Method and system for improving ceramsite sand production process based on coal gangue

Abstract

The invention relates to the technical field of ceramsite sand production processes, and discloses a ceramsite sand production process improvement method and system based on coal gangue. The method comprises the following steps: classifying and analyzing coal gangue through a ray imaging device and an intelligent recognition algorithm, and separating by a high-pressure injection device to obtain a high-purity raw material; coal gangue powder is prepared through a multi-stage crushing and vortex powder selecting system; mixing and proportioning the powder with bauxite and kaolin in proportion; granulating and forming by adopting a dual rotary granulating system to obtain raw material balls; calcining and cooling by utilizing kiln tail waste heat; and carrying out strength detection and particle size separation on the product to obtain the modified ceramsite sand. According to the invention, the accurate control of the production process of the coal gangue ceramsite sand and the stable improvement of the product quality are realized.

Description

Technical Field

[0001] The present application relates to the field of ceramsite sand production technology, and in particular to an improved method and system for ceramsite sand production technology based on coal gangue. Background Art

[0002] At present, the resource utilization of coal gangue has become an important direction for the sustainable development of the coal industry. Traditional methods for treating coal gangue mainly include landfilling, stacking, simple crushing and reuse. With the development of technology, the process of preparing expanded clay sand from coal gangue has gradually matured. The coal gangue is mainly converted into oil well fracturing proppant through crushing, granulation, calcination and other processes. In the existing technology, manual experience is used to adjust the process parameters, and the product quality is evaluated through laboratory offline testing. The entire production process lacks real-time monitoring and intelligent control methods. At the same time, the traditional mechanical sorting method has a low sorting efficiency for coal gangue, the material performance fluctuates greatly during the granulation process, and the product quality stability is poor.

[0003] However, the existing technology has the following shortcomings: first, the coal gangue raw material sorting process relies on manual experience and judgment, lacks accurate online detection and intelligent classification methods, resulting in large fluctuations in raw material quality; second, the setting and adjustment of granulation process parameters lack scientific basis, and fixed parameters are often used for production, which cannot adapt to the performance differences of different batches of raw materials; third, product quality detection mainly relies on manual sampling and laboratory testing, with a long detection cycle, making it difficult to timely discover and solve quality problems in the production process; finally, the entire production process lacks data collection, analysis and feedback mechanisms, and cannot achieve the optimization adjustment of process parameters and dynamic control of product quality. Summary of the invention

[0004] The present application provides a method and system for improving the production process of expanded clay sand based on coal gangue, which is used to address the problems of low raw material sorting efficiency, inaccurate granulation parameter control, and delayed quality detection in the existing process for preparing expanded clay sand from coal gangue, so as to achieve precise control of the production process of expanded clay sand from coal gangue and stable improvement of product quality.

[0005] In the first aspect, the present application provides a method for improving the production process of ceramsite sand based on coal gangue, comprising: obtaining coal gangue material characteristic data through a radiographic imaging device, classifying and analyzing the material characteristic data using an intelligent recognition algorithm, and separating through a high-pressure blowing device according to the recognition result to obtain high-purity sorted coal gangue raw materials; according to the particle size characteristics of the high-purity sorted coal gangue raw materials, cascade crushing is performed using a multi-stage crushing device, and fine classification is performed through an eddy current powder selection system to obtain coal gangue powder; the coal gangue powder is mixed with bauxite according to a preset oxide ratio, A certain amount of kaolin is added, and the mixture is precisely proportioned by an intelligent batching system to obtain a uniform mixed batch; the uniform mixed batch is granulated by a double rotary granulation system, the predetermined water content is controlled, and the mixture is formed under the synergistic effect of stirring and rotation to obtain a raw material ball with high sphericity; the high sphericity raw material ball is subjected to stepwise heat utilization by utilizing the waste heat at the tail of the kiln, calcined at a predetermined temperature, and cooled by a cooling system to obtain a preliminary ceramsite sand product; the preliminary ceramsite sand product is subjected to quality inspection, the strength is inspected by a pressure testing system, and the particle size is sorted by a grading system to obtain a modified ceramsite sand.

[0006] In a second aspect, the present application provides a system for improving the production process of ceramsite sand based on coal gangue, comprising:

[0007] An acquisition module is used to obtain characteristic data of coal gangue materials through a radiographic imaging device, classify and analyze the material characteristic data using an intelligent recognition algorithm, and separate the material through a high-pressure blowing device according to the recognition results to obtain high-purity sorted coal gangue raw materials;

[0008] A classification module is used to classify the high-purity gangue raw materials according to their particle size characteristics, using a multi-stage crushing device for cascade crushing, and finely classifying them through an eddy current powder selection system to obtain gangue powder;

[0009] A mixing module is used to mix the gangue powder with bauxite according to a preset oxide ratio, add a certain amount of kaolin, and accurately proportion the mixture through an intelligent batching system to obtain a uniform mixed batch;

[0010] A control module is used to granulate the uniformly mixed ingredients through a double rotating granulation system, control the predetermined moisture content, and form them under the synergistic effect of stirring and rotating to obtain raw material balls with high sphericity;

[0011] The calcining module is used to utilize the waste heat from the kiln tail to perform step-by-step heat utilization on the raw material balls with high sphericity, calcine them at a predetermined temperature, and cool them down through a cooling system to obtain a preliminary ceramsite sand product;

[0012] The detection module is used to perform quality inspection on the preliminary ceramsite sand product, detect the strength through a pressure testing system, and perform particle size sorting through a grading system to obtain modified ceramsite sand.

[0013] In the technical solution provided by the present application, a radiographic imaging device and an intelligent recognition algorithm are introduced into the production process of gangue ceramsite sand to classify and analyze the material characteristic data, and a high-pressure blowing device is combined to realize the efficient sorting of the gangue raw materials, which greatly improves the stability of the raw material quality and provides a quality-controlled basic material for subsequent processing. At the same time, a multi-stage crushing device and an eddy current powder selection system are used for fine classification to ensure the uniformity and stability of the material particle size distribution. The intelligent batching system is used to accurately proportion the gangue powder, bauxite and kaolin, thereby realizing the precise control of the material components, and then a double rotation system is used. The granulation system granulates the mixed ingredients, and the sphericity and uniformity of the raw ball are ensured through the synergistic effect of stirring and rotation. The waste heat from the kiln tail is then used to perform step-by-step heat utilization and predetermined temperature calcination on the raw ball, which not only reduces energy consumption but also ensures the physical properties of the product. Finally, the pressure testing system and grading system are used to carry out comprehensive quality inspection and grading of the product, thus achieving controllability and consistency of product quality, and realizing full automated intelligent control from raw materials to finished products, which significantly improves production efficiency and product quality stability, reduces errors caused by manual intervention and experience-based judgment, and achieves efficient use of energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0015] Figure 1 It is a schematic diagram of an embodiment of a method for improving the production process of ceramsite sand based on coal gangue in an embodiment of the present application;

