An online monitoring management method and system for the whole cycle of ash-carbon-iron-zinc separation in blast furnace bag dust removal

Abstract

The present invention discloses an online monitoring management method and system for the whole cycle of carbon, iron and zinc separation of blast furnace bag dust removal ash, and relates to the field of dust removal control technology. The method includes: dividing the whole cycle blast furnace bag dust removal ash carbon, iron and zinc separation process into multiple separation stages, performing automatic monitoring of each separation stage in turn, and evaluating the separation state of the current stage according to the stage monitoring index set. If the stage separation evaluation result is qualified, the next separation stage is postponed. If it is unqualified, separation compensation calculation is performed, and carbon, iron and zinc separation is regulated according to the separation compensation strategy. The present invention solves the technical problems that the prior art lacks real-time control means, separation deviations are difficult to correct quickly, and affect the separation efficiency and resource recovery rate of blast furnace bag dust removal ash, and achieves the technical effect of improving the separation efficiency and resource recovery rate of blast furnace bag dust removal ash through staged real-time monitoring and dynamic regulation.

Description

Technical Field

[0001] The present invention relates to the technical field of dust removal control, and in particular to an online monitoring and management method and system for the entire cycle of ash-carbon-iron-zinc separation in a blast furnace bag dust removal system. Background Art

[0002] Blast furnace bag dust is an important by-product produced during the blast furnace smelting process, which contains valuable metal resources such as carbon, iron, and zinc. However, due to the complex composition of dust ash, fine particles and a certain amount of impurities, its recovery and utilization has always been one of the difficulties in the field of resource recycling. Traditional separation methods usually use a single physical or chemical method, which makes it difficult to balance the separation efficiency and economy of carbon, iron, and zinc. In addition, due to the lack of real-time monitoring and intelligent control during the separation process, separation deviations are often not discovered and corrected in time, resulting in waste of resources and economic losses. Summary of the invention

[0003] The present application provides an online monitoring and management method and system for the entire cycle of carbon, iron and zinc separation of blast furnace bag dust collector ash, which is used to solve the technical problems in the prior art that there is a lack of real-time control means, separation deviations are difficult to correct quickly, and the separation efficiency of carbon, iron and zinc in blast furnace bag dust collector ash and the resource recovery rate are affected.

[0004] The first aspect of the present application provides an online monitoring and management method for the whole cycle of carbon, iron and zinc separation of blast furnace bag dust collector ash, the method comprising: dividing the whole cycle blast furnace bag dust collector ash carbon, iron and zinc separation process into multiple separation stages, each separation stage corresponding to a key cycle node and a corresponding monitoring indicator set; performing automated monitoring of each separation stage in sequence according to the node order to obtain a stage monitoring indicator set; performing an assessment of the carbon, iron and zinc separation status of blast furnace bag dust collector ash in the current stage based on the stage monitoring indicator set to generate a stage separation assessment result; if the stage separation assessment result is qualified, then proceeding to the next separation stage, if it is unqualified, performing separation compensation calculation according to the stage separation assessment result to generate a separation compensation strategy; and performing carbon, iron and zinc separation regulation and control with reference to the separation compensation strategy.

[0005] According to a second aspect of the present application, there is provided an online monitoring and management system for the entire cycle of carbon, iron and zinc separation of blast furnace bag dust collector ash, the system comprising: a separation stage division module, the separation stage division module being used to divide the entire cycle blast furnace bag dust collector ash carbon, iron and zinc separation process into multiple separation stages, each separation stage corresponding to a key cycle node and a corresponding monitoring indicator set; an automated monitoring module, the automated monitoring module being used to perform automated monitoring of each separation stage in sequence according to the node sequence to obtain a stage monitoring indicator set; a separation state evaluation module, the separation state evaluation module being used to evaluate the carbon, iron and zinc separation state of blast furnace bag dust collector ash in the current stage based on the stage monitoring indicator set to generate a stage separation evaluation result; a separation compensation calculation module, the separation compensation calculation module being used to postpone entering the next separation stage if the stage separation evaluation result is qualified, and if it is unqualified, to perform separation compensation calculation according to the stage separation evaluation result to generate a separation compensation strategy; a carbon, iron and zinc separation control module, the carbon, iron and zinc separation control module being used to perform carbon, iron and zinc separation control with reference to the separation compensation strategy.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The present application provides an online monitoring management method and system for the whole cycle of carbon, iron and zinc separation of blast furnace bag dust removal ash, which relates to the field of dust removal control technology. The carbon, iron and zinc separation process of blast furnace bag dust removal ash is divided into multiple stages, key nodes and monitoring indicators are set, and stage data is automatically monitored and generated in sequence to evaluate the separation status of the current stage. If qualified, it will enter the next stage. If unqualified, the compensation strategy is calculated through error analysis and the separation process is dynamically regulated to achieve full-cycle intelligent management. It solves the technical problems in the prior art that there is a lack of real-time control means, separation deviations are difficult to correct quickly, and the separation efficiency and resource recovery rate of blast furnace bag dust removal ash are affected. It achieves the technical effect of improving the separation efficiency and resource recovery rate of blast furnace bag dust removal ash through real-time monitoring and dynamic regulation in stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0009] Figure 1 A schematic flow chart of an online monitoring and management method for the entire cycle of separation of ash, carbon, iron and zinc in a blast furnace bag dust removal system provided in an embodiment of the present application;

[0010] Figure 2A schematic structural diagram of an online monitoring and management system for the entire cycle of ash-carbon-iron-zinc separation in a blast furnace bag dust removal system provided in an embodiment of the present application.

[0011] Explanation of the reference numerals: separation stage division module 11 , automation monitoring module 12 , separation state evaluation module 13 , separation compensation calculation module 14 , carbon-iron-zinc separation control module 15 . DETAILED DESCRIPTION

[0012] The present application provides an online monitoring and management method and system for the entire cycle of carbon, iron and zinc separation of blast furnace bag dust collector ash, which is used to solve the technical problems in the prior art that there is a lack of real-time control means, separation deviations are difficult to correct quickly, and the separation efficiency of carbon, iron and zinc in blast furnace bag dust collector ash and the resource recovery rate are affected.

