Method for monitoring off-center loads in multi-point, multi-corridor steel silos

Abstract

This invention relates to an anti-eccentric load monitoring system for multi-point, multi-corridor discharge steel silos, comprising: multiple pressure sensors; multiple K-wave radar level imagers; and a computer for processing pressure data collected from various measuring points by the multiple pressure sensors, and three-dimensional images of the actual elevation H distribution of materials within the steel silo collected by the K-wave radar level imagers. The system calculates and displays the actual three-dimensional image of the materials within the steel silo, the homogenized three-dimensional image, the internal void areas of the materials, and the eccentric load distribution image. This invention also discloses an anti-eccentric load monitoring method for multi-point, multi-corridor discharge steel silos. By displaying and comparing the actual three-dimensional image and the homogenized three-dimensional image of the materials within the steel silo on a computer, this invention can accurately monitor the maximum load gradient area of ​​the materials, as well as potential arching and void areas, providing a more intuitive and reliable guidance for the safe unloading of materials from the steel silo.

Description

Technical Field

[0001] This invention relates to the field of steel silo storage and storage status monitoring technology, and in particular to a method for preventing off-center load monitoring of multi-point, multi-corridor steel silos. Background Technology

[0002] Currently, the largest steel silos used for storing powdery materials have a volume of up to 100,000 m³. 3 The steel silo has a diameter of 60m, a height of 45m, a maximum storage height of over 35m, and a maximum storage load of over 80,000 tons. The silo is cylindrical, with walls welded from steel plates of varying thicknesses, making it susceptible to large impact loads and exhibiting poor resistance to eccentric loads. Material is discharged sequentially from multiple conical hoppers evenly distributed at the silo bottom. Due to the large diameter of the silo bottom, uneven discharge is prone to occur, leading to eccentric loads on the silo. When the material level is high, the probability of material caking and arching increases significantly, resulting in hollow areas within the material. If these hollow areas are large, a sudden collapse of material at higher levels can cause substantial impact loads on the silo walls.

[0003] Conventional steel silo level systems typically employ level measuring devices such as weighted hammers, rotary paddles, capacitive sensors, and radar sensors. Although the principles of these various level gauges differ, they can only measure the height of a single point on the material surface. They cannot obtain information on the overall elevation of the material or internal voids, making it difficult to effectively monitor material imbalances and collapses. Summary of the Invention

[0004] To address the limitations of existing technologies in monitoring material imbalance and collapse within steel silos, this invention aims to provide a method for monitoring imbalance in multi-point, multi-corridor discharge steel silos. This method can accurately detect the maximum load gradient area of ​​materials and potential arching or hollow areas, providing a more intuitive and reliable guide for the safe unloading of materials from the silos.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for monitoring off-center loads in a multi-point, multi-corridor steel silo discharge system, the method comprising the following sequential steps:

[0006] (1) Multiple pressure sensors transmit the collected pressure data to the computer;

[0007] (2) The computer receives and processes the pressure sensor data to obtain a three-dimensional image of the material's theoretical elevation h distribution;

[0008] (3) Multiple K-wave radar level imagers scan the surface shape of the material in the steel silo and transmit the scanning signals to the computer for processing to obtain a three-dimensional image of the actual elevation H distribution of the material in the steel silo.

[0009] (4) The computer compares the three-dimensional image of the actual elevation H of the material with the three-dimensional image of the theoretical elevation h of the material. If the actual elevation H of the material is different from the theoretical elevation h of the material, it indicates that there are arched or hollow areas inside the material.

[0010] The large gradient elevation change points in the actual elevation H distribution are areas with a high risk of material collapse. The large gradient elevation change points refer to the elevation change between two adjacent elevation points that is greater than x1·arctanα, where α is the material repose angle and x1 is the horizontal distance between the two adjacent elevation points.

[0011] The large gradient pressure change points in the pressure distribution data at the bottom of the silo are areas where the material is severely unbalanced. The large gradient pressure change points are those where the pressure value change is greater than ρgsx2·arctanα, where x2 is the horizontal distance between two adjacent pressure measurement points, ρ is the material density, and g is the local gravitational acceleration.

[0012] The anti-offset load monitoring system includes:

[0013] Multiple pressure sensors are used to measure the pressure on the upper edge of the unloading cone, the bottom edge of the steel silo, the inclined surface of the unloading cone, and the lower side of the steel silo wall;

[0014] Multiple K-wave radar level imagers are used to scan the surface shape of the materials in the steel silo and obtain a three-dimensional image of the actual elevation H distribution of the materials in the steel silo.

