Sinter mixture precise proportioning closed loop control method based on dry basis online detection

Abstract

The application discloses a sintering mixture precise proportioning closed-loop control method based on dry base online detection, which comprises the following steps: (1) a dry base online detection collection system is built to collect real-time data of raw materials for proportioning of sintering; (2) wet base component parameters of each raw material are corrected in real time through a dry base conversion formula to eliminate the measurement error caused by water doping; (3) a multivariate coupling dry base proportioning optimization model is constructed to solve the real-time wet base target discharge flow of each raw material; (4) an improved fuzzy PID control algorithm is used for dynamic regulation and control to correct proportioning execution parameters in real time; (5) deviation closed-loop correction is carried out in the whole process, and a deviation accumulation statistical mechanism is established; (6) a multistage threshold early warning mechanism is set; when the parameter fluctuation exceeds the preset threshold, an audible and light early warning is automatically triggered, and a fault-tolerant control strategy is started. The application realizes precise, stable and full-automatic closed-loop proportioning of all components of sintering mixture, and improves the quality stability of sinter.

Description

Technical Field

[0001] This invention relates to the field of sintering production line technology, and more specifically to a closed-loop control method for precise batching of sintering mixtures based on dry-based online detection. Background Technology

[0002] Currently, most sintering production lines adopt the traditional control mode of wet-basis fixed-value batching + manual timed calibration. This means that the feed flow rate of each raw material is preset based on the wet-basis moisture content of the incoming raw materials, quantitative feeding is achieved through a belt-driven loss-in-weight scale, and manual adjustments to the batching parameters are made by sampling and testing the raw material composition and moisture content every 2-4 hours. However, this traditional control method has revealed many inherent defects in long-term industrial applications and cannot adapt to the current demands of intelligent and refined sintering production. Specifically: 1) Sintering raw materials such as iron ore powder, blended ore, quicklime, dolomite, and return ore are affected by open-air storage, weather and rainfall, material drying, and batch differences of incoming materials. The moisture content fluctuates greatly in real time. Conventional wet basis feeding can only ensure a constant material weight, but cannot ensure the stability of the effective components on a dry basis. Even a small fluctuation in the moisture content of the raw materials will cause a large deviation in the total iron and basicity of the mixture, resulting in excessive fluctuations in the composition of sintered ore. 2) Traditional batching relies on offline sampling and testing, with a detection lag time of 2 to 4 hours. The detection lag is serious and is a typical open-loop lag control. Moreover, when the composition and moisture of the raw materials change suddenly, there is no real-time closed-loop control capability. The control system cannot detect and correct the feeding parameters in time, which will result in long-term batching deviations, causing a series of problems such as fluctuations in sintering conditions, unstable sinter quality, and increased fuel consumption. 3) Sintering batching involves the synergistic proportion of 6 to 8 raw materials. The moisture and composition fluctuations of each raw material are coupled with each other, and the multi-variable coupling interference is strong. If the parameters are manually corrected based on experience, the subjectivity is strong, the calibration accuracy is poor, and the lag is strong. It is impossible to achieve the optimal adjustment of multi-parameter synergy, and it is very easy to have local over- or under-mixing problems, resulting in uneven air permeability of the mixture and unstable sintering and combustion. 4) The existing batching system simply executes fixed-value feeding without establishing a batching deviation statistics, traceability and dynamic compensation model. The long-term accumulation of system errors such as zero drift of belt scale, material adhesion and equipment operation wear will further reduce the batching accuracy, resulting in a low pass rate of sintered finished product indicators and high production energy consumption.

[0003] Therefore, how to develop a closed-loop control method for precise batching of sintering mixtures based on dry-based online detection to meet the production requirements of modern sintering production for high precision, high stability, and fully automated control of mixture composition has become an urgent problem to be solved. Summary of the Invention

[0004] The technical problem to be solved by this invention is to provide a closed-loop control method for precise batching of sintering mixtures based on online dry basis detection. By real-time online detection of dry basis components and moisture, dynamic proportioning model calculation, adaptive correction of feed flow rate, and deviation closed-loop feedback adjustment, the method completely eliminates the impact of raw material moisture and component fluctuations on batching accuracy from the source, realizing precise, stable, and fully automatic closed-loop batching of all components of the sintering mixture, improving the quality stability of sintered ore, and reducing production energy consumption and raw material loss.