[0013] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0014] It should be noted that the terms "including" and "having" and any variations thereof in the specification of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or server that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or modules that are not clearly listed or inherent to these processes, methods, products or devices.

[0015] Embodiment 1, as Figure 1 As shown, the present application provides an online monitoring and management method for the whole cycle of ash carbon iron zinc separation of blast furnace bag dust removal, the method comprising:

[0016] P10: The whole cycle blast furnace bag dust removal ash carbon iron zinc separation process is divided into multiple separation stages, each separation stage corresponds to a key cycle node and a corresponding set of monitoring indicators.

[0017] Specifically, in the full-cycle blast furnace bag dust ash carbon, iron and zinc separation management method, dividing the separation process into multiple separation stages is the basis for achieving accurate monitoring and efficient separation. Among them, each separation stage corresponds to a key cycle node, that is, the operation link that plays a decisive role in the separation effect of carbon, iron and zinc in the separation process. For example, the first stage may be the collection and preliminary separation of bag dust ash, and the key cycle node is the particle size classification of dust particles; the second stage may be the carbon separation stage, and the key node is the temperature control of the separation furnace; and in the zinc recovery stage, the key node may be related to the airflow control of high-temperature volatilization treatment.

[0018] To ensure that the key nodes of each stage can be fully monitored, it is necessary to define a set of monitoring indicators, that is, a set of data indicators that can quantify the separation status of the current stage. For example, particle size distribution, separation efficiency, temperature, pressure and operation time. The monitoring of these indicators not only helps to evaluate the stage separation effect, but also provides data support for subsequent compensation calculations.

[0019] For example, key data in the separation process can be collected in real time by introducing online sensors (such as spectral analysis sensors, temperature and pressure sensors). For example, spectral analysis sensors can be used to detect changes in the content of carbon, iron and zinc in dust removal ash in real time, and temperature sensors can ensure that the separation furnace remains in the optimal operating temperature range. In addition, after data collection, it is processed and transmitted through an industrial control system (such as a SCADA system) to form a digital mapping of the separation status in the background.

[0020] In order to manage these monitoring indicator sets more effectively, the data can be prioritized according to actual needs. For example, the carbon separation stage focuses more on the residual carbon content, while the priority monitoring target of the zinc separation stage is the recovery rate and purity of zinc. In this way, the monitoring effect of each key node can be optimized in a targeted manner, thus laying the foundation for subsequent status assessment and compensation strategies.

[0021] In short, dividing the full-cycle separation process into multiple stages and setting corresponding key cycle nodes and monitoring indicator sets for each stage not only realizes the structuring and refinement of separation management, but also ensures the real-time and accuracy of monitoring data through technical means, providing strong technical support for subsequent intelligent evaluation and separation regulation.

[0022] P20: Execute the automated monitoring of each separation stage in sequence according to the node order to obtain the stage monitoring indicator set.

[0023] Optionally, the automated monitoring of each separation stage is performed in sequence according to the node order. This process is guided by the preset key cycle nodes, and the key parameters of the separation stage are collected and analyzed in real time through automated equipment and systems to form a stage monitoring indicator set. The stage monitoring indicator set refers to a set of data that reflects the separation status and process effect of the current stage, such as separation efficiency, component content, temperature and pressure.

[0024] Exemplarily, automated monitoring can be achieved in the following ways: First, deploy adaptive sensors and collection equipment at the core nodes of each separation stage. For example, in the carbon separation stage, a thermogravimetric analysis sensor can be used to monitor the residual mass change of carbon in the dust removal ash; in the iron separation stage, a magnetic induction sensor is used to monitor the content of magnetic materials; in the zinc volatilization stage, a gas phase spectrometer is used to analyze the change of zinc content in the flue gas in real time. Then, the collected monitoring data is transmitted to the central monitoring platform in real time through the data acquisition and control system. During the transmission process, the data is preliminarily processed, such as filtering noise data, standardizing index values, and eliminating abnormal values.

[0025] By performing automated monitoring of each separation stage in sequence, we can not only accurately grasp the dynamic changes of the separation process, but also ensure the real-time, complete and efficient data collection, thus laying the foundation for the full-cycle blast furnace bag dust removal ash carbon iron zinc separation management.

[0026] P30: Based on the stage monitoring indicator set, the current stage of blast furnace bag dust removal ash carbon iron zinc separation status evaluation is performed to generate a stage separation evaluation result.

[0027] In a possible embodiment of the present application, the current stage of separation status of ash carbon iron zinc in blast furnace bag dust removal is evaluated based on the stage monitoring index set, and a stage separation evaluation result is generated through comprehensive analysis of the monitoring data to provide a scientific basis for subsequent separation compensation and regulation. The stage separation evaluation result can be a conclusive indicator or status information reflecting whether the effect of the current separation stage meets the standard.

[0028] For example, first, key parameters reflecting the separation status are extracted from the stage monitoring indicators, such as carbon separation efficiency, iron recovery rate, zinc purity, and equipment operating status (such as temperature, pressure, flow rate, etc.). These parameters are processed in multiple dimensions through a real-time data analysis platform, including outlier detection, trend analysis, and correlation evaluation. For example, if the carbon separation efficiency suddenly drops at a certain moment, this change can be identified through an outlier detection algorithm, and the possible cause can be found by combining the change trend of flow and temperature.

[0029] Secondly, the process of evaluation needs to introduce a process standard reference model, that is, a set of separation state benchmark values ​​based on historical data and theoretical models. By comparing the actual monitoring data with the reference model, the degree of deviation of the current separation state is evaluated. For example, in the iron separation stage, if the recovery rate of magnetic iron is lower than the benchmark value range, the system determines that there is a separation anomaly in this stage and calculates the deviation size.

