Automatic combustion system of potassium sulfate Mannheim furnace

Abstract

The invention relates to the technical field of intelligent control, in particular to a potassium sulfate Mannheim furnace automatic combustion system which comprises a sensing layer, an analysis layer and a control layer. In-furnace temperature images are continuously collected through the sensing layer, after the sensing layer collects the in-furnace temperature images, the in-furnace temperature images are synchronously stored, and the analysis layer further calls the stored in-furnace temperature images from the sensing layer and analyzes an in-furnace temperature imbalance area based on the in-furnace temperature images. According to the method, the temperature image in the furnace is captured, so that the temperature imbalance area in the furnace is analyzed according to the captured temperature image in the furnace, and the ventilation opening and the temperature control station of the Mannheim furnace are intelligently controlled based on the analysis result, so that the temperature of the Mannheim furnace is adaptively controlled in this way; a balanced reaction environment is provided for firing of potassium sulfate, the labor management cost of the Mannheim furnace is reduced, and the management effects of higher response efficiency and higher control precision are provided for temperature control of the Mannheim furnace.

Description

Technical Field

[0001] The invention relates to the technical field of intelligent control, and in particular to an automatic combustion system of a potassium sulfate Mannheim furnace. Background Art

[0002] The Mannheim furnace is a key equipment used in the production of chemical products such as sulfate. It transforms raw materials in the furnace through high-temperature reactions and specific process. Its structure is complex and precise, with efficient heating and reaction systems, strict control of temperature, pressure and other conditions, and plays an indispensable role in the production of potassium sulfate and other products.

[0003] The invention patent with application number 201911018664.X discloses a temperature control method for an intermediate frequency furnace, characterized in that the control method comprises: S1, obtaining the current temperature of the material to be heated in the intermediate frequency furnace; S2, calculating the difference between the current temperature and the target temperature; S3, matching in the intermediate frequency furnace history database based on the current temperature and the difference to obtain a first input electric power; S4, adjusting the first input electric power to obtain a second input electric power; S5, controlling the power supply power of the intermediate frequency furnace according to the second input electric power; S6, repeating S1 to S5 until heating is completed: wherein, step S4 comprises:

[0004] Obtain a third input electric power and an actual temperature rise in a preset time period before the current moment; calculate a theoretical temperature rise corresponding to the third input electric power; calculate a ratio of the theoretical temperature rise to the actual temperature rise; and based on the ratio, adjust the first input electric power to obtain the second input electric power.

[0005] This application aims to solve the problem that "the existing medium frequency furnace control system has the characteristics of low reliability, slow response speed, weak anti-interference ability, and there is a risk of failure during frequent power startup."

[0006] However, for the Mannheim furnace, in the process of producing potassium sulfate, temperature control is currently mainly based on set values ​​and manual control of manual observation instruments as the main control means. This method has low response efficiency and control accuracy for the global temperature control of the Mannheim furnace, and is obviously not suitable for frequent temperature control scenarios.

[0007] Therefore, we proposed an automatic combustion system for potassium sulfate Mannheim furnace. Summary of the invention

[0008] In view of the above-mentioned shortcomings of the prior art, the present invention provides a potassium sulfate Mannheim furnace automatic combustion system, which solves the technical problems raised in the above-mentioned background technology.

[0009] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0010] The automatic combustion system of potassium sulfate Mannheim furnace includes: perception layer, analysis layer and control layer;

[0011] The temperature image inside the furnace is continuously collected through the perception layer. After collecting the temperature image inside the furnace, the perception layer stores the temperature image inside the furnace synchronously. The analysis layer further retrieves the temperature image inside the furnace stored in the perception layer, analyzes the temperature imbalance area inside the furnace based on the temperature image inside the furnace, and sets the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace based on the analysis result of the temperature imbalance area inside the furnace. The control layer receives the control strategy of the vents and temperature control stations set in the analysis layer, controls the operation of the vents and temperature control stations based on the control strategy, and synchronously jumps to the operation stage of the perception layer;

[0012] The analysis layer includes an analysis module, a selection module and a strategy module. The analysis module is used to receive the furnace temperature image stored in the perception layer, and analyze the degree of temperature imbalance in the furnace based on the furnace temperature image. The selection module is used to receive the analysis result of the degree of temperature imbalance in the furnace, and select the temperature imbalance area in the furnace based on the analysis result of the degree of temperature imbalance in the furnace. The strategy module is used to receive the temperature imbalance area in the furnace selected by the selection module, and set the ventilation opening and temperature control station control strategy on the surface of the Mannheim furnace in combination with the temperature imbalance area in the furnace.