[0005] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The innovative aspect of the closed-loop control method for precise batching of sintering mixtures based on dry-based online detection of the present invention lies in including the following steps: (1) A dry basis online detection and acquisition system consisting of an online microwave moisture analyzer, an online X-ray fluorescence component analyzer, a belt loss-in-weight scale, a temperature sensor, and a flow acquisition module was built to collect real-time data on the raw materials used in sintering and to obtain the instantaneous flow rate, real-time moisture content, and wet basis composition parameters of each raw material in real time. (2) Based on the real-time moisture content of each raw material, the wet basis composition parameters of each raw material are corrected in real time by using the dry basis conversion formula to eliminate the measurement error caused by moisture contamination. (3) With the sintering target basicity, target total iron content and target magnesium-silicon ratio as constraints, and with the minimum dry basis batching deviation and the minimum raw material consumption as optimization objectives, a multivariate coupled dry basis batching optimization model is constructed to solve the real-time wet basis target feed flow rate corresponding to each raw material; (4) The real-time wet basis target feed flow rate obtained above is sent to the loss-in-weight scale control system of each batching belt, and the improved fuzzy PID control algorithm is used to dynamically regulate and correct the batching execution parameters in real time. (5) Full-process deviation closed-loop correction, and establish a deviation accumulation statistics mechanism to perform gradient compensation for continuous positive / negative deviations in order to eliminate equipment system errors and process accumulation errors; (6) Monitor raw material moisture content changes, composition fluctuations, feed flow rate deviations, and abnormal equipment conditions in real time, and set up a multi-level threshold early warning mechanism; when the parameter fluctuation exceeds the preset threshold, automatically trigger an audible and visual early warning and start the fault-tolerant control strategy.

[0006] Preferably, in step (1) above, the raw materials used for sintering include mixed ore, iron ore powder, quicklime, dolomite, fuel, and return ore.

[0007] Preferably, in step (1) above, the wet basis composition parameters include total iron, silicon dioxide, calcium oxide and magnesium oxide composition parameters, and the acquisition frequency is set to 100ms / time.

[0008] Preferably, in step (2) above, the dry basis conversion formula includes: (1) (2) Through the above real-time conversion, the effective component content and dry basis feed amount of each raw material can be accurately obtained.

[0009] Preferably, in step (3) above, the multivariate coupled dry basis proportioning optimization model has a built-in deviation weight coefficient. For quicklime and fuel, which are highly sensitive to sintering conditions, a high weight correction coefficient is set to ensure the stability of core batching parameters. For ordinary iron ore powder, a conventional weight coefficient is set to achieve differentiated and accurate proportioning of multiple raw materials.

[0010] Preferably, in step (3) above, the real-time dry basis composition parameters obtained in step (2) are imported into the multivariate coupled dry basis ratio optimization model, and then combined with the sintering machine speed, hourly output and target total amount of mixture parameters, the optimal dry basis ratio of each raw material is solved in real time through iterative calculation, and then the real-time wet basis target feed flow rate of each raw material is obtained by reverse calculation.

[0011] Preferably, in step (4) above, the improved fuzzy PID control algorithm performs dynamic PID parameter self-tuning based on the deviation between the real-time actual feed flow rate and the target feed flow rate, the rate of change of flow rate, and the cumulative amount of deviation.

[0012] Preferably, in step (4) above, a feedforward compensation mechanism is introduced to suppress instantaneous flow fluctuations in response to the starting and stopping of the batching belt, material adhesion, and zero-point drift of the weighing body, so as to ensure that the flow rate of each raw material is quickly and accurately tracked to the target value.

[0013] Preferably, in step (5) above, the whole process deviation closed-loop correction specifically involves: real-time acquisition of the overall dry basis composition parameters of the mixed material after mixing, comparison with the preset target composition parameters for sintering, calculation of the basicity deviation, total iron deviation, and silicon-magnesium composition deviation of the mixed material, and then inputting the above composition deviation data back into the multivariate coupled dry basis ratio optimization model to achieve the whole closed-loop control of raw material detection - model calculation - material feeding execution - finished product feedback - parameter correction.

