Instantaneous sensing and early warning method and system for inorganic salt impurities content in urea

Abstract

The application discloses a method for instant sensing and early warning of inorganic salt impurity content in urea, comprising: calibration reference database, including: pure urea solution density-temperature-concentration reference curve and data table calibration, typical inorganic salt impurity conductivity characteristic calibration and mixed urea solution conductivity-impurity content mapping relationship calibration; real-time acquisition of field data; calculation of inorganic salt impurity static content, including: calculation of total inorganic salt impurity concentration in the current urea solution; combined with urea dissolving tank geometric parameters and real-time liquid level data of the urea dissolving tank, the total mass of the inorganic salt impurity in the dissolving tank is calculated; dynamic calculation of impurity increment per unit time, based on multiple groups of static impurity mass data collected in continuous time sequence, the increase of the inorganic salt impurity in the urea dissolving system per unit time is calculated; dynamic calibration based on historical data; hierarchical early warning and interlocking control. Corresponding systems, electronic devices and computer readable storage media are also disclosed.

Description

Technical Field

[0001] This invention relates to the field of flue gas denitrification material quality testing, industrial online analysis, automatic monitoring, and safety interlock control technology in coal-fired power plants. Specifically, it relates to a method and system for real-time sensing and early warning of inorganic salt impurities in urea used for denitrification. It is particularly suitable for scenarios such as coal-fired power plants, thermal power plants, centralized heating units in industrial parks, and denitrification devices of industrial kilns that use urea hydrolysis or urea pyrolysis processes to achieve flue gas denitrification. It is specifically designed for quality testing, online monitoring, abnormal early warning, and equipment interlock control of low-cost inorganic salt impurities such as ammonium chloride, sodium sulfate, sodium chloride, and ammonium sulfate mixed in with incoming urea. Background Technology

[0002] Currently, the mainstream flue gas denitrification technology for coal-fired power plants and large industrial boilers in China is Selective Catalytic Reduction (SCR). This technology boasts high denitrification efficiency and stable operation, making it a core process for meeting national ultra-low emission standards for air pollutants. SCR denitrification systems require a reducing agent to remove nitrogen oxides (NOx) from the flue gas. X The nitrogen and water are reduced to nitrogen and water. At present, most thermal power units in China have abandoned high-risk reducing agents such as liquid ammonia and ammonia water, and have fully adopted solid granular urea as a reducing agent precursor. The solid urea is converted into ammonia (NH3) through urea hydrolysis or urea pyrolysis processes. The ammonia is then sent to the SCR reactor to complete the denitrification reaction.

[0003] Urea, as a core bulk consumable in denitrification systems, directly determines three key indicators based on its product quality: first, denitrification efficiency; insufficient effective components in urea will directly lead to a decrease in ammonia production and NO... X The failure to meet denitrification standards leads to environmental violations; secondly, it affects catalyst lifespan, as impurity ions can cause catalyst poisoning and deactivation, significantly increasing catalyst replacement costs; thirdly, it compromises the operational safety of the entire denitrification system and upstream hydrolysis / pyrolysis equipment, as corrosive impurities can cause equipment corrosion and leaks, leading to unplanned unit shutdowns. Therefore, quality control of incoming urea is the first line of defense for the safe, stable, economical, and environmentally friendly operation of a thermal power plant's denitrification system.

[0004] In recent years, the denitrification urea market has been characterized by fierce price competition. To reduce costs and win bids at low prices, some suppliers have commonly adulterated genuine urea with inexpensive inorganic salts such as ammonium chloride, sodium sulfate, and sodium chloride. This practice has caused significant economic losses, environmental risks, and potential safety hazards to major power generation companies. Based on the actual operation of coal-fired power plants nationwide, current urea quality control and testing technologies have seven major technical deficiencies:

[0005] 1. Adulterated urea causes multiple equipment malfunctions and operational risks. When urea mixed with inorganic salt impurities enters the hydrolysis / pyrolysis system, it creates a chain of hazards: First, the effective denitrification component is significantly reduced. Inorganic salts do not participate in the hydrolysis ammonia production reaction, resulting in a lower proportion of effective urea for the same mass of adulterated urea. This leads to insufficient total ammonia production and deterioration in quality, directly causing increased NO levels in the flue gas. X Failure to meet emission standards triggers environmental penalties; under the electricity spot trading model, NO X Exceeding standards and reducing unit load will result in significant losses in electricity price assessments; secondly, severe equipment corrosion and adulteration with chloride ions (Cl) from impurities... - The first is a highly corrosive ion. Under the high-temperature conditions of urea hydrolysis and pyrolysis, it can rapidly corrode equipment such as hydrolyzers, pipelines, and valves, causing short-term corrosion, perforation, and media leakage, forcing the urea system to shut down, and even triggering unplanned unit shutdowns. The second is catalyst poisoning and deactivation. Salt impurities carried in the hydrolysis steam adhere to the surface of the SCR catalyst, clogging the catalyst micropores and destroying active sites, resulting in permanent catalyst poisoning and deactivation. Catalysts are expensive and have long replacement cycles; deactivation not only reduces denitrification efficiency but also incurs huge equipment replacement costs. The third is corrosion of flue gas and auxiliary equipment. Salt impurities diffuse with the flue gas to the denitrification flue gas duct, induced draft fan, and other equipment, causing corrosion of the flue gas duct inner wall, fan failure, and further expanding the scope of equipment damage.

[0006] 2. Traditional sampling methods have extremely poor representativeness and cannot reflect the true quality of materials. Currently, urea for denitrification in power plants is mainly divided into two categories: bulk urea in tank trucks and a small amount of bagged urea. Tank truck urea is the mainstream transportation method. Traditional sampling methods have inherent defects: First, the sampling points for tank trucks are limited. Tank trucks can only use samplers through the top loading and unloading port. The sampling location is fixed and the sampling range is extremely small, which cannot cover the entire batch of material. During unloading, urea is transported by gas pressure, and the sampler is easily blocked by particulate urea, making it difficult to collect materials containing impurities. Second, the total amount of sample after reduction is too low. The total amount of urea sampled on-site from a single tank truck is only 2-3 kg. After reduction processing such as traditional quartering, the final sample weight sent to the laboratory for testing is less than 50 g. Using a 50 g sample to represent the quality of the entire 25 tons of urea in a truck is seriously insufficient in representativeness, and the probability of missing adulterated materials is extremely high. Third, the sampling of bagged urea also has defects. Bagged bulk urea is sampled according to the conventional bulk material sampling rules. After sampling, it still needs to be reduced, and the small sample after reduction also cannot represent the quality of the entire batch of material. In summary, the traditional offline sampling method cannot fundamentally solve the problem of unrepresentative samples, which is an important reason for the failure of urea quality control.

[0007] 3. The variety of impurities is complex, and traditional testing methods are cumbersome and prone to error. The types of inorganic salt impurities mixed in urea are not fixed, and the adulterants vary between different suppliers and batches. Traditional chemical analysis methods have significant shortcomings: First, qualitative analysis must be performed before quantitative analysis, which is a complex process. Traditional testing must first determine the specific types of impurities (sodium sulfate, ammonium chloride, sodium chloride, etc.) through chemical experiments, and then match the corresponding testing methods for different ions. The testing process is lengthy and requires a high level of expertise from the testing personnel. Second, matrix interference is severe, resulting in large detection errors: Urea aqueous solutions contain a large number of urea hydrate molecules and trace amounts of urea hydrolysis products. These substances seriously interfere with the detection of chloride ions, sulfate ions, ammonium ions, and sodium ions by traditional titration and colorimetric methods, resulting in large deviations in the final test results and failing to accurately reflect the true content of impurities.

[0008] 4. Long manual testing cycles and significant on-site truck congestion lead to passive production. Currently, all urea purity and impurity content tests rely on manual chemical analysis in laboratories, with the entire testing process taking over 5 hours. Analysis of on-site unloading conditions shows that the complete unloading time for a single urea tanker is approximately 2.5 hours. From vehicle arrival at the plant, sampling, waiting for test results, to confirmation of compliance and completion of unloading, the entire process takes 7-8 hours. To ensure continuous power plant production, a passive approach of unloading first and waiting for test results is commonly adopted: unloading begins immediately after urea sampling, and by the time test results are available several hours later, all the adulterated urea has already entered the dissolving tanks and hydrolysis system. Discovering quality problems at this point is often irreversible, resulting in equipment damage and environmental violations. Strictly adhering to the principle of unloading only after passing testing would cause a large number of tankers to be congested within the plant area for extended periods, resulting in extremely low material turnover efficiency and impacting the power plant's normal material supply.

[0009] 5. The workload for bulk material acceptance is enormous, resulting in high labor costs. Denitrification urea is a major consumable material for power plants, with a single power plant consuming between 50 and 300 tons of urea daily. A typical tanker truck has a capacity of approximately 25 tons, and at least two tanker trucks enter the plant daily. Industry regulations require strict vehicle-to-vehicle acceptance to prevent the mixing of substandard materials. Under the current model, two dedicated laboratory personnel are needed at full capacity to handle urea sampling, sample preparation, testing, and data recording, which barely meets daily acceptance needs. As unit loads increase and urea consumption rises, the workload for acceptance will further increase, leading to a continuous rise in labor costs. Furthermore, manual operation carries management risks such as human error and data tampering.

[0010] 6. Lacks dynamic monitoring and predictive capabilities, only capable of post-event detection, unable to provide early warnings. Traditional detection technologies can only achieve static testing of a single batch, obtaining impurity content data at a specific moment and sampling point. They cannot continuously monitor the changing trends of impurity content throughout the entire process of urea unloading and dissolution, nor can they calculate the rate of increase of impurities per unit time. Traditional technologies are completely unable to identify different adulteration patterns, such as localized adulteration, early-stage adulteration, and uniform adulteration throughout the entire vehicle, and lack the functions of early warning, tiered alarms, and equipment interlocking. Risk management is entirely relegated to the post-event remedial stage.

[0011] 7. The testing system lacks an automatic calibration mechanism, resulting in a continuous decline in accuracy over long-term operation.