[0016] Figure 2 This is a schematic diagram of an embodiment of an improved system for producing expanded clay sand based on coal gangue in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The present application embodiment provides a method and system for improving the production process of ceramsite sand based on coal gangue. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described here can be implemented in an order other than the content illustrated or described here. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0018] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In the embodiment of the present application, an embodiment of the improved method for producing ceramsite sand based on coal gangue includes:

[0019] Step S101, obtaining coal gangue material characteristic data through a radiographic imaging device, classifying and analyzing the material characteristic data using an intelligent recognition algorithm, and separating through a high-pressure blowing device according to the recognition result to obtain high-purity sorted coal gangue raw materials;

[0020] Step S102, according to the particle size characteristics of the high-purity sorted coal gangue raw material, a multi-stage crushing device is used for cascade crushing, and a vortex powder selection system is used for fine classification to obtain coal gangue powder;

[0021] Step S103, mixing the gangue powder with bauxite according to a preset oxide ratio, adding a certain amount of kaolin, and accurately proportioning through an intelligent batching system to obtain a uniform mixed batch;

[0022] Step S104, granulating the uniformly mixed ingredients through a double rotating granulation system, controlling the predetermined water content, and forming them under the synergistic effect of stirring and rotating to obtain raw material balls with high sphericity;

[0023] Step S105, using the waste heat from the kiln tail to perform stepwise heat utilization on the high sphericity raw material balls, calcining them at a predetermined temperature, cooling them through a cooling system, and obtaining a preliminary ceramsite sand product;

[0024] Step S106, perform quality inspection on the preliminary ceramsite sand product, test the strength through a pressure testing system, and perform particle size sorting through a grading system to obtain modified ceramsite sand.

[0025] It is understandable that the execution subject of the present application may be a system for improving the production process of ceramsite sand based on coal gangue, or a terminal or a server, which is not limited here. The present application embodiment is described by taking a server as the execution subject as an example.

[0026] Specifically, the gangue is scanned by an X-ray imaging device to collect density data, morphology data and composition data. The working principle of the X-ray imaging device is to use the different attenuation coefficients of different materials to X-rays, receive the intensity of transmitted X-rays through the detector, and obtain the density distribution image of the material. For example, when X-rays pass through gangue, the density of coal is about 1.3-1.5g / cm 3 The density of gangue is about 2.5-2.8g / cm. By analyzing the difference in X-ray transmission intensity, coal and gangue can be distinguished.

[0027] The deep convolutional neural network receives these data and performs feature extraction. The network structure includes an input layer, multiple convolutional layers, a pooling layer, and a fully connected layer. The convolutional layer extracts local features of the material, such as edges and textures; the pooling layer reduces the dimension of the feature map to reduce the amount of calculation; the fully connected layer combines the features to form a classification result. The system compares the extracted features with the preset standard feature library, calculates the similarity value, and realizes the intelligent classification of coal gangue. Based on the classification results, the high-pressure blowing device separates the materials. The blowing system includes a high-pressure gas source, an electric control valve group, and a nozzle array. According to the classification mark, the system adjusts the injection parameters in real time: the air pressure is controlled within the range of 0.6-0.8MPa, the injection angle is dynamically adjusted between 30-45 degrees, and the injection time is accurate to milliseconds. The separated high-purity coal gangue enters the multi-stage crushing system.

[0028] The crushing process uses a jaw crusher and an impact crusher in series. The jaw crusher processes raw materials of 300-500mm, and crushes the materials to a coarse crushing state by adjusting the crushing chamber gap (50-80mm) and the feeding speed (80-120t / h). The impact crusher receives the coarse crushed materials and dynamically adjusts the rotor speed and the plate hammer gap to reduce the material particle size to below 20mm. The eddy current powder selection system performs fine classification of the crushed materials. The system consists of a classification chamber, a powder selection wheel and a fan. The air flow velocity gradient in the classification chamber is reasonably distributed, the coarse particles fall under the action of gravity, and the fine particles rise with the air flow. The powder selection wheel speed is adjusted within the range of 900-1200r / min, and the powder selection wind speed of 15-20m / s is used to achieve accurate classification of the materials and obtain coal gangue powder above 500 mesh.

[0029] The intelligent batching system accurately mixes gangue powder, bauxite and kaolin. The system collects the oxide content data of each component and calculates Al 2 O 3 :SiO 2The molar ratio of bauxite is determined according to the target ratio of 1.3-1.6:1. Kaolin is added at a ratio of 10% of the total mass. The system detects the mixing uniformity of each component online and adjusts the speed and mixing time of the mixing equipment. The double rotating granulation system granulates the mixed ingredients. The system includes a high-speed stirring rod (speed 800-1000r / min) and a low-speed rotating inner cylinder (speed 30-50r / min). By adjusting the speed ratio of the two, the material is uniformly balled under the synergistic effect of shear force and centrifugal force. The system monitors the moisture content of the material in real time and controls it at around 15% to ensure the molding quality of the raw material balls.

[0030] The heat cascade utilization system uses 500℃ kiln tail exhaust gas to pre-dry the raw material balls. The system adjusts the residence time of the raw material balls in the dryer according to the exhaust gas temperature and the moisture content of the material to reduce the moisture content to 8%. The material then enters the rotary kiln for calcination, and the temperature in the kiln gradually increases from 800℃ to 1300-1350℃ to ensure that the material is fully reacted. The cooling system uses a single-cylinder cooler to control the cooling rate so that the product temperature drops steadily to room temperature. The quality inspection system conducts a comprehensive inspection of the ceramsite sand products. The compressive strength test is carried out on a universal testing machine, and the test pressure is accurate to 0.1MPa. The vibration screening system contains multiple layers of standard screens to accurately classify the products according to the particle size grade. The system correlates the strength data and particle size distribution data of the material and screens out qualified products.

[0031] In the embodiment of the present application, by introducing a radiographic imaging device and an intelligent recognition algorithm into the production process of coal gangue ceramsite sand to classify and analyze the material characteristic data, a high-pressure blowing device is combined to realize the efficient sorting of coal gangue raw materials, which greatly improves the stability of raw material quality and provides quality-controlled basic materials for subsequent processing. At the same time, a multi-stage crushing device and an eddy current powder selection system are used for fine classification to ensure the uniformity and stability of the material particle size distribution. The intelligent batching system is used to accurately proportion coal gangue powder, bauxite and kaolin to realize the precise control of material components, and then a double rotary granulation system is used. The system granulates the mixed ingredients, and the sphericity and uniformity of the raw ball are ensured through the synergistic effect of stirring and rotation. The waste heat from the kiln tail is then used to perform step-by-step heat utilization and predetermined temperature calcination on the raw ball, which not only reduces energy consumption but also ensures the physical properties of the product. Finally, the pressure testing system and grading system are used to carry out comprehensive quality inspection and grading of the product, thus achieving controllability and consistency of product quality, and realizing full automated intelligent control from raw materials to finished products, which significantly improves production efficiency and product quality stability, reduces errors caused by manual intervention and experience-based judgment, and achieves efficient use of energy.

