A method and system for controlling the emission of impurities from a urea hydrolyser

Abstract

The application relates to the technical field of urea hydrolyzers, in particular to a urea hydrolyzer impurity blowdown control method and system. The method comprises the following steps: selecting multiple impurity sensing points according to the structural parameters of the urea hydrolyzer; obtaining feedback data of each impurity sensing point, generating an impurity monitoring package according to all the feedback data; and judging whether to generate a blowdown instruction according to a preset blowdown control model and the impurity monitoring package. Through analysis of the feedback data of each impurity sensing point, multiple impurity mapping indexes are extracted, so that the accurate identification and regional diagnosis of the impurity accumulation risk in the hydrolyzer are realized. Meanwhile, by dynamically adjusting the blowdown control parameters, the online early warning and blowdown efficiency of the impurity accumulation in the hydrolyzer are improved, the labor intensity of the operation personnel is reduced, the inhalation of impurity gas is avoided, and the personal safety is ensured.

Description

Technical Field

[0001] This application relates to the field of urea hydrolyzer technology, and in particular to a method and system for controlling the discharge of impurities from a urea hydrolyzer. Background Technology

[0002] The urea hydrolyzer is the core equipment in the urea hydrolysis system, used to decompose urea waste liquid into ammonia and carbon dioxide. However, during long-term operation, additives and impurities such as biuret and phosphates carried in the urea raw material, minerals dissolved in the demineralized water, and mechanical impurities mixed in during loading, unloading, and transportation will gradually concentrate and deposit within the hydrolyzer. The accumulation of these impurities will bring a series of problems: First, the deposits adhere to the heat exchange surface, forming a scale layer, which significantly reduces heat exchange efficiency and leads to increased steam consumption; second, the accumulation of impurities will occupy the effective volume, shorten the material residence time, and affect the hydrolysis rate; in severe cases, it can also block the drain pipe, causing the system to shut down.

[0003] Currently, the treatment of impurities in hydrolyzers mainly relies on the experience and judgment of operators and periodic manual sludge removal. This method has significant shortcomings: on the one hand, it is difficult to accurately determine the timing of sludge removal; removing sludge too early leads to waste of working fluid and heat, while removing it too late can easily cause impurities to affect the equipment; on the other hand, manual operation requires on-site valve opening for sludge removal, which is not only labor-intensive but also poses a safety hazard of inhaling toxic gases such as residual ammonia. In addition, manual operation makes it difficult to avoid air being drawn into the system, which may cause oxidation and corrosion or affect subsequent processes. Summary of the Invention

[0004] The purpose of this application is to address the aforementioned technical problems by providing a method and system for controlling the discharge of impurities from a urea hydrolyzer, aiming to improve the efficiency of impurity monitoring and discharge in the urea hydrolyzer and ensure its operational efficiency.

[0005] In some embodiments of this application, a multi-source sensing network and multiple impurity regions are constructed. By analyzing the feedback data from each impurity sensing point, multiple impurity mapping indicators are extracted, thereby achieving accurate identification and regional diagnosis of the risk of impurity accumulation in the hydrolyzer. At the same time, by dynamically adjusting the sewage discharge control parameters, the online early warning and sewage discharge efficiency for impurity accumulation in the hydrolyzer are improved, reducing the labor intensity of operators, avoiding the inhalation of impurity gases, and ensuring personal safety.

[0006] In some embodiments of this application, an auxiliary diagnostic model is added. By collecting the auxiliary operating package (i.e., operating parameters) of the hydrolyzer, the current operating condition is matched with the disturbance scenario library, and the judgment logic of impurity accumulation risk is dynamically adjusted to avoid "false alarms" or "missed alarms" caused by operating condition disturbances, thereby improving the impurity monitoring accuracy of the urea hydrolyzer and eliminating invalid emissions.

[0007] In some embodiments of this application, a method for controlling the discharge of impurities from a urea hydrolyzer is provided, including: Multiple impurity sensing points were selected based on the structural parameters of the urea hydrolyzer. Acquire feedback data from each impurity sensing point and generate an impurity monitoring package based on all feedback data; The system determines whether to generate a discharge instruction based on the preset discharge control model and impurity monitoring package.

[0008] In some embodiments of this application, the preset sewage control model includes: Multiple impurity zones were set based on historical monitoring data from the urea hydrolyzer. Select the target impurity region sequentially from all impurity regions; A sub-model for processing the target impurity region is constructed based on all impurity sensing points; Establish a wastewater control strategy for the target impurity area; Set up processing sub-models for each impurity region in sequence, and establish an impurity monitoring model based on all processing sub-models; Set the sewage discharge control strategy for each impurity area in sequence, and establish a sewage discharge strategy library based on all sewage discharge control strategies. Construct an auxiliary diagnostic model; A pollution control model is constructed based on the impurity monitoring model, the pollution discharge strategy library, and the auxiliary diagnostic model.