[0012] Both manual laboratory testing and simple online testing equipment lack historical data calibration functions. As environmental temperature changes, reagents fail, sensors age, and operating conditions fluctuate, testing errors will gradually accumulate, resulting in distorted test results after long-term operation, rendering control standards ineffective.

[0013] In summary, there is currently no mature technical solution in China that integrates online real-time detection, dynamic trend analysis, graded early warning, and automatic interlocking for inorganic salt impurities in denitrified urea. The traditional offline testing and fixed-point sampling mode is completely unable to meet the current development needs of thermal power plants for safe production, environmental management, cost reduction and efficiency improvement. Summary of the Invention

[0014] The purpose of this invention is to overcome the shortcomings of the prior art and provide a method and system for real-time sensing and early warning of inorganic salt impurity content in urea, which combines the physicochemical properties of urea, solution density characteristics, and the response mechanism of inorganic salt ion conductivity. Its technical objective is:

[0015] 1. Abandoning the offline sampling mode for particulate materials, we adopt online detection of the entire area using a homogeneous solution after urea dissolution, which completely solves the problem of poor sample representativeness;

[0016] 2. Based on the physicochemical properties of electrical conductivity, it enables direct quantification of various inorganic salt impurities without qualitative analysis, eliminating interference from the urea matrix and reducing detection errors;

[0017] 3. Enables simultaneous inspection and unloading, reducing response time to less than 1 minute and completely solving the problem of vehicle congestion on site;

[0018] 4. Construct a dual detection system for static impurity content and dynamic impurity increment to achieve trend prediction and graded early warning;

[0019] 5. Configure an automatic historical data calibration mechanism to ensure the long-term detection accuracy of the system;

[0020] 6. Enables automated, unattended testing, replacing dedicated laboratory staff and reducing labor costs;

[0021] 7. Add multi-level alarm and equipment interlock functions to automatically block unqualified urea from entering the system when impurities exceed the standard, thereby preventing major risks such as equipment damage, environmental violations, and unit shutdowns from the source.

[0022] The first aspect of this invention is to provide a method for real-time sensing and early warning of inorganic salt impurities in urea, applied to the urea hydrolysis / pyrolysis process in flue gas denitrification of coal-fired power plants. This method is used for real-time detection, dynamic trend analysis, and graded early warning control of inorganic salt impurities such as ammonium chloride, sodium sulfate, sodium chloride, and ammonium sulfate in incoming denitrification urea. The method includes the following steps:

[0023] S1, the calibration benchmark database, includes: calibration of the density-temperature-concentration benchmark curve and data table of pure urea solution, calibration of the conductivity characteristics of typical inorganic salt impurities, and calibration of the conductivity-impurity content mapping relationship of mixed urea solution, thereby constructing three core benchmark databases;

[0024] S2, real-time acquisition of field data, including: in the urea dissolution process, real-time acquisition of urea solution concentration, solution temperature, solution conductivity data, real-time liquid level data of urea dissolution tank and geometric parameter data of urea dissolution tank, and synchronous acquisition of time sequence operation data;

[0025] S3, calculate the static content of inorganic salt impurities, including: based on the benchmark database calibrated in S1, quantitatively calculate the total concentration of inorganic salt impurities in the current urea solution by combining the real-time collected data on solution temperature, urea solution concentration and solution conductivity; then calculate the total mass of inorganic salt impurities in the current dissolving tank by combining the total concentration of inorganic salt impurities in the current urea solution with the geometric parameters of the urea dissolving tank and the real-time liquid level data of the urea dissolving tank;

[0026] S4, dynamically calculate the increase in impurities per unit time, including: calculating the increase in inorganic salt impurities in the urea dissolution system per unit time based on multiple sets of static impurity mass data collected in a continuous time series, and obtaining the impurity growth rate index;

[0027] S5, based on historical data, performs dynamic calibration, including: retrieving on-site urea unloading log, historical unloading time and historical urea consumption data, and performing deviation calibration on the total concentration of inorganic salt impurities in the current urea solution, the total mass of inorganic salt impurities in the current dissolving tank and the increase in inorganic salt impurities in the urea dissolving system per unit time obtained in S3 and S4, and correcting the output results of the detection model to obtain the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data;

[0028] S6, perform graded pre-alarm and interlock control, including: pre-setting multi-level impurity content thresholds and unit time impurity increment thresholds, comparing the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data with the preset thresholds, and executing graded reminders, early warnings, emergency alarms and urea unloading equipment interlock stop operation based on the comparison results.

[0029] Preferably, S1 includes:

[0030] S11, calibrating the density standard of pure urea solution, including: selecting analytical grade urea reagent as the calibration raw material, preparing pure urea solutions with a mass fraction of 5%~50%, with the concentration gradient set sequentially in 5% increments; controlling the solution temperature range of 25℃~50℃, with the temperature gradient set sequentially in 5℃ increments; conducting density tests according to NY / T887-2010 "Determination of Density of Liquid Fertilizers", setting up parallel samples for each working condition, and controlling the absolute difference of parallel test results to be no greater than 0.003g / mL; the solution preparation and sample pretreatment are performed according to GB / T8571 "Laboratory Sample Preparation of Compound Fertilizers" and GB / T29400-2012 "Determination of Trace Anions in Fertilizers by Ion Chromatography", ensuring that the solution is fully dissolved and free from bubble interference; finally, compiling a data table of pure urea solution density at different concentrations and temperatures to form a pure urea density standard database;

[0031] S12, the conductivity effect of typical inorganic salt impurities was screened and calibrated, including: identifying the mainstream adulterant inorganic salt impurities in denitrified urea as sodium sulfate, ammonium sulfate, ammonium chloride, and sodium chloride; preparing 50% concentration urea solutions containing the above four types of inorganic salts; controlling the ambient temperature at 25℃; preparing two groups of samples with impurity contents of 5.22 g / L and 3.82 g / L respectively; and measuring the conductivity values ​​of urea solutions with different inorganic salt contents in each group; comparing the conductivity response intensity of the four types of inorganic salts under the same contents and operating conditions; selecting sodium sulfate, which has the most significant conductivity effect, as the characteristic reference material for quantitative calculation of impurities; and compiling a comparative data table of conductivity of the four typical inorganic salts under standard operating conditions.

[0032] S13, perform full-condition calibration of the conductivity-impurity content of the mixed solution, including: based on the concentration and temperature gradients in S11, and using sodium sulfate selected in S12 as the characteristic impurity, prepare urea mixed solutions with different base concentrations, temperatures, and sodium sulfate impurity contents; the sodium sulfate impurity content gradients are set to 0.25 g / L, 0.50 g / L, 0.75 g / L, 1.00 g / L, 1.5 g / L, 2.0 g / L, 3.0 g / L, 4.0 g / L, 5.0 g / L, and 6.0 g / L, with increased detection points for low impurity content ranges; the conductivity of the mixed solution is measured under each operating condition, and the correspondence between urea concentration, temperature, sodium sulfate content, and conductivity is recorded for all operating conditions, generating a conductivity-impurity content mapping data table and fitting curve, and constructing a baseline database of mixed solution conductivity; simultaneously, the detection interval of impurity concentration is dynamically adjusted according to the conductivity change range, with increased detection points in ranges of drastic conductivity changes and simplified detection points in ranges of gradual changes.

[0033] Preferably, in step S2, the urea solution concentration is the real-time mass concentration of the urea solution; the solution temperature is the real-time temperature of the urea solution; the solution conductivity data is the real-time conductivity of the urea solution; the real-time liquid level data of the urea dissolving tank is the internal liquid level height of the urea dissolving tank; the geometric parameter data of the urea dissolving tank is the fixed radius dimension of the urea dissolving tank; the synchronously acquired time-series operation data includes millisecond-level time-series timestamp data; the real-time acquired field data includes all parameters continuously acquired by online sensors, with an acquisition frequency of not less than 1 time / second.

[0034] Preferably, S3 includes:

[0035] S31, Calculate the concentration of impurities in the solution, including: retrieving the mixed solution conductivity benchmark database calibrated in S1; and calculating the equivalent concentration x of sodium sulfate in the current urea solution (unit: kg / m³) based on the real-time collected urea solution concentration, solution temperature, and solution conductivity using data table interpolation or a fitting function. 3 ;

[0036] S32, calculate the total mass of impurities in the dissolving tank, including: calculating the total mass m of inorganic salt impurities in the dissolving tank based on formula (1): (1);

[0037] Where m represents the total mass of inorganic salt impurities in the dissolving tank, in kg; and x represents the concentration of inorganic salt impurity ions in the solution, in kg / m³. 3 ; r represents the radius of the urea dissolving tank, in meters; h represents the real-time liquid level in the urea dissolving tank, in meters.

[0038] Preferably, step S4 includes: extracting two adjacent time nodes. Corresponding inorganic salt impurity mass The total amount of urea dissolved in the storage tank per unit time is kept stable by default. The increment of impurities per unit time is calculated according to the differential formula (2). : (2);

[0039] A 1-minute time interval was selected as the standard statistical period, and the impurity increment data was continuously output every minute to form an impurity growth trend curve.

[0040] Preferably, S5 includes: retrieving the on-site urea unloading electronic ledger to obtain the historical total unloading time of vehicles, the total amount of urea unloading per hour, and historical batch impurity detection data; using the historical actual unloading volume as a benchmark, correcting the deviation of the impurity concentration, total impurity mass, and impurity increment per unit time calculated in real time, eliminating system errors caused by material dissolution rate fluctuations, sensor zero-point drift, and environmental interference; the calibration cycle is set to automatic calibration once a day, and manual forced calibration is performed after major overhaul or sensor replacement.

[0041] Preferably, step S6 includes pre-setting four threshold standards, corresponding to four levels of control actions:

[0042] (1) Reminder threshold: When the increase in inorganic salts per unit time accounts for 0.68% of the urea mass, i.e. 6.8g / kg, an audible and visual reminder is triggered to prompt the on-duty maintenance personnel to pay attention to the urea quality;

[0043] (2) Level 1 warning threshold: When the total content of inorganic salts accounts for 0.9% of the urea mass, i.e. 9g / kg, a continuous audible and visual warning is triggered, and an abnormal data is recorded in a pop-up window and an abnormal log is pushed.