[0032] In a specific embodiment, the process of executing step S101 may specifically include the following steps:

[0033] (1) Scanning the coal gangue with an X-ray imaging device to obtain density data, morphology data, and composition data of the coal gangue;

[0034] (2) Input the density data, morphology data and composition data into a deep convolutional neural network, analyze the data through multi-layer feature extraction, and generate a characteristic map of coal gangue;

[0035] (3) Compare the coal gangue feature map with the preset standard feature data, calculate the similarity value, and classify and mark the coal gangue;

[0036] (4) According to the classification mark information, the injection pressure, injection angle and injection time of the high-pressure injection device are adjusted to establish a corresponding relationship between the injection parameters and the classification mark;

[0037] (5) Controlling the high-pressure blowing device to separate materials and adjusting the blowing intensity in real time through the correspondence between the blowing parameters and the classification marks;

[0038] (6) Based on the correlation data between the marking information of the separated materials and the injection parameters, the separation effect is evaluated, and the injection parameters are adjusted to obtain high-purity sorted coal gangue raw materials.

[0039] Specifically, an X-ray imaging device is used to scan the gangue, wherein the X-ray imaging device includes an X-ray generator, a detector array and a data acquisition unit. When the X-rays generated by the X-ray generator penetrate the gangue material, attenuation occurs, and the degree of attenuation depends on the density, composition and other characteristics of the material. The detector array receives the attenuated X-ray signals, and the data acquisition unit converts these signals into digital information to generate a three-dimensional data matrix containing density distribution, morphological characteristics and material composition. The deep convolutional neural network processes and analyzes the collected data, and the network architecture includes an input layer, multiple convolutional layers, a pooling layer and a fully connected layer. The input layer receives the three-dimensional data matrix, the first-level convolutional layer extracts basic features such as local features such as edges and textures, and the pooling layer performs dimensionality reduction compression on the feature map. The second-level convolutional layer combines basic features to form more complex feature patterns, such as the shape contour and internal structure of the material. Finally, these features are integrated through the fully connected layer to output the feature map of the gangue, which contains information in multiple dimensions such as density distribution map, morphological feature map and component distribution map.

[0040] The characteristic spectrum is compared and analyzed with the preset standard characteristic database, which stores the standard characteristic spectrum of pure coal, pure gangue and mixtures of different proportions. The cosine similarity algorithm is used to calculate the similarity between the sample to be analyzed and the standard sample. The calculation formula is:

[0041]

[0042] where xi Represents the feature vector of the sample to be analyzed, y i Represents the eigenvector of the standard sample, and n is the dimension of the eigenvector. The gangue is classified and marked according to the calculated similarity value, and the samples with similarity greater than the threshold are marked as the corresponding category. The injection parameter control model is established based on the classification label information. The model input is the classification label of the material, and the output is the three key parameters of injection pressure, injection angle and injection time. The injection pressure ranges from 0.6-0.8MPa and is dynamically adjusted according to the material density; the injection angle varies in the range of 30-45 degrees, which is adapted to the movement trajectory of the material; the injection time is accurate to milliseconds to ensure that the injection effect is accurate and effective.

[0043] The high-pressure blowing device performs material separation operations. The device includes a high-pressure gas source, an electric control valve group and a nozzle array. The control unit adjusts the blowing intensity in real time according to the output of the blowing parameter model, and adopts differentiated separation strategies for different types of materials. At the same time, the actual effect data of the separation process will be collected and fed back to optimize the blowing parameter model.

[0044] The evaluation of separation effect is based on the correspondence between material marking information and actual injection parameters. The evaluation indicators include multiple dimensions such as separation accuracy, separation efficiency and energy consumption level. The classification accuracy is calculated by comparing the expected classification of materials with the actual classification results; the separation efficiency is evaluated based on the processing volume and energy consumption per unit time; based on these evaluation results, the parameter settings of the injection parameter model are continuously optimized.

[0045] For example, the raw data obtained by scanning with an X-ray imaging device showed that a batch of gangue samples showed obvious differences in density, morphology and composition. After being processed by a deep convolutional neural network, the generated feature maps clearly reflect these differences: the density value of the pure coal area is low and evenly distributed, the particles are relatively regular in morphology, and the carbon content in the composition characteristics is high; while the gangue area shows a higher density value, irregular morphological characteristics, and a higher silicon and aluminum content. Comparing these feature maps with the samples in the standard database, the calculated similarity value distribution shows that the samples contain about two main components, coal and gangue. Based on this classification result, the injection parameter control model sets differentiated processing parameters for different areas: for the coal area with lower density, a lower injection pressure and a larger injection angle are used; for the gangue area with higher density, the injection pressure is increased and the injection angle is reduced accordingly. In the actual separation process, the separation effect evaluation data shows that the accuracy of material separation has been significantly improved through this parameterized control strategy, and the energy utilization efficiency of the separation process has also reached a high level.

[0046] In a specific embodiment, the process of executing step S102 may specifically include the following steps:

[0047] (1) High-purity sorted coal gangue raw materials are uniformly fed by a vibrating feeder, and material flow data is collected by a material detection device;

[0048] (2) adjusting the crushing gap and feed speed of the jaw crusher according to the material flow data to crush the high-purity sorted coal gangue raw materials into a coarse crushing state;

[0049] (3) Detect the particle size of the coarsely crushed material, transmit the particle size data to the impact crusher control system, and dynamically adjust the crushing chamber gap;

[0050] (4) The crushed material is transported to the eddy current powder selection system to collect the particle size distribution data and concentration distribution data of the material;

[0051] (5) According to the particle size distribution data and concentration distribution data, the rotation speed and powder selection air volume of the eddy current powder selection system are adjusted to separate the materials;

[0052] (6) Perform online particle size analysis on the separated materials, collect the materials that meet the fineness requirements as coal gangue powder, and return the materials that do not meet the requirements to the crushing system for further processing.