[0015] The computer is used to process the pressure data of each measuring point collected by multiple pressure sensors, as well as the three-dimensional image of the actual elevation H distribution of the material in the steel silo collected by the K-wave radar level imager. It calculates and displays the actual three-dimensional image of the material in the steel silo, the three-dimensional image after homogenization, the hollow area inside the material, and the image of the off-center load distribution.

[0016] The outputs of the multiple pressure sensors and the multiple K-wave radar level imagers are all connected to the input of the computer.

[0017] The multiple pressure sensors are arranged at the upper edge of each unloading cone, the bottom edge of the steel silo, the inclined surface of each unloading cone, and the lower side of the steel silo wall;

[0018] The multiple K-wave radar level imagers are all circular and evenly arranged on the top of the steel plate silo.

[0019] Step (2) specifically refers to: if the data of the pressure sensor corresponding to the bottom of the steel silo is 0, it means that a hollow area has appeared at the corresponding position of the pressure sensor, and the computer issues an instruction to unload the material in the vicinity; if the data of the pressure sensor is not all 0 or not all 0, the non-zero data is taken and transmitted to the computer for data processing.

[0020] The distribution of pressure sensors along the bottom edge of the steel silo is divided into grids. A grid line A is taken along the upper edge of the unloading cone, corresponding to the bottom surface of the vertical cross-section of the material. The combination of multiple pressure sensor data distributed on grid line A is denoted as p, where p = p1, p2, p3...p n The pressure sensor data p is converted into the theoretical elevation h of the material using the following formula:

[0021] p=ρgsh

[0022] In the formula: s is the area of ​​the material bottom surface corresponding to the pressure sensor, which is considered as a unit area; ρ is the material density; g is the local gravitational acceleration; h = h1, h2, h3…h n ;

[0023] The multiple pressure sensors x along this straight line are labeled x1, x2, x3…x from left to right. n Then the pressure sensor position and theoretical elevation h are represented in a rectangular coordinate system as (x1,h1), (x2,h2), (x3,h3), ..., (x n ,h n The upper surface of the vertical section of the material is considered as a curve, and the curve function of the upper surface of the material is calculated using the Lagrange interpolation method:

[0024]

[0025] In the formula, i, j = 1, 2, 3…n;

[0026] The theoretical elevation h of any point on the upper surface of the vertical cross section of the material is obtained through L(x), and the pressure data p at the point without measuring points is calculated based on the theoretical elevation h.

[0027] Using the above method, the data from multiple pressure sensors that form a straight line are calculated one by one to obtain the distribution of the material's theoretical elevation h in the spatial coordinates of the steel silo. This distribution is then converted into a three-dimensional image by a computer system, resulting in a three-dimensional image of the material's theoretical elevation h distribution.

[0028] As can be seen from the above technical solution, the beneficial effects of the present invention are as follows: First, by displaying the actual three-dimensional image of the material in the steel silo on a computer and comparing it with the homogenized three-dimensional image, the maximum load gradient area of ​​the material and the possible arching and hollow areas can be accurately monitored, providing a more intuitive and reliable guide for the safe unloading of the steel silo; Second, by judging the change points of large gradient elevation in the elevation distribution as areas with a high risk of material collapse, it can effectively prevent the collapse of the silo due to uneven material storage; Third, by judging the change points of large gradient pressure in the pressure distribution data at the bottom of the silo as areas with more serious material unbalance, it can effectively prevent the danger of uneven material storage leading to excessive impact on the silo body or even collapse. Attached Figure Description

[0029] Figure 1 This is a system composition diagram of the present invention;

[0030] Figure 2 This is a schematic diagram showing the arrangement of the pressure sensors;

[0031] Figure 3 This is a schematic diagram showing the location of the K-wave radar level imager situated at the top of the reservoir.

[0032] Figure 4 This is a schematic diagram of the grid division at the bottom of the reservoir.

[0033] Figure 5 This is a schematic diagram of the vertical cross-section of the material at grid line A. Detailed Implementation

[0034] like Figure 1 , Figure 2 , Figure 3 As shown, an anti-eccentric load monitoring system for multi-point, multi-corridor steel silos includes:

[0035] Multiple pressure sensors 1 are used to measure the pressure on the upper edge of the unloading cone 4, the bottom edge of the steel silo, the inclined surface of the unloading cone 4, and the lower side of the steel silo wall.

[0036] Multiple K-wave radar level imagers 2 are used to scan the surface shape of the materials in the steel silo and obtain a three-dimensional image of the actual elevation H distribution of the materials in the steel silo.

[0037] Computer 3 is used to process the pressure data of each measuring point collected by multiple pressure sensors, as well as the three-dimensional image of the actual elevation H distribution of the material in the steel silo collected by K-wave radar level imager 2. It calculates and displays the actual three-dimensional image of the material in the steel silo, the three-dimensional image after homogenization, the hollow area inside the material and the image of the off-center load distribution.

