of a second aspect of the present invention using a data processing device 4000, wherein a set I3000 of acquired time-scale temperature maps M[T] is provided to the method 3000 as input data; Includes.

[0089] To carry out the process, a fourth aspect of the present invention provides a system for measuring the thickness of a batch 2005 of material 1001a floating in a pool 2006 of molten material in an electric melting furnace 1002 for glass or stone, with reference to Figure 2, the system is: - Acquisition device 2010 configured to acquire a set I3000 of time-scale temperature maps M[T] of the surface of batch 2005 of material 1001a within the internal region 2009 of furnace 1002; - A data processing device 4000 according to any embodiment of a second aspect of the present invention, wherein the data processing device 4000 and the acquisition device 2010 are connected to each other by wire or wireless means for data transfer; It is equipped with.

[0090] In a preferred embodiment, the acquisition device 2010 may include an infrared camera configured to acquire a set I3000 of time-scale infrared maps M[T], and the infrared camera or data processing device 4000 includes means for converting the set of time-scale infrared maps into a set I3000 of time-scale temperature maps.

[0091] Examples of acquisition devices equipped with an infrared camera adapted to acquire infrared maps in a glass or stone melting furnace have been described in the art, for example, in Japanese Patent Publication No. 53-39204 (JEOL Ltd., April 11, 1978), Japanese Patent Publication No. 7-216422 (Nippon Steel Corporation, August 15, 1995), Japanese Patent Publication No. 2010-2150 (Takuma Corporation, January 7, 2010), and U.S. Patent Application Publication No. 2018 / 231875 (Canada [CA], representing Her Majesty the Queen through the Minister of Natural Resources, August 16, 2018).

[0092] One example of an infrared camera is the OPTRIS® PI 400 infrared camera sold by OPTRIS® infrared sensing. This camera features an optical resolution of 382 x 288 pixels and an IR FPA 25 μm x 25 μm sensor with a spectral range of 7.5 μm to 13 μm. The detection temperature range is 0 to 250°C or 150°C to 900°C.

[0093] The acquisition device 2010 may be equipped with heat and dust shields in the form of protective windows and / or gas shields positioned in front of the objective lens. In a preferred embodiment, the acquisition device may be an infrared camera positioned in front of a flared cylindrical section adapted to be positioned in front of an opening in the wall 2001 of an electric melting furnace 1002 for glass or stone. The cylindrical section may be equipped with injection nozzles for spraying an inert gas, such as nitrogen, towards the objective lens, thereby purging dust and dissipating heat from the furnace. The camera may also be isolated from the furnace atmosphere between the cylindrical section and the camera lens by a non-removable infrared-transmitting window.

[0094] The means of converting infrared maps to temperature maps rely on estimations from blackbody or graybody radiation. Such means are extensively described in the 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 ASTM-E1256 "Standard test methods for radiation thermometers (single-wavelength type)".

[0095] Depending on the position of the acquisition device 2010 relative to the surface of batch 2005 of material 1001a, the temperature or infrared map may exhibit perspective distortion. In this context, in certain embodiments, the data processing device 4000 may further provide means for correcting the perspective view of the acquisition device by applying a perspective or homography transformation function to each temperature map in a set I3000 of time-scale temperature maps M[T].

[0096] Examples of perspective or homography transformation functions are provided in the OPENCV Python library. These can also be implemented as part of a computer implementation method 3000 according to a first aspect of the present invention.

[0097] In all its embodiments, but not limited thereto, the present invention can be applied to many processes for manufacturing glass products such as glass wool, rock wool, stone wool, textile glass yarn, plate glass, or hollow glass. List of References Patent Documents

[0098] Japanese Patent Publication No. 53-39204 (JEOL Ltd., April 11, 1978)

[0099] U.S. Patent No. 4,194,077 (OWENS CORNING FIBERGLASS CORP [US], March 18, 1980)

[0100] International Publication No. 80 / 02833 (OWENS CORNING FIBERGLASS CORP [US], December 24, 1980)

[0101] US Patent No. 4,409,012 (OWENS ILLINOIS INC [US], October 11, 1983)

[0102] Japanese Patent Publication No. Hei 6-56432 (Nippon Electric Glass Co., Ltd., March 1, 1994)

[0103] Japanese Patent Publication No. 7-216422 (Nippon Steel Corporation, August 15, 1995)

[0104] International Publication No. 02 / 48057 (SOFTWARE & TECH GLAS GMBH [DE], June 20, 2002)

[0105] European Patent Application Publication No. 1,655,570 (FRANKE MATTHIAS[DE], May 10, 2006)

[0106] Japanese Patent Publication No. 2010-2150 (Takuma Corporation, January 7, 2010)

[0107] International Publication No. 2018 / 104695 (LAND INSTRUMENTS INTERNATIONAL LTD [GB], June 14, 2018)

[0108] U.S. Patent Application Publication No. 2018 / 231875 (Canada [CA], on behalf of Her Majesty the Queen through the Minister of Natural Resources, August 16, 2018)

[0109] International Publication No. 2022 / 242843 (GLASS SERVICE AS [CZ], November 24, 2022) Non-patent literature

[0110] Technical specification IEC 62492-1 TS "Industrial process control equipment - Infrared thermometers - Part 1: Technical data for infrared thermometers"