[0053] Specifically, during the crushing and grading process of coal gangue, the vibrating feeder generates directional vibration through electromagnetic drive, with a vibration frequency range of 600-900 times / minute and an amplitude adjustable between 2-5 mm, thereby achieving uniform transportation of materials. The material detection device consists of a weighing sensor and a photoelectric sensor. The weighing sensor collects the mass of the material passing through per unit time in real time, and the photoelectric sensor detects the thickness of the material layer. The data of the two are combined to calculate the material flow rate. The flow calculation formula is:

[0054] Q=M×V×D

[0055] Where Q is the material flow rate (t / h), M is the material mass per unit area (t / m), V is the conveying speed (m / h), and D is the cross-sectional area of ​​the material layer (m). The jaw crusher dynamically adjusts the working parameters according to the flow data. The crushing gap is controlled by a hydraulic cylinder. When the flow rate increases, the gap increases accordingly, and vice versa, it decreases and remains within the range of 50-80 mm. The feeding speed is controlled by the feeder speed, which is proportional to the material flow rate and maintained at 80-120 tons / hour. The coarsely crushed material enters the particle size detection device through the conveyor belt, and the particle size distribution is measured by the laser diffraction method. The detection device emits a laser beam with a wavelength of 650 nanometers, which forms a diffraction pattern after passing through the material, which is received and converted into an electrical signal by a photoelectric detector, and the particle size data is obtained after solution.

[0056] After receiving the particle size data, the impact crusher adjusts the gap of the crushing chamber through the PID control algorithm. The control equation is:

[0057]

[0058] Where u(t) is the control value, i.e. the gap adjustment value; e(t) is the error signal, i.e. the difference between the actual particle size and the target particle size; K p , K i , K d They are proportional, integral and differential coefficients respectively. The gap adjustment range is 15-30 mm, ensuring that the material is crushed to less than 20 mm. t is the time variable, indicating the current moment, in seconds, and τ is the integral variable, the time range from 0 to t, in seconds.

[0059] The crushed materials enter the eddy current powder selection system, which includes a grading chamber and a powder selection wheel. Multiple pressure sensors and concentration sensors are arranged in the grading chamber to collect the spatial distribution data of the materials in real time. The speed of the powder selection wheel is adjustable in the range of 900-1200 rpm, and a stable eddy current field is formed with a powder selection wind speed of 15-20 m / s. According to the particle size distribution and concentration distribution data collected by the sensor, the fuzzy control algorithm is used to adjust the speed and air volume. The separated materials are tested by an online laser particle size analyzer, and 500 mesh (particle size ≤ 0.028 mm) is set as the qualified standard. The test data shows the material particle size distribution curve, and the characteristic particle sizes such as D50 and D90 are calculated and compared with the standard requirements. The qualified materials are transported to the finished product warehouse, and the unqualified materials are returned to the crushing system for reprocessing.

[0060] For example: In actual production, the weighing sensor of the vibrating feeder detects that the material mass per unit area is 0.8 tons / square meter, the photoelectric sensor measures that the cross-sectional area corresponding to the material layer thickness is 0.3 square meters, the conveying speed is 300 meters / hour, and the calculated material flow rate is 72 tons / hour. Based on this flow value, the jaw crusher sets the crushing gap to 65 mm. The laser particle size detection after coarse crushing shows that the D90 value of the material is 78 mm, which is 20 mm greater than the target value. After receiving this data, the impact crusher adjusts the crushing chamber gap to 25 mm through PID control. After secondary crushing, the material enters the eddy current powder selection system. The pressure sensor detects the pressure distribution at different heights of the grading cavity. Combined with the concentration sensor data, it is determined that the powder selection wheel speed is 1050 rpm and the wind speed is 17 meters / second. The final online particle size analyzer test results show that the D90 value of the separated fine powder is 0.025 mm, which meets the standard requirements of 500 mesh.

[0061] In a specific embodiment, the process of executing step S103 may specifically include the following steps:

[0062] (1) Collecting data on the alumina content and silicon oxide content of the gangue powder and calculating the oxide ratio of the gangue powder;

[0063] (2) The amount of bauxite added is calculated according to the oxide ratio, and the gangue powder and bauxite are put into the mixing bin by a speed-adjustable belt weighing feeder;

[0064] (3) measuring the amount of kaolin added by a weighing device, and delivering the kaolin to a mixing bin at a ratio of 10% of the total material mass;

[0065] (4) Detect the moisture content of the materials in the mixing bin and transmit the detection data to the intelligent batching system;

[0066] (5) Analyze the uniformity of material mixing through the intelligent batching system and adjust the mixing rate and mixing time of the mixing bin;

[0067] (6) The mixed materials are tested for composition, and when the test data meets the ratio requirements, they are collected as uniformly mixed ingredients.

[0068] Specifically, the aluminum oxide Al in the coal gangue powder was collected by X-ray fluorescence analyzer. 2 O 3 and silicon oxide (SiO 2 ) content data. X-ray fluorescence analyzer realizes elemental analysis by exciting the characteristic X-rays of atoms. Each element has its characteristic wavelength, and the element content is determined by detecting the intensity of X-rays at different wavelengths. The oxide ratio is calculated using the following formula:

[0069]

[0070] Where R is the molar ratio of aluminum oxide to silicon oxide, C Al is the alumina content in coal gangue, f Al is the aluminum oxide conversion coefficient, P Al is the purity coefficient of coal gangue, C Si is the silicon oxide content, f Si is the silicon oxide conversion coefficient, P Si is the silicon oxide purity coefficient, and α, β, γ, and δ are correction coefficients.

[0071] The required amount of bauxite is calculated based on the target molar ratio (1.3-1.6:1) using the following formula:

[0072]

[0073] Among them, M add is the amount of bauxite added, R target is the target ratio, R current is the current ratio, W total is the total material mass, ρ Al is the density of alumina in bauxite, h Al is the alumina content in bauxite, ρ Siis the density of silicon oxide in bauxite, h Si is the silicon oxide content in bauxite, η is the conversion efficiency, μ is the mixing coefficient, and θ is the compensation factor.

[0074] The speed-adjustable belt weighing feeder performs batching according to the calculation results. The feeder is driven by a variable frequency motor with a speed range of 0-60 rpm and a weighing accuracy of ±0.5%. The conveying speed is linearly related to the motor frequency, and the precise control of the feeding amount is achieved through the PID control algorithm. Kaolin is added using a screw feeder equipped with a high-precision weighing sensor to control the addition amount to 10% of the total material mass. Multiple infrared moisture sensors are set in the mixing bin to measure the moisture content of the material using the near-infrared reflection principle. The sensor emits near-infrared light (wavelength 1.94 microns), and water molecules have characteristic absorption of this wavelength of light. The moisture content is calculated by detecting the intensity of the reflected light. The data acquisition frequency is 10 times / second, and the sampling points are evenly distributed at different positions in the mixing bin.

[0075] The variance coefficient method is used to analyze the uniformity of material mixing. Samples are taken at different positions in the mixing bin and the component content is determined. The speed range of the agitator is 20-120 rpm, and stepless speed regulation is achieved through frequency conversion control. The mixing time is dynamically adjusted according to the uniformity test results, generally within the range of 15-30 minutes. The test data is transmitted in real time via industrial Ethernet. The component detection uses an online X-ray diffractometer to determine the phase composition of the material through the Bragg diffraction principle. The diffraction peak intensity of each phase in the material is compared and analyzed, and the deviation between the actual ratio and the theoretical ratio is calculated. When the content deviation of each component is controlled within ±3%, it is judged as a qualified product.