[0013] The analysis logic of the temperature imbalance degree in the furnace in the analysis module is expressed as:

[0014]

[0015] Where: d is the degree of furnace temperature imbalance determined based on a group of furnace temperature images; n is the set of fan-shaped furnace temperature images in the furnace temperature image; sim(i,i+1) is the similarity between the i-th group of fan-shaped furnace temperature images and the i+1-th group of fan-shaped furnace temperature images; D is the degree of furnace temperature imbalance; d max d min is the maximum and minimum value of each group of furnace temperature imbalance degree obtained based on formula (1); m is the set of furnace temperature imbalance degree obtained based on formula (1); d j is the value of the temperature imbalance degree in the jth group of furnaces;

[0016] in, Table pair In sim(i,i+1), the two sets of fan-shaped furnace temperature images used to calculate similarity are always two adjacent sets of fan-shaped furnace temperature images. Medium, d j With d j+1The corresponding furnace temperature images are based on the time series and are always two adjacent groups of furnace temperature images. The greater the degree of furnace temperature imbalance D, the more unbalanced the temperatures in various areas of the furnace are. Conversely, the more balanced the temperatures in various areas of the furnace are.

[0017] Furthermore, the sensing layer includes a sensing module, a logic module and a storage module. The sensing module is used to sense the temperature in the furnace and obtain the temperature image in the furnace. The logic module is used to set the operating logic of the sensing module, feed back the operating logic of the sensing module to the sensing module, and control the sensing module to operate based on the set logic. The storage module is used to receive the temperature image in the furnace obtained by the operation of the sensing module in real time and store the temperature image in the furnace.

[0018] Among them, the sensor module is integrated by several groups of thermal imaging sensors, the several groups of thermal imaging sensors are no less than three groups, and the several groups of thermal imaging sensors are distributed on the top wall of the furnace in a circular array. The temperature sensing range of each group of thermal imaging sensors coincides with the area where the cross-section of the furnace is located. When the storage module stores the temperature image of the furnace, the temperature image of the furnace is synchronously acquired in the sensor module. The corresponding temperature image of the furnace is further marked based on the sensing timestamp, and then stored in the storage module.

[0019] Furthermore, each thermal imaging sensor in the sensing module operates synchronously based on the logic set by the logic module, and segments and reconstructs the acquired furnace temperature image after each operation to obtain a set of new furnace temperature images, which are recorded as real furnace temperature images. The receiving target of the storage module is the real furnace temperature image obtained by segmentation and reconstruction in the sensing module.

[0020] The segmentation and reconstruction logic of the furnace temperature image is expressed as follows:

[0021] A set of values ​​representing the length is obtained by dividing the circumference of the furnace circle by the number of deployed thermal imaging sensors. Segmentation points are picked at the edge of the furnace temperature image based on the length values, so that the midpoint of the image edge contour portion between each two adjacent segmentation points coincides with the deployment position of the thermal imaging sensor, and the number of deployed thermal imaging sensors between each two adjacent segmentation points is unique. The furnace temperature image is segmented based on a line connecting the segmentation points and the center point of the furnace temperature image to obtain a number of fan-shaped furnace temperature images. From the number of fan-shaped furnace temperature images obtained based on the segmentation operation of each furnace temperature image, the fan-shaped furnace temperature image where the thermal imaging sensor deployment position of the furnace temperature image source is located is picked, and the number of picked fan-shaped furnace temperature images are spliced ​​to reconstruct a real furnace temperature image.

[0022] Among them, several groups of picked fan-shaped furnace temperature images are spliced ​​together and arranged based on their corresponding thermal imaging sensor deployment positions.

[0023] Furthermore, the sensor module operation logic set in the logic module, that is, the operation frequency of the sensor module is set, and the sensor module operation logic set in the logic module is customized by the system end user;

[0024] The storage module runs synchronously to traverse the furnace temperature images stored internally, converts each furnace temperature image into a grayscale image, and iteratively converts the source furnace temperature image with the converted grayscale image;

[0025] The furnace temperature images stored in the storage module are copied in real time during the operation phase of the analysis layer, and all the furnace temperature images stored in the storage module are fed back to the analysis layer, and only the backup furnace temperature images are retained;

[0026] The logical representation of converting the furnace temperature image into a grayscale image in the storage module is:

[0027]

[0028] Where: G(a) is the target grayscale value of pixel a in the furnace temperature image; T is the temperature of pixel a; T max 、T min is the highest and lowest temperature of the furnace temperature image; λ is the correction;

[0029] Among them, correction λ∈(0.9, 0.99), the value of correction λ obeys, the closer the distance between pixel a and the raw material used for preparing potassium sulfate in the furnace temperature image, the larger the value of correction λ, conversely, the smaller the value of correction λ, based on the above formula, the target gray value of each pixel in the furnace temperature image is calculated to obtain the grayscale image of the furnace temperature image.