[0014] Preferably, in step (6) above, the fault-tolerant control strategy is to slowly correct the feeding parameters to avoid sintering condition oscillations caused by large parameter adjustments, and to ensure the stable and continuous operation of the batching system.

[0015] The beneficial effects of this invention are: (1) This invention completely eliminates the influence of raw material moisture and composition fluctuations on batching accuracy from the source by real-time online detection of dry basis composition and moisture, dynamic proportioning model calculation, adaptive correction of feed flow rate, and deviation closed-loop feedback adjustment, thereby achieving accurate, stable, and fully automatic closed-loop batching of all components of sintering mixture, improving the quality stability of sintered ore, and reducing production energy consumption and raw material loss. (2) The present invention realizes real-time closed-loop control of the whole process, with no detection lag, and can respond to the dynamic changes of raw material composition, moisture and working conditions in real time, eliminate the lag deviation of batching, and ensure the continuous stability of the mixture composition. (3) The present invention has a built-in deviation statistics and gradient compensation mechanism, which can automatically eliminate the cumulative errors of the system such as zero drift of belt scale, material adhesion, and equipment wear, without the need for frequent manual calibration, greatly reducing the intensity of manual labor, and realizing the fully automatic and stable operation of the batching system; (4) This invention significantly improves the uniformity of sintering mixture composition and the qualified rate of sintered ore by precise and stable batching control, stabilizes the sintering endpoint and combustion conditions, effectively reduces the consumption of sintering solid fuel and the return rate of ore, and ensures the smooth operation of the blast furnace, providing core technical support for achieving energy saving, consumption reduction, quality improvement and efficiency enhancement. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in 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 of the closed-loop control method for precise batching of sintering mixtures based on dry-based online detection, as per the present invention. Detailed Implementation

[0018] The technical solution of the present invention will be clearly and completely described below through specific embodiments.

[0019] The present invention provides a closed-loop control method for precise batching of sintering mixtures based on dry-based online detection, such as... Figure 1 As shown, it includes the following steps: (1) A dry basis online detection and acquisition system consisting of an online microwave moisture analyzer, an online X-ray fluorescence component analyzer, a belt loss-in-weight scale, a temperature sensor, and a flow acquisition module was built to collect data on the raw materials used in sintering in real time and obtain the instantaneous flow rate, real-time moisture content, and wet basis composition parameters of each raw material in real time.

[0020] The raw materials used in the sintering process of this invention include mixed ore, iron ore powder, quicklime, dolomite, fuel, and recycled ore.

[0021] The wet basis composition parameters of this invention include total iron, silicon dioxide, calcium oxide, and magnesium oxide, and the acquisition frequency is set to 100ms / time to achieve full coverage of raw material parameters and acquisition without lag.

[0022] (2) Based on the real-time moisture content of each raw material, the wet basis composition parameters of each raw material are corrected in real time by using the dry basis conversion formula to eliminate the measurement error caused by moisture contamination.

[0023] The above dry basis conversion formulas include: (1) (2) Through the above real-time conversion, the effective component content and dry basis feed amount of each raw material are accurately obtained, abandoning the traditional wet basis metering mode and avoiding the interference of moisture fluctuations from the source.

[0024] (3) With the sintering target basicity, target total iron content and target magnesium-silicon ratio as constraints, and with the minimum dry basis batching deviation and the minimum raw material consumption as optimization objectives, a multivariate coupled dry basis batching optimization model is constructed to solve the real-time wet basis target feed flow rate corresponding to each raw material.

[0025] The aforementioned multivariate coupled dry basis proportioning optimization model incorporates a deviation weighting coefficient. For raw materials such as quicklime and fuel, which are highly sensitive to sintering conditions, a high-weight correction coefficient is set to prioritize ensuring the stability of core batching parameters. For ordinary iron ore powder, a conventional weighting coefficient is set to achieve precise proportioning of multiple raw materials.