[0076] For example: A batch of coal gangue powder was measured by X-ray fluorescence analysis to have an alumina content of 25% and a silica content of 60%. Substituting these data into the oxide ratio calculation formula, taking into account the influence of the conversion coefficient and the purity coefficient, the current molar ratio is calculated to be 0.8:1. In order to achieve the target ratio of 1.4:1, the bauxite addition calculation formula is substituted, and the required bauxite addition amount is calculated by taking into account the alumina content of 75% and the silica content of 15% in the bauxite, as well as various influencing factors. The speed-adjustable belt weighing feeder feeds the material according to the calculated results, and simultaneously adds kaolin accounting for 10% of the total amount. During the mixing process, the moisture content data detected by the infrared sensor shows that the moisture content of the material is distributed between 12-14%. The mixing uniformity is analyzed by the variance coefficient method. When the variance coefficient is less than 3%, the stirring rate is adjusted to 80 rpm and the mixing time is set to 20 minutes. The final X-ray diffraction analysis showed that the molar ratio of aluminum oxide to silicon oxide in the mixture reached 1.39:1, and the content deviation of each component was within 2%, meeting the process requirements.

[0077] In a specific embodiment, the process of executing step S104 may specifically include the following steps:

[0078] (1) Measuring the feed rate data of uniformly mixed ingredients through a feed rate sensor, and inputting the feed rate data into a granulation control system;

[0079] (2) Collecting moisture content data of the uniformly mixed ingredients using a moisture detector and comparing the moisture content data with process parameters;

[0080] (3) Adjust the water spraying amount of the water spraying device according to the moisture content data to control the moisture content of the uniformly mixed ingredients within the process requirements;

[0081] (4) collecting the rotation speed data of the high-speed stirring rod and the rotation speed data of the low-speed rotating inner cylinder, and calculating the rotation speed ratio data of the two;

[0082] (5) adjusting the speeds of the high-speed stirring rod and the low-speed rotating inner cylinder according to the speed ratio data to establish a speed coordinated control relationship;

[0083] (6) The sphericity of the material at the discharge end of the pelletizing system is tested, and the test data is fed back to the pelletizing control system, and the material that meets the sphericity requirements is collected as high-sphericity raw material balls.

[0084] Specifically, in the gangue granulation process, the feed speed sensor uses the laser displacement principle to measure the material conveying speed. The laser transmitter emits a laser beam with a wavelength of 650 nanometers, which is received by the photoelectric receiver after being reflected by the material surface, and the material movement speed is calculated according to the Doppler frequency shift principle. The sampling frequency of the sensor is 1000Hz, and the measurement accuracy reaches ±0.1 m / s. The feed speed data is closely related to the moisture content of the material, so the moisture detector uses near-infrared reflection technology to monitor the moisture content in real time. The moisture detector emits near-infrared light with a wavelength of 1.94 microns. Since water molecules have characteristic absorption of this wavelength, the intensity of reflected light is negatively correlated with the moisture content of the material. The detector collects data every 0.1 seconds and compares the data with the moisture content range (12-15%) required by the process. The water spray device dynamically adjusts the water spray amount according to the moisture content detection results. The device consists of a variable frequency water pump, an electromagnetic flowmeter and multiple atomizing nozzles. The pump frequency is linearly related to the water spray amount, and the electromagnetic flowmeter feeds back the actual water spray amount in real time. When the moisture content is detected to be lower than 12%, the water spraying volume is increased; when the moisture content is higher than 15%, the water spraying volume is reduced. The precise adjustment of the water spraying volume is achieved through the PID control algorithm.

[0085] The granulating equipment includes a high-speed stirring rod and a low-speed rotating inner drum, both of which are driven by a variable frequency motor. The speed range of the high-speed stirring rod is 800-1000 rpm, and the speed data is collected by the Hall sensor; the speed range of the low-speed rotating inner drum is 30-50 rpm, and the speed data is collected by the photoelectric encoder. The two sets of data are transmitted in real time through the industrial bus, and the speed ratio is calculated. The speed ratio is directly related to the molding quality of the raw material balls. The high-speed stirring rod provides shear force and dispersion force, and the low-speed rotating inner drum provides extrusion force and rolling force. The speed coordination relationship is established through the fuzzy control algorithm: when the material particle size is coarse, the speed of the high-speed stirring rod is increased to enhance the dispersion effect; when the material is easy to agglomerate, the speed of the low-speed inner drum is increased to enhance the rolling effect.

[0086] Sphericity detection uses machine vision technology. The image of the raw ball is captured by an industrial camera with an image resolution of 4096×3072 pixels. The raw ball contour is extracted through an edge detection algorithm and the roundness parameters are calculated. The roundness calculation takes into account factors such as the ratio of the maximum inscribed circle radius to the minimum circumscribed circle radius and the change in contour curvature. The detection data is fed back through industrial Ethernet and used to adjust the pelletizing parameters.

[0087] For example, the feed speed sensor detected that the initial material conveying speed was 2 m / s, and the moisture content collected by the near-infrared moisture detector was 10%, which was lower than the process requirements. According to the moisture content deviation, the water spray device adjusted the pump frequency to 35 Hz, and the actual water spray volume was monitored by the flow meter to reach 2 liters / minute. After adjustment, the moisture content rose to 13%, meeting the process requirements. At the same time, the Hall sensor detected that the speed of the high-speed stirring rod was 850 rpm, and the photoelectric encoder detected that the speed of the low-speed inner drum was 40 rpm. The calculated speed ratio was 21.25. Due to the large initial material particle size, the fuzzy controller increased the speed of the high-speed stirring rod to 900 rpm. The machine vision system detects the raw material balls at the discharge end. The roundness value of the first batch of raw material balls is 0.85. By increasing the speed of the low-speed inner drum to 45 rpm, the roundness value of the second batch of raw material balls is increased to 0.92, meeting the process requirements. Throughout the process, the collection, processing and feedback of each detection data form a closed-loop control, realizing the real-time optimization of granulation parameters.

[0088] In a specific embodiment, the process of executing step S105 may specifically include the following steps:

[0089] (1) Collect the temperature data of the exhaust gas at the kiln tail through the temperature sensor and input the temperature data into the heat cascade utilization control system;

[0090] (2) adjusting the feeding speed of the high sphericity raw balls according to the temperature data, and drying the high sphericity raw balls in a drying machine at the end of the kiln;

[0091] (3) The moisture content data of the dried material is collected by a humidity detection device, and the moisture content data is analyzed and processed;

[0092] (4) adjusting the rotation speed and temperature of the rotary kiln according to the moisture content data, and calcining the material in stages;

[0093] (5) Collect temperature distribution data and material transmission speed data in the kiln, and calculate the residence time of materials in different temperature zones;

[0094] (6) Compare the temperature zone residence time data with the process parameters, adjust the cooling rate of the cooling system, cool the calcined material, and collect it as a preliminary ceramsite sand product.