[0038] The outputs of the multiple pressure sensors 1 and the multiple K-wave radar level imagers 2 are all connected to the input of the computer 3.

[0039] The multiple pressure sensors 1 are arranged at the upper edge of each unloading cone 4, the bottom edge of the steel silo, the inclined surface of each unloading cone 4, and the lower side of the steel silo wall. The multiple K-wave radar level imagers 2 are all circular and evenly arranged on the top of the steel silo. The upper end of the steel silo is provided with a feed inlet 6, and the lower end of the steel silo is provided with a discharge outlet 5.

[0040] This method includes the following steps in sequence:

[0041] (1) Multiple pressure sensors 1 transmit the collected pressure data to computer 3;

[0042] (2) Computer 3 receives data from pressure sensor 1 and processes it to obtain a three-dimensional image of the material theoretical elevation h distribution;

[0043] (3) Multiple K-wave radar level imagers 2 scan the surface shape of the material in the steel silo and transmit the scanning signal to computer 3 for processing to obtain a three-dimensional image of the actual elevation H distribution of the material in the steel silo.

[0044] (4) Computer 3 compares the three-dimensional image of the actual elevation H of the material with the three-dimensional image of the theoretical elevation h of the material. If the actual elevation H of the material is different from the theoretical elevation h of the material, it indicates that there are arched or hollow areas inside the material.

[0045] The large gradient elevation change points in the actual elevation H distribution are areas with a high risk of material collapse. The large gradient elevation change points refer to the elevation change between two adjacent elevation points that is greater than x1·arctanα, where α is the material repose angle and x1 is the horizontal distance between the two adjacent elevation points.

[0046] The large gradient pressure change points in the pressure distribution data at the bottom of the silo are areas where the material is severely unbalanced. The large gradient pressure change points are those where the pressure value change is greater than ρgsx2·arctanα, where x2 is the horizontal distance between two adjacent pressure measurement points, ρ is the material density, and g is the local gravitational acceleration.

[0047] The specific steps (2) are as follows: if the data of the pressure sensor 1 corresponding to the bottom of the steel silo is 0, it means that a hollow area has appeared at the corresponding position of the pressure sensor 1, and the computer 3 issues an instruction to unload the material in the vicinity; if the data of the pressure sensor 1 is not all 0 or not all 0, the non-zero data is transmitted to the computer 3 for data processing.

[0048] The distribution of pressure sensors along the bottom edge of the steel silo is divided into grids. A grid line A is taken along the upper edge of the unloading cone, corresponding to the bottom surface of the vertical cross-section of the material. The combination of multiple pressure sensor data distributed on grid line A is denoted as p, where p = p1, p2, p3...pn The pressure sensor data p is converted into the theoretical elevation h of the material using the following formula:

[0049] p=ρgsh

[0050] In the formula: s is the area of ​​the material bottom surface corresponding to the pressure sensor, which is considered as a unit area; ρ is the material density; g is the local gravitational acceleration; h = h1, h2, h3…h n ;

[0051] The multiple pressure sensors x along this straight line are labeled x1, x2, x3…x from left to right. n Then the pressure sensor position and theoretical elevation h are represented in a rectangular coordinate system as (x1,h1), (x2,h2), (x3,h3), ..., (x n ,h n The upper surface of the vertical cross-section of the material is considered as a curve, such as... Figure 5 As shown, the curve function of the material's upper surface is calculated using the Lagrange interpolation method:

[0052]

[0053] In the formula, i, j = 1, 2, 3…n;

[0054] The theoretical elevation h of any point on the vertical cross-section of the material is obtained through L(x), and the pressure data p at the location without a measuring point is calculated based on the theoretical elevation h; the location without a measuring point refers to the location at the bottom of the silo where no pressure sensor is installed.

[0055] Using the above method, the data from multiple pressure sensors 1 that form a straight line are calculated one by one to obtain the distribution of the material's theoretical elevation h in the spatial coordinates of the steel silo. The data is then converted into a three-dimensional image by the computer system 3 to obtain a three-dimensional image of the material's theoretical elevation h distribution.

[0056] This invention, by displaying actual three-dimensional images of the materials inside the steel silo on a computer and comparing them with homogenized three-dimensional images, can accurately monitor the areas of maximum load gradient of the materials, as well as potential arching and hollow areas, providing a more intuitive and reliable guide for the safe unloading of the steel silo. By identifying large elevation gradients in the elevation distribution as areas with a high risk of material collapse, it can effectively prevent uneven material storage in the steel silo. Furthermore, by identifying large pressure gradients in the pressure distribution data at the bottom of the silo as areas with severe material imbalance, it can effectively prevent the danger of uneven storage leading to excessive impact on the silo body or even collapse.

