A speed intelligent control system of a chain and flight conveyor

Abstract

The application belongs to the technical field of chain grate machine speed control, and particularly discloses a kind of chain grate machine speed intelligent control systems, the system includes: machine speed determination module, regulation and control determination module, machine speed correction module, effect evaluation module and machine speed optimization module;The application determines initial machine speed by collecting the surface moisture distribution map and the material layer thickness distribution map of green ball material and based on the preset moisture content-thickness-machine speed mapping relationship, realizes the fine perception of the spatial distribution of the key attributes of material, and then quantitatively evaluates the uneven state of the material, avoids the risk of local insufficient drying or over-drying from the source, simultaneously collects the moisture content and material temperature of the outlet material after the chain grate machine runs at the initial machine speed, and analyzes the deviation from the target value to determine whether machine speed regulation is needed, realizes timely predictive intervention on machine speed, thereby effectively guarantees the stability of the final product quality and reduces batch quality defects.

Description

Technical Field

[0001] This invention belongs to the field of chain grate machine speed control technology, and relates to an intelligent control system for chain grate machine speed. Background Technology

[0002] The chain grate machine is a core thermal equipment in iron and steel metallurgical pellet production, primarily responsible for drying, preheating, and partially oxidizing water-containing green pellets. Its operating speed directly affects pellet quality, energy consumption, and production capacity. Excessive speed will result in insufficient residence time of the green pellets in the preheating section, leading to inadequate drying and preheating. Upon entering the high-temperature section, the pellets are prone to bursting and pulverizing due to rapid moisture evaporation or thermal stress. Insufficient speed, on the other hand, will significantly restrict production efficiency and cause energy waste and the risk of material overburning.

[0003] For example, Chinese invention patent CN103940236B discloses a chain grate machine speed control system, which measures the amount of raw material and the thickness of the material layer at several points, and adjusts the operating speed of the chain grate machine accordingly. This technical solution achieves control over the material layer thickness to a certain extent, which helps prevent grate burn-out and airflow short circuits, and plays a positive role in ensuring smooth basic production.

[0004] The existing technologies mentioned above have the following shortcomings: 1. The current technology is mainly based on the material quantity and the thickness measurement of green pellets at limited points. It is impossible to obtain a spatial distribution map of surface moisture content that can directly reflect the drying characteristics of green pellets, as well as a material layer thickness distribution map that reflects the uniformity of the fabric. It lacks a fine perception of the spatial distribution of key material attributes, and therefore cannot quantify the initial uneven state of the material when it enters the chain grate machine. As a result, it is impossible to compensate for the risk of local under-drying or over-drying from the source.

[0005] 2. The current speed control of the chain grate machine mainly relies on the state of the raw material at its inlet, without further detection and analysis of the state at the outlet of the chain grate machine. As a result, it is impossible to identify the drying effect deviation caused by internal operating condition fluctuations, and it is also impossible to make timely predictive intervention and closed-loop correction of the machine speed, thus failing to ensure the stability of product quality. Summary of the Invention

[0006] In view of this, in order to solve the problems mentioned in the background technology, an intelligent control system for the speed of a chain grate machine is proposed.

[0007] The objective of this invention can be achieved through the following technical solution: This invention provides an intelligent control system for the speed of a chain grate machine, comprising: a speed determination module, which collects a surface moisture content distribution map and a material layer thickness distribution map of the water-containing raw balls at the inlet of the chain grate machine, and determines the initial speed of the chain grate machine according to a preset moisture content-thickness-speed mapping relationship.

[0008] The control and judgment module runs the chain grate machine at the initial speed and collects the moisture content and material temperature of the dried pellets at each sampling time within a preset time period at the outlet end. It analyzes the deviation from the preset target value to determine whether the machine speed needs to be adjusted.

[0009] The machine speed correction module calculates the corrected machine speed of the chain grate machine based on the deviation analysis results if machine speed adjustment is required.

[0010] The effect evaluation module adjusts the chain grate machine to the correct speed and, after a preset stabilization period, re-collects the moisture content and material temperature of the dried pellets at each sampling time within the preset time period at the outlet end to evaluate whether the drying effect meets the standard.

[0011] If the speed optimization module meets the evaluation criteria, it will use the corrected speed as the initial speed benchmark for subsequent water-bearing pellet processing; otherwise, it will return to the speed correction module.

[0012] Compared with the prior art, the beneficial effects of the present invention are as follows: (1) The present invention collects the surface moisture content distribution map and material layer thickness distribution map of the water-containing raw balls, and determines the initial machine speed based on the preset mapping relationship, thereby realizing the fine perception of the spatial distribution of key material attributes, and then quantitatively assesses the uneven state of the feed, thus avoiding the risk of local insufficient drying or over-drying from the source.