[0030] Furthermore, the sim(i,i+1) is obtained by the following formula:

[0031]

[0032] Where: u is the set of pixels in the fan-shaped furnace temperature image; H(i) v is the gray value of the similarity of the vth group in the i-th group of fan-shaped furnace temperature images; H(i+1) v is the gray value of the similarity of the vth group in the i+1th group of fan-shaped furnace temperature images; γ is the adjustment factor;

[0033] in, Table pair The average, Table pair The adjustment factor γ takes the value of 1 or -1 and obeys When , the adjustment factor γ=1, When , the adjustment factor γ = -1.

[0034] Furthermore, the selection module is set with a trigger threshold for selecting the temperature imbalance area in the furnace. After receiving the analysis result of the temperature imbalance degree in the furnace, the selection module compares the analysis result with the trigger threshold for selecting the temperature imbalance area in the furnace to decide whether to perform the selection operation of the temperature imbalance area in the furnace. When the decision result is no, the module jumps to the analysis module operation stage. When the decision result is yes:

[0035] Based on the thermal imaging sensor that is the source of each fan-shaped furnace temperature image in each group of furnace temperature images, the grayscale average value of each fan-shaped furnace temperature image is calculated in each group, and the difference between the maximum grayscale average value and the minimum grayscale average value is further calculated. The selection threshold is set, and the difference of each group is compared with the selection threshold. The corresponding area of ​​the source fan-shaped furnace temperature image group with the difference within the selection threshold is selected, and the selected area is recorded as the furnace temperature imbalance area.

[0036] Furthermore, the control strategy for the vents and temperature control stations on the surface of the Mannheim furnace set in the strategy module is:

[0037] The vents closest to the area of ​​temperature imbalance are opened or closed;

[0038] The temperature control station closest to the temperature imbalance area performs temperature adjustment operations:

[0039] C=(H norr -H now )×k;

[0040] Where: C is the target adjustment temperature; H now is the gray value of the current temperature imbalance area; H norr is the standard gray value prepared for potassium sulfate; k is the ratio of temperature change to gray value change;

[0041] Among them, H now <H norr , the vent closest to the temperature imbalance area is opened, H now >H norr , the vents closest to the temperature imbalance area are closed, and the ratio k of temperature change to gray value change is a known value determined a priori.

[0042] Furthermore, the control layer includes a receiving module, a waiting module and a jumping module. The receiving module is used for the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace set by the strategy module in the analysis layer, and the operation of the Mannheim furnace is controlled based on the control strategy. The waiting module is used for setting the delay trigger time, and the delay trigger time is applied to control the operation of the jumping module. The jumping module is used for system jump to jump to the perception layer for operation.

[0043] Among them, the delayed trigger time set in the waiting module is applied to the start timestamp of the Mannheim furnace based on the control strategy. After the accumulated timing of the start timestamp of the Mannheim furnace based on the control strategy reaches the delayed trigger time, the jump module triggers the operation.

[0044] Furthermore, the delay trigger time set in the waiting module is subject to:

[0045]

[0046] Where: t is the delayed trigger time; t0 is the delayed trigger time base; g d is the total amount of temperature imbalance area in the furnace; g all It is the total amount of fan-shaped furnace temperature images in a furnace temperature image;

[0047] The delayed trigger time base t0 is customized by the system user, and the delayed trigger time base t0 is not less than three seconds.

[0048] Furthermore, the analysis module is interactively connected to a storage module via a local area network, the storage module is interactively connected to a logic module and a sensor module via a local area network, the analysis module is interactively connected to a selection module and a strategy module via a local area network, the strategy module is interactively connected to a receiving module via a local area network, and the receiving module is interactively connected to a waiting module and a jump module via a local area network.

[0049] Compared with the known public technology, the technical solution provided by the present invention has the following beneficial effects:

[0050] The present invention provides a potassium sulfate Mannheim furnace automatic combustion system. During operation, the system captures a temperature image in the furnace through thermal imaging sensing technology, analyzes the temperature imbalance area in the furnace according to the captured temperature image in the furnace, and then intelligently controls the vents and temperature control stations of the Mannheim furnace based on the analysis result, so that the temperature of the Mannheim furnace is adaptively controlled in this way, providing a balanced reaction environment for the firing of potassium sulfate, thereby greatly reducing the manual management cost of the Mannheim furnace and providing a management effect with higher response efficiency and higher control accuracy for the temperature control of the Mannheim furnace. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0052] Figure 1It is a schematic diagram of the structure of the automatic combustion system of the potassium sulfate Mannheim furnace;

[0053] Figure 2 This is a schematic diagram of the structure of the potassium sulfate Mannheim furnace in the present invention;

[0054] Figure 3 It is a schematic diagram of the principle of furnace temperature image segmentation and reconstruction in the present invention. DETAILED DESCRIPTION

[0055] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] The present invention will be further described below in conjunction with the embodiments.

