Method for controlling the amount of dry quenching dedusting desulfurizer

Abstract

The present application relates to the technical field of coke production, and particularly relates to a dry quenching dedusting desulfurizer dosage control method, which comprises the following steps: collecting flue gas parameters, reagent supply parameters and equipment influence parameters in the dry quenching dedusting process based on a preset collection frequency; performing desulfurization efficiency analysis on the flue gas parameters to obtain a desulfurization efficiency index; performing reagent utilization rate analysis on the desulfurization efficiency index in combination with the reagent supply parameters to obtain a reagent desulfurization utilization rate; in response to the desulfurization efficiency index being less than a preset efficiency threshold value and / or the reagent desulfurization utilization rate being less than a preset utilization rate threshold value, calculating a desulfurization efficiency index gap and / or a reagent desulfurization utilization rate gap, and performing heterogeneous fusion of the desulfurization efficiency index gap and / or the reagent desulfurization utilization rate gap with the equipment influence parameters to obtain a desulfurization parameter adjustment feature set; inputting the desulfurization parameter adjustment feature set into a preset reagent control parameter optimization model to obtain reagent control parameters, and using the reagent control parameters to control the desulfurization reagent. The method can improve the stability and reliability of the desulfurization system.

Description

Technical Field

[0001] This invention relates to the technical field of coke production, and in particular to a method for controlling the dosage of dry quenching coke dust removal and desulfurization agent. Background Technology

[0002] In industrial production processes such as steel and coking, dry quenching technology is widely used due to its advantages such as energy saving and environmental protection. As a key link in ensuring clean production, the precise control of the amount of desulfurizing agent used in dry quenching directly affects the pollutant removal efficiency and production cost.

[0003] Currently, in the dry quenching coke dust removal and desulfurization process, desulfurizing agents such as powdered calcium hydroxide are usually quantitatively conveyed using unloading valves and screw feeders. This quantitative conveying method has obvious drawbacks. It does not fully consider the real-time changes in flue gas parameters, nor does it take into account the supply parameters of the desulfurizing agent itself and the operating status of the equipment. When the concentration of sulfur dioxide in the flue gas suddenly increases or the flue gas velocity fluctuates, the quantitatively added desulfurizing agent may be insufficient, resulting in the desulfurization efficiency failing to meet the standards and causing excessive emissions of pollutants such as sulfur dioxide. On the other hand, when the flue gas parameters are low, excessive addition of desulfurizing agent not only wastes the agent and increases production costs, but may also increase the burden on subsequent treatment due to increased by-products, and may even cause problems such as equipment blockage. Summary of the Invention

[0004] This invention provides a method for controlling the dosage of desulfurizing agent in dry quenching dust removal, which can improve the stability and reliability of desulfurization systems, maintain the stability of desulfurization efficiency, and reduce production costs, and can effectively solve the problems in the background art.

[0005] To achieve the above objectives, the present invention provides a method for controlling the dosage of dry quenching coke dust removal and desulfurization agent, comprising:

[0006] Based on the preset acquisition frequency, flue gas parameters, reagent supply parameters, and equipment impact parameters are collected during the dry quenching, dust removal, and desulfurization process.

[0007] The desulfurization efficiency was analyzed based on the flue gas parameters to obtain the desulfurization efficiency index;

[0008] Based on the reagent supply parameters, the desulfurization efficiency index is analyzed to obtain the reagent desulfurization utilization rate.

[0009] In response to the desulfurization efficiency index being less than a preset efficiency threshold, and / or the chemical desulfurization utilization rate being less than a preset utilization rate threshold, the desulfurization efficiency index gap and / or the chemical desulfurization utilization rate gap are calculated and heterogeneously fused with the equipment influence parameters to obtain a desulfurization parameter adjustment feature set.

[0010] The desulfurization parameter adjustment feature set is input into a preset reagent control parameter optimization model to obtain reagent control parameters, and desulfurization reagent control is performed accordingly.

[0011] In one possible design, the flue gas parameters include at least the inlet and outlet sulfur dioxide concentrations, flue gas velocity, and flue gas concentration.

[0012] In one possible design, the agent supply parameters include at least the desulfurizer delivery rate, the desulfurizer concentration, and the desulfurizer activity characteristics.

[0013] In one possible design, the equipment's influencing parameters include at least the pressure difference between the inlet and outlet of the desulfurization tower and the resistance of the bag filter.

[0014] In one possible design, the formula for calculating the desulfurization efficiency index is: Where P is the desulfurization efficiency index, and C in The concentration of sulfur dioxide at the air inlet, C out This refers to the sulfur dioxide concentration at the gas outlet.

[0015] In one possible design, the desulfurization efficiency index is analyzed by combining the reagent supply parameters to obtain the reagent desulfurization utilization rate:

[0016] Integrate the desulfurization efficiency index with the reagent supply parameters;

[0017] In chronological order, the desulfurization efficiency index at the same moment is matched one by one with the corresponding reagent supply parameters;

[0018] Obtain the amount of desulfurizing agent added and the amount actually involved in the reaction;

[0019] Obtain the delivery rate and concentration of the desulfurizing agent, and calculate the amount of pure desulfurizing agent added per unit time.