[0013] (2) This invention collects the moisture content and temperature of the material at the outlet after the chain grate machine runs at the initial speed, and analyzes the deviation from the target value to determine whether the machine speed needs to be adjusted. This enables timely predictive intervention on the machine speed, thereby effectively ensuring the stability of the final product quality and reducing batch quality defects.

[0014] (3) This invention re-evaluates the drying effect after performing machine speed correction and uses the corrected machine speed that meets the standard as the initial machine speed benchmark for subsequent green pellet material processing. It dynamically adapts to the long-term changes in material characteristics and working conditions, and continuously iterates and optimizes the control strategy, thereby continuously improving the adaptability and stability of production and reducing the reliance on human experience.

[0015] (4) This invention analyzes the deviation between moisture content and material temperature to intelligently select the dominant influence and determine the control direction. This method effectively overcomes the problem of target conflict and oscillation adjustment in multi-parameter control systems, can accurately identify the current main contradiction, and implement targeted control accordingly, thereby improving the decision-making intelligence and response accuracy of machine speed control. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram showing the connections of the various modules in the system of the present invention.

[0018] Figure 2 This is a schematic diagram illustrating whether the speed adjustment determination step is required in this invention.

[0019] Figure 3 This is a schematic diagram showing the connection steps for evaluating whether the drying effect of the present invention meets the standard. Detailed Implementation

[0020] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1 As shown, the present invention provides an intelligent control system for the speed of a chain grate machine. The system includes: a speed determination module, a control judgment module, a speed correction module, an effect evaluation module, and a speed optimization module.

[0022] In the above, the control judgment module is connected to the machine speed determination module and the machine speed correction module, the machine speed correction module is connected to the effect evaluation module and the machine speed optimization module, and the effect evaluation module is also connected to the machine speed optimization module.

[0023] The speed determination module collects surface moisture content distribution maps and material layer thickness distribution maps of the water-containing raw balls at the inlet of the chain grate machine, and determines the initial speed of the chain grate machine according to the preset moisture content-thickness-speed mapping relationship.

[0024] For example, the acquisition of the surface moisture content distribution map of the water-containing green pellets includes: when the water-containing green pellets reach the inlet of the chain grate machine, the near-infrared spectral imaging device moves the detection point according to the preset grid path, and after positioning at each grid point, emits a detection beam containing multiple characteristic wavelengths to the surface of the green pellets, wherein the grid path refers to a rectangular scanning grid divided on the material layer plane with a spacing of no more than 10cm.

[0025] Specifically, the multiple characteristic wavelengths include, but are not limited to, wavelengths near the 1150nm, 1450nm, and 1940nm bands. Among them, the 1450nm and 1940nm bands are sensitive to changes in moisture content and can be used as the main analytical bands. The 1150nm band is less affected by moisture content and can be used as a reference band to eliminate background interference.

[0026] The optical detector synchronously receives the reflected light signal and converts it into an analog electrical signal, which is then converted into digital spectral data by an analog-to-digital converter.

[0027] Based on the digital spectral data, the absorbance ratio of a specific wavelength pair is calculated, and the moisture content of each grid point is determined according to the pre-established absorbance ratio-moisture content calibration curve.

[0028] Specifically, calculating the absorbance ratio of a specific wavelength pair includes: selecting a specific wavelength sensitive to moisture content as the analysis wavelength, and selecting a specific wavelength minimally affected by moisture content and suitable for characterizing background interference as the reference wavelength. The absorbance at the analysis wavelength and the absorbance at the reference wavelength are extracted from the digital spectral data, and their ratio is used as the absorbance ratio. In one specific embodiment, to accurately capture the absorption characteristics of moisture content and eliminate background interference, the analysis wavelength can be selected from wavelengths around 1450 nm or 1940 nm, and the reference wavelength can be selected from wavelengths around 1150 nm or 1750 nm.

[0029] It should be noted that the absorbance ratio-moisture content calibration curve refers to a mathematical model or data lookup table pre-established using standard laboratory methods to map the measured absorbance ratio to moisture content. The method for obtaining this curve includes: preparing multiple standard green bulb samples with known and different moisture contents; measuring the digital spectral data of each standard sample under the same conditions as the actual test using the near-infrared spectral imaging device; processing the spectral data of each standard sample according to the same wavelength selection and calculation method to obtain a series of absorbance ratios; correlating the known moisture content of each standard sample with its corresponding absorbance ratio; and using regression analysis to fit the quantitative relationship between the absorbance ratio and moisture content, thereby obtaining the absorbance ratio-moisture content calibration curve. The calibration curve can be fitted using quadratic polynomial regression analysis.

[0030] After completing the scanning of the entire material layer surface, a surface moisture content distribution map is generated in real time based on the moisture content of each grid point using a spatial interpolation algorithm. The spatial interpolation algorithm can also be a commonly used algorithm in this field, such as Kriging interpolation or cubic spline interpolation, depending on the size of the material layer. For example, Kriging interpolation is preferred for large-sized material layers.