Claims

1. A method for monitoring eccentric load in a multi-point, multi-corridor steel silo discharge monitoring system, characterized in that: The method includes the following steps in sequence: (1) Multiple pressure sensors transmit the collected pressure data to the computer; (2) The computer receives and processes the pressure sensor data to obtain a three-dimensional image of the material's theoretical elevation h distribution; (3) Multiple K-wave radar level imagers scan the surface shape of the material in the steel silo and transmit the scanning signals to the computer for processing to obtain a three-dimensional image of the actual elevation H distribution of the material in the steel silo. (4) The computer compares the three-dimensional image of the material's actual elevation H distribution with the three-dimensional image of the material's theoretical elevation h distribution. If the material's actual elevation H and the material's theoretical elevation h are different, it indicates that there are arched or hollow areas inside the material. The computer calculates and displays the actual three-dimensional image, the homogenized three-dimensional image, the hollow areas inside the material, and the image of the eccentric load distribution of the material in the steel silo. The large gradient elevation change points in the actual elevation H distribution are areas with a high risk of material collapse. The large gradient elevation change points refer to the elevation change between two adjacent elevation points that is greater than x1·arctanα, where α is the material repose angle and x1 is the horizontal distance between the two adjacent elevation points. The large gradient pressure change points in the pressure distribution data at the bottom of the silo are areas where the material is severely unbalanced. The large gradient pressure change points are those where the pressure value change is greater than ρgsx2·atctanα, where x2 is the horizontal distance between two adjacent pressure measurement points, ρ is the material density, and g is the local gravitational acceleration. The anti-off-center load monitoring system includes: Multiple pressure sensors are used to measure the pressure on the upper edge of the unloading cone, the bottom edge of the steel silo, the inclined surface of the unloading cone, and the lower side of the steel silo wall; Multiple K-wave radar level imagers are used to scan the surface shape of the materials in the steel silo and obtain a three-dimensional image of the actual elevation H distribution of the materials in the steel silo. The computer is used to process the pressure data of each measuring point collected by multiple pressure sensors, as well as the three-dimensional image of the actual elevation H distribution of the material in the steel silo collected by the K-wave radar level imager. It calculates and displays the actual three-dimensional image of the material in the steel silo, the three-dimensional image after homogenization, the hollow area inside the material, and the image of the off-center load distribution. The outputs of the multiple pressure sensors and the multiple K-wave radar level imagers are all connected to the input of the computer. The multiple pressure sensors are arranged at the upper edge of each unloading cone, the bottom edge of the steel silo, the inclined surface of each unloading cone, and the lower side of the steel silo wall; The multiple K-wave radar level imagers are all circular and evenly arranged on the top of the steel plate silo.

2. The method for monitoring off-center load according to claim 1, characterized in that: Step (2) specifically refers to: if the data of the pressure sensor corresponding to the bottom of the steel silo is 0, it means that a hollow area has appeared at the corresponding position of the pressure sensor, and the computer issues an instruction to unload the material in the vicinity; if the data of the pressure sensor is not all 0 or not all 0, the non-zero data is taken and transmitted to the computer for data processing. The distribution of pressure sensors along the bottom edge of the steel silo is divided into grids. A grid line A is taken along the upper edge of the unloading cone, corresponding to the bottom surface of the vertical cross-section of the material. The combination of pressure data from multiple pressure sensors distributed on grid line A is denoted as p, where p = p1, p2, p3...p n The pressure sensor data p is converted into the theoretical elevation h of the material using the following formula: p = ρhsh In the formula: s is the area of ​​the material bottom surface corresponding to the pressure sensor, which is considered as a unit area; ρ is the material density; g is the local gravitational acceleration; h = h1, h2, h3…h n ; The multiple pressure sensors x along this straight line are labeled x1, x2, x3…x from left to right. n Then, the pressure sensor position and theoretical elevation h are represented in a rectangular coordinate system as (x1,h1), (x2,h2), (x3,h3), ..., (x n ,h n The upper surface of the vertical section of the material is considered as a curve, and the curve function of the upper surface of the material is calculated using the Lagrange interpolation method: In the formula, i, j = 1, 2, 3…n; The theoretical elevation h of any point on the upper surface of the vertical cross section of the material is obtained through L(x), and the pressure data p at the point without measuring points is calculated based on the theoretical elevation h. Using the above method, the data from multiple pressure sensors that form a straight line are calculated one by one to obtain the distribution of the material's theoretical elevation h in the spatial coordinates of the steel silo. This data is then converted into a three-dimensional image by a computer to obtain a three-dimensional image of the material's theoretical elevation h distribution.

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