[0057] Embodiment 1:

[0058] The potassium sulfate Mannheim furnace automatic combustion system of this embodiment is as follows: Figure 1 As shown, it includes: perception layer, analysis layer and control layer;

[0059] The temperature image inside the furnace is continuously collected through the perception layer. After collecting the temperature image inside the furnace, the perception layer stores the temperature image inside the furnace synchronously. The analysis layer further retrieves the temperature image inside the furnace stored in the perception layer, analyzes the temperature imbalance area inside the furnace based on the temperature image inside the furnace, and sets the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace based on the analysis result of the temperature imbalance area inside the furnace. The control layer receives the control strategy of the vents and temperature control stations set in the analysis layer, controls the operation of the vents and temperature control stations based on the control strategy, and synchronously jumps to the operation stage of the perception layer;

[0060] The perception layer includes a sensor module, a logic module and a storage module. The sensor module is used to sense the temperature in the furnace and obtain the temperature image in the furnace. The logic module is used to set the operation logic of the sensor module, feed back the operation logic of the sensor module to the sensor module, and control the sensor module to operate based on the set logic. The storage module is used to receive the temperature image in the furnace obtained by the operation of the sensor module in real time and store the temperature image in the furnace.

[0061] The sensing module is integrated by a plurality of groups of thermal imaging sensors, the plurality of groups of thermal imaging sensors are not less than three groups, and the plurality of groups of thermal imaging sensors are distributed on the top wall of the furnace in a circular array, and the temperature sensing range of each group of thermal imaging sensors coincides with the area where the cross section of the furnace is located. When the storage module stores the temperature image of the furnace, the sensing timestamp of the temperature image of the furnace is synchronously obtained in the sensing module, and the corresponding temperature image of the furnace is further marked based on the sensing timestamp, and then stored in the storage module;

[0062] The analysis layer includes an analysis module, a selection module and a strategy module. The analysis module is used to receive the furnace temperature image stored in the perception layer, and analyze the degree of temperature imbalance in the furnace based on the furnace temperature image. The selection module is used to receive the analysis result of the degree of temperature imbalance in the furnace, and select the temperature imbalance area in the furnace based on the analysis result of the degree of temperature imbalance in the furnace. The strategy module is used to receive the temperature imbalance area in the furnace selected by the selection module, and set the ventilation opening and temperature control station control strategy on the surface of the Mannheim furnace in combination with the temperature imbalance area in the furnace.

[0063] The analysis logic of the furnace temperature imbalance degree in the analysis module is expressed as:

[0064]

[0065] Where: d is the degree of furnace temperature imbalance determined based on a group of furnace temperature images; n is the set of fan-shaped furnace temperature images in the furnace temperature image; sim(i,i+1) is the similarity between the i-th group of fan-shaped furnace temperature images and the i+1-th group of fan-shaped furnace temperature images; D is the degree of furnace temperature imbalance; d max d min is the maximum and minimum value of each group of furnace temperature imbalance degree obtained based on formula (1); m is the set of furnace temperature imbalance degree obtained based on formula (1); d j is the value of the temperature imbalance degree in the jth group of furnaces;

[0066] in, Table pair In sim(i,i+1), the two sets of fan-shaped furnace temperature images used to calculate similarity are always two adjacent sets of fan-shaped furnace temperature images. Medium, d j With d j+1 The corresponding furnace temperature images are based on the time sequence and are always two adjacent groups of furnace temperature images. The greater the furnace temperature imbalance degree D, the more unbalanced the temperature of each area in the furnace is. Conversely, the more balanced the temperature of each area in the furnace is.

[0067] The control layer includes a receiving module, a waiting module and a jumping module. The receiving module is used to analyze the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace set by the strategy module in the layer, and control the operation of the Mannheim furnace based on the control strategy. The waiting module is used to set the delay trigger time, and the delay trigger time is applied to control the operation of the jumping module. The jumping module is used for system jump, and jumps to the perception layer for operation;

[0068] Among them, the delayed trigger time set in the waiting module is applied to the start timestamp of the Mannheim furnace based on the control strategy operation. After the accumulated timing of the start timestamp of the Mannheim furnace based on the control strategy operation reaches the delayed trigger time, the jump module triggers the operation;

[0069] The analysis module is interactively connected to the storage module through the local area network, the storage module is interactively connected to the logic module and the sensor module through the local area network, the analysis module is interactively connected to the selection module and the strategy module through the local area network, the strategy module is interactively connected to the receiving module through the local area network, and the receiving module is interactively connected to the waiting module and the jump module through the local area network.