[0031] In one specific embodiment, generating the surface moisture content distribution map includes: using each grid point whose moisture content has been calculated as a known data point, and estimating the moisture content of the unmeasured area based on its planar coordinate position and moisture content using an inverse distance weighted interpolation algorithm. The resolution of the interpolation output grid is set to 1cm × 1cm. By traversing each pixel of the output grid and calculating its weighted average with the surrounding known data points, a complete, continuous, and visualized surface moisture content distribution map is finally generated.

[0032] For example, the acquisition of the thickness distribution map of the water-containing raw pellets includes: when the water-containing raw pellets reach the inlet of the chain grate machine, the laser ranging sensors arranged in a rectangular grid are triggered simultaneously.

[0033] The sensor emits a laser measurement beam onto the surface of the water-containing raw material layer and calculates the vertical distance of each measurement point relative to the fixed installation reference plane by measuring the flight time of the laser beam from emission to reception. The fixed installation reference plane refers to the unified installation reference plane of all laser rangefinders. This plane is parallel to the grate surface of the chain grate machine when it is unloaded. The height of the plane is determined by the equipment installation calibration. For example, the vertical distance between the reference plane and the unloaded grate surface is preset to 2m. This distance is used as the grate height value for subsequent calculation of the material layer thickness.

[0034] The method for calculating the vertical distance of each measurement point relative to the fixed mounting reference plane includes: obtaining the flight time required for the laser beam to travel from emission to reflection from the surface of the material layer and back to the sensor. According to the formula Calculate the measured distance from the sensor to the measurement point on the material layer surface. In the formula For the speed of light, take a value of m / s.

[0035] The actual material layer thickness at each measurement point is obtained by calculating the difference between the vertical distance and the preset height of the chain grate bed.

[0036] After scanning the entire material layer surface, a material layer thickness distribution map is generated based on the actual material layer thickness at each measurement point using a spatial interpolation algorithm.

[0037] It should be added that the preset moisture content-thickness-machine speed mapping relationship refers to a three-dimensional data model or set of rules established through historical production data or experimental data to characterize the correspondence between the overall state of green pellet materials and the optimal initial machine speed.

[0038] For example, taking experimental data as an example, the specific method for establishing the preset moisture content-thickness-machine speed mapping relationship includes:

[0039] Samples of green pellets with different moisture contents and material layer thicknesses were selected. For each sample with different moisture contents and material layer thicknesses, drying experiments were conducted at different machine speeds under fixed drying temperatures and durations. The lowest machine speed that met the drying quality requirements was recorded as the compliant machine speed for that sample group.

[0040] Before each drying experiment, an online near-infrared moisture meter was used to scan the material surface to obtain its surface moisture content distribution map, and a laser rangefinder was used to scan the material surface to obtain its material layer thickness distribution map. Moisture content distribution parameters and material layer thickness distribution parameters were extracted from the surface moisture content distribution map and the material layer thickness distribution map, respectively, and the comprehensive moisture content coefficient and the comprehensive thickness coefficient were calculated accordingly.

[0041] Each successful drying experiment achieving the target machine speed, along with the combined coefficient of moisture content and the combined coefficient of thickness, is treated as a single data point. All data points are then grouped according to their target machine speed values. Within each target machine speed group, the corresponding combined coefficients of moisture content and thickness are further grouped into separate sets.

[0042] From the sets of comprehensive coefficients for moisture content and thickness corresponding to each compliant machine speed, the numerical distribution ranges of the two comprehensive coefficients under that compliant machine speed are statistically determined. Specifically, the minimum and maximum values ​​of the data in each set are selected to form the intervals for the comprehensive coefficients for moisture content and thickness corresponding to that compliant machine speed. Finally, a two-dimensional lookup table is formed with the comprehensive coefficients for moisture content and thickness as inputs and the compliant machine speed as the output, serving as the mapping relationship between moisture content, thickness, and machine speed.

[0043] For example, determining the initial speed of the chain grate machine includes: extracting surface moisture content distribution parameters and material layer thickness distribution parameters from the surface moisture content distribution map and the material layer thickness distribution map, respectively.

[0044] Specifically, the surface moisture content distribution parameters include, but are not limited to, average moisture content, standard deviation of moisture content distribution, skewness of moisture content distribution, and area proportion of high moisture content regions. The material layer thickness distribution parameters include, but are not limited to, average material layer thickness, thickness range, standard deviation of thickness distribution, and area proportion of thickness exceeding limits.

[0045] The surface moisture content distribution parameters and the material layer thickness distribution parameters are normalized respectively, and the normalized surface moisture content distribution parameters and material layer thickness distribution parameters are weighted and summed according to their preset weight factors to obtain the comprehensive moisture content coefficient and the comprehensive thickness coefficient.