[0095] Specifically, in the gangue granulation process, the feed speed sensor uses the laser displacement principle to measure the material conveying speed. The laser transmitter emits a laser beam with a wavelength of 650 nanometers, which is received by the photoelectric receiver after being reflected by the material surface. The material movement speed is calculated according to the Doppler frequency shift principle. The sampling frequency of the sensor is 1000Hz, and the measurement accuracy reaches ±0.1 m / s. The feed speed data is closely related to the moisture content of the material, so the moisture detector uses near-infrared reflection technology to monitor the moisture content in real time. The moisture detector emits near-infrared light with a wavelength of 1.94 microns. Since water molecules have characteristic absorption of this wavelength, the intensity of the reflected light is negatively correlated with the moisture content of the material. The detector collects data every 0.1 seconds and compares the data with the moisture content range (12-15%) required by the process.

[0096] The water spraying device dynamically adjusts the water spraying amount according to the moisture content detection result. The device consists of a variable frequency water pump, an electromagnetic flowmeter and multiple atomizing nozzles. The water pump frequency is linearly related to the water spraying amount, and the electromagnetic flowmeter feeds back the actual water spraying amount in real time. When the moisture content is detected to be lower than 12%, the water spraying amount is increased; when the moisture content is higher than 15%, the water spraying amount is reduced. The precise adjustment of the water spraying amount is achieved through the PID control algorithm. The granulation equipment includes a high-speed stirring rod and a low-speed rotating inner cylinder, both of which are driven by a variable frequency motor. The speed range of the high-speed stirring rod is 800-1000 rpm, and the speed data is collected by the Hall sensor; the speed range of the low-speed rotating inner cylinder is 30-50 rpm, and the speed data is collected by the photoelectric encoder. The two sets of data are transmitted in real time through the industrial bus, and the speed ratio is calculated.

[0097] The speed ratio is directly related to the molding quality of the raw ball. The high-speed stirring rod provides shear force and dispersion force, and the low-speed rotating inner drum provides extrusion force and rolling force. The fuzzy control algorithm is used to establish a coordinated relationship between the speeds: when the material particle size is coarse, the speed of the high-speed stirring rod is increased to enhance the dispersion effect; when the material is prone to agglomeration, the speed of the low-speed inner drum is increased to enhance the rolling effect. The sphericity detection uses machine vision technology, and the image of the raw ball is captured by an industrial camera with an image resolution of 4096×3072 pixels. The raw ball contour is extracted by the edge detection algorithm, and the roundness parameters are calculated. The roundness calculation takes into account factors such as the ratio of the maximum inscribed circle radius to the minimum circumscribed circle radius and the change in contour curvature. The detection data is fed back through the industrial Ethernet and used to adjust the granulation parameters.

[0098] For example: the feed speed sensor detects that the initial material conveying speed is 2 meters per second, and the moisture content collected by the near-infrared moisture detector is 10%, which is lower than the process requirements. According to the moisture content deviation, the water spray device adjusts the water pump frequency to 35Hz, and the actual water spray volume is monitored by the flow meter to reach 2 liters per minute. After adjustment, the moisture content rises to 13%, meeting the process requirements. At the same time, the Hall sensor detects that the speed of the high-speed stirring rod is 850 rpm, and the photoelectric encoder detects that the speed of the low-speed inner drum is 40 rpm. The calculated speed ratio is 21.25. Due to the large particle size of the initial material, the fuzzy controller increases the speed of the high-speed stirring rod to 900 rpm. The machine vision system detects the raw material balls at the discharge end. The roundness value of the first batch of raw material balls is 0.85. By increasing the speed of the low-speed inner drum to 45 rpm, the roundness value of the second batch of raw material balls is increased to 0.92, meeting the process requirements.

[0099] In a specific embodiment, the process of executing step S106 may specifically include the following steps:

[0100] (1) Sampling the preliminary ceramsite sand product through a sampling device to collect material density data and surface morphology data;

[0101] (2) Input the material density data and surface morphology data into the pressure testing system, and use the universal testing machine to test the compressive strength of the sample;

[0102] (3) Establish material strength distribution curve based on compressive strength test data and calculate the average strength value and dispersion coefficient of the material;

[0103] (4) Classifying the preliminary ceramsite sand product by a vibrating screening device and collecting material mass distribution data in different particle size ranges;

[0104] (5) Correlate and analyze the material mass distribution data with the strength distribution curve to calculate the qualified rate of each particle size interval;

[0105] (6) Screen out materials that meet the quality requirements based on the qualified rate data, and collect the materials as modified expanded clay sand.

[0106] Specifically, the quality inspection process of the preliminary ceramsite sand product begins with the operation of the automatic sampling device, which adopts a rotary sampler structure, composed of multiple equally divided sample cylinders, and the rotating shaft is driven by a servo motor for uniform sampling. The volume of each sample cylinder is 200 ml, and samples are taken at different positions of the production line at the same time to ensure that the samples are representative. After sampling, the material density is measured by a true density meter. The device is based on the Archimedes principle and uses the drainage method to determine the density value. The surface morphology data is collected using scanning electron microscopy technology, with an electron beam acceleration voltage of 20 kV, and the surface microscopic morphology image is obtained through a secondary electron detector. The core equipment of the pressure testing system is an electro-hydraulic servo universal testing machine with a maximum test force of 100 kN and a displacement accuracy of 0.001 mm. The testing machine is equipped with a special indenter and a base, and the surface of the indenter is hardened to ensure uniform force. The test process adopts displacement control, with a loading rate of 0.5 mm / min, and the force-displacement curve data is collected in real time through a force sensor and a displacement sensor. The sampling frequency of the digital acquisition card is 1000 Hz to ensure the continuity and accuracy of data acquisition.

[0107] The establishment of the strength distribution curve is based on the test data of a large number of samples, and each batch tests no less than 30 samples. The normal distribution model is used for data processing. When calculating the average strength value, the maximum and minimum values ​​are eliminated to reduce the influence of outliers. The dispersion coefficient calculation takes into account the ratio of the sample standard deviation to the average value, reflecting the degree of dispersion of the strength data. The strength distribution curve is fitted by the least squares method to obtain a mathematical model of the change of strength with particle size. The vibration screening device adopts a multi-layer standard screen structure, and the screens are 40 mesh, 60 mesh, 80 mesh, and 100 mesh from top to bottom. The power of the vibration motor is 2 kilowatts, the vibration frequency can be adjusted in the range of 20-60 Hz, and the amplitude is adjustable from 0-10 mm. Each layer of the screen is equipped with a weight sensor to collect material quality data of each particle size range in real time. The data collection interval is 1 second, and the data is stored and processed through the industrial controller.