[0044] (3) Level 2 alarm threshold: When the total content of inorganic salts accounts for 0.93% of the urea mass, i.e. 9.3g / kg, a high-intensity audible and visual alarm is triggered, the system locks the current batch of urea data and uploads it to the plant monitoring platform;

[0045] (4) Emergency interlock alarm threshold: When the total inorganic salt content accounts for 1.2% of the urea mass, i.e. 12g / kg, the highest level alarm is triggered and an interlock signal is output, automatically stopping the operation of the urea unloading equipment;

[0046] The second aspect of the present invention provides a real-time sensing and early warning system for the content of inorganic salt impurities in urea, used to realize the real-time sensing and early warning method for the content of inorganic salt impurities in urea in the first aspect, including a reference database module, a field parameter acquisition module, an impurity content calculation module, a dynamic incremental analysis module, a data calibration module, and a graded alarm and interlock control module; each module is connected in sequence to communicate with each other and work together to complete the entire process of real-time sensing, quantitative calculation, dynamic analysis, data calibration, graded alarm, and equipment interlock for inorganic salt impurities in urea;

[0047] The benchmark database module (102) is used to calibrate the benchmark database, including: calibrating the density-temperature-concentration benchmark curve and data table of pure urea solution, calibrating the conductivity characteristics of typical inorganic salt impurities, and calibrating the conductivity-impurity content mapping relationship of mixed urea solution, thereby constructing three core benchmark databases.

[0048] The field parameter acquisition module (102) is deployed in the urea dissolving tank and urea delivery pipeline to collect field data in real time, including: in the urea dissolving process, real-time data on urea solution concentration, solution temperature, solution conductivity, real-time liquid level data of the urea dissolving tank and geometric parameter data of the urea dissolving tank, and synchronously collecting time-series operation data;

[0049] The impurity content calculation module (103) is connected to the benchmark database module and the field parameter acquisition module respectively, and is used to calculate the static content of inorganic salt impurities, including: quantitatively calculating the total concentration of inorganic salt impurities in the current urea solution based on the benchmark database calibrated by S1, combined with the real-time acquired solution temperature, urea solution concentration and solution conductivity data; and then calculating the total mass of inorganic salt impurities in the current dissolving tank by combining the total concentration of inorganic salt impurities in the current urea solution with the geometric parameters of the urea dissolving tank and the real-time liquid level data of the urea dissolving tank.

[0050] The dynamic incremental analysis module (104) is connected to the impurity content calculation module and is used to dynamically calculate the impurity increment per unit time, including: calculating the increase of inorganic salt impurities in the urea dissolution system per unit time based on multiple sets of static impurity mass data collected in a continuous time series, and obtaining the impurity growth rate index.

[0051] The data calibration module (105) is connected to the dynamic incremental analysis module and is used to perform dynamic calibration based on historical data. This includes: retrieving on-site urea unloading log, historical unloading time and historical urea consumption data; performing deviation calibration on the total concentration of inorganic salt impurities in the current urea solution, the total mass of inorganic salt impurities in the current dissolving tank and the increase in inorganic salt impurities in the urea dissolving system per unit time obtained in S3 and S4; and correcting the output results of the detection model to obtain the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data.

[0052] The graded alarm and interlock control module (106) is connected to the data calibration module and has built-in multi-level impurity thresholds for graded pre-alarm and interlock control, including: pre-setting multi-level impurity content thresholds and unit time impurity increment thresholds, comparing the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data with the preset thresholds, and executing graded reminders, early warnings, emergency alarms and urea unloading equipment interlock stop operations based on the comparison results.

[0053] Preferably, the system further includes a human-computer interaction and data storage module, which is connected to all functional modules and is used to realize parameter setting, data display, log storage, ledger retrieval and remote data upload functions.

[0054] Preferably, the on-site parameter acquisition module includes four types of online detection sensors: solution concentration sensor, temperature sensor, conductivity sensor, and liquid level sensor. The solution concentration sensor, temperature sensor, and conductivity sensor are integrated and installed inside the urea dissolving tank's outlet pipeline, directly contacting the urea solution for detection. The liquid level sensor is installed on the top of the urea dissolving tank and uses a non-contact radar level gauge to detect the liquid level height inside the tank.

[0055] The beneficial effects of the method and system of the present invention are as follows:

[0056] 1. In terms of environmental benefits, it can intercept adulterated urea with high impurities in real time, ensuring the quality and output of ammonia in the denitrification system and ensuring the NO content in flue gas. x Long-term stable emission compliance avoids environmental administrative penalties and accountability for exceeding emission standards; prevents catalyst poisoning and deactivation, ensures long-term efficient operation of the denitrification system, and reduces fluctuations in pollutant emissions.

[0057] 2. In terms of safety and production benefits, it effectively prevents corrosive impurities such as chloride ions from entering the hydrolyzer, pipelines, and flue, significantly reducing the probability of equipment corrosion, leakage, and damage, extending equipment service life, and reducing the frequency of equipment maintenance; the high-impurity urea automatic interlock shutdown avoids unplanned unit shutdowns and equipment safety accidents caused by unqualified materials entering the system, and improves the operational reliability of the entire denitrification system and main unit.

[0058] 3. In terms of economic benefits, labor costs are saved by replacing two full-time laboratory personnel, resulting in significant annual savings in salaries, social security, and benefits. Equipment costs are also reduced by decreasing the frequency of corrosion damage and replacement of equipment such as hydrolyzers, pipelines, catalysts, and flues, thus lowering equipment maintenance and procurement expenses. Furthermore, performance evaluation losses are mitigated by avoiding NO... X Direct economic losses caused by exceeding standards, unit outages, and electricity spot trading price assessments; improved material turnover efficiency, elimination of tanker truck congestion issues, improved material transportation and unloading efficiency, and reduced logistics delay costs.

[0059] 4. In terms of management efficiency, it realizes full-process automation, datafication, and traceability of urea quality testing. All test data and alarm logs are stored for a long time to meet the requirements of enterprise's refined management and audit traceability. Data can be remotely uploaded to the whole plant control platform, adapting to the trend of intelligent and digital transformation of thermal power plants. Standardized testing process and hierarchical alarm logic unify urea quality control standards and reduce human management differences. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of the present invention, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0061] Figure 1 A flowchart of a method for real-time sensing and early warning of inorganic salt impurities in urea according to an embodiment of the present invention;

[0062] Figure 2 This is a schematic diagram of the principle architecture of a real-time sensing and early warning system for the content of inorganic salt impurities in urea according to an embodiment of the present invention.

[0063] Figure 3 This is a structural diagram of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0064] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for 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 the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0066] In the description of this invention, it should be noted that, unless otherwise explicitly 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 of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0067] The core technical principles of this invention include the density characteristic principle of pure urea solution, the conductivity response principle of inorganic salt ions, the conductivity-impurity content mapping principle of mixed solutions, the global impurity mass conversion principle, the time-series data difference and impurity increment analysis principle, the historical data dynamic calibration principle, and the historical data dynamic calibration principle. Among these:

[0068] The density characteristics of pure urea solution are specifically characterized by the density-temperature-concentration correlation principle. When solid urea dissolves in water, it forms a homogeneous and stable urea aqueous solution. Under fixed atmospheric pressure, the density of the urea solution is determined only by two variables: the solution's mass concentration and the ambient temperature. The higher the solution concentration, the greater the density; at the same concentration, the higher the ambient temperature, the lower the solution density. The embodiments of this invention strictly adhere to current national testing standards for calibration. Concentration range A standardized database was established to determine the density benchmark values ​​of pure urea solutions within a specific temperature range. This database serves to differentiate physicochemical parameter changes caused by urea concentration variations, temperature fluctuations, and the incorporation of inorganic salt impurities. It also aims to eliminate interference from fluctuations in urea's operating conditions on conductivity test results, ensuring the specificity of impurity detection. The experimental process strictly adhered to parallel sample testing requirements, and the absolute density difference between parallel samples was recorded. Meanwhile, in accordance with the national standards for fertilizer sample preparation and anion detection, solution pretreatment was performed to eliminate test interference such as air bubbles and undissolved particles, ensuring the accuracy of the baseline data.

[0069] The principle of inorganic salt ion conductivity response is specifically characterized by the conductivity effect of typical inorganic salt impurities. When electrolytes dissolve in water, they dissociate into freely moving anions and cations. The higher the ion concentration, the stronger the conductivity of the solution, and the higher the corresponding conductivity value. The mainstream adulterants in denitrified urea—sodium sulfate (Na₂SO₄), ammonium sulfate ((NH₄)₂SO₄), ammonium chloride (NH₄Cl), and sodium chloride (NaCl)—are all strong electrolytes. They completely dissociate upon dissolving in urea aqueous solution, significantly increasing the solution's conductivity. Pure urea, however, is a non-electrolyte, and its solution has extremely low conductivity. Based on this principle, the conductivity of pure urea solution is only a baseline value. After the addition of inorganic salt impurities, the conductivity increases systematically with increasing impurity content. By comparing the conductivity response intensity of the four mainstream adulterants at the same concentration and temperature, this invention selects sodium sulfate, which has the most significant conductivity effect, as a characteristic reference substance. This establishes a unified standard for quantitative calculation of impurities, which can equivalently characterize the total content of all similar inorganic salt impurities.

[0070] Based on the principle of mapping conductivity to impurity content in mixed solutions, under the premise of fixed urea concentration and temperature, the conductivity of a urea mixed solution exhibits an approximately linear relationship with the content of inorganic salt impurities. The embodiments of this invention cover all on-site operating conditions, sequentially calibrating conductivity values ​​at different urea concentrations (5%~50%, step size 5%), different temperatures (25℃~50℃, step size 5℃), and different sodium sulfate impurity contents (0~6.0 g / L, with more frequent measurement points in the low-content range), forming a complete four-dimensional data table and fitting curve of concentration-temperature-impurity-conductivity. During on-site operation, the system accurately calculates the equivalent inorganic salt impurity concentration in the solution using interpolation or function fitting based on the three real-time acquired operating parameters (urea concentration, temperature, and conductivity). For impurity ranges with drastic conductivity changes, more frequent measurement points are added to further improve the detection sensitivity of trace impurities.