[0046] In one specific embodiment, the calculation process of the comprehensive moisture content coefficient is as follows: Multiple extracted surface moisture content distribution parameters are normalized to obtain dimensionless values ​​for each parameter. The normalized average moisture content, standard deviation of moisture content distribution, skewness of moisture content distribution, and area proportion of high moisture content regions are then weighted and summed according to their preset weighting factors to obtain the comprehensive moisture content coefficient.

[0047] The formula for calculating the comprehensive moisture content coefficient is: Comprehensive Moisture Content Coefficient In the formula , , and These represent the normalized average moisture content, the standard deviation of moisture content distribution, the skewness of moisture content distribution, and the percentage of area with high moisture content, respectively. , , and These are preset weighting factors for each corresponding parameter, used to quantify the relative importance of different moisture content distribution characteristics on drying difficulty. For example, in a configuration... , , , .

[0048] The purpose of this design is to calculate a comprehensive moisture content coefficient through weighted fusion. On the one hand, by utilizing weight allocation, it can reflect the actual weight of different characteristics, such as average moisture content, distribution uniformity, distribution asymmetry, and local high moisture content areas, on different dimensions of the overall drying difficulty, reflecting the different contributions of each characteristic to the adjustment needs of the drying process. On the other hand, it can directly integrate information from multiple moisture content distribution characteristics, comprehensively considering their combined impact on the initial speed setting of the chain grate machine.

[0049] The weights can be set based on material characteristics, drying process requirements, and actual engineering experience, or they can be obtained through experimental data. For example, historical data on different moisture content distribution states and their corresponding drying effects can be collected first. The correlation coefficient between each moisture content distribution characteristic parameter and the required machine speed adjustment can be calculated. The contribution of each characteristic to the machine speed setting can be determined through regression analysis. After normalization, the contribution is converted into the weight of each moisture content distribution parameter, and the sum of the weights is 1. In this way, the comprehensive moisture content coefficient can be accurately quantified.

[0050] The analysis method for the thickness comprehensive coefficient is similar to that for the moisture content comprehensive coefficient. The calculation process for the thickness comprehensive coefficient is as follows: the extracted multiple material layer thickness distribution parameters are normalized to obtain dimensionless values ​​for each parameter. The normalized average material layer thickness, thickness range, thickness distribution standard deviation, and the proportion of the area exceeding the thickness limit are linearly weighted and summed according to their preset weighting factors to obtain the thickness comprehensive coefficient.

[0051] The initial operating speed of the chain grate machine is obtained by matching and searching the moisture content comprehensive coefficient and the thickness comprehensive coefficient in the preset moisture content-thickness-machine speed mapping relationship.

[0052] The control and determination module runs the chain grate machine at the initial machine speed and collects the moisture content and material temperature of the dried pellets at each sampling time within a preset time period at the outlet end. It analyzes the deviation from the preset target value to determine whether machine speed control is required.

[0053] The preset time period refers to the data collection window set at the outlet after the chain grate machine has been running at a stable speed, in order to evaluate its drying effect. The method for obtaining this data is as follows: by analyzing historical production data, the typical process fluctuation cycles of moisture content and material temperature under stable operating conditions are determined, and the preset time period is set to be no less than two of these fluctuation cycles to ensure that the collected data effectively reflects the stable state of the process and avoids instantaneous interference.

[0054] It should be noted that the preset target value includes a preset target moisture content and a preset target material temperature. The preset target moisture content refers to the optimal outlet moisture content range that the green pellet material should achieve after completing the chain grate drying process to meet the quality requirements of the subsequent roasting process. This value is obtained based on the process requirements of the green pellet material in the subsequent roasting process, determined through systematic laboratory research and industrial trials. Specific methods include: preparing dried green pellet samples with different moisture contents, conducting experiments under simulated subsequent roasting conditions, systematically analyzing key indicators such as the compressive strength and metallurgical properties of the finished pellets, and finally determining the moisture content range that yields the best overall performance as the preset target moisture content.

[0055] The preset target material temperature refers to the temperature range at the outlet of the chain grate machine that the green pellets should be in to achieve the best preheating effect. This value is obtained based on the roasting characteristics of the material and is optimized and determined through historical production data and thermal calibration results. Specifically, the method includes: using the thermal calculation model of the chain grate machine system, combined with the material reaction kinetics, to calculate the theoretical material temperature range that ensures sufficient preheating of the material while avoiding local overheating. Then, by comprehensively analyzing a large amount of outlet material temperature data recorded under stable and high-quality production conditions, this theoretical range is verified and corrected to finally determine the preset target material temperature.

[0056] For example, the moisture content of the dried pellets at each sampling time within a preset time period at the collection outlet end includes: multiple online near-infrared moisture meters are arranged at equal intervals along the width of the material layer at the outlet end of the chain grate machine. The equal interval arrangement means that the center distance between adjacent moisture meters is equal and the distance is not greater than one-tenth of the width of the material layer, so as to ensure that the local moisture content deviation caused by uneven material distribution or drying on the cross section of the material layer can be effectively detected.