[0030] If the accuracy of the assessment needs to be further improved, a machine learning model (such as support vector machine SVM or decision tree) can be used to classify the state of the stage data, learn the influencing factors of the separation state from historical data, and classify the separation state of the current stage, such as "meeting the standard" or "not meeting the standard". After the assessment is completed, the staged separation assessment results are generated. If the separation state is qualified, the assessment result will trigger the system to enter the next separation stage; if the separation state is unqualified, the result will be marked with the abnormal category and the scope of influence, and enter the compensation calculation module.

[0031] In addition, the evaluation results can also be displayed through the data visualization system, such as presenting the time trend of carbon, iron and zinc separation efficiency and the deviation of each indicator from the benchmark value in the form of a chart. This visualization method not only helps operators to intuitively understand the separation status, but also facilitates the rapid location of problems.

[0032] P40: If the stage separation evaluation result is qualified, then proceed to the next separation stage; if it is unqualified, then perform separation compensation calculation according to the stage separation evaluation result to generate a separation compensation strategy.

[0033] Furthermore, step P40 of the embodiment of the present application also includes:

[0034] P41: If the stage-by-stage separation assessment result is unqualified, a separation error analysis is performed based on the stage-by-stage separation assessment result, and a target separation compensation mode is obtained by matching the separation error analysis result, wherein the separation compensation mode includes a current stage extension compensation mode and a subsequent stage associated compensation mode; P42: According to the target separation compensation mode, the corresponding compensation calculation unit is activated to perform separation compensation calculation to generate the separation compensation strategy.

[0035] It should be understood that the stability and flexibility of the entire separation process can be ensured by dynamically responding to the separation evaluation results. For example, if the stage separation evaluation result is qualified, it can be directly postponed to the next separation stage and continue to perform the separation operation according to the established process. When the evaluation result is unqualified, the compensation mechanism is automatically activated to generate an accurate separation compensation strategy based on the status data of the current stage to ensure the overall separation effect.

[0036] Optionally, when the separation state is unqualified, the separation error of the current stage is first analyzed. Separation error analysis refers to the identification of key abnormal factors through multi-dimensional analysis of the deviation between the monitoring data and the target value. For example, in the carbon separation stage, if the residual carbon content is detected to be higher than the standard, the potential source of error can be traced in combination with parameters such as separation temperature and gas flow rate. In order to achieve the accuracy of error analysis, an error causal analysis model can be introduced to quantify the contribution of each influencing factor by comparing the current separation index with historical data or process model benchmark values. At the same time, a comprehensive analysis of the possible impact of errors on subsequent separation stages is performed to ensure that the problem solution is forward-looking.

[0037] After the error analysis is completed, the target separation compensation mode will be matched according to the separation error analysis results, and the target separation compensation mode includes the current stage extension compensation mode and the subsequent stage associated compensation mode. Among them, the current stage extension compensation mode is applicable to the situation where the separation effect can be corrected by adjusting the time or parameters in the current stage. For example, in the iron separation stage, if the magnetic separation efficiency is low, it can be compensated by extending the magnetic separation time or increasing the magnetic field strength. The subsequent stage associated compensation mode is applicable to the situation where the current error has little effect on the subsequent stage and the compensation is more suitable for execution in the subsequent stage. For example, in the zinc separation stage, if the zinc volatilization rate is low, the subsequent condensation stage can be compensated by optimizing the collection efficiency. The matching process can automatically select the optimal compensation mode by comparing the error analysis results with the process compensation rules, combining the compensation cost and the scope of influence.

[0038] After determining the compensation mode, activate the corresponding compensation calculation unit to dynamically adjust the operating parameters according to the compensation needs. The compensation calculation unit is a core module that integrates multi-parameter optimization and can output the optimal control scheme according to different separation goals (such as efficiency, purity, recovery rate, etc.). For example, in the extended compensation mode of the current stage, the compensation calculation unit will re-evaluate key parameters such as separation time, temperature or pressure, and calculate the minimum adjustment amount to achieve the goal. In the associated compensation mode of the subsequent stage, the compensation calculation unit can simulate the error transmission effect, predict its impact on the subsequent stage, and formulate a compensation scheme for the optimization parameters (such as starting conditions or time windows).

[0039] In order to ensure the scientificity and economy of the compensation strategy, the compensation calculation unit can use a multi-objective optimization algorithm (such as particle swarm optimization or genetic algorithm) to balance the separation efficiency, compensation cost and time consumption to generate a global optimal solution. The generated compensation strategy is output in the form of digital instructions and executed by an automated control system. For example, in the extended compensation mode of the current stage, the operating time and temperature of the equipment will be adjusted in real time; in the associated compensation mode of the subsequent stage, the startup parameters of the subsequent equipment can be optimized or the process cycle can be extended. Whether it is through extended compensation in the current stage or associated compensation in the subsequent stage, the problem can be solved efficiently, and the impact of errors on the full-cycle carbon, iron and zinc separation process can be minimized, thereby achieving a dual increase in resource recovery rate and economic benefits.

[0040] Further, based on the staged separation evaluation results, separation error analysis is performed, and step P41 of the embodiment of the present application further includes:

[0041] P41-1: Calculate the separation deviation value between the stage separation evaluation result and the target separation standard; P41-2: Classify and analyze the separation deviation value to obtain the classification analysis result, which includes the separation efficiency deviation and separation quality deviation of carbon, iron and zinc; P41-3: Based on the classification analysis result, determine the error source and quantify the degree of influence to generate the separation error analysis result.

[0042] Optionally, separation error analysis is performed based on the staged separation evaluation results. Based on the actual monitoring data of the current stage and combined with the target separation standards, separation anomalies are comprehensively quantified to provide strong support for subsequent compensation decisions.

[0043] First, the separation deviation value is calculated based on the staged separation evaluation results and the target separation standard. The separation deviation value refers to the numerical difference between the actual separation effect (such as the residual content of carbon, the recovery rate of iron, the purity of zinc, etc.) and the target standard value, which is used to quantify the degree of separation deviation at the current stage. For example, if the target value of the residual content of carbon is 3%, and the actual measured value is 5%, the carbon separation deviation value is +2%. This quantitative calculation lays the foundation for subsequent error classification and analysis.