[0020] The actual amount of desulfurization involved in the reaction is calculated by combining the desulfurization efficiency index and the sulfur dioxide content in the flue gas.

[0021] The desulfurization utilization rate of the reagent is calculated based on the actual amount of desulfurization agent involved in the reaction and the amount of pure desulfurizing agent added per unit time.

[0022] In one possible design, the agent control parameters include the discharge valve opening degree, dosing interval, single dosing time, and dilution water flow rate.

[0023] In one possible design, the preset sampling frequency adopts a dynamic adjustment mechanism. The initial sampling frequency is determined based on the planned flue gas processing volume, and the subsequent sampling frequency is generated based on the desulfurization parameter adjustment feature set.

[0024] In one possible design, when collecting the flue gas parameters, at least two independent sensor systems are used to simultaneously collect the inlet and outlet sulfur dioxide concentrations, flue gas velocity, and flue gas concentration, and the data collected by the two sensor systems are cross-validated and fused.

[0025] In one possible design, the preset drug control parameter tuning model is a reinforcement learning model.

[0026] The technical solution of this invention can achieve the following technical effects:

[0027] By collecting flue gas parameters, reagent characteristics, and equipment status at high frequency, the system can dynamically sense changes in operating conditions, avoiding the lag of traditional fixed dosing modes. When the desulfurization efficiency or reagent utilization rate is lower than the threshold, the system can accurately identify demand deviations by calculating the "gap value" and integrating equipment parameters, providing a data basis for optimization.

[0028] By combining flue gas parameters and reagent characteristics, the dosage can be dynamically adjusted to prevent pollutants from exceeding the standard and to avoid waste caused by excessive dosage. Through reagent utilization analysis, the matching between desulfurizing agent activity and delivery rate can be optimized to reduce ineffective consumption. By introducing parameters such as desulfurization tower pressure difference and bag filter resistance, the risk of equipment blockage can be predicted, and the dosage strategy can be adjusted to reduce by-product accumulation and extend equipment life.

[0029] The preset drug control parameter tuning model can handle nonlinear and multivariable coupled problems, output optimal control parameters, and surpass the limitations of traditional PID control; it integrates parameters of different dimensions into a parameter tuning feature set, improving the model's generalization ability to complex working conditions.

[0030] It ensures desulfurization efficiency meets standards in real time, avoids the risk of excessive sulfur dioxide emissions, and complies with stringent environmental regulations; reduces waste of desulfurizing agents, while lowering the cost of subsequent by-product treatment and equipment maintenance; and through continuous analysis of parameters such as pressure difference and resistance, it can provide early warning of bag filter blockage or desulfurization tower scaling, assisting in the formulation of maintenance plans and reducing unplanned downtime. Attached Figure Description

[0031] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0032] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0033] This application will now be described with reference to the accompanying drawings.

[0034] like Figure 1As shown, the method for controlling the dosage of dry quenching coke dust removal and desulfurization agent of the present invention specifically includes the following steps:

[0035] S1. Based on the preset acquisition frequency, collect flue gas parameters, reagent supply parameters and equipment impact parameters during the dry quenching, dust removal and desulfurization process;

[0036] S2. Analyze the desulfurization efficiency of the flue gas parameters to obtain the desulfurization efficiency index;

[0037] S3. Based on the reagent supply parameters, perform reagent utilization analysis on the desulfurization efficiency index to obtain the reagent desulfurization utilization rate;

[0038] S4. In response to the desulfurization efficiency index being less than a preset efficiency threshold, and / or the chemical desulfurization utilization rate being less than a preset utilization rate threshold, calculate the desulfurization efficiency index gap, and / or the chemical desulfurization utilization rate gap, and fuse them with the equipment influence parameters to obtain a desulfurization parameter adjustment feature set.

[0039] S5. Input the desulfurization parameter adjustment feature set into the preset reagent control parameter optimization model to obtain the reagent control parameters, and use them to control the desulfurization reagents.

[0040] In this embodiment, by collecting flue gas parameters, reagent supply parameters, and equipment impact parameters in real time, and combining them with desulfurization efficiency analysis and reagent utilization rate analysis, this method can dynamically sense changes in the production process and adaptively adjust the dosage of desulfurizing agent according to these changes; enabling the desulfurization system to maintain high desulfurization efficiency under various operating conditions, while avoiding waste of reagents.

[0041] This method not only considers changes in flue gas parameters, but also combines reagent supply parameters and equipment operating status. By comprehensively analyzing these parameters, it achieves comprehensive optimization and control of desulfurizing agent dosage, enabling the desulfurization system to minimize production costs and subsequent treatment burden while ensuring desulfurization efficiency.

[0042] By inputting the desulfurization parameter adjustment feature set into the preset reagent control parameter optimization model, this method can automatically obtain the optimal reagent control parameters, making the control of the desulfurization system more scientific and reasonable, reducing the interference of human factors, and improving control accuracy and efficiency.

[0043] Because this method can monitor equipment-affecting parameters in real time and combine them with other parameters for comprehensive analysis, it can identify potential problems or risks in the equipment in advance, which helps to take timely measures for maintenance or adjustment and avoid equipment failure or production accidents.