[0108] The correlation analysis between mass distribution data and strength distribution curve adopts multiple regression method to establish the relationship model between particle size, density and strength. The qualified rate calculation of each particle size interval is based on this model, while considering factors such as density uniformity and surface defects. The qualified judgment criteria include: strength not less than 95% of the design value, density fluctuation range not exceeding 3%, and no obvious cracks and pores on the surface.

[0109] The quantitative sampling and verification adopts a secondary sampling scheme. The first sampling quantity is 1% of the total batch. If unqualified is found, the sampling ratio is increased to 2% for re-inspection. The final screening is based on the process requirements, and the materials that meet all quality indicators are collected as qualified products. Specific example: When sampling and analyzing from the production line, a representative sample is obtained by a rotary sample divider, and the true density is measured to be 1.4 g / cm3. Scanning electron microscope observation shows that the particle surface is relatively smooth and the pore distribution is uniform. The sample is sent to the universal testing machine for compressive strength testing. At a loading rate of 0.5 mm / min, a complete force-displacement curve is obtained, and the average compressive strength and dispersion coefficient are calculated. At the same time, the vibration screening device classifies large batches of materials and collects mass distribution data for each particle size range. These data are input into the correlation analysis model to obtain performance indicators of different particle size ranges, and the screening criteria for qualified products are determined based on this, and finally a modified ceramsite sand product that meets the process requirements is obtained.

[0110] The above describes the improved method for producing ceramsite sand based on coal gangue in the embodiment of the present application. The following describes the improved system for producing ceramsite sand based on coal gangue in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the improved system for producing ceramsite sand based on coal gangue includes:

[0111] The acquisition module 201 is used to obtain the characteristic data of the coal gangue material through the radiographic imaging device, classify and analyze the material characteristic data using the intelligent recognition algorithm, and separate the material through the high-pressure blowing device according to the recognition result to obtain high-purity sorted coal gangue raw materials;

[0112] The classification module 202 is used to obtain the coal gangue powder by using a multi-stage crushing device for cascade crushing according to the particle size characteristics of the high-purity coal gangue raw material and finely classifying it through an eddy current powder selection system;

[0113] A mixing module 203 is used to mix the gangue powder with bauxite according to a preset oxide ratio, add a certain amount of kaolin, and accurately proportion the mixture through an intelligent batching system to obtain a uniform mixed batch;

[0114] The control module 204 is used to granulate the uniformly mixed ingredients through a double rotating granulation system, control the predetermined water content, and form them under the synergistic effect of stirring and rotating to obtain raw material balls with high sphericity;

[0115] The calcining module 205 is used to utilize the waste heat from the kiln tail to perform stepwise heat utilization on the raw material balls with high sphericity, calcine them at a predetermined temperature, and cool them down through a cooling system to obtain a preliminary ceramsite sand product;

[0116] The detection module 206 is used to perform quality detection on the preliminary ceramsite sand product, detect the strength through a pressure testing system, and perform particle size sorting through a grading system to obtain modified ceramsite sand.

[0117] Through the synergy of the above-mentioned components, by introducing radiographic imaging devices and intelligent recognition algorithms in the production process of gangue ceramsite sand to classify and analyze material characteristic data, combined with high-pressure blowing devices, efficient sorting of gangue raw materials is achieved, which greatly improves the stability of raw material quality and provides quality-controlled basic materials for subsequent processing. At the same time, multi-stage crushing devices and eddy current powder selection systems are used for fine classification to ensure the uniformity and stability of material particle size distribution. The intelligent batching system is used to accurately proportion gangue powder, bauxite and kaolin, achieving precise control of material components, and then the double rotary The granulation system granulates the mixed ingredients, and the sphericity and uniformity of the raw balls are ensured through the synergistic effect of stirring and rotation. The waste heat from the kiln is then used to perform step-by-step heat utilization and predetermined temperature calcination on the raw balls, which not only reduces energy consumption but also ensures the physical properties of the products. Finally, the pressure testing system and grading system are used to carry out comprehensive quality inspection and grading of the products, thus achieving controllability and consistency of product quality, and realizing full automated intelligent control from raw materials to finished products, which significantly improves production efficiency and product quality stability, reduces errors caused by manual intervention and experience-based judgment, and achieves efficient use of energy.

[0118] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for improving the production process of ceramsite sand based on coal gangue, characterized in that: include: The characteristic data of the coal gangue material is obtained by a radiographic imaging device, the material characteristic data is classified and analyzed by an intelligent recognition algorithm, and the material is separated by a high-pressure blowing device according to the recognition result to obtain high-purity sorted coal gangue raw materials; According to the particle size characteristics of the high-purity sorted coal gangue raw material, a multi-stage crushing device is used for cascade crushing, and a vortex powder selection system is used for fine classification to obtain coal gangue powder; The gangue powder is mixed with bauxite according to a preset oxide ratio, a certain amount of kaolin is added, and the mixture is accurately proportioned by an intelligent batching system to obtain a uniform mixed batch; The uniformly mixed ingredients are granulated by a double rotating granulation system, the predetermined water content is controlled, and the mixture is formed under the synergistic effect of stirring and rotating to obtain raw material balls with high sphericity; The high sphericity raw material balls are subjected to stepwise heat utilization by utilizing the waste heat from the kiln tail, calcined at a predetermined temperature, and cooled by a cooling system to obtain a preliminary ceramsite sand product; The preliminary expanded clay sand product is subjected to quality inspection, its strength is tested by a pressure testing system, and its particle size is sorted by a grading system to obtain modified expanded clay sand.

2. The improved method for producing ceramsite sand based on coal gangue according to claim 1, characterized in that: The method comprises: obtaining the characteristic data of the coal gangue material by a radiographic imaging device, classifying and analyzing the characteristic data of the material by using an intelligent recognition algorithm, and separating the material by a high-pressure blowing device according to the recognition result to obtain high-purity sorted coal gangue raw materials, including: Use X-ray imaging device to scan the coal gangue to obtain the density data, morphology data and composition data of the coal gangue; The density data, morphology data and composition data are input into a deep convolutional neural network, and the data are analyzed by multi-layer feature extraction to generate a coal gangue feature map; Compare the coal gangue characteristic map with preset standard characteristic data, calculate the similarity value, and classify and mark the coal gangue; According to the classification mark information, the injection pressure, injection angle and injection time of the high-pressure injection device are adjusted to establish a corresponding relationship between the injection parameters and the classification mark; By means of the correspondence between the spraying parameters and the classification marks, the high-pressure spraying device is controlled to separate the materials and the spraying intensity is adjusted in real time; According to the correlation data between the marking information of the separated materials and the injection parameters, the separation effect is evaluated, and the injection parameters are adjusted to obtain high-purity sorted coal gangue raw materials.