[0009] In some embodiments of this application, the processing sub-model for constructing the target impurity region includes: Set multiple impurity mapping indices for the target impurity region; Generate mapping values ​​of each impurity sensing point to the target impurity region; Multiple associated sensing points in the target impurity region are selected based on all mapping values; A sub-model for processing the target impurity region is established based on all associated sensing points and all impurity mapping indicators.

[0010] In some embodiments of this application, the construction of the auxiliary diagnostic model includes: Establish a basic diagnostic strategy for the target impurity region; The basic diagnostic strategy includes: standard thresholds for each impurity mapping index in the target impurity region; Set the basic diagnostic strategies for each impurity region in sequence; Obtain all impurity mapping indices for each impurity region and establish a mapping index set; Multiple disturbance indicators and correction cycles were set based on historical monitoring data of the urea hydrolyzer. Multiple disturbance scenarios are established based on all disturbance indicators; Select the target disturbance scene sequentially from all disturbance scenes; Set a compensation sub-strategy for the target disturbance scenario, wherein the compensation sub-strategy includes correction values ​​for all impurity mapping indices in the mapping index set; Set the compensation sub-strategies for each disturbance scenario in sequence; An auxiliary diagnostic model is constructed, which includes all basic diagnostic strategies and all compensation sub-strategies.

[0011] In some embodiments of this application, the step of determining whether to generate a discharge instruction includes: Multiple feedback time points can be preset; Obtain the impurity monitoring package and auxiliary operation package at the current feedback time point; The primary correction strategy is set according to the auxiliary operation package; Select the areas to be evaluated sequentially from all impurity areas; The processing sub-model for the region to be evaluated is set as the execution processing model; Generate a mapping parameter package for the region to be evaluated based on the execution processing model and the impurity monitoring package; Generate the pollution discharge evaluation value f for the area to be evaluated based on the mapping parameter package; Preset discharge evaluation threshold F1; If f > F1, generate a sewage discharge instruction for the area to be evaluated; The system sequentially determines whether to generate a discharge command for each impurity area.

[0012] In some embodiments of this application, generating the pollution discharge evaluation value f of the area to be evaluated includes: f=U1×[ Y(i)×(k i -g i ×k' i )]; Where U1 is a preset fixed coefficient; θ1 is the number of impurity mapping indicators in the region to be evaluated; k i It generates a real-time reference value for the i-th impurity mapping index in the region to be evaluated based on the mapping parameter package; k' i The safety threshold for the i-th impurity mapping index is set based on the basic diagnostic strategy for the region to be evaluated; g i The correction coefficient for the i-th impurity mapping index in the region to be evaluated is set according to the first-level correction strategy; Y(i) is the selection coefficient; if (k i -g i ×k' i If (k) > 0, Y(i) = 1; if (k) > 0, Y(i) = 1 i -gi×k' i If )<0, Y(i)=0.

[0013] In some embodiments of this application, the step of setting a first-level correction strategy based on the auxiliary runtime package includes: Multiple time intervals can be set according to the auxiliary operation package; Select the target time interval sequentially within the entire time interval; The runtime scenario for the target time range is generated based on the auxiliary runtime package; Generate similarity values ​​between the running scenario and each disturbance scenario, and set the execution compensation strategy for the target time interval based on all similarity values; Set the execution compensation strategy for each time interval in sequence; A first-level correction strategy is set based on all execution compensation strategies.

[0014] In some embodiments of this application, a urea hydrolyzer impurity discharge control system is provided, comprising: The central processing unit is used to select multiple impurity sensing points based on the structural parameters of the urea hydrolyzer. The data acquisition unit includes multiple acquisition sub-modules; The acquisition submodule is set at each impurity sensing point, and the data acquisition unit is used to acquire feedback data from each impurity sensing point. The data acquisition unit is also used to generate an impurity monitoring package based on all feedback data; The central processing unit includes: The first processing module is used to establish a sewage control model; The second processing module is used to determine whether to generate a discharge instruction based on the discharge control model and the impurity monitoring package.