[0057] During the continuous operation of the chain grate machine at its initial speed, all online near-infrared moisture meters are simultaneously triggered, and the reflectance spectral signals of the dried green balls passing through the outlet are collected according to the preset sampling frequency. The preset sampling frequency is set according to the speed of the chain grate machine. In a specific embodiment, it is set to 2 times / minute when the machine speed is greater than or equal to 1m / min, and 1 time / minute when the machine speed is less than 1m / min, to ensure that all areas of the material layer are covered.

[0058] The reflectance spectral signals collected from each measurement point are converted into digital spectral data in real time, and the real-time moisture content of each measurement point is calculated based on the digital spectral data. The calculation method of the real-time moisture content of each measurement point is the same as the calculation method of the moisture content described above, and will not be repeated here.

[0059] The real-time moisture content of all measurement points within each sampling time is arithmetically averaged to obtain the moisture content of the dried pellets at the outlet end within a preset time period at each sampling time.

[0060] For example, the material temperature of the dried pellets at each sampling time within a preset time period at the collection outlet end includes: multiple non-contact infrared thermometers are arranged at equal intervals along the width direction of the material layer above the chain grate machine outlet end.

[0061] During the continuous operation of the chain grate machine at its initial speed, all infrared thermometers are simultaneously triggered to scan the surface of the dried green pellet material layer passing through the outlet end according to the preset sampling frequency, and the material temperature at each measurement point at each sampling time is collected simultaneously.

[0062] The material temperatures collected at all measurement points at each sampling time are grouped into a material temperature group, and the material temperatures in the material temperature group are sorted in descending order. The median of the sorted group is then taken as the material temperature at the sampling time.

[0063] The material temperature at the outlet end is determined by the material temperature at multiple consecutive sampling times within a preset time period after drying, representing the temperature of the raw pellets.

[0064] Please see Figure 2As shown, for example, determining whether machine speed adjustment is needed includes: calculating the average deviation between the moisture content and material temperature of the dried pellets at each sampling time within a preset time period at the outlet and the preset target value.

[0065] Specifically, the calculation process for the average deviation of moisture content and the average deviation of material temperature is as follows: based on the moisture content and material temperature of the dried pellets at each sampling time within a preset time period at the outlet, the deviations from the preset target values ​​are calculated respectively to obtain the moisture content deviation sequence and the material temperature deviation sequence.

[0066] The arithmetic mean of the moisture content deviation sequence and the material temperature deviation sequence are calculated respectively to obtain the average deviation of moisture content and the average deviation of material temperature.

[0067] Based on whether the average deviation of moisture content and the average deviation of material temperature exceed the allowable fluctuation range of the process, it is determined whether the moisture content status and the material temperature status are abnormal.

[0068] It is worth noting that the allowable fluctuation ranges of various process parameters involved in this invention are determined comprehensively based on pellet production process standards, equipment safety operation requirements, and long-term production practice data. These ranges serve as the benchmark for subsequent status judgment, machine speed correction, and effect evaluation, and are specifically defined as follows: The allowable fluctuation range of moisture content is the allowable deviation interval near the preset target moisture content value. In one specific embodiment, this range is ±0.5%. For example, if the preset target moisture content is 9.0%, then the acceptable outlet moisture content range is 8.5% to 9.5%. The allowable fluctuation range of material temperature is the allowable deviation interval near the preset target material temperature value. In one specific embodiment, this range is ±15℃. To ensure stable equipment operation, the maximum allowable change in machine speed of the chain grate machine within adjacent control cycles is set to ±0.2 m / min.

[0069] If either the moisture content or the material temperature is determined to be abnormal, then the machine speed needs to be adjusted.

[0070] If both the moisture content and the material temperature are normal, the changing trends of the moisture content and the material temperature are analyzed based on multiple sampled values ​​within the preset time period.

[0071] The analysis of the changing trends of moisture content and material temperature includes: Step A1, performing linear regression analysis on multiple moisture content sampling values ​​collected in chronological order within the preset time period to calculate the slope of moisture content change. Similarly, performing linear regression analysis on multiple material temperature sampling values ​​to calculate the slope of material temperature change.

[0072] Step A2: Calculate the absolute value of the difference between the current value of moisture content and the boundary value of the allowable fluctuation range of the process. Divide this difference by the absolute value of the rate of change of moisture content to obtain the estimated time when the moisture content parameter is expected to exceed the allowable range. Similarly, calculate the estimated time when the material temperature parameter is expected to exceed the allowable range.

[0073] If either the estimated moisture content or the estimated material temperature exceeds the preset warning time threshold in step A3, it is determined that the parameter is about to exceed the allowable fluctuation range of the process; otherwise, the parameter change trend is determined to be normal.

[0074] If the changing trend indicates that the parameter will exceed its process allowable fluctuation range, then it is determined that machine speed adjustment is required; otherwise, it is determined that machine speed adjustment is not required.