[0044] After the deviation value is calculated, the separation deviation is classified and analyzed to refine the source of the anomaly and evaluate its specific manifestation. The results of the classification analysis include separation efficiency deviation and separation quality deviation, where the separation efficiency deviation measures the separation ability of the target component (such as carbon, iron, and zinc) during the separation process. For example, if the iron recovery rate target is 95% and the actual is 90%, there is an efficiency deviation. The separation quality deviation can reflect the deviation of the purity of the target component after separation or other quality indicators. For example, the purity target of zinc is 98%, and the actual measured value is 96%, which indicates that there is a quality deviation.

[0045] By introducing multidimensional data classification algorithms (such as K-means clustering), the deviation data can be classified and the specific forms of abnormalities can be marked, such as "high efficiency and low quality" or "double low deviation". This classification helps to quickly identify problems of different natures in the separation process, so as to conduct subsequent analysis more targetedly.

[0046] After the classification analysis is completed, the error sources are further identified and their impact on the separation effect is quantified. The error source is the specific reason for the separation deviation, which may be related to operating parameters, equipment performance or environmental conditions. For example, low carbon separation efficiency may be due to insufficient separation time or unstable temperature control.

[0047] In order to accurately identify the source of error, a causal analysis model can be introduced to trace back possible error factors, and the operating parameters that contribute most to the current deviation can be determined by combining monitoring data and historical models. For example, low carbon separation efficiency may be due to insufficient separation time, and low zinc purity may be related to temperature fluctuations or insufficient gas volatility. Furthermore, in order to quantify the degree of impact, regression analysis and sensitivity analysis techniques can be used. Regression analysis can estimate the quantitative contribution of each error source to the deviation, such as deriving the influence coefficient of temperature on zinc volatility through an equation; sensitivity analysis is used for priority sorting to clarify which error sources are the key factors affecting the separation effect.

[0048] Finally, the separation error analysis results are generated, including the specific data and classification results of each separation deviation value, the key error sources that cause the deviation and their impact. Accurate analysis of separation anomalies is achieved. It not only helps to accurately locate the cause of the problem, but also provides data support for the generation of targeted compensation strategies, thereby effectively improving the overall efficiency and stability of carbon, iron and zinc separation.

[0049] Further, according to the separation error analysis result, the target separation compensation mode is matched and obtained. In the embodiment of the present application, step P41 further includes:

[0050] P41-4: Based on the separation error analysis results, multiple indicator deviation errors are extracted to perform deviation evaluation of error indicators, and high-impact error indicators are screened to obtain; P41-5: Based on the high-impact error indicators, the impact of the current node error on subsequent nodes is evaluated. If the impact on subsequent nodes is large, the current stage extension compensation mode is preferably used as the target separation compensation mode; P41-6: If the current node error has a small impact on subsequent nodes, an associated node matching analysis is performed based on the high-impact error indicators to obtain multiple candidate compensation nodes, and the multiple candidate compensation nodes may include the current separation stage; P41-7: Based on the multiple candidate compensation nodes, compensation node optimization is performed, and the target separation compensation mode is determined based on the optimal compensation node.

[0051] Optionally, a target separation compensation mode is obtained according to the separation error analysis result to ensure the efficiency and stability of the separation process.

[0052] First, based on the results of the separation error analysis, a deviation assessment is performed on the deviation errors of multiple indicators. Deviation assessment refers to screening out the error indicators that have the most significant impact on the separation effect by quantifying the degree of deviation and impact weight of each indicator. For example, in the carbon separation stage, if the temperature deviation is strongly correlated with the carbon separation efficiency and the contribution rate is 80%, the temperature deviation is marked as a high-impact error indicator. To ensure the scientific nature of the screening, a weight analysis model can be used to calculate the impact weight of each deviation indicator through historical data and real-time monitoring data. Indicators with higher weights are preferentially selected as high-impact error indicators to provide a basis for subsequent compensation decisions.

[0053] After determining the high-impact error indicators, their impact on subsequent separation nodes is evaluated. Impact evaluation refers to analyzing whether the error of the current node will significantly affect the separation effect of subsequent nodes. For example, if the gas temperature deviation in the current zinc volatilization stage may cause a decrease in the zinc recovery rate in the condensation stage, it is considered that the impact on subsequent nodes is greater.

[0054] For example, in order to achieve the accuracy of the evaluation, a transfer effect analysis model can be introduced to quantify the potential impact of errors on subsequent nodes by simulating the error transfer process in the process flow. If the impact is large, it is preferred to extend the compensation mode in the current stage, that is, to solve the problem directly at the current node by extending the operation time of the current separation stage or adjusting the parameters. For example, in the iron separation stage, the recovery rate can be improved by increasing the magnetic separation time or increasing the magnetic field strength, thereby avoiding the error from being transferred to the subsequent stage.

[0055] If the evaluation finds that the error of the current node has little impact on the subsequent nodes, the high-impact error index is subjected to an associated node matching analysis. Associated node matching refers to finding the most suitable node for performing compensation by analyzing other nodes associated with the current node (including the current stage and the subsequent stage). Exemplarily, a correlation analysis can be used to generate multiple alternative compensation nodes by combining the high-impact error index with the operating conditions of the alternative nodes. These nodes may include the current separation stage and the subsequent separation stage. For example, in the carbon separation stage, if effective compensation cannot be achieved by extending the time, the subsequent iron separation stage can be selected to adjust the equipment parameters to indirectly optimize the separation effect.

[0056] After obtaining multiple candidate compensation nodes, it is necessary to optimize these nodes to determine the optimal compensation node and the target separation compensation mode. The compensation node optimization can be performed by a multi-objective optimization algorithm to comprehensively weigh factors such as compensation efficiency, cost and time, and select the optimal compensation node to minimize the impact of errors on the separation effect.