[0070] In this embodiment, the logic module runs to set the operation logic of the sensor module, feeds back the operation logic of the sensor module to the sensor module, controls the sensor module to run based on the set logic, the sensor module senses the temperature in the furnace in real time, obtains the temperature image in the furnace, the storage module synchronously receives the temperature image in the furnace obtained by the operation of the sensor module in real time, stores the temperature image in the furnace, the analysis module post-operates to receive the temperature image in the furnace stored in the perception layer, analyzes the temperature imbalance degree in the furnace based on the temperature image in the furnace, the selection module further receives the temperature imbalance degree analysis result in the furnace, and the temperature image in the furnace is analyzed based on the temperature image in the furnace. The imbalance degree analysis result selects the temperature imbalance area in the furnace, and then the strategy module receives the temperature imbalance area in the furnace selected by the selection module, and sets the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace in combination with the temperature imbalance area in the furnace. The receiving module further analyzes the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace set by the strategy module in the layer, controls the operation of the Mannheim furnace based on the control strategy, waits for the module to synchronously set the delayed trigger time, applies the delayed trigger time to control the jump module to run, and finally jumps through the jump module system to jump to the perception layer to run.

[0071] Through the operation of the system in the above embodiment, the operating temperature control of the Mannheim furnace is separated from manual management as much as possible, saving a lot of manual management costs. At the same time, the self-operation temperature control of the Mannheim furnace is intelligent, thereby providing a stable reaction environment for the preparation of potassium sulfate.

[0072] See also Figure 2 The figure further illustrates the internal structure of the Mannheim furnace, see Figure 3 Based on the arrows in the figure, the figure shows the segmentation and reconstruction process of the temperature image in the furnace.

[0073] Embodiment 2:

[0074] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The automatic combustion system of potassium sulfate Mannheim furnace in Example 1 is further described in detail:

[0075] Each thermal imaging sensor in the sensing module operates synchronously based on the logic set by the logic module, and segments and reconstructs the acquired furnace temperature image after each operation to obtain a set of new furnace temperature images, which are recorded as real furnace temperature images. The receiving target of the storage module is the real furnace temperature image obtained by segmentation and reconstruction in the sensing module.

[0076] The segmentation and reconstruction logic of the furnace temperature image is expressed as:

[0077] A set of values ​​representing the length is obtained by dividing the circumference of the furnace circle by the number of deployed thermal imaging sensors. Segmentation points are picked at the edge of the furnace temperature image based on the length values, so that the midpoint of the image edge contour portion between each two adjacent segmentation points coincides with the deployment position of the thermal imaging sensor, and the number of deployed thermal imaging sensors between each two adjacent segmentation points is unique. The furnace temperature image is segmented based on a line connecting the segmentation points and the center point of the furnace temperature image to obtain a number of fan-shaped furnace temperature images. From the number of fan-shaped furnace temperature images obtained based on the segmentation operation of each furnace temperature image, the fan-shaped furnace temperature image where the thermal imaging sensor deployment position of the furnace temperature image source is located is picked, and the number of picked fan-shaped furnace temperature images are spliced ​​to reconstruct a real furnace temperature image.

[0078] Among them, several groups of picked fan-shaped furnace temperature images are spliced ​​together and arranged based on their corresponding thermal imaging sensor deployment positions;

[0079] The sensor module operation logic set in the logic module, that is, the operation frequency of the sensor module is set. The sensor module operation logic set in the logic module is customized by the system end user;

[0080] The storage module runs synchronously and traverses the furnace temperature images stored internally, converts each furnace temperature image into a grayscale image, and iteratively converts the source furnace temperature image with the converted grayscale image;

[0081] The furnace temperature images stored in the storage module are copied in real time during the operation of the analysis layer. All the furnace temperature images stored in the storage module are fed back to the analysis layer, and only the backup furnace temperature images are retained.

[0082] The logical representation of converting the furnace temperature image into a grayscale image in the storage module is:

[0083]

[0084] Where: G(a) is the target grayscale value of pixel a in the furnace temperature image; T is the temperature of pixel a; T max 、T min is the highest and lowest temperature of the furnace temperature image; λ is the correction;

[0085] Among them, correction λ∈(0.9, 0.99), the value of correction λ obeys, the closer the distance between pixel a and the raw material used for preparing potassium sulfate in the furnace temperature image, the larger the value of correction λ, conversely, the smaller the value of correction λ, based on the above formula, the target gray value of each pixel in the furnace temperature image is calculated to obtain the grayscale image of the furnace temperature image.

[0086] In this embodiment, through the above settings, further operation logic support is provided for the operation of the system in Example 1, and the grayscale image conversion of the temperature image in the furnace is performed with the limited grayscale image conversion logic, providing operation data support for the operation of the system in Example 1.