3. The improved method for producing ceramsite sand based on coal gangue according to claim 1, characterized in that: According to the particle size characteristics of the high-purity sorted coal gangue raw material, a multi-stage crushing device is used for cascade crushing, and a vortex powder selection system is used for fine classification to obtain coal gangue powder, including: The high-purity sorted coal gangue raw material is uniformly fed by a vibrating feeder, and material flow data is collected by a material detection device; According to the material flow data, the crushing gap and the feed speed of the jaw crusher are adjusted to crush the high-purity sorted coal gangue raw material into a coarse crushing state; Performing particle size detection on the coarsely crushed material, transmitting the particle size data to the impact crusher control system, and dynamically adjusting the crushing chamber gap; The crushed materials are transported to the eddy current powder selection system to collect the particle size distribution data and concentration distribution data of the materials; According to the particle size distribution data and the concentration distribution data, the rotation speed and the powder selection air volume of the eddy current powder selection system are adjusted to separate the materials; The separated materials are subjected to online particle size analysis, and the materials that meet the fineness requirements are collected as coal gangue powder, while the materials that do not meet the requirements are returned to the crushing system for further processing.

4. The improved method for producing ceramsite sand based on coal gangue according to claim 1, characterized in that: The gangue powder is mixed with bauxite according to a preset oxide ratio, a quantitative amount of kaolin is added, and a uniform mixed batch is obtained by accurately proportioning through an intelligent batching system, including: Collecting the aluminum oxide content data and the silicon oxide content data of the gangue powder, and calculating the oxide ratio of the gangue powder; The amount of bauxite added is calculated according to the oxide ratio, and the gangue powder and bauxite are fed into a mixing bin by a speed-adjustable belt weighing feeder; The amount of kaolin added is measured by a weighing device, and the kaolin is transported to the mixing bin at a ratio of 10% of the total material mass; Test the moisture content of materials in the mixing bin and transmit the test data to the intelligent batching system; The intelligent batching system is used to analyze the uniformity of material mixing and adjust the mixing rate and mixing time of the mixing bin; The mixed materials are tested for composition, and when the test data meets the ratio requirements, they are collected as uniformly mixed ingredients.

5. The improved method for producing ceramsite sand based on coal gangue according to claim 1, characterized in that: The uniformly mixed ingredients are granulated by a double rotating granulation system, the predetermined water content is controlled, and the mixture is formed under the synergistic effect of stirring and rotating to obtain raw material balls with high sphericity, including: Measuring the feed speed data of the uniformly mixed ingredients by a feed speed sensor, and inputting the feed speed data into a granulation control system; The moisture content data of the uniformly mixed ingredients are collected by a moisture detector, and the moisture content data are compared with the process parameters; adjusting the water spraying amount of the water spraying device according to the moisture content data to control the moisture content of the uniformly mixed ingredients within the range required by the process; Collect the rotation speed data of the high-speed stirring rod and the rotation speed data of the low-speed rotating inner cylinder, and calculate the rotation speed ratio data of the two; According to the speed ratio data, the speeds of the high-speed stirring rod and the low-speed rotating inner cylinder are adjusted to establish a speed coordinated control relationship; The sphericity of the material at the discharge end of the pelletizing system is tested, and the test data is fed back to the pelletizing control system, and the materials that meet the sphericity requirements are collected as high-sphericity raw balls.

6. The improved method for producing ceramsite sand based on coal gangue according to claim 1, characterized in that: The method of utilizing the waste heat from the kiln tail to perform stepwise heat utilization on the raw material balls with high sphericity, calcining them at a predetermined temperature, and cooling them through a cooling system to obtain a preliminary ceramsite sand product includes: The temperature data of the exhaust gas at the kiln tail is collected by a temperature sensor, and the temperature data is input into a heat cascade utilization control system; adjusting the feeding speed of the high sphericity raw balls according to the temperature data, and drying the high sphericity raw balls in a drying machine at the end of a kiln; The moisture content data of the dried material is collected by the humidity detection device, and the moisture content data is analyzed and processed; According to the moisture content data, the rotation speed and temperature of the rotary kiln are adjusted to calcine the material in stages; Collect temperature distribution data and material transmission speed data in the kiln, and calculate the residence time of materials in different temperature zones; The temperature zone residence time data is compared with the process parameters, the cooling rate of the cooling system is adjusted, the calcined material is cooled and collected as a preliminary ceramsite sand product.

7. The improved method for producing ceramsite sand based on coal gangue according to claim 1, characterized in that: The preliminary ceramsite sand product is subjected to quality inspection, strength inspection by a pressure testing system, and particle size sorting by a grading system to obtain modified ceramsite sand, including: Sampling the preliminary ceramsite sand product by means of a sampling device to collect material density data and surface morphology data; The material density data and surface morphology data are input into a pressure testing system, and a universal testing machine is used to perform a compressive strength test on the sample; Establish material strength distribution curve based on compressive strength test data, and calculate the average strength value and dispersion coefficient of the material; The preliminary ceramsite sand product is graded by a vibrating screening device to collect material mass distribution data in different particle size ranges; Correlation analysis is performed on the material mass distribution data and the strength distribution curve to calculate the qualified rate of each particle size interval; The materials that meet the quality requirements are screened out based on the qualified rate data, and the materials are collected as modified expanded clay sand.

8. A system for improving the production process of ceramsite sand based on coal gangue, used to implement the method for improving the production process of ceramsite sand based on coal gangue as described in any one of claims 1 to 7, characterized in that: include: An acquisition module is used to obtain characteristic data of coal gangue materials through a radiographic imaging device, classify and analyze the material characteristic data using an intelligent recognition algorithm, and separate the material through a high-pressure blowing device according to the recognition results to obtain high-purity sorted coal gangue raw materials; A classification module is used to classify the high-purity gangue raw materials according to their particle size characteristics, using a multi-stage crushing device for cascade crushing, and finely classifying them through an eddy current powder selection system to obtain gangue powder; A mixing module is used to mix the gangue powder with bauxite according to a preset oxide ratio, add a certain amount of kaolin, and accurately proportion the mixture through an intelligent batching system to obtain a uniform mixed batch; A control module is used to granulate the uniformly mixed ingredients through a double rotating granulation system, control the predetermined moisture content, and form them under the synergistic effect of stirring and rotating to obtain raw material balls with high sphericity; The calcining module is used to utilize the waste heat from the kiln tail to perform step-by-step heat utilization on the raw material balls with high sphericity, calcine them at a predetermined temperature, and cool them down through a cooling system to obtain a preliminary ceramsite sand product; The detection module is used to perform quality inspection on the preliminary ceramsite sand product, detect the strength through a pressure testing system, and perform particle size sorting through a grading system to obtain modified ceramsite sand.

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