[0044] By comprehensively considering multiple parameters and making dynamic adaptive adjustments, this method can significantly improve the stability and reliability of the desulfurization system. When flue gas parameters fluctuate or equipment operating conditions change, the desulfurization system can respond quickly and adjust the dosage of desulfurizing agent, thereby maintaining stable desulfurization efficiency and low production costs.

[0045] In some embodiments of the present invention, regarding step S1:

[0046] The flue gas parameters include at least the inlet and outlet sulfur dioxide concentrations, flue gas velocity, and flue gas concentration.

[0047] The concentration of sulfur dioxide at the inlet and outlet is a key indicator for measuring the desulfurization effect. High-precision sulfur dioxide sensors can be installed at the flue gas inlet and outlet of the dry quenching dust removal and desulfurization system. At the same time, in order to ensure the accuracy and stability of the data, the sensors need to be calibrated and maintained regularly. Standard gas can be used to verify the sensors, and the sensor parameters should be adjusted in a timely manner based on the verification results.

[0048] Flue gas velocity has a significant impact on the desulfurization reaction process. Pitot tubes or thermal mass flow meters should be installed at appropriate locations in the flue. Pitot tubes calculate flue gas velocity using Bernoulli's equation by measuring the dynamic and static pressures of the flue gas. Thermal mass flow meters, on the other hand, determine the flow rate by measuring the heat transfer between the heating element and the gas, based on the principle of heat conduction. Similarly, these measuring devices need to be calibrated regularly, and standard flow meters can be used for calibration.

[0049] Flue gas concentration is related to the load and operating status of the desulfurization system; a laser scattering dust concentration meter can be used, which calculates the concentration of particulate matter in the flue gas by emitting a laser beam into the flue gas and based on the intensity and distribution of the scattered light in the flue gas; to ensure measurement accuracy, the dust concentration meter needs to be cleaned and calibrated regularly to remove dust and contaminants from the surface of the optical components.

[0050] The agent supply parameters include at least the desulfurizer delivery rate, desulfurizer concentration, and desulfurizer activity characteristics;

[0051] Install a screw feeder speed sensor or a weighing feeder on the desulfurizing agent conveying pipeline to obtain the conveying rate of the desulfurizing agent; the screw feeder speed sensor calculates the conveying volume of the desulfurizing agent by measuring the rotation speed of the screw blades and combining it with the structural parameters of the screw feeder; the weighing feeder directly measures the weight of the desulfurizing agent conveyed per unit time; these devices are calibrated regularly to ensure the accuracy of the measurements;

[0052] For desulfurizing agents such as powdered calcium hydroxide, an online concentration analyzer can be used for concentration detection; the measurement results of the online concentration analyzer should be verified and calibrated regularly using chemical analysis methods to ensure data reliability;

[0053] The activity of desulfurizing agents has a significant impact on desulfurization efficiency. By periodically sampling and analyzing samples in the laboratory, parameters such as the content of active ingredients and specific surface area of ​​desulfurizing agents are measured to evaluate their activity. At the same time, combined with the actual desulfurization effect at the production site, a correlation model between the activity of desulfurizing agents and the actual desulfurization efficiency is established to more accurately grasp the activity characteristics of desulfurizing agents.

[0054] The equipment influencing parameters include at least the pressure difference between the inlet and outlet of the desulfurization tower and the resistance of the bag filter.

[0055] Differential pressure transmitters are installed at the inlet and outlet of the desulfurization tower to measure the pressure difference between the inlet and outlet of the desulfurization tower. The differential pressure transmitters detect the pressure difference between the two ends and convert it into an electrical signal output. The differential pressure transmitters are calibrated regularly to ensure measurement accuracy.

[0056] Pressure sensors are installed at the inlet and outlet of the bag filter to measure the pressure difference between the inlet and outlet, thereby obtaining the resistance of the bag filter; the pressure sensor converts the pressure signal into an electrical signal output; the pressure sensor is calibrated regularly to ensure the accuracy of the measurement data;

[0057] The preset sampling frequency is determined based on the planned flue gas processing volume. In the dry quenching coke production process, the planned flue gas processing volume is an important indicator for measuring the production scale and flue gas generation. Generally, the larger the planned flue gas processing volume, the more flue gas volume needs to be processed per unit time, and the faster the content of pollutants (such as sulfur dioxide) in the flue gas may change. The real-time requirements for adjusting the amount of desulfurizing agent are also higher.