[0015] In some embodiments of this application, the first processing module is further configured to: Multiple impurity zones were set based on historical monitoring data from the urea hydrolyzer. Select the target impurity region sequentially from all impurity regions; A sub-model for processing the target impurity region is constructed based on all impurity sensing points; Establish a wastewater control strategy for the target impurity area; Set up processing sub-models for each impurity region in sequence, and establish an impurity monitoring model based on all processing sub-models; Set the sewage discharge control strategy for each impurity area in sequence, and establish a sewage discharge strategy library based on all sewage discharge control strategies. Construct an auxiliary diagnostic model; Based on the impurity monitoring model, the wastewater discharge strategy library, and the auxiliary diagnostic model, a wastewater discharge control model is constructed. The sub-model for constructing the target impurity region includes: Set multiple impurity mapping indices for the target impurity region; Generate mapping values ​​of each impurity sensing point to the target impurity region; Multiple associated sensing points in the target impurity region are selected based on all mapping values; A sub-model for processing the target impurity region is established based on all associated sensing points and all impurity mapping indicators. The construction of the auxiliary diagnostic model includes: Establish a basic diagnostic strategy for the target impurity region; The basic diagnostic strategy includes: standard thresholds for each impurity mapping index in the target impurity region; Set the basic diagnostic strategies for each impurity region in sequence; Obtain all impurity mapping indices for each impurity region and establish a mapping index set; Multiple disturbance indicators and correction cycles were set based on historical monitoring data of the urea hydrolyzer. Multiple disturbance scenarios are established based on all disturbance indicators; Select the target disturbance scene sequentially from all disturbance scenes; Set a compensation sub-strategy for the target disturbance scenario, wherein the compensation sub-strategy includes correction values ​​for all impurity mapping indices in the mapping index set; Set the compensation sub-strategies for each disturbance scenario in sequence; An auxiliary diagnostic model is constructed, which includes all basic diagnostic strategies and all compensation sub-strategies.

[0016] In some embodiments of this application, the second processing module is further configured to: Multiple feedback time points can be preset; Obtain the impurity monitoring package and auxiliary operation package at the current feedback time point; The primary correction strategy is set according to the auxiliary operation package; Select the areas to be evaluated sequentially from all impurity areas; The processing sub-model for the region to be evaluated is set as the execution processing model; Generate a mapping parameter package for the region to be evaluated based on the execution processing model and the impurity monitoring package; Generate the pollution discharge evaluation value f for the area to be evaluated based on the mapping parameter package; Preset discharge evaluation threshold F1; If f > F1, generate a sewage discharge instruction for the area to be evaluated; The system sequentially determines whether to generate a discharge command for each impurity area.

[0017] Compared with the prior art, the wastewater discharge control method and system for urea hydrolyzers described in this application have the following advantages: By constructing a multi-source sensing network and multiple impurity regions, and analyzing the feedback data from each impurity sensing point, various impurity mapping indicators are extracted. This enables accurate identification and regional diagnosis of the risk of impurity accumulation within the hydrolyzer. Simultaneously, by dynamically adjusting the discharge control parameters, the online early warning and discharge efficiency for impurity accumulation within the hydrolyzer are improved, reducing the labor intensity of operators, preventing the inhalation of impurity gases, and ensuring personal safety.

[0018] An auxiliary diagnostic model is added. By collecting the auxiliary operating package (i.e. operating parameters) of the hydrolyzer, the current operating condition is matched with the disturbance scenario library, and the judgment logic of impurity accumulation risk is dynamically adjusted to avoid "false alarms" or "missed alarms" caused by operating condition disturbances. This improves the impurity monitoring accuracy of the urea hydrolyzer and eliminates ineffective emissions. Attached Figure Description

[0019] Figure 1 This is a schematic flowchart of a method for controlling the discharge of impurities from a urea hydrolyzer, according to a preferred embodiment of this application. Detailed Implementation

[0020] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are used to illustrate this application, but are not intended to limit the scope of this application.

[0021] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0022] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, "a plurality of" means two or more.

[0023] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0024] like Figure 1 As shown in the preferred embodiment of this application, a method for controlling the discharge of impurities from a urea hydrolyzer is characterized by comprising: S101: Select multiple impurity sensing points based on the structural parameters of the urea hydrolyzer; S102: Obtain feedback data from each impurity sensing point and generate an impurity monitoring package based on all feedback data; S103: Determine whether to generate a discharge instruction based on the preset discharge control model and impurity monitoring package.

[0025] Specifically, based on historical impurity accumulation risk parameters within the hydrolyzer, multiple impurity zones are defined (including areas where impurity accumulation risk may occur, such as: level gauge pressure taps, heat exchanger tube walls, bottom of the equipment, below the heat exchanger tube bundle, and below the gas-liquid interface). Based on the selected impurity zones and the hydrolyzer's structural parameters, multiple monitoring points (i.e., equipment points capable of collecting relevant data on impurity content within the hydrolyzer) are set. Multiple impurity sensing points are generated from all monitoring points, with each individual impurity sensing point representing a monitoring point.

[0026] Specifically, multiple impurity mapping indicators are set based on historical monitoring. These indicators include, but are not limited to, parameters that indirectly characterize changes in deposition rate, such as turbidity, conductivity, pressure difference, and solution density. Feedback data from each impurity sensing point includes real-time parameters of one or more impurity mapping indicators at the corresponding device location.