[0087] Embodiment 3:

[0088] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The automatic combustion system of potassium sulfate Mannheim furnace in Example 1 is further described in detail:

[0089] sim(i,i+1) is obtained by the following formula:

[0090]

[0091] Where: u is the set of pixels in the fan-shaped furnace temperature image; H(i) v is the gray value of the similarity of the vth group in the i-th group of fan-shaped furnace temperature images; H(i+1) v is the gray value of the similarity of the vth group in the i+1th group of fan-shaped furnace temperature images; γ is the adjustment factor;

[0092] in, Table pair The average, Table pair The adjustment factor γ takes the value of 1 or -1 and obeys When , the adjustment factor γ=1, When , the adjustment factor γ = -1.

[0093] Through the above logic formula, the calculation logic of the parameter sim(i, i+1) used in the furnace temperature imbalance degree analysis logic in Example 1 is further limited.

[0094] like Figure 1 As shown, the selection module is set with a trigger threshold for selecting the temperature imbalance area in the furnace. After receiving the analysis result of the temperature imbalance degree in the furnace, the selection module compares the analysis result with the trigger threshold for selecting the temperature imbalance area in the furnace to decide whether to perform the selection operation of the temperature imbalance area in the furnace. When the decision result is no, it jumps to the analysis module operation stage. When the decision result is yes:

[0095] Based on the thermal imaging sensor that is the source of each fan-shaped furnace temperature image in each group of furnace temperature images, the grayscale average value of each fan-shaped furnace temperature image is calculated in each group, and the difference between the maximum grayscale average value and the minimum grayscale average value is further calculated. The selection threshold is set, and the difference of each group is compared with the selection threshold. The corresponding area of ​​the source fan-shaped furnace temperature image group with the difference within the selection threshold is selected, and the selected area is recorded as the furnace temperature imbalance area.

[0096] Through the above settings, the logic of selecting the temperature imbalance area in the furnace by the selection module in the system analysis layer is further limited.

[0097] Embodiment 4:

[0098] In terms of specific implementation, based on Example 1, this example refers to Figure 1 The automatic combustion system of potassium sulfate Mannheim furnace in Example 1 is further described in detail:

[0099] The control strategies for the ventilation openings and temperature control stations on the surface of the Mannheim furnace set in the strategy module are:

[0100] The vents closest to the area of ​​temperature imbalance are opened or closed;

[0101] The temperature control station closest to the temperature imbalance area performs temperature adjustment operations:

[0102] C=(H norr -H now )×k;

[0103] Where: C is the target adjustment temperature; H now is the gray value of the current temperature imbalance area; H norr is the standard gray value prepared for potassium sulfate; k is the ratio of temperature change to gray value change;

[0104] Among them, H now <H norr , the vent closest to the temperature imbalance area is opened, H now >H norr , the vents closest to the temperature imbalance area are closed, and the ratio k of temperature change to gray value change is a known value determined a priori.

[0105] like Figure 1As shown, the delay trigger time set in the waiting module follows:

[0106]

[0107] Where: t is the delayed trigger time; t0 is the delayed trigger time base; g d is the total amount of temperature imbalance area in the furnace; g all It is the total amount of fan-shaped furnace temperature images in a furnace temperature image;

[0108] The delayed trigger time base t0 is customized by the system user, and the delayed trigger time base t0 is not less than three seconds.

[0109] In this embodiment, the control strategy of the ventilation openings and temperature control stations on the surface of the Mannheim furnace set in the strategy module is further logically limited to ensure the stable operation of the strategy module and provide a control strategy for the control layer to intelligently control the Mannheim furnace.

[0110] In summary, in the above embodiment, during operation, the system uses thermal imaging sensing technology to capture the temperature image inside the furnace, and then analyzes the temperature imbalance area inside the furnace based on the captured temperature image inside the furnace, and then based on the analysis result, intelligently controls the vents and temperature control stations of the Mannheim furnace, so that the temperature of the Mannheim furnace can be adaptively controlled in this way, providing a balanced reaction environment for the firing of potassium sulfate, thereby greatly reducing the manual management cost of the Mannheim furnace and providing a management effect with higher response efficiency and higher control accuracy for the temperature control of the Mannheim furnace.