[0071] The principle of global impurity mass conversion is used to convert the global impurity mass of the dissolving tank. Conductivity and concentration parameters measured on-site are pipeline / local solution parameters, only reflecting the impurity concentration in a local area. The urea dissolving tank is a regular cylindrical tank with fixed geometric parameters (radius). Combined with the liquid level height collected by a real-time radar level gauge, the total volume of the solution inside the tank can be calculated using the cylinder volume formula. Then, combined with the impurity concentration in the solution, the total mass of inorganic salt impurities in the entire urea solution can be calculated. This principle achieves a leap from local point detection to whole-tank global detection, overcoming the limitation of traditional sampling which only represents local materials. The detection results can completely represent the true impurity level of the entire vehicle and batch of urea.

[0072] Based on the principles of time-series data differential analysis and impurity increment analysis, urea unloading and dissolution are continuous processes. The system continuously collects multiple sets of time-series data on the total mass of impurities at fixed time intervals (1 minute). By extracting the mass data from two adjacent time points and performing differential calculations, the mass of newly added inorganic salt impurities per unit time can be obtained, which is the impurity growth rate. The impurity increment data can intuitively reflect the uniformity of adulteration in urea materials. If the impurity increment per unit time is stable, it indicates that the entire truckload of urea is uniformly adulterated; if the increment fluctuates, it indicates localized adulteration. Combining the increment data and static impurity content as dual indicators, multi-dimensional quality judgment can be achieved, and risk development trends can be predicted.

[0073] The principle of dynamic calibration based on historical data arises because the urea unloading rate and dissolution rate at power plants fluctuate slightly with varying loads and operating conditions. Simultaneously, long-term sensor operation can lead to zero-point drift and temperature drift, resulting in errors in the detection system. This invention, based on the principle of dynamic calibration using historical data, retrieves long-term historical unloading records, hourly unloading volumes, and historical detection data to establish a deviation correction coefficient. Data calibration is automatically performed daily. Manual forced calibration can be performed after sensor replacement or equipment overhaul to continuously correct detection results, ensuring the long-term accuracy and stability of the system.

[0074] Based on the principle of multi-level threshold hierarchical control, and combined with power plant on-site safety operation experience, equipment corrosion tolerance limits, and environmental protection control requirements, this embodiment of the invention sets four-level gradient thresholds, corresponding to four control actions: alert, first-level warning, second-level alarm, and emergency interlock shutdown. Different levels of anomalies correspond to different handling requirements. Minor anomalies only alert maintenance personnel, moderate anomalies are logged and warned, and severe anomalies trigger high-intensity alarms. When the extreme risk threshold is reached, the urea unloading equipment is directly interlocked and stopped, forcibly preventing unqualified materials from entering the system, thus realizing the safety control logic of "hierarchical control, gradient handling, and extreme blocking".

[0075] Example 1

[0076] The first embodiment of this invention relates to a method for real-time sensing and early warning of inorganic salt impurities in urea, which consists of six steps: benchmark database calibration, real-time acquisition of field parameters, static impurity content calculation, dynamic calculation of impurity increment per unit time, dynamic calibration of historical data, and graded early warning and interlocking control. Each step includes detailed sub-processes, test standards, calculation formulas, operating parameters, and threshold standards, fully covering the entire process from laboratory calibration to field implementation.

[0077] This method is based entirely on the homogeneous solution after urea is dissolved. The detection is completed simultaneously with the unloading of urea. The response time for a single batch is less than 1 minute. It eliminates the need for manual sampling, sample preparation, sample reduction, and laboratory testing. It is suitable for the daily high consumption and multiple vehicle arrivals of bulk materials in power plants and can achieve fully automated detection and acceptance for each vehicle.

[0078] like Figure 1 As shown, the first aspect of this invention is to provide a method for real-time sensing and early warning of the content of inorganic salt impurities in urea, applied to the urea hydrolysis / pyrolysis process in flue gas denitrification of coal-fired power plants. This method is used for real-time detection, dynamic trend analysis, and graded early warning control of inorganic salt impurities such as ammonium chloride, sodium sulfate, sodium chloride, and ammonium sulfate in incoming denitrification urea. The method includes the following steps:

[0079] S1, the calibration benchmark database, includes: calibration of the density-temperature-concentration benchmark curve and data table of pure urea solution, calibration of the conductivity characteristics of typical inorganic salt impurities, and calibration of the conductivity-impurity content mapping relationship of mixed urea solution, thereby constructing three core benchmark databases;

[0080] S2, real-time acquisition of field data, including: in the urea dissolution process, real-time acquisition of urea solution concentration, solution temperature, solution conductivity data, real-time liquid level data of urea dissolution tank and geometric parameter data of urea dissolution tank, and synchronous acquisition of time sequence operation data;

[0081] S3, calculate the static content of inorganic salt impurities, including: based on the benchmark database calibrated in S1, quantitatively calculate the total concentration of inorganic salt impurities in the current urea solution by combining the real-time collected data on solution temperature, urea solution concentration and solution conductivity; then calculate the total mass of inorganic salt impurities in the current dissolving tank by combining the total concentration of inorganic salt impurities in the current urea solution with the geometric parameters of the urea dissolving tank and the real-time liquid level data of the urea dissolving tank;

[0082] S4, dynamically calculate the increase in impurities per unit time, including: calculating the increase in inorganic salt impurities in the urea dissolution system per unit time based on multiple sets of static impurity mass data collected in a continuous time series, and obtaining the impurity growth rate index;

[0083] S5, based on historical data, performs dynamic calibration, including: retrieving on-site urea unloading log, historical unloading time and historical urea consumption data, and performing deviation calibration on the total concentration of inorganic salt impurities in the current urea solution, the total mass of inorganic salt impurities in the current dissolving tank and the increase in inorganic salt impurities in the urea dissolving system per unit time obtained in S3 and S4, and correcting the output results of the detection model to obtain the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data;

[0084] S6, perform graded pre-alarm and interlock control, including: pre-setting multi-level impurity content thresholds and unit time impurity increment thresholds, comparing the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data with the preset thresholds, and executing graded reminders, early warnings, emergency alarms and urea unloading equipment interlock stop operation based on the comparison results.

[0085] In a preferred embodiment, S1 includes:

[0086] S11, Calibrate the density standard of pure urea solution, including: selecting analytical grade urea reagent as the calibration raw material, preparing pure urea solutions with a mass fraction of 5%~50%, and setting the concentration gradient in increments of 5%; controlling the solution temperature range to 25℃~50℃, with the temperature gradient set in increments of 5℃; conducting density testing according to "Determination of Density of Liquid Fertilizers" (NY / T887-2010), setting up parallel samples for each working condition, and controlling the absolute difference of parallel test results to be no greater than 0.003g / mL; solution preparation and sample pretreatment according to "Repair..." The laboratory sample preparation for mixed fertilizers (GB / T8571) and the determination of trace anions in fertilizers by ion chromatography (GB / T29400-2012) were performed to ensure that the solution was fully dissolved and free from bubble interference. Finally, a data table of pure urea solution density at different concentrations and temperatures was compiled to form a pure urea density benchmark database. In this embodiment, step S11 clarifies the variation law of pure urea solution density with concentration and temperature, distinguishes the differences in physicochemical parameters caused by the physicochemical properties of urea itself and inorganic salt impurities, and eliminates the interference of urea concentration and temperature fluctuations on conductivity detection results.

[0087] S12 involves screening and calibrating the conductivity effects of typical inorganic salt impurities, including: identifying the mainstream adulterant inorganic salt impurities in denitrified urea as sodium sulfate, ammonium sulfate, ammonium chloride, and sodium chloride; preparing urea solutions with a concentration of 50% containing the above four types of inorganic salts; controlling the ambient temperature at 25℃; preparing two groups of samples with impurity contents of 5.22 g / L and 3.82 g / L respectively; and detecting the conductivity values ​​of urea solutions with different inorganic salt contents in each group; comparing the conductivity response intensity of the four types of inorganic salts under the same contents and operating conditions; selecting sodium sulfate, which has the most significant conductivity effect, as the characteristic reference material for quantitative calculation of impurities; and compiling a comparative data table of conductivity of the four typical inorganic salts under standard operating conditions. In this embodiment, step S12 screens out the characteristic impurity with the highest response sensitivity as the calculation benchmark, unifies the quantitative calculation standard for impurities, takes into account the comprehensive detection effect of various adulterant salts, and improves the sensitivity of impurity identification and quantification.

[0088] S13, perform full-condition calibration of the conductivity-impurity content of the mixed solution, including: based on the concentration and temperature gradients in S11, and using sodium sulfate selected in S12 as the characteristic impurity, prepare urea mixed solutions with different base concentrations (5%~50%, gradient 5%), different temperatures (25℃~50℃, gradient 5℃), and different sodium sulfate impurity contents (0g / L~6.0g / L); wherein the sodium sulfate impurity content gradient is set to 0.25g / L, 0.50g / L, 0.75g / L, 1.00g / L, 1.5g / L, 2.0g / L, 3.0g / L, 4.0g / L, 5.0g / L, and 6.0g / L, targeting low impurity content... The system involves: 1) Increasing the density of detection points within a given range; 2) Detecting the conductivity of the mixed solution under each operating condition, recording the correspondence between urea concentration, temperature, sodium sulfate content, and conductivity under all operating conditions, generating a conductivity-impurity content mapping data table and fitting curve, and constructing a baseline database for the conductivity of the mixed solution; 3) Dynamically adjusting the impurity concentration detection interval based on the conductivity variation, increasing the density of detection points in areas of drastic conductivity changes, and simplifying detection points in areas of gradual changes; 4) In this embodiment, step S13 covers all operating conditions on-site, establishing a quantitative correspondence between conductivity and impurity content across all dimensions, achieving accurate interpolation calculation of impurity content under any operating condition, and further improving the detection accuracy of trace impurities by increasing the density of detection points in low-impurity ranges.

[0089] Step S1 establishes a standardized benchmark data system that can be matched to all working conditions on site through step-by-step calibration, eliminating the interference of urea, ambient temperature, and solution concentration on impurity detection, providing accurate data basis for subsequent quantitative calculation of impurities, and reducing the inherent error of the detection system from the source.