[0058] In this embodiment, flue gas parameters, reagent supply parameters, and equipment impact parameters are categorized and collected in a targeted manner, covering core dimensions from pollutant concentration and fluid characteristics to reagent supply and equipment operating status. This ensures the accurate acquisition of key data such as inlet and outlet sulfur dioxide concentrations and flue gas velocity. Comprehensive and accurate data provides a reliable basis for subsequent desulfurization efficiency analysis and reagent utilization analysis, avoiding analytical errors caused by missing or biased data, and laying a solid foundation for precise control of desulfurizing agent dosage. A regular calibration and maintenance plan is developed for various data acquisition devices. This mechanism effectively reduces measurement errors caused by long-term operation, ensures long-term data stability and reliability, avoids erroneous data collection caused by equipment failure or performance degradation, reduces the risk of misjudgment of desulfurizing agent dosage due to data distortion, and ensures the stable operation of the entire control system. Based on the planned flue gas treatment volume... The initial acquisition frequency is determined to fully consider the parameter variation characteristics under different production scales. When the processing volume is large, the acquisition frequency of key parameters is increased to promptly capture rapid changes in pollutant concentration, flow rate, etc. Simultaneously, the subsequent acquisition frequency is dynamically adjusted based on the desulfurization parameter adjustment feature set, enabling rapid response during operating condition fluctuations and appropriate reduction of frequency to save resources when operating conditions are stable. This satisfies real-time monitoring needs while balancing data processing costs and equipment operating losses, thereby improving system operating efficiency. The simultaneous acquisition of three types of parameters—flue gas, reagents, and equipment—establishes the correlation between material flow, energy flow, and equipment status during the production process. This allows the system to more comprehensively and deeply understand the desulfurization process, thereby achieving precise dynamic control of desulfurizing agent dosage. This effectively solves the drawbacks of traditional quantitative delivery methods, reduces pollutant emissions, saves production costs, and improves the overall performance and economic benefits of the dry quenching coke dust removal and desulfurization system.

[0059] More specifically, select at least two sensor systems with high precision, high stability and good anti-interference capabilities, and install them in key locations to collect flue gas parameters; ensure that the two sensor systems are independent of each other, including in terms of power supply, signal transmission and processing, so as to avoid the failure of one system affecting the normal operation of the other system;

[0060] Two sensor systems simultaneously collect flue gas parameters such as inlet and outlet sulfur dioxide concentration, flue gas velocity, and flue gas concentration at a preset acquisition frequency; the collected data should include information such as timestamps and sensor identifiers for subsequent data processing and analysis.

[0061] The data collected by the two sensor systems are synchronized in time to ensure consistency across the timeline. Statistical methods or algorithms are used to cross-validate the data collected by the two sensor systems to determine their consistency and accuracy. If significant differences are found between the data collected by the two sensor systems, an alarm mechanism is triggered to prompt operators to check and handle the issue.

[0062] For data that passes cross-validation, data fusion algorithms such as weighted averaging and Kalman filtering are used to fuse the two sets of data to obtain more accurate and reliable flue gas parameter values. During the data fusion process, factors such as sensor accuracy, stability, and anti-interference ability should be considered, and weights should be allocated reasonably to ensure the accuracy and reliability of the fusion results.

[0063] In this embodiment, by setting up two independent sensor systems and performing cross-validation and fusion processing, the risk of inaccurate data due to sensor failure or error can be significantly reduced, improving the accuracy and reliability of data acquisition. Redundant design and data processing methods enable the system to maintain stable operation when facing sensor failure or external interference, reducing system failures or downtime caused by inaccurate data. Accurate and reliable flue gas parameter data provide strong support for the precise control of desulfurizing agent dosage, which helps to improve desulfurization efficiency, reduce production costs, and reduce environmental impact.

[0064] In some embodiments of the present invention, regarding step S2:

[0065] Because measuring equipment may have errors or be affected by external interference, outliers may exist in the collected flue gas parameters. For these outliers, statistical methods are used to identify and process them. Reasonable upper and lower thresholds are set, and data exceeding the thresholds are regarded as outliers. They are replaced by the average of adjacent normal data or by interpolation to ensure the accuracy of subsequent analysis. The dimensions and numerical ranges of different flue gas parameters may vary greatly. In order to eliminate the influence of dimensions on the analysis results, the collected flue gas parameters are normalized.

[0066] Based on the principles of chemical reaction and actual production experience, a desulfurization efficiency calculation model was established.

[0067] To ensure the accuracy of the desulfurization efficiency index calculation, the established model needs to be verified and calibrated. During the coke production process, actual production data under different operating conditions are selected, and the desulfurization efficiency index is calculated using the established model and compared with the desulfurization efficiency data manually detected in the laboratory. If the calculated results deviate significantly from the actual test results, the reasons are analyzed and the correction coefficients in the model are adjusted. At the same time, new production data are collected regularly to continuously optimize the model parameters so that the desulfurization efficiency index can accurately reflect the actual desulfurization effect.

[0068] The formula for calculating the desulfurization efficiency index is: ,

[0069] Where P is the desulfurization efficiency index, and C in The concentration of sulfur dioxide at the air inlet, C out The concentration of sulfur dioxide at the outlet;

[0070] In this embodiment, by using statistical methods to set reasonable upper and lower thresholds, outliers are identified and processed. They are replaced with the average of adjacent normal data or by interpolation, effectively removing noise and interference from the data, improving the accuracy and reliability of the data, and providing a high-quality data foundation for subsequent desulfurization efficiency analysis, avoiding deviations in analysis results caused by outliers. Normalization eliminates the influence of dimensions on the analysis results, enabling different parameters to be compared and analyzed on the same scale, further improving the usability of the data and the accuracy of the analysis, ensuring the stability and effectiveness of the desulfurization efficiency calculation model. The desulfurization efficiency calculation model is established by combining chemical reaction principles and actual production experience, fully considering dry... The physical and chemical changes during the quenching, dust removal, and desulfurization process enable the model to more accurately reflect the actual situation. This modeling method, which combines theory and practice, improves the rationality and scientific validity of the model, providing strong support for accurately calculating the desulfurization efficiency index. In the coke production process, by selecting actual production data under different operating conditions, using the established model to calculate the desulfurization efficiency index, and comparing it with desulfurization efficiency data manually detected in the laboratory, the accuracy and reliability of the model can be intuitively evaluated, and problems and deficiencies in the model can be identified in a timely manner. The process of analyzing and correcting deviations can continuously improve the model, enhance its adaptability to actual production conditions, and enable the model to more accurately reflect the actual desulfurization effect.