[0111] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. Potassium sulfate Mannheim furnace automatic combustion system, characterized in that: include: Perception layer, analysis layer and control layer; The temperature image inside the furnace is continuously collected through the perception layer. After collecting the temperature image inside the furnace, the perception layer stores the temperature image inside the furnace synchronously. The analysis layer further retrieves the temperature image inside the furnace stored in the perception layer, analyzes the temperature imbalance area inside the furnace based on the temperature image inside the furnace, and sets the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace based on the analysis result of the temperature imbalance area inside the furnace. The control layer receives the control strategy of the vents and temperature control stations set in the analysis layer, controls the operation of the vents and temperature control stations based on the control strategy, and synchronously jumps to the operation stage of the perception layer; The analysis layer includes an analysis module, a selection module and a strategy module. The analysis module is used to receive the furnace temperature image stored in the perception layer, and analyze the degree of temperature imbalance in the furnace based on the furnace temperature image. The selection module is used to receive the analysis result of the degree of temperature imbalance in the furnace, and select the temperature imbalance area in the furnace based on the analysis result of the degree of temperature imbalance in the furnace. The strategy module is used to receive the temperature imbalance area in the furnace selected by the selection module, and set the ventilation opening and temperature control station control strategy on the surface of the Mannheim furnace in combination with the temperature imbalance area in the furnace. The analysis logic of the temperature imbalance degree in the furnace in the analysis module is expressed as: Where: d is the degree of furnace temperature imbalance determined based on a group of furnace temperature images; n is the set of fan-shaped furnace temperature images in the furnace temperature image; sim(i,i+1) is the similarity between the i-th group of fan-shaped furnace temperature images and the i+1-th group of fan-shaped furnace temperature images; D is the degree of furnace temperature imbalance; d max d min is the maximum and minimum value of each group of furnace temperature imbalance degree obtained based on formula (1); m is the set of furnace temperature imbalance degree obtained based on formula (1); d j is the value of the temperature imbalance degree in the jth group of furnaces; in, Table pair In sim(i,i+1), the two sets of fan-shaped furnace temperature images used to calculate similarity are always two adjacent sets of fan-shaped furnace temperature images. Medium, d j With d j+1 The corresponding furnace temperature images are based on the time series and are always two adjacent groups of furnace temperature images. The greater the degree of furnace temperature imbalance D, the more unbalanced the temperatures in various areas of the furnace are. Conversely, the more balanced the temperatures in various areas of the furnace are.

2. The potassium sulfate Mannheim furnace automatic combustion system according to claim 1, characterized in that: The sensing layer includes a sensing module, a logic module and a storage module. The sensing module is used to sense the temperature in the furnace and obtain the temperature image in the furnace. The logic module is used to set the operating logic of the sensing module, feed back the operating logic of the sensing module to the sensing module, and control the sensing module to operate based on the set logic. The storage module is used to receive the temperature image in the furnace obtained by the operation of the sensing module in real time and store the temperature image in the furnace. Among them, the sensor module is integrated by several groups of thermal imaging sensors, the several groups of thermal imaging sensors are no less than three groups, and the several groups of thermal imaging sensors are distributed on the top wall of the furnace in a circular array. The temperature sensing range of each group of thermal imaging sensors coincides with the area where the cross-section of the furnace is located. When the storage module stores the temperature image of the furnace, the temperature image of the furnace is synchronously acquired in the sensor module. The corresponding temperature image of the furnace is further marked based on the sensing timestamp, and then stored in the storage module.

3. The potassium sulfate Mannheim furnace automatic combustion system according to claim 2, characterized in that: Each thermal imaging sensor in the sensing module operates synchronously based on the logic set by the logic module, and segments and reconstructs the acquired furnace temperature image after each operation to obtain a set of new furnace temperature images, which are recorded as real furnace temperature images. The receiving target of the storage module is the real furnace temperature image obtained by segmentation and reconstruction in the sensing module; The segmentation and reconstruction logic of the furnace temperature image is expressed as follows: A set of values ​​representing the length is obtained by dividing the circumference of the furnace circle by the number of deployed thermal imaging sensors. Segmentation points are picked at the edge of the furnace temperature image based on the length values, so that the midpoint of the image edge contour portion between each two adjacent segmentation points coincides with the deployment position of the thermal imaging sensor, and the number of deployed thermal imaging sensors between each two adjacent segmentation points is unique. The furnace temperature image is segmented based on a line connecting the segmentation points and the center point of the furnace temperature image to obtain a number of fan-shaped furnace temperature images. From the number of fan-shaped furnace temperature images obtained based on the segmentation operation of each furnace temperature image, the fan-shaped furnace temperature image where the thermal imaging sensor deployment position of the furnace temperature image source is located is picked, and the number of picked fan-shaped furnace temperature images are spliced ​​to reconstruct a real furnace temperature image. Among them, several groups of picked fan-shaped furnace temperature images are spliced ​​together and arranged based on their corresponding thermal imaging sensor deployment positions.