[0057] Further, based on the multiple candidate compensation nodes, the compensation node optimization is performed, and step P41-7 of the embodiment of the present application further includes:

[0058] P41-71: Extract evaluation indicators of the multiple alternative compensation nodes, the evaluation indicators include separation compensation efficiency, compensation cost, compensation time and correlation influence between nodes; P41-72: Standardize the evaluation indicators to generate an indicator evaluation matrix; P41-73: Based on the indicator evaluation matrix, calculate the relative distance between each alternative compensation node and the ideal solution and the negative ideal solution; P41-74: According to the relative distance, calculate the relative closeness of each alternative compensation node; P41-75: Sort the multiple alternative compensation nodes according to the relative closeness, and select the node with the highest relative closeness as the optimal compensation node.

[0059] Specifically, compensation nodes are optimized based on multiple candidate compensation nodes to ensure the scientificity and economy of the separation compensation strategy.

[0060] First, evaluation indicators are extracted from multiple candidate compensation nodes. These indicators are used to quantify the compensation effect and cost of each node to ensure that the preferred nodes can achieve efficient compensation. The evaluation indicators may include: separation compensation efficiency, that is, the degree to which the separation effect of carbon, iron and zinc is improved after the execution of the compensation node, which is the core indicator for evaluating the compensation effect. Compensation cost, that is, the resource consumption required to perform the compensation operation, such as energy, time and equipment operating costs. Compensation time, that is, the time required to complete the compensation, is a key factor in ensuring the continuity of the separation process. Inter-node correlation influence, that is, the potential impact of the compensation node on the separation effect of the subsequent stage. These indicators provide a data basis for subsequent calculations and are the core parameters for the optimization of compensation nodes.

[0061] Since the dimensions and value ranges of the evaluation indicators are different, in order to facilitate comparison, the indicators need to be standardized so that the values ​​of all indicators are normalized to the same range (such as [0,1]). The following formula can be used for standardization:

[0062] Positive indicators (such as compensation efficiency): ; Negative indicators (such as compensation cost, compensation time): ;in, is the j-th index value of the i-th node, and are the maximum and minimum values ​​of the j-th indicator respectively.

[0063] After using the standardized evaluation index, an index evaluation matrix is ​​constructed, which contains the standardized index values ​​of all candidate compensation nodes and can provide a unified data structure for subsequent calculations.

[0064] Further, based on the index evaluation matrix, the relative distance between each candidate compensation node and the ideal solution and the negative ideal solution is calculated. The ideal solution is the optimal combination of evaluation indicators, indicating the theoretically best compensation node, and the negative ideal solution is the worst combination of evaluation indicators, indicating the theoretically worst compensation node. Exemplarily, for each candidate compensation node, its Euclidean distance from the ideal solution and the negative ideal solution can be calculated as the relative distance.

[0065] Furthermore, by calculating the relative distance between each node and the ideal solution and the negative ideal solution, the relative closeness, that is, the measure of the quality of the node, is further obtained. The relative closeness calculation formula can be: ,in, represents the distance between the alternative compensation node and the ideal solution, Indicates the distance between the alternative compensation node and the negative ideal solution, relative closeness The value of is between 0 and 1. The closer the value is to 1, the closer the node is to the ideal solution and the better the compensation effect.

[0066] Finally, all candidate compensation nodes are sorted according to the calculated relative proximity, and the node with the highest proximity is selected as the optimal compensation node. For example, if the proximity of a node is 0.92, which is higher than other nodes, then the node will be activated first in the subsequent compensation process. Through this complete compensation node optimization process, the optimal compensation node can be determined scientifically and accurately, providing reliable guarantee for the full-cycle optimization of carbon, iron and zinc separation, and further improving separation efficiency and resource utilization.

[0067] Further, according to the optimal compensation node, the target separation compensation mode is determined, and step P41-7 of the embodiment of the present application further includes:

[0068] P41-76: If the optimal compensation node is the current separation stage, the extended compensation mode of the current stage is used as the target separation compensation mode; P41-77: If the optimal compensation node is the associated separation stage, the associated compensation mode of the subsequent stage is used as the target separation compensation mode, and the target separation compensation mode is marked with the optimal compensation node identifier.

[0069] In a possible embodiment of the present application, the most suitable compensation mode is scientifically selected in combination with the attributes of the compensation node and the requirements of the separation stage to provide clear guidance for subsequent execution.

[0070] When the optimal compensation node is located in the current separation stage, the current stage extension compensation mode is preferentially selected as the target separation compensation mode. The current stage extension compensation mode aims to directly correct the separation deviation by optimizing the operating conditions of the current stage to avoid the error from being transmitted to the subsequent stages. The specific implementation includes dynamically adjusting key parameters (such as separation time, temperature, pressure or flow rate) to improve the separation efficiency. For example, in the iron separation stage, if the magnetic separation efficiency is low, the system can extend the magnetic separation time or increase the magnetic field strength to compensate for the deviation. This compensation method is real-time and direct, and can quickly optimize the separation state to ensure that the separation effect of the current stage meets the target standard. Technically, relying on the dynamic parameter optimization algorithm and real-time monitoring system, the compensation effect can be quickly verified through closed-loop control.

[0071] When the optimal compensation node is located in the associated separation stage (i.e., the subsequent stage), the system selects the associated compensation mode of the subsequent stage as the target separation compensation mode. The associated compensation mode indirectly corrects the deviation of the current stage by optimizing the operating conditions at the subsequent separation node. For example, in the zinc volatilization stage, if the volatilization efficiency is lower than the target value, the cooling conditions (such as cooling temperature and air flow velocity) can be adjusted in the subsequent condensation stage to improve the zinc recovery rate. This mode predicts the impact of the current deviation on the subsequent nodes through the error transfer analysis model, and optimizes the subsequent node parameters in a targeted manner. To ensure accurate execution, the compensation mode is marked with the optimal compensation node, indicating the location of the compensation node and the adjustment requirements (such as temperature, pressure, and time). The identification information forms a traceable operation record to ensure the consistency of the execution of the compensation plan, and at the same time provide data support for subsequent optimization.