[0090] In a preferred embodiment, in step S2, the urea solution concentration is the real-time mass concentration of the urea solution; the solution temperature is the real-time temperature of the urea solution; the solution conductivity data is the real-time conductivity of the urea solution; the real-time liquid level data of the urea dissolving tank is the internal liquid level height of the urea dissolving tank; the geometric parameter data of the urea dissolving tank is the fixed radius dimension of the urea dissolving tank; the synchronously acquired time-series operating data includes millisecond-level time-series timestamp data; the real-time acquired field data includes all parameters continuously acquired by online sensors, with an acquisition frequency of not less than 1 time / second. In this embodiment, step S2 enables high-frequency continuous acquisition of real-time field operating parameters, ensuring data time-series continuity and synchronization, and providing complete and real-time raw data for dynamic impurity increment calculation and trend analysis.

[0091] In a preferred embodiment, S3 includes:

[0092] S31, Calculate the concentration of impurities in the solution, including: retrieving the mixed solution conductivity benchmark database calibrated in S1; and calculating the equivalent concentration x of sodium sulfate in the current urea solution (unit: kg / m³) based on the real-time collected urea solution concentration, solution temperature, and solution conductivity using data table interpolation or a fitting function. 3 ;

[0093] S32, calculate the total mass of impurities in the dissolving tank, including: calculating the total mass m of inorganic salt impurities in the dissolving tank based on formula (1):

[0094] (1);

[0095] Where m represents the total mass of inorganic salt impurities in the dissolving tank, in kg; and x represents the concentration of inorganic salt impurity ions in the solution, in kg / m³. 3 ; r represents the radius of the urea dissolving tank, in meters; h represents the real-time liquid level in the urea dissolving tank, in meters.

[0096] In this embodiment, the implementation of step S3, combined with the physicochemical parameters of the solution and the geometric parameters of the dissolving tank, realizes the full-domain conversion from the local impurity concentration of the solution to the total mass of impurities in the whole tank, solving the defect that traditional single-point sampling cannot represent the quality of the whole batch of materials.

[0097] In a preferred embodiment, S4 includes: extracting two adjacent time nodes. Corresponding inorganic salt impurity mass The total amount of urea dissolved in the storage tank per unit time is kept stable by default. The increment of impurities per unit time is calculated according to the differential formula (2). : (2);

[0098] A 1-minute time interval was selected as the standard statistical period, and the impurity increment data was continuously output every minute to form an impurity growth trend curve.

[0099] In this embodiment, step S4 calculates the impurity growth rate by time-series difference, which not only detects the instantaneous impurity content, but also predicts the overall adulteration degree and uniformity of urea materials, and identifies different adulteration modes such as local adulteration and whole-vehicle adulteration.

[0100] In a preferred embodiment, S5 includes: retrieving the on-site urea unloading electronic ledger to obtain the historical total unloading time of vehicles, the total amount of urea unloading per hour, and historical batch impurity detection data; using the historical actual unloading volume as a benchmark, correcting the deviation of the impurity concentration, total impurity mass, and impurity increment per unit time calculated in real time, eliminating system errors caused by material dissolution rate fluctuations, sensor zero-point drift, and environmental interference; setting the calibration cycle to automatic calibration once a day, and manual forced calibration after major overhaul or sensor replacement.

[0101] In this embodiment, step S5 uses long-term historical operating data to iteratively optimize the detection model, compensate for detection deviations caused by equipment aging and operating condition fluctuations, and ensure the detection stability and accuracy of the system under long-term operating conditions.

[0102] In a preferred embodiment, S6 includes pre-setting four threshold standards, corresponding to four levels of control actions:

[0103] (1) Reminder threshold: When the increase in inorganic salts per unit time accounts for 0.68% of the urea mass, i.e. 6.8g / kg, an audible and visual reminder is triggered to prompt the on-duty maintenance personnel to pay attention to the urea quality;

[0104] (2) Level 1 warning threshold: When the total content of inorganic salts accounts for 0.9% of the urea mass, i.e. 9g / kg, a continuous audible and visual warning is triggered, and an abnormal data is recorded in a pop-up window and an abnormal log is pushed.

[0105] (3) Level 2 alarm threshold: When the total content of inorganic salts accounts for 0.93% of the urea mass, i.e. 9.3g / kg, a high-intensity audible and visual alarm is triggered, the system locks the current batch of urea data and uploads it to the plant monitoring platform;

[0106] (4) Emergency interlock alarm threshold: When the total inorganic salt content accounts for 1.2% of the urea mass, i.e. 12g / kg, the highest level alarm is triggered and an interlock signal is output, automatically stopping the operation of the urea unloading equipment;

[0107] In this embodiment, step S6 uses multi-level thresholds to achieve gradient control, distinguishing between minor, moderate, and severe abnormal operating conditions, taking into account both the flexibility of operation and maintenance and the ability to forcibly block major risks, thus preventing high-impurity urea from entering the denitrification system from the source.

[0108] The real-time sensing and early warning method in this embodiment completes the detection throughout the synchronous process of urea unloading and dissolution. The response time for a single batch of urea from arrival at the plant to unloading and completion of testing is less than 1 minute, eliminating the need for separate offline sampling and laboratory testing. This completely solves the problem of on-site vehicle congestion caused by the traditional manual testing cycle of up to 5 hours and the waiting time of 7-8 hours for unloading a whole truck. It enables simultaneous unloading and testing, significantly improving the efficiency of urea acceptance upon arrival at the plant. In addition, the method in this embodiment is for granular denitrification urea, and the entire process is based on the homogeneous solution after urea dissolution. It abandons the traditional fixed-point sampling on the top of the tank truck and sampling during the unloading process, eliminating the need for manual sampling, sample reduction, and laboratory chemical analysis. This completely solves the industry problems of fixed sampling locations for granular urea, easy clogging of samplers, and the fact that the sample size after sample reduction is less than 50g, resulting in extremely poor sample representativeness. The test results can fully represent the true quality of the entire tank and truck of urea. Third, the method in this embodiment can simultaneously identify multiple mainstream adulterated inorganic salts such as ammonium chloride, sodium sulfate, and sodium chloride, without the need for prior qualitative analysis of impurities, and can directly complete quantitative detection. This overcomes the cumbersome process of traditional testing, which requires first qualitatively identifying the types of impurities and then matching the corresponding testing methods. It also solves the problem that urea hydration molecules and hydrolysis products interfere with the accurate detection of anions and cations by traditional chemical analysis methods, reducing detection errors. Fourth, the method in this embodiment is suitable for daily use in power plants. For bulk material acceptance scenarios involving urea consumption, single tanker loading capacity of 25t, and daily intake of 2 or more truckloads, simultaneous inspection and acceptance of each truck can be achieved without the need to add manual testing positions. This replaces the manual acceptance mode that requires 2 laboratory technicians to be on duty at full capacity, significantly reducing the workload of manual urea acceptance upon arrival at the plant and reducing the company's labor costs.

[0109] The method embodiments of this invention are divided into two main implementation stages: the laboratory benchmark calibration stage and the field online operation stage. The laboratory stage completes the experimental calibration of the three-layer benchmark database, providing data for field testing. The field stage completes equipment installation, parameter configuration, and system debugging, subsequently entering a fully automated online detection, analysis, early warning, and interlocking operation mode. The following provides a detailed description of the specific implementation methods, operational details, parameter standards, calculation formulas, and operating requirements for each step.

[0110] Step S1: Benchmark Database Calibration Procedure (Laboratory Implementation)

[0111] This step is the preliminary calibration work, which is completed in a laboratory environment. It is divided into three sub-steps: S11 pure urea density calibration, S12 inorganic salt conductivity effect screening, and S13 mixed solution conductivity-impurity content calibration. All tests strictly follow the corresponding national standards.

[0112] S11, Calibration of the density of pure urea solution, including the following sub-steps:

[0113] Experimental materials and equipment: urea solution was prepared using analytical grade urea reagent and deionized water; the testing equipment met the requirements of "Determination of Density of Liquid Fertilizer" (NY / T887-2010); glassware was pre-weighed, cleaned and dried.

[0114] Concentration gradient setting: Configure 10 groups of pure urea solutions with mass fractions of 5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%, with a concentration step of 5%, covering the entire operating concentration range on site.

[0115] Temperature gradient setting: The temperature of each group of concentration solutions is controlled at 25℃, 30℃, 35℃, 40℃, 45℃ and 50℃ respectively, with a temperature step of 5℃, to match the temperature range of the on-site urea dissolution conditions.

[0116] Solution pretreatment: Stir and dissolve the urea according to the requirements of "Laboratory Sample Preparation of Compound Fertilizers" (GB / T8571) and "Determination of Trace Anions in Fertilizers by Ion Chromatography" (GB / T29400-2012) to ensure complete dissolution of urea. Let the solution stand to eliminate air bubbles and remove interference from air bubbles on density detection.

[0117] Density testing and parallel sample requirements: Two parallel measurements should be conducted for each working condition. The absolute difference between the parallel measurement results should be ≤0.003g / mL. The average value should be taken as the final density value.

[0118] Data processing: Summarize the density values ​​corresponding to all concentrations and temperatures to form a pure urea density benchmark data table, as shown in Table 1. The data is then entered into the benchmark database module of the system for permanent storage.

[0119] Table 1

[0120]

[0121] S12, Screening and calibration of typical inorganic salt impurity conductivity effects, includes the following sub-steps:

[0122] Experimental materials: The mainstream adulterants in denitrified urea were selected, namely sodium sulfate (Na2SO4), ammonium sulfate ((NH4)2SO4), ammonium chloride (NH4Cl) and sodium chloride (NaCl); the basic solution was a 50% mass fraction urea solution, and the ambient temperature was kept constant at 25℃.

[0123] Impurity content settings: Two gradient samples with impurity contents of 5.22 g / L and 3.82 g / L were prepared for each group of salts.

[0124] Conductivity testing: The conductivity of the solution was tested group by group using a high-precision conductivity meter (unit: μs / cm), and all data were recorded.