[0071] In some embodiments of the present invention, regarding step S3:

[0072] After obtaining the desulfurization efficiency index, it is integrated with the reagent supply parameters;

[0073] In chronological order, the desulfurization efficiency index at the same moment is matched with the corresponding reagent supply parameters to form complete analytical data, which prepares for subsequent analysis of reagent utilization.

[0074] Chemical desulfurization utilization rate refers to the degree to which the desulfurizing agent is effectively utilized in the actual desulfurization reaction; when calculating, both the amount of desulfurizing agent added and the amount actually participating in the reaction should be taken into account.

[0075] Taking powdered calcium hydroxide desulfurizer as an example, if the delivery rate and concentration of the desulfurizer are known at a certain moment, the amount of pure desulfurizer added per unit time can be obtained by multiplying the delivery rate by the concentration.

[0076] The actual amount of desulfurization involved in the reaction is calculated by combining the desulfurization efficiency index and the sulfur dioxide content in the flue gas. First, the mass of sulfur dioxide removed per unit time is calculated based on the sulfur dioxide concentration at the flue gas inlet, the flue gas volume flow rate, and the desulfurization efficiency index. Then, based on the chemical principle of the desulfurization reaction, the theoretical amount of desulfurization involved in the reaction is obtained.

[0077] The desulfurization utilization rate is calculated based on the actual amount of desulfurization agent involved in the reaction and the amount of pure desulfurizing agent added per unit time. The desulfurization utilization rate is obtained by dividing the actual amount of desulfurization agent involved in the reaction by the actual amount of desulfurizing agent added and then multiplying by 100%.

[0078] In this embodiment, the desulfurization efficiency index and reagent supply parameters are integrated and matched one-to-one in chronological order to form complete analytical data, comprehensively considering key factors in the desulfurization process. The desulfurization efficiency index reflects the overall effect of the desulfurization process, while the reagent supply parameters reflect the input of desulfurizing agents. The combination of the two provides a complete and systematic data foundation for subsequent in-depth analysis of reagent utilization, avoiding analytical biases caused by missing or mismatched data, and ensuring the accuracy and reliability of the analytical results. It lays a solid foundation for subsequent accurate calculation of reagent desulfurization utilization and in-depth analysis of factors affecting reagent utilization. It helps to comprehensively and deeply understand the utilization of reagents in the desulfurization process and provides strong support for optimizing the control of desulfurizing agent dosage. When calculating the reagent desulfurization utilization, the input amount of desulfurizing agent and the actual participation of the reaction are comprehensively considered. The appropriate amount can accurately reflect the effective utilization of desulfurizing agents in actual desulfurization reactions; it avoids the one-sidedness caused by only considering the input amount or only considering the reaction amount, making the calculation results closer to the actual situation. Taking powdered calcium hydroxide desulfurizing agent as an example, the calculation process is explained in detail. First, the input amount of pure desulfurizing agent per unit time is calculated by the delivery rate and concentration of the desulfurizing agent. Then, the actual amount of desulfurizing agent participating in the reaction is calculated by combining the desulfurization efficiency index and the sulfur dioxide content in the flue gas. Finally, the desulfurization utilization rate of the agent is obtained by dividing the actual amount of desulfurizing agent input by the actual amount of desulfurizing agent. This specific and operable calculation method provides clear guidance for practical applications, making it easy to accurately evaluate the desulfurization utilization rate of agents in industrial processes such as coke production. By accurately calculating the desulfurization utilization rate of agents, the utilization of desulfurizing agents in actual production processes can be clearly understood.

[0079] In some embodiments of the present invention, regarding step S4,

[0080] The preset efficiency threshold is determined comprehensively based on factors such as environmental protection requirements, process standards, and the company's own cost control objectives in the coke production process.

[0081] When the desulfurization efficiency index is less than the preset efficiency threshold, the desulfurization efficiency index gap is calculated; the gap value is equal to the preset efficiency threshold minus the actual desulfurization efficiency index; the desulfurization efficiency index gap directly reflects the gap between the current desulfurization efficiency and the expected target.

[0082] The preset utilization rate threshold is determined based on the cost of the desulfurizing agent, the chemical principle of the desulfurization reaction, and actual production experience; a higher desulfurization utilization rate means lower cost and higher efficiency, but various factors in actual production need to be considered comprehensively.