[0075] If speed adjustment is required, the speed correction module calculates the corrected speed of the chain grate machine based on the deviation analysis results.

[0076] For example, the calculation of the corrected speed of the chain grate machine includes: taking the absolute values ​​of the moisture content deviation and the material temperature deviation respectively, and comparing their magnitudes.

[0077] The parameter corresponding to the average deviation with the largest absolute value is taken as the dominant control parameter, and the sign of the average deviation is taken as the dominant control direction. A negative deviation indicates that the parameter is lower than the target value, and the machine speed needs to be increased. A positive deviation indicates that the parameter is higher than the target value, and the machine speed needs to be decreased.

[0078] Using the absolute value of the average deviation of the dominant control parameter as input and its upper limit of allowable process deviation as a benchmark, the dominant influence degree is calculated, and the dominant influence degree is multiplied by a preset benchmark correction amount to obtain the preliminary machine speed correction value.

[0079] Specifically, the formula for calculating the influence of moisture content is as follows: In the formula For the degree of influence of moisture content, This represents the absolute value of the moisture content deviation. This represents the upper limit of the allowable fluctuation range for the process.

[0080] It should be noted that the preset benchmark correction amount refers to the basic change in the operating speed of the chain grate machine required under a unit dominant influence degree. Obtaining this benchmark correction amount involves: determining the maximum allowable change in the machine speed based on the equipment characteristics and process requirements of the chain grate machine; multiplying the maximum allowable change by a preset proportional coefficient; and using the product as the benchmark correction amount. The proportional coefficient... The value of is determined through system step response testing: a unit step signal is applied to the control system, and the overshoot and settling time of the system output are observed. The optimal value is selected to ensure the system exhibits the best dynamic response characteristics while avoiding over-adjustment, based on the principle of ensuring response speed. Value, usually The value range is from 0.3 to 0.6.

[0081] By combining the initial speed correction value with its dominant control direction, the initial speed is adjusted to obtain the theoretical corrected speed.

[0082] The corrected speed of the chain grate machine is obtained by applying equipment operation constraints to the theoretical corrected speed.

[0083] To prevent the equipment from operating beyond its limits, the equipment operation constraint processing includes: comparing the theoretical corrected speed with the preset upper and lower limits of the chain grate machine's speed. If the theoretical corrected speed is higher than the upper limit, the corrected speed is set to the upper limit. If the theoretical corrected speed is lower than the lower limit, the corrected speed is set to the lower limit. If the theoretical corrected speed is between the upper and lower limits, the theoretical corrected speed is directly used as the corrected speed. The upper and lower limits are determined based on the chain grate machine model.

[0084] The effect evaluation module adjusts the chain grate machine to the correct speed and, after a preset stabilization period, re-collects the moisture content and material temperature of the dried pellets at each sampling time within a preset time period at the outlet end to evaluate whether the drying effect meets the standard.

[0085] Specifically, the preset stabilization time refers to the minimum waiting time from the completion of machine speed adjustment to the system's thermal equilibrium, ensuring that the outlet parameters reflect the effect of the corrected machine speed. The method for obtaining the preset stabilization time includes: obtaining the effective process length of the chain grate, and then using the ratio of the effective process length to the corrected machine speed as the stabilization time, thereby ensuring that the material at the outlet has been completely processed under the corrected machine speed.

[0086] Please see Figure 3 As shown, exemplarily, the evaluation of whether the drying effect meets the standard includes:

[0087] Q1. The moisture content and material temperature of the dried green balls collected at each sampling time within the preset time period are compared with their allowable fluctuation ranges in the process.

[0088] Q2. If the moisture content and material temperature at all sampling times are within the allowable fluctuation range of the process, then the drying effect is deemed to meet the standard.

[0089] Q3. If the moisture content or material temperature at any sampling time exceeds the allowable fluctuation range of the process, a comprehensive evaluation of the drying effect will be triggered.

[0090] Furthermore, the comprehensive evaluation of the triggered drying effect includes: Q3-1, based on the moisture content and material temperature of the dried green balls at each sampling time within a preset time period at the outlet, analyzing the changing trends of the moisture content and material temperature of the dried green balls during the evaluation period.

[0091] Q3-2. Obtain the historical trend of the moisture content and material temperature of the dried green balls during a time period equal to the preset stabilization duration before the current machine speed correction operation is executed.

[0092] Q3-3. Compare the trend of change during the evaluation period with the historical trend of change, and evaluate the moisture content correction efficiency and the material temperature correction efficiency. If both the moisture content correction efficiency and the material temperature correction efficiency meet the preset requirements, the drying effect is judged to be up to standard; otherwise, the drying effect is judged to be down to standard.

[0093] It should be noted that the preset requirements include a moisture content correction efficiency threshold and a material temperature correction efficiency threshold. The moisture content correction efficiency threshold refers to the minimum efficiency standard that the drying system should achieve in adjusting moisture content, and the material temperature correction efficiency threshold refers to the minimum efficiency standard that the drying system should achieve in adjusting material temperature.