[0125] Screening of reference materials: By comparing the conductivity values ​​of four types of salts at the same content, sodium sulfate was found to have the highest conductivity response intensity and was selected as the characteristic reference material for quantitative calculation of impurities. Through market research and on-site investigation of urea hydrolysis, SO4... 2- CL - Na + NH4 + SO42 is a major impurity ion in urea. Based on theoretical estimations, for the same conductivity, SO422... 2- The quality is significantly higher than that of CL - Na + and NH4 + Since the differences are not significant, the salt with the greatest conductivity effect is used as the immediate impurity for calculation. A certain molar concentration of Na₂SO₄, (NH₄)₂SO₄, NH₄Cl, and NaCl were selected, and their conductivity was tested. It was determined that for the same conductivity, Na₂SO₄ has the most significant impurity effect. This method uses Na₂SO₄ as the analytical impurity for related research and early warning.

[0126] Data storage: The conductivity comparison data of the four types of salts (as shown in Table 2, conductivity of typical inorganic salts at different concentrations at 25℃ (unit: μs / cm)) will be entered into the database as an auxiliary reference for impurity types.

[0127] Table 2

[0128]

[0129] S13, Calibration of mixed solution conductivity-impurity content under full operating conditions, including the following sub-steps:

[0130] Basic operating conditions: The urea concentration gradient (5%~50%, step size 5%) and temperature gradient (25℃~50℃, step size 5℃) of S11 are used, and the impurity is sodium sulfate selected in S12.

[0131] Set impurity content gradients: Set impurity contents: 0 g / L, 0.25 g / L, 0.50 g / L, 0.75 g / L, 1.00 g / L, 1.5 g / L, 2.0 g / L, 3.0 g / L, 4.0 g / L, 5.0 g / L, 6.0 g / L; Increase the number of measurement points in the low impurity range of 0~2.0 g / L to improve the accuracy of trace impurity detection.

[0132] Operating condition testing: Corresponding mixed solutions were prepared, and conductivity was measured after constant temperature. Four sets of data were recorded: concentration, temperature, impurity content, and conductivity, as shown in Table 3. Based on the conductivity of urea solutions with different temperatures, concentrations, and (NH4)2SO4 contents, a list of conductivity curves for Na2SO4 content was obtained. Depending on the on-site urea dissolution, urea solutions ranging from 5% to 50% were tested at a density of 5%; temperatures ranging from 25℃ to 50℃ were tested at a density of 5℃; and impurity content (as Na2SO4) ranging from 0.2 g / L to 5.5 g / L was accurately measured, with higher density measurements taken during periods of low impurity content.

[0133] Table 3

[0134]

[0135] It should be noted that Table 3 is based on experience and can be adjusted according to experiments. For example, if the conductivity changes significantly at a certain impurity concentration interval, the frequency can be increased appropriately; if the change is not obvious at a certain measurement interval, the frequency can be decreased appropriately. All of these are within the protection range of the anti-counterfeiting measures.

[0136] Dynamic optimization of measuring points: During the experiment, observe the change in conductivity. Add additional measuring points in the range of drastic change, and maintain the original gradient in the range of gradual change.

[0137] Data modeling: Based on the experimental data, draw the conductivity-impurity content relationship curve, organize it into a standardized data table, and enter it into the system's benchmark database module, and configure interpolation calculation and curve fitting algorithms.

[0138] Step S2, real-time data acquisition steps (long-term field operation), includes:

[0139] Sensor installation layout: A concentration sensor, temperature sensor, and conductivity sensor are sequentially installed on the outlet pipeline of the urea dissolving tank, with the sensor probes directly immersed in the urea solution. A radar level gauge is installed on the top of the dissolving tank for non-contact detection of the liquid level. All sensors are industrial explosion-proof, meeting the explosion-proof requirements of the denitrification workshop.

[0140] List of parameters to be collected: The following parameters are collected in real time: urea solution mass concentration, solution real-time temperature, solution conductivity, dissolving tank liquid level height, dissolving tank fixed radius (static parameter, entered into the system at once), and millisecond-level time sequence timestamp.

[0141] Acquisition frequency: All dynamic parameters are acquired at a frequency of 1 time / second to ensure data continuity and real-time performance.

[0142] Signal transmission: The sensor outputs a standard 4-20mA industrial analog signal, which is transmitted to the PLC processing unit through a shielded cable to resist on-site electromagnetic interference.

[0143] Step S3, the static content calculation of inorganic salt impurities, is automatically completed by the system PLC and consists of two sub-steps: calculation of solution impurity concentration and calculation of total impurity mass in the entire tank.

[0144] S31, Calculation of solution impurity concentration: The PLC retrieves the four-dimensional data table from the reference database and, combined with the three dynamic parameters of urea concentration, temperature, and conductivity acquired in real time, calculates the equivalent sodium sulfate impurity concentration x (unit: kg / m³) in the current solution using linear interpolation. 3 ).

[0145] S32, Calculate the total mass of impurities in the dissolving tank: Call the fixed parameters, urea dissolving tank radius r (m), urea dissolving tank real-time liquid level h (m), and use formula (1) to calculate the total mass m of inorganic salt impurities in the dissolving tank. The calculation results are stored and displayed in real time.

[0146] (1);

[0147] Where m represents the total mass of inorganic salt impurities in the dissolving tank, in kg; and x represents the concentration of inorganic salt impurity ions in the solution, in kg / m³. 3 ; r represents the radius of the urea dissolving tank, in meters; h represents the real-time liquid level in the urea dissolving tank, in meters.

[0148] Step S4, the dynamic calculation step of impurity increment per unit time, includes:

[0149] Time-series data caching: The system's dynamic incremental analysis module continuously caches all historical static impurity quality data for a period of no less than 1 hour.

[0150] Time interval setting: Select 1 minute as the standard statistical period, and extract two adjacent time nodes. Corresponding inorganic salt impurity mass .

[0151] Calculate the increment: By default, the amount of urea dissolved in the dissolving tank is the same per unit time. Calculate the increment of impurities per unit time according to the differential formula (2). :

[0152] (2);

[0153] Trend display: Based on multiple consecutive groups Automatically plot the impurity growth trend curve and display it in real time on the human-machine interface.

[0154] Step S5, historical data dynamic calibration step, includes:

[0155] Historical data retrieval: The system automatically reads locally stored urea unloading logs, historical unloading durations, hourly unloading volumes, and historical monitoring data.

[0156] Automatic calibration: The system performs an automatic calibration once daily at midnight, generating correction coefficients based on historical material consumption data, and adjusting the real-time calculations accordingly. The data is corrected for deviations to compensate for sensor drift and operating condition fluctuation errors.

[0157] Manual calibration: After sensor replacement, equipment overhaul, or system restart, maintenance personnel can perform manual forced calibration via the touchscreen to ensure data accuracy.

[0158] Application of calibration results: The final data after calibration is sent to the graded alarm module for threshold comparison.

[0159] Step S6, Hierarchical pre-alarm and interlocking control steps

[0160] The system has four built-in fixed thresholds, and automatically compares and executes control actions throughout the process. The threshold standards and corresponding actions are set as follows:

[0161] Warning threshold: The percentage of inorganic salt increment to urea mass per unit time is 0.68% (6.8 g / kg). When the trigger condition is met, a low-level audio-visual warning is activated to alert on-site maintenance personnel to pay attention to urea quality; no mandatory action is required.

[0162] Level 1 warning threshold: Total inorganic salt content as a percentage of urea mass = 0.9% (9g / kg). Upon triggering, a continuous audible and visual warning will be activated, and the system will automatically record abnormal data, generate logs, and store them.

[0163] Level 2 alarm threshold: Total inorganic salt content as a percentage of urea mass = 0.93% (9.3 g / kg). Upon triggering, a high-intensity audible and visual alarm will be activated, the current batch data will be locked, and the data will be uploaded to the plant monitoring platform.

[0164] Emergency interlock threshold: Total inorganic salt content as a percentage of urea mass = 1.2% (12g / kg). Upon triggering, the highest level alarm will activate, and the relay will simultaneously output a passive switch signal to cut off the control circuit of the urea unloading equipment, automatically stopping the unloading operation and forcibly blocking unqualified materials.

[0165] All alarm actions and interlock actions generate independent logs, which are permanently stored and support later traceability and query.

[0166] Example 2

[0167] The second embodiment of the present invention relates to a real-time sensing and early warning system for the content of inorganic salt impurities in urea. As a dedicated hardware and software integrated system for implementing the method of the first embodiment, it is divided into six modules: a baseline database module, a field parameter acquisition module, an impurity content calculation module, a dynamic incremental analysis module, a data calibration module, and a graded alarm and interlocking control module. In addition, an independent human-machine interaction and data storage module is set up outside the system for human-machine interaction and data storage. All system hardware uses industrial explosion-proof, dustproof, waterproof, and corrosion-resistant components, suitable for the harsh environment of denitrification workshops in thermal power plants; the core computing unit uses an industrial PLC, with strong anti-interference capabilities and can operate continuously 24 / 7; sensors acquire data at high frequency, and modules use industrial standard signal communication, ensuring high stability; it is equipped with a local touchscreen and human-machine interface, supporting local operation, data viewing, and parameter setting; it also has remote data upload capabilities, allowing access to intelligent management platforms such as power plant SIS and MIS; multi-level audible and visual alarms distinguish abnormal levels, and relay interlocking signals can be directly connected to existing electrical control circuits on-site to achieve automatic shutdown. The system operates fully automatically, eliminating the need for dedicated laboratory personnel and replacing the manual work of two laboratory technicians, thus significantly reducing the company's labor and management costs.

[0168] The system embodiment of the present invention includes seven modules, and the hardware configuration, function implementation, installation and debugging methods of each module are described in detail:

[0169] 1. Benchmark Database Module

[0170] It uses an industrial-grade solid-state drive as the storage medium to store a three-layer calibration database; it has built-in data retrieval, linear interpolation, and curve fitting algorithms, communicates with the PLC in real time, and responds to data retrieval requests in milliseconds; it has a power-loss data protection function, so data is not lost after power failure.

[0171] 2. On-site parameter acquisition module

[0172] It consists of a concentration sensor, a temperature sensor, a conductivity sensor, and a radar level gauge, all of which are explosion-proof industrial-grade components. After installation according to the pipeline and tank layout, zero-point calibration and range calibration are performed after wiring. The output 4-20mA standard signal is connected to the PLC analog input module.