[0083] When the desulfurization utilization rate of the reagent is less than the preset utilization rate threshold, the desulfurization utilization rate gap of the reagent is calculated; the gap value is equal to the preset utilization rate threshold minus the actual desulfurization utilization rate of the reagent, and the desulfurization utilization rate gap of the reagent reflects the insufficiency in the utilization of the reagent;

[0084] The pressure difference between the inlet and outlet of the desulfurization tower reflects the flow resistance of the gas inside the tower and the progress of the desulfurization reaction. An excessively large pressure difference indicates that there are problems such as blockage inside the desulfurization tower or uneven distribution of the desulfurizing agent, which affects the desulfurization efficiency. An excessively small pressure difference indicates that the desulfurizing agent is not filled enough or the reaction is incomplete.

[0085] The resistance of a bag filter is an important indicator for measuring its operating status. Excessive resistance indicates that too much dust or desulfurization byproducts may have accumulated on the surface of the filter bag, making it difficult for gas to pass through and affecting the normal operation of the entire dust removal and desulfurization system. Insufficient resistance may mean that the filter bag is not performing its proper filtering function.

[0086] Because the desulfurization efficiency index gap, the chemical desulfurization utilization rate gap, and the different dimensions and numerical ranges of equipment influence parameters are different, direct fusion may lead to the amplification or reduction of the influence of certain parameters on the results; therefore, it is necessary to first standardize these data and convert them into dimensionless values ​​so that they can be compared and analyzed on the same scale.

[0087] Based on the degree of influence of each parameter on the desulfurization process, different weights are assigned, and then weighted fusion is performed to obtain the desulfurization parameter adjustment feature set;

[0088] The desulfurization parameter adjustment feature set can comprehensively and accurately reflect the problems and directions that need to be adjusted in the current dry quenching, dust removal, and desulfurization process.

[0089] In this embodiment, by calculating the desulfurization efficiency index gap and the reagent desulfurization utilization rate gap, the difference between the current desulfurization efficiency and the expected target, and between the actual utilization of the reagent and the ideal state, can be intuitively reflected. This allows operators and managers to clearly understand the specific problems existing in the desulfurization process, providing clear goals and directions for subsequent parameter adjustments. If the desulfurization efficiency index gap is large, it indicates that the current desulfurization efficiency is far from meeting the requirements, and parameter adjustments should focus on improving desulfurization efficiency. If the reagent desulfurization utilization rate gap is significant, it indicates that there are problems with reagent utilization, and the method of reagent use should be optimized. The analysis of the pressure difference between the inlet and outlet of the desulfurization tower and the resistance of the bag filter can accurately reflect the operating status of the desulfurization and dust removal equipment. Abnormal changes in pressure difference and resistance can promptly indicate potential problems inside the equipment, providing a basis for equipment maintenance and adjustment, and helping to ensure the stable operation of the entire dust removal and desulfurization system. Due to the different dimensions and numbers of the desulfurization efficiency index gap, reagent desulfurization utilization rate gap, and equipment influence parameters, the analysis of these factors can provide a clear understanding of the specific problems existing in the desulfurization process. Direct fusion of parameters with different value ranges can lead to unreasonable amplification or reduction of their influence on the results. Data standardization converts these parameters into dimensionless values, enabling comparison and analysis on the same scale, avoiding biases caused by dimensional differences, and improving the accuracy and scientific rigor of subsequent fusion calculations. Different weights are assigned to each parameter based on its impact on the desulfurization process, and then weighted fusion is performed to obtain a desulfurization parameter adjustment feature set. This method comprehensively considers factors such as desulfurization efficiency, reagent utilization, and equipment operation. Different parameters have varying degrees of influence on desulfurization effects in actual production. By rationally allocating weights, the overall operating status of the system can be more accurately reflected, providing a more reliable basis for subsequent reagent control parameter optimization, thereby improving the accuracy of decision-making. The desulfurization parameter adjustment feature set is a feature vector that integrates information on desulfurization efficiency, reagent utilization, and equipment operating status. It can comprehensively and accurately reflect the problems and necessary adjustments in the current dry quenching, dust removal, and desulfurization process.

[0090] Furthermore, the subsequent acquisition frequency is generated based on the desulfurization parameter adjustment feature set; the desulfurization parameter adjustment feature set contains key information such as the desulfurization efficiency index gap, the chemical desulfurization utilization rate gap, and equipment influence parameters, which can reflect the actual status and existing problems of the current desulfurization process;

[0091] When the desulfurization parameter adjustment feature set shows that the desulfurization efficiency or reagent utilization rate is not up to standard, it means that the flue gas parameters may have changed significantly. At this time, it is necessary to increase the sampling frequency in order to capture parameter changes more promptly and adjust the amount of desulfurizing agent.

[0092] The specific adjustment strategy is as follows: when the desulfurization efficiency index gap or the chemical desulfurization utilization rate gap exceeds a certain preset threshold, the sampling frequency is increased to multiple times per second to ensure real-time capture of parameter changes; when the gap is within a stable range or has decreased, the sampling frequency can be gradually reduced to the initial sampling frequency or lower to reduce the data processing burden.

[0093] In this embodiment, dynamically adjusting the acquisition frequency ensures timely capture of parameter changes when flue gas parameters fluctuate significantly, providing accurate data support for precise control of desulfurizer dosage. When flue gas parameters are stable, reducing the acquisition frequency reduces unnecessary data redundancy and processing burden, improving system operating efficiency. By adjusting the acquisition frequency in real time and combining it with desulfurization parameter adjustment feature sets for data analysis, the required desulfurizer dosage can be determined more accurately, thereby achieving precise control of desulfurizer dosage, improving desulfurization efficiency, and reducing production costs. The mechanism of dynamically adjusting the acquisition frequency enables the system to better adapt to different production conditions and flue gas parameter changes, maintaining system stability and reliability.