[0094] The moisture content correction efficiency threshold and the material temperature correction efficiency threshold are determined by the following method: Historical production data is collected, and control cycle data deemed to have achieved the drying effect target are extracted as samples. For each sample, its moisture content correction efficiency and material temperature correction efficiency are calculated, where the formula for calculating the moisture content correction efficiency is as follows: In the formula For moisture content correction efficiency. The absolute value of the deviation in moisture content before adjustment, if If so, no correction is needed, and the efficiency is directly judged to meet the standard. The absolute value of the moisture content deviation after adjustment is used, and the material temperature correction efficiency is calculated similarly. Statistical analysis is performed on all obtained moisture content correction efficiencies and material temperature correction efficiencies, and the 25th percentile of their distributions is taken as the corresponding moisture content correction efficiency threshold and material temperature correction efficiency threshold.

[0095] If the speed optimization module meets the evaluation criteria, it will use the corrected speed as the initial speed benchmark for subsequent water-containing pellet processing; otherwise, it will return to the speed correction module. If the number of consecutive returns to the speed correction module exceeds a preset threshold and the criteria are still not met, an audible and visual alarm will be triggered, prompting manual intervention. The preset threshold is set based on historical data or process requirements.

[0096] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0097] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0098] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0099] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0100] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart control system for the speed of a chain grate machine, characterized in that: The system includes: The machine speed determination module collects the surface moisture content distribution map and material layer thickness distribution map of the water-containing raw balls at the inlet of the chain grate machine, and determines the initial machine speed of the chain grate machine according to the preset moisture content-thickness-machine speed mapping relationship. The control and judgment module runs the chain grate machine at the initial machine speed and collects the moisture content and material temperature of the dried pellets at each sampling time within a preset time period at the outlet end. It analyzes the deviation from the preset target value to determine whether machine speed control is required. The machine speed correction module calculates the corrected machine speed of the chain grate machine based on the deviation analysis results if machine speed adjustment is required. The effect evaluation module adjusts the chain grate machine to the correct speed and, after a preset stabilization period, re-collects the moisture content and material temperature of the dried pellets at the outlet end at each sampling time within the preset time period to evaluate whether the drying effect meets the standard. If the machine speed optimization module meets the evaluation criteria, it will use the corrected machine speed as the initial machine speed benchmark for subsequent water-containing pelletizing treatment; otherwise, it will return to the machine speed correction module. The absolute values ​​of the moisture content deviation and the material temperature deviation are taken and compared. The parameter corresponding to the average deviation with the larger absolute value is taken as the dominant control parameter, and the sign of the average deviation is taken as the dominant control direction. Using the absolute value of the average deviation of the dominant control parameter as input and its upper limit of process allowable deviation as a benchmark, the dominant influence degree is calculated, and the dominant influence degree is multiplied by a preset benchmark correction amount to obtain the preliminary machine speed correction value. The preliminary machine speed correction value is combined with its dominant control direction to adjust the initial machine speed to obtain the theoretical corrected machine speed. The theoretical corrected machine speed is subjected to equipment operation constraint processing to obtain the corrected machine speed of the chain grate machine.

2. The intelligent control system for the speed of a chain grate machine according to claim 1, characterized in that: The surface moisture content distribution map of the collected hydrous bulbs includes: When the water-containing green pellets reach the entrance of the chain grate machine, the near-infrared spectral imaging device moves the detection point according to the preset grid path. After positioning at each grid point, it emits a detection beam containing multiple characteristic wavelengths onto the surface of the green pellets. The optical detector synchronously receives the reflected light signal and converts it into an analog electrical signal, which is then converted into digital spectral data by an analog-to-digital converter. Based on the digital spectral data, the absorbance ratio of a specific wavelength pair is calculated, and the moisture content of each grid point is determined according to the pre-established absorbance ratio-moisture content calibration curve. After completing the scanning of the entire material layer surface, a surface moisture content distribution map is generated in real time based on the moisture content of each grid point using a spatial interpolation algorithm.

3. The intelligent control system for the speed of a chain grate machine according to claim 1, characterized in that: The thickness distribution map of the water-bearing green pellets collected includes: When the water-containing raw balls reach the entrance of the chain grate machine, the laser ranging sensors arranged in a rectangular grid are triggered simultaneously. The sensor emits a laser measurement beam toward the surface of the water-containing raw pellet layer, and calculates the vertical distance of each measurement point relative to the fixed installation reference plane by measuring the flight time of the laser beam from emission to reception. The difference between the vertical distance and the preset height of the chain grate bed is calculated to obtain the actual material layer thickness at each measurement point; After scanning the entire material layer surface, a material layer thickness distribution map is generated based on the actual material layer thickness at each measurement point using a spatial interpolation algorithm.