[0173] 3. Impurity content calculation module

[0174] The core is a Siemens / Mitsubishi / Rockwell series industrial PLC, with built-in formula calculation program and interpolation calculation program; it can complete the calculation of solution impurity concentration and total tank impurity mass; the PLC is equipped with a power-off retention register to save intermediate calculation data when power is off.

[0175] 4. Dynamic Incremental Analysis Module

[0176] Integrated into the PLC, it is equipped with a large-capacity time-series data buffer to continuously store historical quality data; it has a built-in differential calculation program to automatically calculate the impurity increment per unit time, generate trend data, and upload it to the human-machine interface.

[0177] 5. Data calibration module

[0178] It is divided into a local ledger storage unit and a deviation correction algorithm unit. The ledger stores urea unloading data over the years; the algorithm unit dynamically generates correction coefficients based on historical data, automatically calibrates daily, and supports a manual calibration trigger button.

[0179] 6. Hierarchical alarm and interlocking control module

[0180] It includes multiple sets of audible and visual alarms (differentiated by level) and relay output modules; different thresholds correspond to different audible and visual circuits, and the interlocking relay contacts are connected in series to the main control circuit of the urea unloading equipment to achieve electrical interlocking shutdown.

[0181] 7. Human-computer interaction and data storage module

[0182] An industrial explosion-proof touch screen is configured locally to enable parameter setting, data display, curve viewing, and log querying; a large-capacity storage unit stores detection data, alarm logs, and operation records for a storage time of ≥3 years; an Ethernet / 4G communication module is configured to connect to the power plant's SIS system to enable remote data upload.

[0183] In this embodiment, all modules of the system are integrated into an integrated industrial explosion-proof control cabinet. The cabinet has an IP65 protection rating, is dustproof, waterproof, and corrosion-resistant, and is adaptable to operating temperatures from -10℃ to 60℃. It is directly fixed to the local platform in the denitrification workshop, and the wiring uses explosion-proof conduit, complying with power plant safety regulations. The system can achieve full-condition operation adaptability, including:

[0184] 1. Material consumption adaptation: Adaptable to power plant daily urea consumption of 50t~300t, single 25t tanker trucks, and bulk material conditions with 2 or more trucks entering the plant daily, supporting fully automatic detection for each truck.

[0185] 2. Environmental and working condition adaptability: The sensors, control cabinets, and PLCs are all adapted to the high temperature, dust, corrosive flue gas, and complex electromagnetic environment of thermal power plants.

[0186] 3. Operation mode adaptation: Supports continuous operation 24 / 7, meeting the requirements for continuous operation of the denitrification system throughout the year.

[0187] 4. Management mode adaptation: It can operate independently locally or be connected to the power plant's intelligent management and control platform to meet the management needs of power plants of different sizes.

[0188] Figure 2As shown, the second aspect of the present invention provides a real-time sensing and early warning system for the content of inorganic salt impurities in urea, used to realize the real-time sensing and early warning method for the content of inorganic salt impurities in urea in the first aspect, including a reference database module, a field parameter acquisition module, an impurity content calculation module, a dynamic incremental analysis module, a data calibration module, and a graded alarm and interlock control module; each module is connected in sequence to communicate with each other and work together to complete the entire process of real-time sensing, quantitative calculation, dynamic analysis, data calibration, graded alarm, and equipment interlock for inorganic salt impurities in urea;

[0189] The benchmark database module 101 is used to store a pure urea solution density benchmark database, a typical inorganic salt conductivity comparison database, and a mixed solution conductivity-impurity content mapping database, providing data support for quantitative impurity calculation. In this embodiment, the benchmark database module includes a local storage unit and a data retrieval unit. The local storage unit uses an industrial-grade solid-state drive to store the three types of calibration databases, supporting permanent local data storage. The data retrieval unit has interpolation calculation and curve fitting functions, and can quickly match corresponding benchmark data according to real-time operating parameters. Using the benchmark database module of this embodiment can ensure the safe storage and fast retrieval of benchmark data, adapt to the complex electromagnetic environment of industrial sites, and support high-frequency, real-time data matching calculations.

[0190] The on-site parameter acquisition module 102 is deployed in the urea dissolving tank and urea delivery pipeline. It is used to collect urea solution concentration, solution temperature, solution conductivity, dissolving tank level, dissolving tank geometric parameters, and time-series timestamp data in real time, and upload the collected data to the impurity content calculation module in real time. In this embodiment, the on-site parameter acquisition module includes four types of online detection sensors: solution concentration sensor, temperature sensor, conductivity sensor, and level sensor. All sensors are industrial explosion-proof and suitable for explosion-proof conditions in power plant denitrification workshops. The solution concentration sensor, temperature sensor, and conductivity sensor are integrated and installed inside the urea dissolving tank outlet pipeline, directly contacting the urea solution for detection. The level sensor is installed on the top of the urea dissolving tank and uses a non-contact radar level gauge to detect the liquid level height inside the tank. All sensor output signals are standard 4-20mA industrial analog signals with a sampling frequency ≥ 1 time / second. The sensor layout conforms to the urea dissolving process flow, the explosion-proof design meets power plant safety regulations, high-frequency acquisition ensures real-time data, and the analog signal transmission has strong anti-interference capabilities, making it suitable for complex industrial environments.

[0191] The impurity content calculation module 103 is connected to both the benchmark database module and the field parameter acquisition module. It retrieves benchmark data and real-time acquired data to calculate the concentration of inorganic salt impurities in the urea solution and the total mass of impurities in the dissolving tank. In this embodiment, the impurity content calculation module uses an industrial PLC programmable logic controller as its core computing unit. It has built-in formula calculation programs and data table interpolation programs, enabling automatic calculation of solution impurity concentration and total mass of impurities in the dissolving tank. The PLC has power-off protection, retaining current calculation data and intermediate results after power failure. Using a mainstream industrial PLC as the computing core ensures high stability, strong anti-interference capabilities, and adaptability to continuous industrial operation. Power-off protection prevents data loss and guarantees the continuity of the testing process.

[0192] The dynamic incremental analysis module 104, connected to the impurity content calculation module, receives continuous time-series impurity quality data, calculates the increase in inorganic salt impurities per unit time, and generates impurity growth trend data. In this embodiment, the dynamic incremental analysis module has a built-in time-series data caching unit and a differential operation unit. The time-series data caching unit continuously stores at least one hour of historical impurity quality data. The differential operation unit extracts the preceding and following time-series data at fixed time intervals, calculates the impurity increment per unit time, and automatically generates a real-time trend curve. By using the dynamic incremental analysis module to cache historical data for a long time, the integrity of incremental calculation and trend analysis is ensured, and the change pattern of impurity content is intuitively displayed, making it easier for maintenance personnel to judge the adulteration trend of urea.

[0193] The data calibration module 105 is connected to the dynamic incremental analysis module and the human-computer interaction and data storage module, respectively. It is used to retrieve historical ledger data and perform deviation calibration on impurity concentration, impurity mass, and impurity increment data. In this embodiment, the data calibration module has a built-in historical ledger database and a deviation correction algorithm unit. The historical ledger database stores urea unloading time, hourly unloading volume, and historical detection data. The deviation correction algorithm unit establishes correction coefficients based on historical material data and automatically calibrates real-time detection results, supporting both automatic daily calibration and manual forced calibration modes. By using the data calibration module, relying on historical big data, the system error is continuously corrected, solving the problem of decreased detection accuracy caused by sensor drift and operating condition fluctuations, and ensuring long-term stable operation of the system.

[0194] The graded alarm and interlocking control module 106 is connected to the data calibration module and has built-in multi-level impurity thresholds for comparing calibrated data with preset thresholds to execute reminder, warning, alarm, and equipment interlocking shutdown actions. In this embodiment, the graded alarm and interlocking control module includes an audible and visual reminder unit, an audible and visual warning unit, an audible and visual alarm unit, and an interlocking output unit. The audible and visual reminder unit, audible and visual warning unit, and audible and visual alarm unit correspond to the reminder, first-level warning, and second-level alarm conditions in the four-level threshold, respectively, and use different timbre and brightness audible and visual signals to distinguish the alarm level. The interlocking output unit is a relay switch output interface, corresponding to the emergency interlocking alarm threshold, and outputs a passive switch signal in series to connect to the urea unloading equipment control circuit to achieve automatic shutdown. Using the graded alarm and interlocking control module of this embodiment, the multi-level audible and visual signals clearly distinguish the abnormal level, making it easy for on-site personnel to quickly identify the degree of risk. The relay interlocking signal is safe and reliable and can be directly connected to the existing equipment control system on site to achieve emergency risk blocking.

[0195] In a preferred embodiment, the system further includes a human-machine interaction and data storage module, which is connected to all functional modules and is used to realize parameter setting, data display, log storage, ledger retrieval, and remote data upload functions. In this embodiment, the human-machine interaction and data storage module includes an industrial touch screen, a large-capacity storage unit, and a data upload and communication unit. The industrial touch screen is located in the local control cabinet of the denitrification workshop and is used for parameter threshold setting, real-time data display, historical curve viewing, and abnormal log query. The large-capacity storage unit stores all detection data, alarm logs, and operation records for a storage time of not less than 3 years. The data upload and communication unit adopts the Ethernet / 4G industrial communication protocol to remotely upload data to the power plant's SIS system and production monitoring platform. The local human-machine interaction is convenient, the large-capacity storage meets the requirements of industrial data traceability, and the remote communication enables centralized monitoring of the entire plant, adapting to the power plant's intelligent management and control system.

[0196] The system in this embodiment adopts an integrated cabinet structure, with all modules integrated inside an industrial explosion-proof control cabinet. The control cabinet is dustproof, waterproof, corrosion-resistant, and anti-electromagnetic interference, and is adaptable to the operating environment temperature. It is perfectly suited to the on-site environment of denitrification workshops in coal-fired power plants; its integrated design facilitates on-site installation, wiring, and maintenance, and its protection level meets the stringent environmental requirements of industrial sites, extending the service life of the equipment. Furthermore, the system requires no supporting laboratory testing equipment, manual sampling tools, or sample reduction equipment; it operates fully automatically, eliminating the need for dedicated laboratory personnel on-site, thus simplifying on-site facilities, reducing equipment and labor inputs, and lowering the overall operating costs for enterprises.