[0027] In a preferred embodiment of this application, the preset sewage control model includes: Multiple impurity zones were set based on historical monitoring data from the urea hydrolyzer. Select the target impurity region sequentially from all impurity regions; A sub-model for processing the target impurity region is constructed based on all impurity sensing points; Establish a wastewater control strategy for the target impurity area; Set up processing sub-models for each impurity region in sequence, and establish an impurity monitoring model based on all processing sub-models; Set the sewage discharge control strategy for each impurity area in sequence, and establish a sewage discharge strategy library based on all sewage discharge control strategies. Construct an auxiliary diagnostic model; A pollution control model is constructed based on the impurity monitoring model, the pollution discharge strategy library, and the auxiliary diagnostic model.

[0028] Specifically, by analyzing historical monitoring data from the hydrolyzer, several impurity zones (i.e., areas prone to impurity accumulation) are identified, and drainage control strategies (i.e., drainage control parameters) are set for each impurity zone. For example, for the bottom of the equipment (impurity zone), the drainage control strategy is: strong disturbance pulses (on for 20 seconds, off for 5 seconds, repeated 6-8 times), utilizing the high-speed flow generated by prolonged operation to carry away thick sludge; brief shutdown allows pressure recovery, creating an impact. If the differential pressure decreases slowly, the number of pulses is gradually increased. Simultaneously, flushing water is turned on to backflush the drain pipe during drainage intervals to prevent pipe blockage. For the area below the heat exchange tube bundle (impurity zone), the drainage control strategy is: first, reduce the liquid level to the normal lower limit (e.g., 30%). During the sludge removal process, the feed valve is briefly opened (5-10 seconds) to allow fresh solution to flow in rapidly from the top, forming a downward impact flow that stirs up the sediment below the tube bundle. Combined with pulse sludge removal, the stirred-up impurities are discharged from the bottom. Based on historical operation records, targeted control parameters (i.e., sludge removal control strategies) can be set for each impurity zone, thereby improving the sludge removal efficiency inside the hydrolyzer.

[0029] Specifically, the sub-model for processing the target impurity region is constructed, including: Set multiple impurity mapping indices for the target impurity region; Generate mapping values ​​of each impurity sensing point to the target impurity region; Multiple associated sensing points in the target impurity region are selected based on all mapping values; A sub-model for processing the target impurity region is established based on all associated sensing points and all impurity mapping indicators.

[0030] Specifically, based on historical monitoring data, impurity mapping indicators (i.e., parameters such as turbidity, conductivity, pressure difference, and solution density) that can map the impurity accumulation state of the target impurity region are selected. For example, for the bottom of the equipment (the impurity region), the impurity mapping indicators are parameters such as the bottom solution concentration and the pressure difference at different heights at the bottom. For the gas-liquid interface, the impurity mapping indicators are parameters such as the pressure pulsation amplitude and the liquid level response time.

[0031] Specifically, it is determined whether the current impurity sensing point can collect relevant parameters of the impurity accumulation state of the target impurity region. If it can be collected, the mapping value between the current impurity sensing point and the target impurity region is set to 1. If it cannot be collected, the mapping value between the current impurity sensing point and the target impurity region is set to 0. The impurity sensing point with a mapping value of 1 is set as the associated sensing point of the target impurity region.

[0032] Specifically, by quantifying the various impurity mapping indices of the target impurity region, the reference values ​​of each impurity mapping index are made to be within the same range.

[0033] Specifically, the processing sub-model can preprocess the raw data collected from each associated sensing point and generate real-time reference values ​​for each impurity mapping index in the target impurity region.

[0034] It is understood that in the above embodiments, a multi-source sensing network and multiple impurity regions are constructed. By analyzing the feedback data of each impurity sensing point, multiple impurity mapping indicators are extracted, thereby achieving accurate identification and regional diagnosis of the risk of impurity accumulation in the hydrolyzer. At the same time, by dynamically adjusting the sewage discharge control parameters, the online early warning and sewage discharge efficiency of impurity accumulation in the hydrolyzer are improved, the labor intensity of operators is reduced, the inhalation of impurity gases is avoided, and personal safety is ensured.

[0035] In a preferred embodiment of this application, the construction of an auxiliary diagnostic model includes: Establish a basic diagnostic strategy for the target impurity region; The basic diagnostic strategy includes: standard thresholds for each impurity mapping index in the target impurity region; Set the basic diagnostic strategies for each impurity region in sequence; Obtain all impurity mapping indices for each impurity region and establish a mapping index set; Multiple disturbance indicators and correction cycles were set based on historical monitoring data of the urea hydrolyzer. Multiple disturbance scenarios are established based on all disturbance indicators; Select the target disturbance scene sequentially from all disturbance scenes; Set a compensation sub-strategy for the target perturbation scenario. The compensation sub-strategy includes the correction values ​​of all impurity mapping indices in the mapping index set. Set the compensation sub-strategies for each disturbance scenario in sequence; Construct an auxiliary diagnostic model, which includes all basic diagnostic strategies and all compensation sub-strategies.