[0094] In some embodiments of the present invention, regarding step S5:

[0095] In practical applications of coke production, machine learning algorithms or mechanism-based mathematical models are used to construct optimal reagent control parameters. Based on historical production data, including flue gas parameters, reagent supply parameters, equipment impact parameters, and corresponding desulfurization efficiency, reagent utilization, and optimal reagent control parameters under different operating conditions, the model is trained and optimized. Data from coke plants under different production loads and different desulfurizer batches are collected, and the neural network model is repeatedly trained to accurately identify the mapping relationship between data features and optimal reagent control parameters.

[0096] The desulfurization parameter adjustment feature set is input into the reagent control parameter optimization model according to the input format and data type specified by the model. Since the desulfurization parameter adjustment feature set has been normalized for data of different dimensions, it ensures that the data meets the input requirements of the model.

[0097] After receiving the desulfurization parameter adjustment feature set, the model performs calculations and inferences through its internal algorithm, and finally outputs the reagent control parameters.

[0098] The reagent control parameters output by the model are transmitted to the actuators of the dry quenching, dust removal, and desulfurization system.

[0099] The drug control parameters include:

[0100] The speed of the screw feeder can be adjusted to change the amount of desulfurizing agent delivered per unit time. When the sulfur dioxide concentration in the flue gas increases and the amount of desulfurizing agent needs to be increased, the speed of the screw feeder can be increased. Conversely, when the flue gas parameters are low, the speed can be reduced to decrease the amount of desulfurizing agent delivered.

[0101] The opening degree of the discharge valve is used to control the flow rate of desulfurizing agent from the storage device into the delivery pipeline. Adjusting the opening degree of the discharge valve can directly affect the delivery rate of the desulfurizing agent. A larger opening degree allows more desulfurizing agent to pass through, increasing the delivery volume; a smaller opening degree limits the amount of desulfurizing agent that can pass through.

[0102] The dosing interval refers to the time interval between two desulfurizer dosing operations. The dosing interval should be set reasonably according to the changes in flue gas parameters and the needs of the desulfurization reaction. When the concentration of pollutants in the flue gas fluctuates greatly, the dosing interval should be shortened so that the amount of desulfurizer added can be adjusted more timely. When the flue gas parameters are relatively stable, the dosing interval can be appropriately extended to reduce unnecessary operations and reduce equipment wear and energy consumption.

[0103] The single-dosage time refers to the duration of each desulfurization agent addition. The length of the single-dosage time affects the amount of desulfurization agent added each time. The appropriate single-dosage time should be determined by combining the desulfurization agent delivery rate and actual needs. When the pollutant concentration is high or the desulfurization reaction requires a rapid response, the single-dosage time can be appropriately extended and the amount of desulfurization agent added per time can be increased. Conversely, the single-dosage time should be shortened.

[0104] In some cases, to improve the flowability and reactivity of the desulfurizing agent, or according to specific process requirements, it is necessary to dilute desulfurizing agents such as powdered calcium hydroxide. By adjusting the flow rate of the dilution water, the concentration of the desulfurizing agent can be controlled. Increasing the flow rate of the dilution water will decrease the concentration of the desulfurizing agent, while decreasing the flow rate of the dilution water will increase the concentration of the desulfurizing agent.

[0105] In this embodiment, by constructing a reagent control parameter optimization model and inputting a desulfurization parameter adjustment feature set, the model can accurately adjust the reagent control parameters based on real-time collected flue gas parameters. When the sulfur dioxide concentration in the flue gas increases or the flue gas velocity fluctuates, the model promptly increases the screw feeder speed, increases the discharge valve opening, shortens the dosing interval, or extends the single dosing time, thereby increasing the amount of desulfurizing agent added to ensure the desulfurization reaction proceeds fully, effectively improving desulfurization efficiency and ensuring that pollutant emissions meet standards. When it is necessary to dilute the desulfurizing agent, the concentration of the desulfurizing agent is controlled by adjusting the dilution water flow rate to better adapt to the desulfurization reaction requirements under different operating conditions, further improving desulfurization efficiency. The model accurately controls the amount of desulfurizing agent added based on a comprehensive analysis of flue gas parameters, reagent supply parameters, and equipment influence parameters. When the flue gas parameters are low, the model reduces the screw feeder speed, decreases the discharge valve opening, extends the dosing interval, or shortens the single dosing time to avoid excessive addition of desulfurizing agent, thereby reducing reagent waste and lowering reagent procurement costs. Reasonable control of the amount of desulfurizing agent reduces the risk of excessive addition. The byproducts generated by the addition of chemicals reduce the burden of subsequent processing and lower processing costs. Simultaneously, it reduces equipment blockage and other problems, lowers equipment maintenance and repair costs, improves production efficiency, and brings significant economic benefits to the enterprise. The chemical control parameter optimization model, trained and optimized based on historical production data, can adapt to the production needs of coke plants under different production loads and different desulfurizer batches. Through repeated training of the neural network model, it accurately identifies the mapping relationship between data features and optimal chemical control parameters, outputting appropriate chemical control parameters regardless of changes in production conditions to ensure the stable operation of the desulfurization system. By comprehensively considering equipment impact parameters and adjusting chemical control parameters, equipment failures can be avoided, equipment operating conditions improved, and the stable operation of the entire dust removal and desulfurization system ensured, reducing losses due to production interruptions. The chemical control parameters output by the model are transmitted to the actuators of the dry quenching coke dust removal and desulfurization system, realizing automated control of desulfurizer dosing, reducing manual intervention, and improving the automation level of the production process.