4. The intelligent control system for the speed of a chain grate machine according to claim 2, characterized in that: The determination of the initial speed of the chain grate machine includes: Surface moisture content distribution parameters and material layer thickness parameters are extracted from the surface moisture content distribution map and the material layer thickness distribution map, respectively; The surface moisture content distribution parameters and the material layer thickness distribution parameters are normalized respectively. The normalized average moisture content, moisture content distribution standard deviation, moisture content distribution skewness, and high moisture content area ratio are weighted and summed according to their preset weight factors to obtain the comprehensive moisture content coefficient. The normalized average material layer thickness, thickness range, thickness distribution standard deviation, and area ratio of the thickness exceeding the limit are linearly weighted and summed according to their preset weighting factors to obtain the comprehensive thickness coefficient. The initial operating speed of the chain grate machine is obtained by matching and searching the moisture content comprehensive coefficient and the thickness comprehensive coefficient in the preset moisture content-thickness-machine speed mapping relationship.

5. The intelligent control system for the speed of a chain grate machine according to claim 1, characterized in that: The moisture content of the dried green pellets at each sampling time within a preset time period at the collection outlet includes: Multiple online near-infrared moisture meters are evenly spaced along the width of the material layer at the outlet end of the chain grate machine; During the continuous operation of the chain grate machine at its initial speed, all online near-infrared moisture meters are simultaneously triggered, and the reflectance spectral signals of the dried pellets passing through the outlet are collected according to the preset sampling frequency. The reflectance spectral signals collected from each measurement point are converted into digital spectral data in real time, and the real-time moisture content of each measurement point is calculated based on the digital spectral data. The real-time moisture content of all measurement points within each sampling time is arithmetically averaged to obtain the moisture content of the dried pellets at the outlet end within a preset time period at each sampling time.

6. The intelligent control system for the speed of a chain grate machine according to claim 1, characterized in that: The material temperature of the dried green pellets at each sampling time within a preset time period at the collection outlet includes: Above the outlet of the chain grate machine, multiple non-contact infrared thermometers are arranged at equal intervals along the width of the material layer. During the continuous operation of the chain grate machine at its initial speed, all infrared thermometers are simultaneously triggered to scan the surface of the dried green pellet material layer passing through the outlet end according to the preset sampling frequency, and the material temperature at each measurement point at each sampling time is collected simultaneously. The material temperatures collected at all measurement points at each sampling time are grouped into a material temperature group, and the material temperatures in the material temperature group are sorted in descending order. The median of the sorted group is then taken as the material temperature at the sampling time. The material temperature at the outlet end is determined by the material temperature at multiple consecutive sampling times within a preset time period after drying, representing the temperature of the raw pellets.

7. The intelligent control system for the speed of a chain grate machine according to claim 1, characterized in that: The determination of whether speed adjustment is needed includes: Based on the moisture content and material temperature of the dried green balls at each sampling time within a preset time period at the outlet, the average deviation from the preset target value is calculated respectively. Based on whether the average deviation of moisture content and the average deviation of material temperature exceed the allowable fluctuation range of the process, determine whether the moisture content status and the material temperature status are abnormal. If either the moisture content or the material temperature is determined to be abnormal, then the machine speed needs to be adjusted. If both the moisture content and the material temperature are normal, the trend of change in moisture content and material temperature is analyzed based on multiple sampled values ​​within the preset time period. If the changing trend indicates that the parameter will exceed its process allowable fluctuation range, then it is determined that machine speed adjustment is required; otherwise, it is determined that machine speed adjustment is not required.

8. The intelligent control system for the speed of a chain grate machine according to claim 1, characterized in that: The evaluation of whether the drying effect meets the standard includes: Q1. The moisture content and material temperature of the dried green balls collected at each sampling time within the preset time period are compared with their allowable fluctuation ranges in the process. Q2. If the moisture content and material temperature at all sampling times are within the allowable fluctuation range of the process, then the drying effect is deemed to be up to standard. Q3. If the moisture content or material temperature at any sampling time exceeds the allowable fluctuation range of the process, a comprehensive evaluation of the drying effect will be triggered.

9. The intelligent control system for the speed of a chain grate machine according to claim 8, characterized in that: The comprehensive evaluation of the triggered drying effect includes: Based on the moisture content and material temperature of the dried green pellets at each sampling time within a preset time period at the outlet, the changing trends of the moisture content and material temperature of the dried green pellets during the evaluation period are analyzed. Obtain the historical trends of moisture content and material temperature of dried green balls recorded during a time period equal to the preset stabilization duration before the current machine speed correction operation is executed. The trend of change during the evaluation period is compared with the historical trend to evaluate the moisture content correction efficiency and the material temperature correction efficiency. If both the moisture content correction efficiency and the material temperature correction efficiency meet the preset requirements, the drying effect is judged to be up to standard; otherwise, the drying effect is judged to be down to standard.

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