[0036] Specifically, by analyzing historical monitoring data, standard thresholds (i.e. safety thresholds) are set for each impurity mapping index. When the real-time reference value of the impurity mapping index is greater than the preset standard threshold, it indicates that the accumulated amount of impurities in the target impurity area has interfered with the normal operation of the hydrolyzer, and timely sewage discharge treatment is required.

[0037] Specifically, by analyzing historical monitoring data, multiple disturbance indicators are generated, including but not limited to: biuret content, denitrification system load change rate, number of unit start-ups and shutdowns, operating ambient temperature, and historical accumulation rate of impurities (i.e., the average accumulation rate in the previous sewage discharge cycle), which are parameters that affect the efficiency of impurity precipitation inside the hydrolyzer.

[0038] Specifically, by quantifying each disturbance index, the reference values ​​of each disturbance index are made to be within the same range. Multiple value intervals for each disturbance index are set sequentially, and multiple disturbance scenarios are set according to the random combination of all value intervals. Among them, the value intervals of each disturbance index corresponding to any two disturbance scenarios are not exactly the same.

[0039] Specifically, by analyzing historical monitoring data, the correction values ​​for each impurity mapping index are set for each target disturbance scenario during a correction cycle (i.e., the correction amount for the standard threshold; for example, the more times there are start-stop cycles, the more impurities are released, and the standard threshold should be lowered and the discharge frequency increased to avoid the rapid accumulation of impurities without timely warning).

[0040] Specifically, the correction period can be set according to historical parameters, and in this application, it is preferably 1 hour.

[0041] It is understandable that in the above embodiments, an auxiliary diagnostic model is added. By collecting the auxiliary operating package (i.e., operating parameters) of the hydrolyzer, the current operating condition is matched with the disturbance scenario library, and the judgment logic of impurity accumulation risk is dynamically adjusted to avoid "false alarms" or "missed alarms" caused by operating condition disturbances, thereby improving the impurity monitoring accuracy of the urea hydrolyzer and eliminating invalid emissions.

[0042] In a preferred embodiment of this application, determining whether a discharge instruction has been generated includes: Multiple feedback time points can be preset; Obtain the impurity monitoring package and auxiliary operation package at the current feedback time point; The primary correction strategy is set according to the auxiliary operation package; Select the areas to be evaluated sequentially from all impurity areas; The processing sub-model for the region to be evaluated is set as the execution processing model; Generate a mapping parameter package for the region to be evaluated based on the execution processing model and the impurity monitoring package; Generate the pollution discharge evaluation value f for the area to be evaluated based on the mapping parameter package; Preset discharge evaluation threshold F1; If f > F1, generate a sewage discharge instruction for the area to be evaluated; The system sequentially determines whether to generate a discharge command for each impurity area.

[0043] Specifically, the impurity monitoring package contains the raw data collected from each associated sensing point in the area to be evaluated, while the mapping parameter package contains the real-time reference values ​​of each impurity mapping index in the area to be evaluated.

[0044] Specifically, the threshold for the pollution discharge assessment value can be set based on historical parameters. When the pollution discharge assessment value of the area to be assessed is greater than the preset threshold, it indicates that there is a risk of impurity accumulation in the area to be assessed. It is necessary to call the pollution discharge control strategy of the area to be assessed according to the pollution discharge instruction to set the corresponding pollution discharge parameters and complete the pollution discharge of impurities in the area to be assessed.

[0045] Specifically, generating the pollution discharge assessment value f for the area to be assessed includes: f=U1×[ Y(i)×(k i -g i ×k' i )]; Where U1 is a preset fixed coefficient; θ1 is the number of impurity mapping indicators in the region to be evaluated; k i It generates a real-time reference value for the i-th impurity mapping index in the region to be evaluated based on the mapping parameter package; k' i The safety threshold for the i-th impurity mapping index is set based on the basic diagnostic strategy for the region to be evaluated; g i The correction coefficient for the i-th impurity mapping index in the region to be evaluated is set according to the first-level correction strategy; Y(i) is the selection coefficient; if (k i -g i ×k' i If (k) > 0, Y(i) = 1; if (k) > 0, Y(i) = 1 i -gi×k' i If )<0, Y(i)=0.

[0046] Specifically, by setting a fixed coefficient, the pollution discharge evaluation value is kept within a preset range.