[0072] Furthermore, step P42 of the embodiment of the present application also includes:

[0073] P42-1: Based on the target separation compensation mode, output the compensation instruction of this stage or the compensation instruction of the associated stage, and the compensation instruction of the associated stage carries the associated node identifier; P42-2: According to the compensation instruction of this stage or the compensation instruction of the associated stage, activate the corresponding target compensation calculation unit; P42-3: The target compensation calculation unit performs separation compensation calculation on the separation error analysis result through the embedded compensation calculation matrix to generate the separation compensation strategy. Among them, each separation stage is configured with a corresponding compensation calculation unit, and each of the compensation calculation units is embedded with a corresponding compensation calculation matrix, and the compensation calculation matrix is ​​constructed by extracting the key separation indicators of the corresponding separation stage.

[0074] It should be understood that by combining the target separation compensation mode, outputting the compensation instruction and implementing accurate calculation through the compensation calculation unit, a targeted separation compensation strategy is finally generated to provide clear guidance for the operation execution.

[0075] First, based on the target separation compensation mode, the compensation instruction of this stage or the compensation instruction of the associated stage is generated and output.

[0076] Exemplarily, when the target compensation mode is the extended compensation mode for the current stage, the system directly generates compensation instructions for this stage applicable to the current node. For example, in the carbon separation stage, if it is detected that the carbon separation efficiency is low, the instruction may be "extend the separation time by 10 minutes" or "increase the separation temperature by +5°C" to achieve rapid correction of the current deviation. When the target compensation mode is the associated compensation mode for the subsequent stage, an associated stage compensation instruction is generated, and the instruction is accompanied by an associated node identifier to clarify the execution node and adjustment parameters of the compensation operation. For example, if the volatilization rate is low during the zinc volatilization stage, the instruction may be "lower the cooling temperature to -3°C during the condensation stage." The associated node identifier not only clarifies the execution location of the compensation, but also provides a tracking basis for subsequent monitoring.

[0077] By outputting compensation instructions, the compensation requirements are converted from the analysis stage to specific operation plans to ensure that the instruction information is clear and executable. According to the generated compensation instructions, the corresponding target compensation calculation unit is automatically activated to perform accurate compensation calculations. Each separation stage is equipped with independent compensation calculation units, which are calculation modules specially designed for the specific separation process of that stage. For example, in the carbon separation stage, the compensation calculation unit focuses on optimizing the separation time, temperature and gas flow rate. In the zinc separation stage, the compensation calculation unit focuses on key parameters such as volatilization efficiency and condensation conditions.

[0078] Activating the target compensation calculation unit makes the compensation calculation more targeted, avoids the redundancy caused by global calculation, and ensures that the system can respond quickly to separation deviations. At the same time, the independence of each calculation unit ensures that operations at different separation stages do not interfere with each other, which can improve the overall operating efficiency of the system.

[0079] At the same time, the target compensation calculation unit calculates the separation error analysis results through its embedded compensation calculation matrix to generate a specific separation compensation strategy. The compensation calculation matrix is ​​a mathematical model constructed by extracting key separation indicators for each separation stage. For example, the matrix for the carbon separation stage may contain parameter weights such as separation time, temperature and gas flow rate. The matrix for the zinc separation stage includes indicators such as volatilization temperature and condensation rate. These matrices are optimized in combination with historical data, process benchmark values ​​and real-time monitoring data to ensure the scientificity and practicality of the calculation results.

[0080] The compensation calculation matrix can be combined with the target compensation mode and the error analysis results, and a multi-objective optimization algorithm (such as genetic algorithm or linear programming) can be used for compensation calculation. For example, if the iron recovery rate is lower than the target value, the compensation calculation matrix may output a compensation strategy of "extending the magnetic separation time by 5 minutes and increasing the magnetic field strength to 1.5T". Finally, a clear separation compensation strategy is generated, including the specific parameter adjustment range and execution steps. Thereby achieving a complete closed-loop management from compensation demand analysis to strategy generation. Regardless of whether the compensation acts on the current stage or the associated stage, it can ensure the scientificity and efficiency of the operation, thereby optimizing the overall effect of carbon, iron and zinc separation and maximizing resource utilization.

[0081] P50: Refer to the separation compensation strategy to regulate the separation of carbon, iron and zinc.

[0082] Specifically, precise control is implemented by referring to the generated separation compensation strategy, and the separation equipment and process parameters are dynamically adjusted to achieve the separation goals efficiently.

[0083] First, according to the separation compensation strategy, control instructions are sent to the corresponding separation equipment. These instructions include specific parameter adjustments, such as separation temperature, time, pressure, gas flow rate, etc. For example, in the carbon separation stage, if the compensation strategy requires extending the operation time and increasing the furnace temperature, the instruction "extend the separation time by 10 minutes and increase the temperature to 300°C" can be sent to the separation equipment. The instructions can be accurately transmitted to the equipment through industrial automation control systems (such as PLC controllers) to ensure the immediacy and accuracy of the operation.

[0084] During the control process, the adjustment effect is tracked through the deployed real-time monitoring sensors. For example, temperature sensors are deployed to monitor the real-time temperature changes in the separation furnace, and flow rate sensors are deployed to monitor the adjustment effect of the gas flow rate. Online spectrometers are deployed to detect changes in the separation efficiency of carbon, iron, and zinc, and the feedback data provided by these sensors is compared with the strategic target value in real time to ensure that the control execution meets expectations.

[0085] If the monitoring data shows that the adjustment effect deviates from the target value, the parameters can be further optimized dynamically. For example, in the iron separation stage, if real-time monitoring shows that the magnetic separation efficiency is still not up to standard, the magnetic field strength can be further increased or the operation mode of the magnetic separator can be optimized. This closed-loop control mechanism combines real-time data feedback with strategy iteration calculation to achieve continuous optimization of the separation process.

[0086] By referring to the separation compensation strategy for carbon, iron and zinc separation control, it is possible to quickly respond to separation deviations, optimize separation effects, reduce resource waste, and improve resource utilization. This precise control can not only significantly improve separation efficiency, but also ensure the stability and continuity of the entire separation process through closed-loop optimization and multi-node collaboration, providing technical support for the full-cycle management of carbon, iron and zinc separation.