[0111] ASTM-E1256 "Standard Test Procedure for Infrared Thermometers (Single Wavelength Type)"

Claims

1. A computer implementation method (3000) for measuring the thickness e of a batch (2005) of material floating in a pool (2006) of molten material in an electric melting furnace (1002) for glass or stone, The above method takes as input data a set (I3000) of time-scale temperature maps M[T] of the batch (2005) of the material and the steady-state temperature T of the batch (2005). ｓ The range of the predicted values ​​R[T] ｓ ] and the threshold σ of the temperature variation ΔT over time Δt are received; The method provides, as output data, the spatial distribution (O3000) of the thickness of the batch (2005) across its surface; The above method (3000) is, (a) In each temperature map M[T] of the provided set I3000, the temperature is the steady-state temperature T of the batch (2005). ｓ The range of the predicted values ​​R[T] ｓ The step of selecting a region located within ] (3001); (b) A step (3002) of calculating the temperature variation ΔT between a plurality of temperature maps that are continuous over a given time range Δt within the same detection area; (c) step (3003) of selecting from the regions of step (b) in which the temperature variation ΔT at time Δt is below the threshold σ provided as input; (d) A step (3004) in which the thickness E of the batch 2005 is calculated for each point of the plurality of temperature maps in each of the regions selected in step (c), wherein the thickness E is calculated as a function E(T) for each point. ｓ The calculation is performed by applying the function E(T ｓ ) In this case, the thickness and the steady-state temperature T ｓ The relationship is defined, and the function E(T ｓ ) shall be based on heat flow transfer obtained through simulation or experiment, or on an empirical model, step (3004); Methods that include...

2. The method further receives, as input data, the temperature T within the internal region of the furnace 1002 that exists above the batch (2005) ｆ , the temperature T of the melt pool (2006) ｍｅｌｔ ; Here, the function E(T) ｓ ) is the heat flow transfer function provided by the following equation, [Math 1] Here, λ is the thermal conductivity of batch 2005, σ is the Stefan-Boltzmann constant, h is the convective heat transfer coefficient of batch 2005, ε is the thermal emissivity of batch 2005, and T ｆ T is the temperature within the internal region of the furnace located above the batch 2005. ｍｅｌｔ T is the temperature of the molten pool 2006. ｓ The method according to claim 1 (3000), wherein is the steady-state temperature of the batch 2005, which is the subject of the calculation of the thickness E.

3. The aforementioned function E(T) ｓ ) is an empirical model provided by the following equation, [Math 2] Here, T １ and T ２ These are the two steady temperatures measured by the experiment of batch 2005, and e １ and e ２ These are, respectively, the aforementioned temperature T １ and T ２ These are the two thicknesses measured by the experiment of batch 2005 corresponding to T ｓ The method according to claim 1 (3000), wherein is the steady-state temperature of the batch 2005, which is the subject of the calculation of the thickness E.

4. The method according to claim 1 or 2 (3000), wherein the threshold value σ of the temperature fluctuation ΔT at the time Δt is 5°C / min, preferably 2°C / min, and more preferably 1°C / min.

5. The steady-state temperature T of batch 2005 ｓ The range of the predicted values ​​R[T] ｓ The method (3000) according to any one of claims 1 to 4, wherein ] is a range of values ​​experimentally determined for batches of similar materials under similar melting conditions, or a defined range of values ​​between two modes of a bimodal distribution calculated from one or more of the temperature maps of the provided set (I3000).

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

7. The method according to claim 6 (3000), further comprising step (f) calculating the velocity of the region detected in step (e) by calculating the displacement of the region over time on the time scale of the set of time-scale temperature maps.

8. The method according to claim 6 or 7 (3000), wherein the object detection function is selected from Otsu's thresholding function, the Laplacian of a Gaussian function, the Hessian determinant, the Gaussian difference, and the watershed-based gray-level blob detection function.

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

10. A computer program (I4001) including instructions, wherein the instructions cause the computer to perform the method described in any one of claims 1 to 8 when the program is executed by the computer.

11. A computer-readable medium (4002) containing instructions, wherein the instructions cause the computer to perform the method according to any one of claims 1 to 8 when the program is executed by the computer.

12. A process for measuring the thickness of a batch (2005) of material (1001a) floating in a pool (2006) of molten material in an electric melting furnace (1002) for glass or stone, wherein the process is: - A step of obtaining a set (I3000) of time-scale temperature maps M[T] of the surface of a batch (2005) of the material (1001a) within the internal region (2009) of the melting furnace (1002); - A step of carrying out the method (3000) according to any one of claims 1 to 8 using a data processing device (4000), wherein the acquired set of time-scale temperature maps M[T] (I3000) is provided to the method (3000) as input data; A process that includes this.

13. A system for measuring the thickness of a batch (2005) of material (1001a) floating in a pool (2006) of molten material in an electric melting furnace (1002) for glass or stone, wherein the system is - An acquisition device (2010) configured to acquire a set (I3000) of time-scale temperature maps M[T] of the surface of a batch 2005 of the material (1001a) within the internal region (2009) of the 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 connected to each other by wire or wireless for data transfer; A system that includes these features.

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 a set of time-scale temperature maps I3000.

15. The system according to claim 13 or 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 to each temperature map of the set of time-scale temperature maps M[T] (I3000).

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