[0047] Specifically, the primary correction strategy is set according to the auxiliary runtime package, including: Multiple time intervals can be set according to the auxiliary operation package; Select the target time interval sequentially within the entire time interval; The runtime scenario for the target time range is generated based on the auxiliary runtime package; Generate similarity values ​​between the running scenario and each disturbance scenario, and set the execution compensation strategy for the target time interval based on all similarity values; Set the execution compensation strategy for each time interval in sequence; A first-level correction strategy is set based on all execution compensation strategies.

[0048] Specifically, the auxiliary operation package includes historical operating curves of various disturbance indicators from the previous discharge time point to the current time point. Based on the set correction period, the time interval between the previous discharge time point and the current time point is evenly divided to set multiple time intervals, where each time interval represents a correction period.

[0049] Specifically, the reference values ​​of each disturbance indicator within the current time interval are obtained, and the corresponding similarity values ​​are set according to the degree of difference between them and the standard reference values ​​of each disturbance indicator corresponding to each disturbance scenario. The smaller the difference, the larger the corresponding similarity value. The mapping relationship between the two can be set according to historical parameters, and the compensation sub-strategy corresponding to the disturbance scenario with the largest similarity value is selected as the compensation strategy to be executed.

[0050] Specifically, by calculating each execution compensation strategy, the overall correction amount of each impurity mapping index (i.e., the sum of the correction amounts within each execution compensation strategy) is generated in sequence. The corresponding correction coefficient is set according to its overall correction amount, and a first-level correction strategy is constructed based on all correction coefficients.

[0051] In another preferred embodiment of the urea hydrolyzer impurity discharge control method based on any of the above preferred embodiments, this preferred embodiment provides a urea hydrolyzer impurity discharge control system, comprising: The central processing unit is used to select multiple impurity sensing points based on the structural parameters of the urea hydrolyzer. The data acquisition unit includes multiple acquisition sub-modules; The acquisition submodule is set at each impurity sensing point, and the data acquisition unit is used to acquire the feedback data of each impurity sensing point. The data acquisition unit is also used to generate an impurity monitoring package based on all feedback data; The central processing unit includes: The first processing module is used to establish a sewage control model; The second processing module is used to determine whether to generate a discharge instruction based on the discharge control model and the impurity monitoring package.

[0052] In a preferred embodiment of this application, the first processing module is further configured to: Multiple impurity zones were set based on historical monitoring data from the urea hydrolyzer. Select the target impurity region sequentially from all impurity regions; A sub-model for processing the target impurity region is constructed based on all impurity sensing points; Establish a wastewater control strategy for the target impurity area; Set up processing sub-models for each impurity region in sequence, and establish an impurity monitoring model based on all processing sub-models; Set the sewage discharge control strategy for each impurity area in sequence, and establish a sewage discharge strategy library based on all sewage discharge control strategies. Construct an auxiliary diagnostic model; Based on the impurity monitoring model, the wastewater discharge strategy library, and the auxiliary diagnostic model, a wastewater discharge control model is constructed. The sub-model for constructing the target impurity region includes: Set multiple impurity mapping indices for the target impurity region; Generate mapping values ​​of each impurity sensing point to the target impurity region; Multiple associated sensing points in the target impurity region are selected based on all mapping values; A sub-model for processing the target impurity region is established based on all associated sensing points and all impurity mapping indicators. Constructing an auxiliary diagnostic model, including: Establish a basic diagnostic strategy for the target impurity region; The basic diagnostic strategy includes: standard thresholds for each impurity mapping index in the target impurity region; Set the basic diagnostic strategies for each impurity region in sequence; Obtain all impurity mapping indices for each impurity region and establish a mapping index set; Multiple disturbance indicators and correction cycles were set based on historical monitoring data of the urea hydrolyzer. Multiple disturbance scenarios are established based on all disturbance indicators; Select the target disturbance scene sequentially from all disturbance scenes; Set a compensation sub-strategy for the target perturbation scenario. The compensation sub-strategy includes the correction values ​​of all impurity mapping indices in the mapping index set. Set the compensation sub-strategies for each disturbance scenario in sequence; Construct an auxiliary diagnostic model, which includes all basic diagnostic strategies and all compensation sub-strategies.

[0053] In a preferred embodiment of this application, the second processing module is further configured to: Multiple feedback time points can be preset; Obtain the impurity monitoring package and auxiliary operation package at the current feedback time point; The primary correction strategy is set according to the auxiliary operation package; Select the areas to be evaluated sequentially from all impurity areas; The processing sub-model for the region to be evaluated is set as the execution processing model; Generate a mapping parameter package for the region to be evaluated based on the execution processing model and the impurity monitoring package; Generate the pollution discharge evaluation value f for the area to be evaluated based on the mapping parameter package; Preset discharge evaluation threshold F1; If f > F1, generate a sewage discharge instruction for the area to be evaluated; The system sequentially determines whether to generate a discharge command for each impurity area.