[0106] Furthermore, the preset reagent control parameter optimization model is a reinforcement learning model. Based on the desulfurization effect feedback after each adjustment of the reagent control parameters, the model's decision-making strategy is automatically optimized to achieve better reagent control parameter optimization.

[0107] The reinforcement learning model can automatically adjust the reagent control parameters according to the real-time changes in flue gas parameters, reagent supply parameters and equipment operating status to achieve adaptive control. Through interaction and feedback learning with the environment, the reinforcement learning model can continuously optimize the decision-making strategy and find the optimal combination of reagent control parameters, thereby improving desulfurization efficiency and reducing production costs.

[0108] Reinforcement learning models can make decisions and adjustments automatically, reducing the need for human intervention and lowering operational difficulty and cost. Reinforcement learning models are highly scalable and can be easily integrated with new sensor data, control strategies, or optimization objectives to adapt to ever-changing production needs and environmental standards.

[0109] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling the dosage of dry quenching coke dust removal and desulfurization agent, characterized in that, include: Based on a preset acquisition frequency, flue gas parameters, reagent supply parameters, and equipment impact parameters are collected during the dry quenching, dust removal, and desulfurization process. The flue gas parameters include at least the inlet and outlet sulfur dioxide concentrations, flue gas velocity, and flue gas concentration. The reagent supply parameters include at least the desulfurizing agent delivery rate, desulfurizing agent concentration, and desulfurizing agent activity characteristics. The desulfurization efficiency was analyzed based on the flue gas parameters to obtain the desulfurization efficiency index; The desulfurization efficiency index is integrated with the reagent supply parameters; the desulfurization efficiency index and the corresponding reagent supply parameters at the same time are matched one by one according to the time sequence; the amount of desulfurizer input and the amount actually participating in the reaction are obtained; the desulfurizer delivery rate and desulfurizer concentration are obtained, and the amount of pure desulfurizer input per unit time is calculated; based on the sulfur dioxide concentration at the flue gas inlet, the flue gas volume flow rate and the desulfurization efficiency index, the mass of sulfur dioxide removed per unit time is calculated; and based on the chemical principle of the desulfurization reaction, the theoretical amount of desulfurization reagent participating in the reaction is obtained. The desulfurization utilization rate of the reagent is calculated based on the actual amount of desulfurization agent involved in the reaction and the amount of pure desulfurizing agent added per unit time. In response to the desulfurization efficiency index being less than a preset efficiency threshold and the chemical desulfurization utilization rate being less than a preset utilization rate threshold, the desulfurization efficiency index gap and the chemical desulfurization utilization rate gap are calculated, and they are heterogeneously fused with the equipment influence parameters to obtain a desulfurization parameter adjustment feature set. The desulfurization parameter adjustment feature set is input into a preset reagent control parameter optimization model to obtain reagent control parameters, and desulfurization reagent control is performed accordingly.

2. The method for controlling the dosage of dry quenching coke dust removal and desulfurization agent according to claim 1, characterized in that, The parameters affecting the equipment include at least the pressure difference between the inlet and outlet of the desulfurization tower and the resistance of the bag filter.

3. The method for controlling the dosage of dry quenching coke dust removal and desulfurization agent according to claim 1, characterized in that, The formula for calculating the desulfurization efficiency index is: ， Where P is the desulfurization efficiency index, and C in The concentration of sulfur dioxide at the air inlet, C out This refers to the sulfur dioxide concentration at the gas outlet.

4. The method for controlling the dosage of dry quenching coke dust removal and desulfurization agent according to claim 1, characterized in that, The controlled parameters for the reagent include the opening degree of the discharge valve, the dosing interval, the dosing time per dose, and the dilution water flow rate.

5. The method for controlling the dosage of dry quenching coke dust removal and desulfurization agent according to claim 1, characterized in that, The preset acquisition frequency adopts a dynamic adjustment mechanism. The initial acquisition frequency is determined based on the planned flue gas processing volume, and the subsequent acquisition frequency is generated based on the desulfurization parameter adjustment feature set.

6. The method for controlling the dosage of dry quenching coke dust removal and desulfurization agent according to claim 1, characterized in that, When collecting the flue gas parameters, at least two independent sensor systems are used to simultaneously collect the inlet and outlet sulfur dioxide concentrations, flue gas velocity, and flue gas concentration, and the data collected by the two sensor systems are cross-validated and fused.

7. The method for controlling the dosage of dry quenching coke dust removal and desulfurization agent according to claim 1, characterized in that, The preset drug control parameter tuning model is a reinforcement learning model.

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