4. The potassium sulfate Mannheim furnace automatic combustion system according to claim 2, characterized in that: The sensor module operation logic set in the logic module, that is, the operation frequency of the sensor module is set. The sensor module operation logic set in the logic module is customized by the system end user; The storage module runs synchronously to traverse the furnace temperature images stored internally, converts each furnace temperature image into a grayscale image, and iteratively converts the source furnace temperature image with the converted grayscale image; The furnace temperature images stored in the storage module are copied in real time during the operation phase of the analysis layer, and all the furnace temperature images stored in the storage module are fed back to the analysis layer, and only the backup furnace temperature images are retained; The logical representation of converting the furnace temperature image into a grayscale image in the storage module is: Where: G(a) is the target grayscale value of pixel a in the furnace temperature image; T is the temperature of pixel a; T max , T min The highest and lowest temperatures in the furnace temperature image; λ is the correction; Among them, correction λ∈(0.9, 0.99), the value of correction λ obeys, the closer the distance between pixel a and the raw material used for preparing potassium sulfate in the furnace temperature image, the larger the value of correction λ, conversely, the smaller the value of correction λ, based on the above formula, the target gray value of each pixel in the furnace temperature image is calculated to obtain the grayscale image of the furnace temperature image.

5. The potassium sulfate Mannheim furnace automatic combustion system according to claim 1, characterized in that: The sim(i,i+1) is obtained by the following formula: Where: u is the set of pixels in the fan-shaped furnace temperature image; H(i) v is the gray value of the similarity of the vth group in the i-th group of fan-shaped furnace temperature images; H(i+1) v is the gray value of the similarity of the vth group in the i+1th group of fan-shaped furnace temperature images; γ is the adjustment factor; in, Table pair The average, Table pair The adjustment factor γ takes the value of 1 or -1 and obeys When , the adjustment factor γ=1, When , the adjustment factor γ = -1.

6. The potassium sulfate Mannheim furnace automatic combustion system according to claim 1, characterized in that: The selection module is set with a trigger threshold for selecting the temperature imbalance area in the furnace. After receiving the analysis result of the temperature imbalance degree in the furnace, the selection module compares the analysis result with the trigger threshold for selecting the temperature imbalance area in the furnace to decide whether to perform the selection operation of the temperature imbalance area in the furnace. When the decision result is no, it jumps to the analysis module operation stage. When the decision result is yes: Based on the thermal imaging sensor that is the source of each fan-shaped furnace temperature image in each group of furnace temperature images, the grayscale average value of each fan-shaped furnace temperature image is calculated in each group, and the difference between the maximum grayscale average value and the minimum grayscale average value is further calculated. The selection threshold is set, and the difference of each group is compared with the selection threshold. The corresponding area of ​​the source fan-shaped furnace temperature image group with the difference within the selection threshold is selected, and the selected area is recorded as the furnace temperature imbalance area.

7. The potassium sulfate Mannheim furnace automatic combustion system according to claim 1, characterized in that: The control strategy for the vents and temperature control stations on the surface of the Mannheim furnace set in the strategy module is: The vents closest to the area of ​​temperature imbalance are opened or closed; The temperature control station closest to the temperature imbalance area performs temperature adjustment operations: C=(H norr -H now )×k; Where: C is the target adjustment temperature; H now is the gray value of the current temperature imbalance area; H norr is the standard gray value prepared for potassium sulfate; k is the ratio of temperature change to gray value change; Among them, H now <H norr , the vent closest to the temperature imbalance area is opened, H now >H norr , the vents closest to the temperature imbalance area are closed, and the ratio k of temperature change to gray value change is a known value determined a priori.

8. The potassium sulfate Mannheim furnace automatic combustion system according to claim 1, characterized in that: The control layer includes a receiving module, a waiting module and a jumping module. The receiving module is used to analyze the control strategy of the vents and temperature control stations on the surface of the Mannheim furnace set by the strategy module in the layer, and control the operation of the Mannheim furnace based on the control strategy. The waiting module is used to set the delay trigger time, and the delay trigger time is applied to control the operation of the jumping module. The jumping module is used for system jump, and jumps to the perception layer for operation; Among them, the delayed trigger time set in the waiting module is applied to the start timestamp of the Mannheim furnace based on the control strategy. After the accumulated timing of the start timestamp of the Mannheim furnace based on the control strategy reaches the delayed trigger time, the jump module triggers the operation.

9. The potassium sulfate Mannheim furnace automatic combustion system according to claim 8, characterized in that: The delay trigger time set in the waiting module is subject to: Where: t is the delayed trigger time; t0 is the delayed trigger time base; g d is the total amount of temperature imbalance area in the furnace; g all It is the total amount of fan-shaped furnace temperature images in a furnace temperature image; The delayed trigger time base t0 is customized by the system user, and the delayed trigger time base t0 is not less than three seconds.

10. The potassium sulfate Mannheim furnace automatic combustion system according to claim 1, characterized in that: The analysis module is interactively connected to the storage module through the local area network, the storage module is interactively connected to the logic module and the sensor module through the local area network, the analysis module is interactively connected to the selection module and the strategy module through the local area network, the strategy module is interactively connected to the receiving module through the local area network, and the receiving module is interactively connected to the waiting module and the jump module through the local area network.

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