[0087] In summary, the embodiments of the present application have at least the following technical effects:

[0088] The present application divides the ash-carbon-iron-zinc separation process of blast furnace bag dust removal into multiple stages, sets key cycle nodes and monitoring indicators, and performs automated monitoring in sequence to form a set of stage monitoring indicators; the separation status is evaluated based on the monitoring data, and if it is qualified, it enters the next stage; if it is unqualified, compensation calculation is performed through error analysis to generate a compensation strategy; the separation process is dynamically controlled with reference to the compensation strategy, the separation efficiency and resource recovery rate are optimized, and intelligent management of the entire cycle is realized.

[0089] The technical effect of improving the separation efficiency of ash, carbon, iron and zinc and the resource recovery rate of blast furnace bag dust collectors has been achieved through real-time monitoring and dynamic regulation in stages.

[0090] Embodiment 2, based on the same inventive concept as the online monitoring and management method for the whole cycle of separation of ash, carbon, iron and zinc in a blast furnace bag dust removal system in the aforementioned embodiment, Figure 2 As shown, the present application provides an online monitoring and management system for the entire cycle of blast furnace bag dust removal ash carbon iron zinc separation. The system and method embodiments in the present application are based on the same inventive concept. The system includes:

[0091] The separation stage division module 11 is used to divide the full-cycle blast furnace bag dust removal ash carbon iron zinc separation process into multiple separation stages, each separation stage corresponds to a key cycle node and a corresponding monitoring indicator set.

[0092] The automatic monitoring module 12 is used to perform automatic monitoring of each separation stage in sequence according to the node sequence to obtain a stage monitoring indicator set.

[0093] The separation state evaluation module 13 is used to evaluate the separation state of ash, carbon, iron and zinc in the blast furnace bag dust collector at the current stage based on the stage monitoring indicator set, and generate a staged separation evaluation result.

[0094] The separation compensation calculation module 14 is used to postpone entering the next separation stage if the staged separation evaluation result is qualified, and to perform separation compensation calculation according to the staged separation evaluation result to generate a separation compensation strategy if it is unqualified.

[0095] The carbon-iron-zinc separation control module 15 is used to perform carbon-iron-zinc separation control with reference to the separation compensation strategy.

[0096] Furthermore, the separation compensation calculation module 14 is further configured to perform the following steps:

[0097] If the staged separation assessment result is unqualified, a separation error analysis is performed based on the staged separation assessment result, and a target separation compensation mode is obtained by matching the separation error analysis result, wherein the separation compensation mode includes a current stage extension compensation mode and a subsequent stage associated compensation mode; according to the target separation compensation mode, a corresponding compensation calculation unit is activated to perform separation compensation calculation to generate the separation compensation strategy.

[0098] Furthermore, the separation compensation calculation module 14 is further configured to perform the following steps:

[0099] Calculate the separation deviation value between the staged separation evaluation result and the target separation standard; classify and analyze the separation deviation value to obtain the classification analysis result, which includes the separation efficiency deviation and separation quality deviation of carbon, iron and zinc; determine the error source and quantify the influence degree according to the classification analysis result, and generate the separation error analysis result.

[0100] Furthermore, the separation compensation calculation module 14 is further configured to perform the following steps:

[0101] According to the separation error analysis result, multiple indicator deviation errors are extracted to perform deviation evaluation of error indicators, and high-impact error indicators are screened; according to the high-impact error indicators, the impact of the current node error on subsequent nodes is evaluated. If the impact on subsequent nodes is large, the current stage extension compensation mode is preferably used as the target separation compensation mode; if the current node error has a small impact on subsequent nodes, an associated node matching analysis is performed according to the high-impact error indicators to obtain multiple candidate compensation nodes, and the multiple candidate compensation nodes may include the current separation stage; based on the multiple candidate compensation nodes, compensation node optimization is performed, and the target separation compensation mode is determined according to the optimal compensation node.

[0102] Furthermore, the separation compensation calculation module 14 is further configured to perform the following steps:

[0103] Extract evaluation indicators of the multiple candidate compensation nodes, the evaluation indicators include separation compensation efficiency, compensation cost, compensation time and correlation influence between nodes; standardize the evaluation indicators to generate an indicator evaluation matrix; based on the indicator evaluation matrix, calculate the relative distance between each candidate compensation node and the ideal solution and the negative ideal solution; according to the relative distance, calculate the relative closeness of each candidate compensation node; sort the multiple candidate compensation nodes according to the relative closeness, and select the node with the highest relative closeness as the optimal compensation node.

[0104] Furthermore, the separation compensation calculation module 14 is further configured to perform the following steps:

[0105] If the optimal compensation node is the current separation stage, the extended compensation mode of the current stage is used as the target separation compensation mode; if the optimal compensation node is the associated separation stage, the associated compensation mode of the subsequent stage is used as the target separation compensation mode, and the target separation compensation mode carries the optimal compensation node identifier.

[0106] Furthermore, the separation compensation calculation module 14 is further configured to perform the following steps:

[0107] Based on the target separation compensation mode, output the compensation instruction of this stage or the compensation instruction of the associated stage, and the compensation instruction of the associated stage carries the associated node identifier; according to the compensation instruction of this stage or the compensation instruction of the associated stage, activate the corresponding target compensation calculation unit; the target compensation calculation unit performs separation compensation calculation on the separation error analysis result through the embedded compensation calculation matrix to generate the separation compensation strategy. Wherein, each separation stage is configured with a corresponding compensation calculation unit, and each of the compensation calculation units is embedded with a corresponding compensation calculation matrix, and the compensation calculation matrix is ​​constructed by extracting the key separation indicators of the corresponding separation stage.