[0054] Based on the first concept of this application, a multi-source sensing network and multiple impurity regions are constructed. By analyzing the feedback data from each impurity sensing point, multiple impurity mapping indicators are extracted, thereby achieving accurate identification and regional diagnosis of the risk of impurity accumulation in the hydrolyzer. At the same time, by dynamically adjusting the sewage discharge control parameters, the online early warning and sewage discharge efficiency for impurity accumulation in the hydrolyzer are improved, reducing the labor intensity of operators, avoiding the inhalation of impurity gases, and ensuring personal safety.

[0055] According to the second concept of this application, an auxiliary diagnostic model is added. By collecting the auxiliary operation package (i.e., operating parameters) of the hydrolyzer, the current operating condition is matched with the disturbance scenario library, and the judgment logic of impurity accumulation risk is dynamically adjusted to avoid "false alarms" or "missed alarms" caused by operating condition disturbances, thereby improving the impurity monitoring accuracy of the urea hydrolyzer and eliminating ineffective emissions.

[0056] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of this application, and these improvements and substitutions should also be considered within the scope of protection of this application.

Claims

1. A method for controlling the discharge of impurities from a urea hydrolyzer, characterized in that, include: Multiple impurity sensing points were selected based on the structural parameters of the urea hydrolyzer. Acquire feedback data from each impurity sensing point and generate an impurity monitoring package based on all feedback data; The system determines whether to generate a discharge instruction based on the preset discharge control model and impurity monitoring package.

2. The method for controlling the discharge of impurities from a urea hydrolyzer as described in claim 1, characterized in that, The preset sewage control model includes: Multiple impurity zones were set based on historical monitoring data from the urea hydrolyzer. Select the target impurity region sequentially from all impurity regions; A sub-model for processing the target impurity region is constructed based on all impurity sensing points; Establish a wastewater control strategy for the target impurity area; Set up processing sub-models for each impurity region in sequence, and establish an impurity monitoring model based on all processing sub-models; Set the sewage discharge control strategy for each impurity area in sequence, and establish a sewage discharge strategy library based on all sewage discharge control strategies. Construct an auxiliary diagnostic model; A pollution control model is constructed based on the impurity monitoring model, the pollution discharge strategy library, and the auxiliary diagnostic model.

3. The method for controlling the discharge of impurities from a urea hydrolyzer as described in claim 2, characterized in that, Construct a sub-model for processing the target impurity region, including: Set multiple impurity mapping indices for the target impurity region; Generate mapping values ​​of each impurity sensing point to the target impurity region; Multiple associated sensing points in the target impurity region are selected based on all mapping values; A sub-model for processing the target impurity region is established based on all associated sensing points and all impurity mapping indicators.

4. The method for controlling the discharge of impurities from a urea hydrolyzer as described in claim 3, characterized in that, The construction of the auxiliary diagnostic model includes: Establish a basic diagnostic strategy for the target impurity region; The basic diagnostic strategy includes: standard thresholds for each impurity mapping index in the target impurity region; Set the basic diagnostic strategies for each impurity region in sequence; Obtain all impurity mapping indices for each impurity region and establish a mapping index set; Multiple disturbance indicators and correction cycles were set based on historical monitoring data of the urea hydrolyzer. Multiple disturbance scenarios are established based on all disturbance indicators; Select the target disturbance scene sequentially from all disturbance scenes; Set a compensation sub-strategy for the target disturbance scenario, wherein the compensation sub-strategy includes correction values ​​for all impurity mapping indices in the mapping index set; Set the compensation sub-strategies for each disturbance scenario in sequence; An auxiliary diagnostic model is constructed, which includes all basic diagnostic strategies and all compensation sub-strategies.

5. The method for controlling the discharge of impurities from a urea hydrolyzer as described in claim 4, characterized in that, The determination of whether to generate a sewage discharge instruction includes: Multiple feedback time points can be preset; Obtain the impurity monitoring package and auxiliary operation package at the current feedback time point; The primary correction strategy is set according to the auxiliary operation package; Select the areas to be evaluated sequentially from all impurity areas; The processing sub-model for the region to be evaluated is set as the execution processing model; Generate a mapping parameter package for the region to be evaluated based on the execution processing model and the impurity monitoring package; Generate the pollution discharge evaluation value f for the area to be evaluated based on the mapping parameter package; Preset discharge evaluation threshold F1; If f > F1, generate a sewage discharge instruction for the area to be evaluated; The system sequentially determines whether to generate a discharge command for each impurity area.