[0026] The present invention imports the real-time dry basis composition parameters obtained in step (2) into the multivariate coupled dry basis ratio optimization model, and then combines the sintering machine speed, hourly output and target total amount of mixture parameters to solve the optimal dry basis ratio of each raw material in real time. Then, it reverses the calculation to obtain the real-time wet basis target feed flow rate of each raw material.

[0027] (4) The real-time wet basis target feed flow rate obtained above is sent to the loss-in-weight scale control system of each batching belt. The improved fuzzy PID control algorithm is used for dynamic regulation and real-time correction of batching execution parameters.

[0028] The improved fuzzy PID control algorithm described above dynamically tunes the PID parameters based on the deviation between the real-time actual feed flow rate and the target feed flow rate, the rate of change of the flow rate, and the cumulative amount of the deviation.

[0029] This invention introduces a feedforward compensation mechanism to address the starting and stopping of the batching belt, material adhesion, and zero-point drift of the weighing body, thereby suppressing instantaneous flow fluctuations and ensuring that the flow rate of each raw material is quickly and accurately tracked to the target value.

[0030] (5) Full-process deviation closed-loop correction, and establish a deviation accumulation statistics mechanism to perform gradient compensation for continuous positive / negative deviations in order to eliminate equipment system errors and process accumulation errors.

[0031] The above-mentioned full-process deviation closed-loop correction is specifically as follows: real-time acquisition of the overall dry basis composition parameters of the mixed material after mixing, comparison with the preset target composition parameters for sintering, calculation of the basicity deviation, total iron deviation, and silicon-magnesium composition deviation of the mixed material, and then inputting the above composition deviation data back into the multivariate coupled dry basis ratio optimization model to realize the full closed-loop control of raw material detection - model calculation - material feeding execution - finished product feedback - parameter correction.

[0032] (6) Monitor raw material moisture content changes, composition fluctuations, feed flow rate deviations, and abnormal equipment conditions in real time, and set up a multi-level threshold early warning mechanism; when the parameter fluctuation exceeds the preset threshold, automatically trigger an audible and visual early warning and start the fault-tolerant control strategy.

[0033] The above fault-tolerant control strategy is to slowly correct the feeding parameters to avoid sintering condition fluctuations caused by large parameter adjustments, and to ensure the stable and continuous operation of the batching system.

[0034] The beneficial effects of this invention are: (1) This invention completely eliminates the influence of raw material moisture and composition fluctuations on batching accuracy from the source by real-time online detection of dry basis composition and moisture, dynamic proportioning model calculation, adaptive correction of feed flow rate, and deviation closed-loop feedback adjustment, thereby achieving accurate, stable, and fully automatic closed-loop batching of all components of sintering mixture, improving the quality stability of sintered ore, and reducing production energy consumption and raw material loss. (2) The present invention realizes real-time closed-loop control of the whole process, with no detection lag, and can respond to the dynamic changes of raw material composition, moisture and working conditions in real time, eliminate the lag deviation of batching, and ensure the continuous stability of the mixture composition. (3) The present invention has a built-in deviation statistics and gradient compensation mechanism, which can automatically eliminate the cumulative errors of the system such as zero drift of belt scale, material adhesion, and equipment wear, without the need for frequent manual calibration, greatly reducing the intensity of manual labor, and realizing the fully automatic and stable operation of the batching system; (4) This invention significantly improves the uniformity of sintering mixture composition and the qualified rate of sintered ore by precise and stable batching control, stabilizes the sintering endpoint and combustion conditions, effectively reduces the consumption of sintering solid fuel and the return rate of ore, and ensures the smooth operation of the blast furnace, providing core technical support for achieving energy saving, consumption reduction, quality improvement and efficiency enhancement.

[0035] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the concept and scope of the present invention. Without departing from the design concept of the present invention, all modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope of the present invention. The technical content for which protection is sought in the present invention has been fully described in the technical requirements.