[0108] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0109] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0110] This specification and the drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An online monitoring and management method for the whole cycle of ash carbon iron zinc separation in blast furnace bag dust removal, characterized in that: The method comprises: The whole cycle blast furnace bag dust removal ash carbon iron zinc separation process is divided into multiple separation stages, each separation stage corresponds to a key cycle node and a corresponding set of monitoring indicators; According to the node sequence, the automated monitoring of each separation stage is performed in turn to obtain the stage monitoring indicator set; Based on the stage monitoring indicator set, the current stage of blast furnace bag dust removal ash carbon iron zinc separation state evaluation is performed to generate a stage separation evaluation result; If the staged separation evaluation result is qualified, the next separation stage is entered; if it is unqualified, a separation compensation calculation is performed according to the staged separation evaluation result to generate a separation compensation strategy; Referring to the separation compensation strategy, carbon, iron and zinc separation regulation is performed; If the staged separation evaluation result is unqualified, separation compensation calculation is performed according to the staged separation evaluation result to generate a separation compensation strategy, including: If the staged separation evaluation result is unqualified, a separation error analysis is performed based on the staged separation evaluation result, and a target separation compensation mode is obtained by matching according to the separation error analysis result, wherein the separation compensation mode includes a current stage extension compensation mode and a subsequent stage association compensation mode; According to the target separation compensation mode, activating a corresponding compensation calculation unit to perform separation compensation calculation and generate the separation compensation strategy; Performing separation error analysis based on the staged separation evaluation results includes: Calculating the separation deviation value between the staged separation evaluation result and the target separation standard; Performing classification analysis on the separation deviation values ​​to obtain classification analysis results, wherein the classification analysis results include separation efficiency deviations and separation quality deviations of carbon, iron, and zinc; According to the classification analysis result, the error source is determined and the influence degree is quantified to generate the separation error analysis result.

2. The online monitoring and management method for the whole cycle of separation of carbon, iron and zinc in blast furnace bag dust removal as claimed in claim 1 is characterized in that: According to the separation error analysis results, the target separation compensation mode is matched and obtained, including: According to the separation error analysis results, multiple index deviation errors are extracted to perform deviation evaluation of error indexes, and high-impact error indexes are screened and obtained; According to the high-impact error index, the impact of the current node error on the subsequent nodes is evaluated. If the impact on the subsequent nodes is large, the current stage extension compensation mode is used as the target separation compensation mode; If the current node error has little impact on subsequent nodes, then performing a matching analysis of associated nodes according to the high-impact error index to obtain multiple candidate compensation nodes, wherein the multiple candidate compensation nodes include the current separation stage; Based on the multiple candidate compensation nodes, a compensation node optimization is performed, and a target separation compensation mode is determined according to the optimal compensation node.

3. The online monitoring and management method for the whole cycle of separation of ash, carbon, iron and zinc in blast furnace bag dust removal as claimed in claim 2 is characterized in that: Based on the multiple candidate compensation nodes, performing compensation node optimization includes: Extracting evaluation indicators of the plurality of candidate compensation nodes, the evaluation indicators including separation compensation efficiency, compensation cost, compensation time and inter-node correlation impact; Standardizing the evaluation indicators to generate an indicator evaluation matrix; Based on the index evaluation matrix, the relative distance between each candidate compensation node and the ideal solution and the negative ideal solution is calculated; Calculating the relative closeness of each candidate compensation node according to the relative distance; The multiple candidate compensation nodes are sorted according to the relative closeness, and the node with the highest relative closeness is selected as the optimal compensation node.

4. The online monitoring and management method for the whole cycle of separation of ash, carbon, iron and zinc in blast furnace bag dust removal as claimed in claim 2 is characterized in that: Determine the target separation compensation mode according to the optimal compensation node, including: If the optimal compensation node is the current separation stage, the current stage extended compensation mode is used as the target separation compensation mode; If the optimal compensation node is the associated separation stage, the associated compensation mode of the subsequent stage is used as the target separation compensation mode, and the target separation compensation mode carries the optimal compensation node identifier.

5. The online monitoring and management method for the whole cycle of ash-carbon-iron-zinc separation in a blast furnace bag dust removal system as claimed in claim 1, characterized in that: According to the target separation compensation mode, activating a corresponding compensation calculation unit to perform separation compensation calculation and generating the separation compensation strategy includes: Based on the target separation compensation mode, outputting a compensation instruction for this stage or a compensation instruction for an associated stage, wherein the compensation instruction for the associated stage carries an associated node identifier; According to the compensation instruction of the current stage or the compensation instruction of the associated stage, activating the corresponding target compensation calculation unit; The target compensation calculation unit performs separation compensation calculation on the separation error analysis result through an embedded compensation calculation matrix to generate the separation compensation strategy.

6. The online monitoring and management method for the whole cycle of separation of carbon, iron and zinc in blast furnace bag dust removal as claimed in claim 5, characterized in that: Each separation stage is configured with a corresponding compensation calculation unit, and each of the compensation calculation units is embedded with a corresponding compensation calculation matrix, and the compensation calculation matrix is ​​constructed by extracting key separation indicators of the corresponding separation stage.

7. An online monitoring and management system for the entire cycle of ash-carbon-iron-zinc separation in blast furnace bag dust removal, characterized in that: The system is used to execute the method according to any one of claims 1 to 6, and the system comprises: A separation stage division module, which is used to divide the full-cycle blast furnace bag dust removal ash carbon iron zinc separation process into multiple separation stages, each separation stage corresponding to a key cycle node and a corresponding monitoring indicator set; An automated monitoring module, which is used to perform automated monitoring of each separation stage in sequence according to the node order to obtain a stage monitoring indicator set; A separation state evaluation module, the separation state evaluation module is used to evaluate the ash-carbon-iron-zinc separation state of the blast furnace bag dust collector at the current stage based on the stage monitoring indicator set, and generate a staged separation evaluation result; A separation compensation calculation module, wherein if the staged separation evaluation result is qualified, the next separation stage is postponed, and if it is unqualified, separation compensation calculation is performed according to the staged separation evaluation result to generate a separation compensation strategy; A carbon-iron-zinc separation control module is used to perform carbon-iron-zinc separation control with reference to the separation compensation strategy.

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