6. The method for controlling the discharge of impurities from a urea hydrolyzer as described in claim 5, characterized in that, The generation of the pollution discharge evaluation value f for the area to be evaluated includes: f=U1×[ Y(i)×(k i -g i ×k' i )]; Where U1 is a preset fixed coefficient; θ1 is the number of impurity mapping indicators in the region to be evaluated; k i It generates a real-time reference value for the i-th impurity mapping index in the region to be evaluated based on the mapping parameter package; k' i The safety threshold for the i-th impurity mapping index is set based on the basic diagnostic strategy for the region to be evaluated; g i The correction coefficient for the i-th impurity mapping index in the region to be evaluated is set according to the first-level correction strategy; Y(i) is the selection coefficient; if (k i -g i ×k' i If (k) > 0, Y(i) = 1; if (k) > 0, Y(i) = 1 i -gi×k' i If )<0, Y(i)=0.

7. The method for controlling the discharge of impurities from a urea hydrolyzer as described in claim 6, characterized in that, The step of setting a first-level correction strategy based on the auxiliary runtime package includes: Multiple time intervals can be set according to the auxiliary operation package; Select the target time interval sequentially within the entire time interval; The runtime scenario for the target time range is generated based on the auxiliary runtime package; Generate similarity values ​​between the running scenario and each disturbance scenario, and set the execution compensation strategy for the target time interval based on all similarity values; Set the execution compensation strategy for each time interval in sequence; A first-level correction strategy is set based on all execution compensation strategies.

8. A urea hydrolyzer impurity discharge control system, employing the urea hydrolyzer impurity discharge control method according to any one of claims 1-7, characterized in that, include: The central processing unit is used to select multiple impurity sensing points based on the structural parameters of the urea hydrolyzer. The data acquisition unit includes multiple acquisition sub-modules; The acquisition submodule is set at each impurity sensing point, and the data acquisition unit is used to acquire feedback data from each impurity sensing point. The data acquisition unit is also used to generate an impurity monitoring package based on all feedback data; The central processing unit includes: The first processing module is used to establish a sewage control model; The second processing module is used to determine whether to generate a discharge instruction based on the discharge control model and the impurity monitoring package.

9. The urea hydrolyzer impurity discharge control system as described in claim 8, characterized in that, The first processing module is also used for: Multiple impurity zones were set based on historical monitoring data from the urea hydrolyzer. Select the target impurity region sequentially from all impurity regions; A sub-model for processing the target impurity region is constructed based on all impurity sensing points; Establish a wastewater control strategy for the target impurity area; Set up processing sub-models for each impurity region in sequence, and establish an impurity monitoring model based on all processing sub-models; Set the sewage discharge control strategy for each impurity area in sequence, and establish a sewage discharge strategy library based on all sewage discharge control strategies. Construct an auxiliary diagnostic model; Based on the impurity monitoring model, the wastewater discharge strategy library, and the auxiliary diagnostic model, a wastewater discharge control model is constructed. The sub-model for constructing the target impurity region includes: Set multiple impurity mapping indices for the target impurity region; Generate mapping values ​​of each impurity sensing point to the target impurity region; Multiple associated sensing points in the target impurity region are selected based on all mapping values; A sub-model for processing the target impurity region is established based on all associated sensing points and all impurity mapping indicators. The construction of the auxiliary diagnostic model includes: Establish a basic diagnostic strategy for the target impurity region; The basic diagnostic strategy includes: standard thresholds for each impurity mapping index in the target impurity region; Set the basic diagnostic strategies for each impurity region in sequence; Obtain all impurity mapping indices for each impurity region and establish a mapping index set; Multiple disturbance indicators and correction cycles were set based on historical monitoring data of the urea hydrolyzer. Multiple disturbance scenarios are established based on all disturbance indicators; Select the target disturbance scene sequentially from all disturbance scenes; Set a compensation sub-strategy for the target disturbance scenario, wherein the compensation sub-strategy includes correction values ​​for all impurity mapping indices in the mapping index set; Set the compensation sub-strategies for each disturbance scenario in sequence; An auxiliary diagnostic model is constructed, which includes all basic diagnostic strategies and all compensation sub-strategies.

10. The urea hydrolyzer impurity discharge control system as described in claim 9, characterized in that, The second processing module is also used for: Multiple feedback time points can be preset; Obtain the impurity monitoring package and auxiliary operation package at the current feedback time point; The primary correction strategy is set according to the auxiliary operation package; Select the areas to be evaluated sequentially from all impurity areas; The processing sub-model for the region to be evaluated is set as the execution processing model; Generate a mapping parameter package for the region to be evaluated based on the execution processing model and the impurity monitoring package; Generate the pollution discharge evaluation value f for the area to be evaluated based on the mapping parameter package; Preset discharge evaluation threshold F1; If f > F1, generate a sewage discharge instruction for the area to be evaluated; The system sequentially determines whether to generate a discharge command for each impurity area.

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