Claims

1. A closed-loop control method for precise batching of sintering mixtures based on dry-based online detection, characterized in that... Includes the following steps: (1) A dry basis online detection and acquisition system consisting of an online microwave moisture analyzer, an online X-ray fluorescence component analyzer, a belt loss-in-weight scale, a temperature sensor, and a flow acquisition module was built to collect real-time data on the raw materials used in sintering and to obtain the instantaneous flow rate, real-time moisture content, and wet basis composition parameters of each raw material in real time. (2) Based on the real-time moisture content of each raw material, the wet basis composition parameters of each raw material are corrected in real time by using the dry basis conversion formula to eliminate the measurement error caused by moisture contamination. (3) With the sintering target basicity, target total iron content and target magnesium-silicon ratio as constraints, and with the minimum dry basis batching deviation and the minimum raw material consumption as optimization objectives, a multivariate coupled dry basis batching optimization model is constructed to solve the real-time wet basis target feed flow rate corresponding to each raw material; (4) The real-time wet basis target feed flow rate obtained above is sent to the loss-in-weight scale control system of each batching belt, and the improved fuzzy PID control algorithm is used to dynamically regulate and correct the batching execution parameters in real time. (5) Full-process deviation closed-loop correction, and establish a deviation accumulation statistics mechanism to perform gradient compensation for continuous positive / negative deviations in order to eliminate equipment system errors and process accumulation errors; (6) Monitor raw material moisture content changes, composition fluctuations, feed flow rate deviations, and abnormal equipment conditions in real time, and set up a multi-level threshold early warning mechanism; when the parameter fluctuation exceeds the preset threshold, automatically trigger an audible and visual early warning and start the fault-tolerant control strategy.

2. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 1, characterized in that: In step (1) above, the raw materials used for sintering include mixed ore, iron ore powder, quicklime, dolomite, fuel, and return ore.

3. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 1, characterized in that: In step (1) above, the wet basis composition parameters include total iron, silicon dioxide, calcium oxide and magnesium oxide composition parameters, and the acquisition frequency is set to 100ms / time.

4. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 1, characterized in that: In step (2) above, the dry basis conversion formula includes: （1） （2） Through the above real-time conversion, the effective component content and dry basis feed amount of each raw material can be accurately obtained.

5. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 1, characterized in that: In step (3) above, the multivariate coupled dry basis ratio optimization model has a built-in deviation weight coefficient. For quicklime and fuel, which are highly sensitive to sintering conditions, a high weight correction coefficient is set to ensure the stability of core batching parameters. For ordinary iron ore powder, a conventional weight coefficient is set to achieve precise ratio of multiple raw materials.

6. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 5, characterized in that: In step (3) above, the real-time dry basis composition parameters obtained in step (2) are imported into the multivariate coupled dry basis ratio optimization model. Then, combined with the sintering machine speed, hourly output and target total amount of mixture parameters, the optimal dry basis ratio of each raw material is solved in real time through iterative calculation. Then, the real-time wet basis target feed flow rate of each raw material is calculated in reverse.

7. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 1, characterized in that: In step (4) above, the improved fuzzy PID control algorithm performs dynamic PID parameter self-tuning based on the deviation between the real-time actual feed flow rate and the target feed flow rate, the rate of change of flow rate, and the cumulative amount of deviation.

8. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 7, characterized in that: In step (4) above, a feedforward compensation mechanism is introduced to address the starting and stopping of the batching belt, material adhesion, and zero-point drift of the weighing body, thereby suppressing instantaneous flow fluctuations and ensuring that the flow rate of each raw material is quickly and accurately tracked to the target value.

9. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 1, characterized in that: In step (5) above, the whole process deviation closed-loop correction specifically involves: real-time acquisition of the overall dry basis composition parameters of the mixed material after mixing, comparison with the preset target composition parameters for sintering, calculation of the basicity deviation, total iron deviation, and silicon-magnesium composition deviation of the mixed material, and then inputting the above composition deviation data back into the multivariate coupled dry basis ratio optimization model to achieve the whole closed-loop control of raw material detection-model calculation-material feeding execution-finished product feedback-parameter correction.

10. The closed-loop control method for precise batching of sintering mixtures based on dry-based online detection according to claim 1, characterized in that: In step (6) above, the fault-tolerant control strategy is to slowly correct the feeding parameters to avoid sintering condition oscillations caused by large parameter adjustments and to ensure the stable and continuous operation of the batching system.

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