[0197] Specific application examples

[0198] Application Example 1: Field Application in a 300MW Coal-fired Power Plant

[0199] A 300MW coal-fired power unit in northern China uses a urea hydrolysis denitrification process, consuming approximately 120 tons of urea daily. An average of five 25-ton urea tank trucks enter the plant daily. The original setup involved two laboratory technicians dedicated to urea sampling and testing, with manual testing taking five hours. This resulted in a long-standing problem of tank truck congestion and repeated instances of adulterated urea entering the system, causing slight corrosion of the hydrolyzer and fluctuations in denitrification efficiency.

[0200] After implementing the technical solution of this invention, the calibration of the three-layer benchmark database in the laboratory was completed, and the data was consistent with the standard data in the disclosure document; the installation and debugging of sensors, control cabinets, and PLCs were completed on-site, and the system was put into fully automatic operation; the detection response time was stabilized at 40-55 seconds, and urea unloading and testing were completed simultaneously, completely eliminating the problem of tanker congestion in the plant area; the system has cumulatively intercepted 7 batches of adulterated urea with excessive inorganic salt impurities, triggered 3 level-two alarms and 1 emergency interlock shutdown, effectively avoiding equipment corrosion and environmental violations; the dedicated laboratory position was eliminated, and 2 laboratory technicians were transferred to other jobs, saving approximately 120,000 yuan in labor costs annually; the corrosion rate of hydrolyzers and catalysts decreased significantly, the frequency of equipment maintenance was reduced by 30%, and equipment maintenance costs were greatly reduced.

[0201] Application Example 2: Field Application of a 600MW Supercritical Unit

[0202] A certain 600MW supercritical thermal power unit consumes 280 tons of urea per day, with an average of 10 tank trucks entering the plant daily. In the past, there have been instances of catalyst poisoning and NO2 contamination due to urea adulteration. X Situations where environmental protection authorities are summoned for talks due to momentary exceedances of standards.

[0203] The implementation results are as follows: the system accurately identifies multiple batches of urea adulterated with ammonium chloride and sodium sulfate through conductivity data, and the trend curve of impurity increment per unit time clearly reflects the adulteration characteristics of the whole vehicle; historical data is automatically calibrated daily, and the detection error has been kept within the allowable range for 12 consecutive months of operation; the multi-level alarm function is operating normally, and maintenance personnel can promptly handle abnormal materials based on reminders and warnings, and there have been no further cases of unqualified urea entering the system; the entire system operates automatically without manual intervention, and the data is uploaded to the power plant's SIS platform in real time to achieve remote centralized monitoring.

[0204] The real-time sensing and early warning methods and systems for inorganic salt impurities in urea disclosed in all embodiments of this invention can be directly and scalably applied to denitrification projects in various coal-fired power plants, thermal power plants, and industrial boilers nationwide that employ urea hydrolysis / thermal denitrification processes. This completely solves a series of problems in the current field of urea quality control for denitrification, such as poor sampling, slow testing, large errors, high labor costs, and post-event risk management. It can simultaneously achieve multiple goals including environmental compliance, safe production, cost reduction and efficiency improvement, and intelligent control, possessing strong industrial applicability, market promotion value, and social value. The entire technical solution can operate stably and continuously for a long time, adapting to harsh industrial conditions and conforming to national policies on air pollution prevention and control and industrial intelligent upgrading.

[0205] The embodiments of the present invention achieve the following technical effects:

[0206] The detection mode has been transformed from offline particle sampling to online full-domain monitoring of the solution, abandoning the traditional particle material detection mode of fixed-point sampling on the top of the tanker and sampling during the unloading process. After the urea is dissolved, the homogeneous solution is monitored online throughout the entire process. Salt impurities are completely dissolved and evenly distributed in the water, fundamentally solving the problems of limited sampling points, sampler clogging, and extremely poor representativeness after sample reduction for particle materials. The test results can truly reflect the quality of the entire batch of urea.

[0207] Leveraging conductivity characteristics, this method enables rapid and quantitative analysis of multiple impurities without qualitative analysis, breaking through the cumbersome process of traditional chemical analysis that requires first qualitatively identifying the types of impurities and then quantitatively detecting them. Utilizing the conductive properties of strong electrolyte ions, conductivity is used as the core detection parameter, allowing for the simultaneous and equivalent detection of four mainstream adulterated inorganic salts: ammonium chloride, sodium sulfate, sodium chloride, and ammonium sulfate. At the same time, it completely eliminates the matrix interference of urea hydration molecules and hydrolysis products on traditional chemical analysis methods, significantly reducing detection errors.

[0208] The testing response speed has been greatly improved, completely solving the problem of vehicle congestion on site. Traditional manual testing takes more than 5 hours, and the entire vehicle unloading process is now much faster. For hours, there was severe congestion at the site; however, in all application embodiments of this invention, the detection response time is less than 1 minute, and urea unloading and testing are carried out simultaneously without waiting for test results. Material turnover efficiency is increased by dozens of times, fully meeting the needs of continuous production on site and eliminating the passive situation of unloading first and then discovering defects.

[0209] In terms of data dimensions, it achieves dual analysis of static content and dynamic increment, enabling trend prediction. Traditional technologies can only detect the static impurity content at a certain moment. The implementation of this invention not only calculates the total static mass of impurities in the whole tank, but also calculates the impurity increment per unit time through time-series difference, analyzes the impurity growth trend and adulteration uniformity, and achieves an upgrade from post-detection to in-process monitoring and pre-detection.

[0210] The system employs multi-level calibration and historical data calibration to ensure stable long-term operational accuracy. First, a three-layer benchmark database is calibrated through national standard tests to ensure the accuracy of basic data. Second, an automatic calibration mechanism is established by introducing historical unloading records and historical test data to compensate for system errors caused by sensor drift and fluctuations in operating conditions, thus solving the problem of declining accuracy of traditional equipment over long-term operation.

[0211] The control logic includes multi-level thresholds and automatic interlocks to build a full-level risk prevention and control system. It sets four-level gradient thresholds, corresponding to four levels of actions: reminder, early warning, alarm, and interlock shutdown, to distinguish different risk levels. Under high-risk conditions, it automatically cuts off the urea unloading equipment and forcibly blocks unqualified materials from entering the system, forming a closed-loop control of monitoring, analysis, early warning, and blocking to prevent major risks such as equipment corrosion, catalyst poisoning, environmental exceedances, and unauthorized unit shutdowns.

[0212] The fully automated, unattended system significantly reduces costs and increases efficiency. The entire method and system eliminates the need for manual sampling, sample preparation, and testing, replacing the full workload of the original two full-time laboratory personnel and greatly reducing labor costs. It also eliminates the need for a large number of laboratory testing equipment, sampling instruments, and sample reduction equipment, reducing hardware investment. The fully automated operation also avoids management risks such as human error and data tampering.

[0213] The present invention also provides a memory that stores multiple instructions for implementing the method as described in Embodiment 1.

[0214] like Figure 3 As shown, the present invention also provides an electronic device, including a processor 301 and a memory 302 connected to the processor 301. The memory 302 stores a plurality of instructions, which can be loaded and executed by the processor to enable the processor to perform the method as described in Embodiment 1.

[0215] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for real-time sensing and early warning of the content of inorganic salt impurities in urea, characterized in that, Includes the following steps: S1, the calibration benchmark database, includes: calibration of the density-temperature-concentration benchmark curve and data table of pure urea solution, calibration of the conductivity characteristics of typical inorganic salt impurities, and calibration of the conductivity-impurity content mapping relationship of mixed urea solution, thereby constructing three core benchmark databases; S2, real-time acquisition of field data, including: in the urea dissolution process, real-time acquisition of urea solution concentration, solution temperature, solution conductivity data, real-time liquid level data of urea dissolution tank and geometric parameter data of urea dissolution tank, and synchronous acquisition of time sequence operation data; S3, calculate the static content of inorganic salt impurities, including: based on the benchmark database calibrated in S1, quantitatively calculate the total concentration of inorganic salt impurities in the current urea solution by combining the real-time collected data on solution temperature, urea solution concentration and solution conductivity; then calculate the total mass of inorganic salt impurities in the current dissolving tank by combining the total concentration of inorganic salt impurities in the current urea solution with the geometric parameters of the urea dissolving tank and the real-time liquid level data of the urea dissolving tank; S4, dynamically calculate the increase in impurities per unit time, including: calculating the increase in inorganic salt impurities in the urea dissolution system per unit time based on multiple sets of static impurity mass data collected in a continuous time series, and obtaining the impurity growth rate index; S5, based on historical data, performs dynamic calibration, including: retrieving on-site urea unloading log, historical unloading time and historical urea consumption data, and performing deviation calibration on the total concentration of inorganic salt impurities in the current urea solution, the total mass of inorganic salt impurities in the current dissolving tank and the increase in inorganic salt impurities in the urea dissolving system per unit time obtained in S3 and S4, and correcting the output results of the detection model to obtain the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data; S6, perform graded pre-alarm and interlock control, including: pre-setting multi-level impurity content thresholds and unit time impurity increment thresholds, comparing the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data with the preset thresholds, and executing graded reminders, early warnings, emergency alarms and urea unloading equipment interlock stop operation based on the comparison results.

2. The method for real-time sensing and early warning of inorganic salt impurity content in urea according to claim 1, characterized in that, S1 includes: S11, calibrating the density standard of pure urea solution, including: selecting analytical grade urea reagent as the calibration raw material, preparing pure urea solutions with a mass fraction of 5%~50%, with the concentration gradient set sequentially in 5% increments; controlling the solution temperature range of 25℃~50℃, with the temperature gradient set sequentially in 5℃ increments; conducting density tests according to NY / T887-2010 "Determination of Density of Liquid Fertilizers", setting up parallel samples for each working condition, and controlling the absolute difference of parallel test results to be no greater than 0.003g / mL; the solution preparation and sample pretreatment are performed according to GB / T8571 "Laboratory Sample Preparation of Compound Fertilizers" and GB / T29400-2012 "Determination of Trace Anions in Fertilizers by Ion Chromatography", ensuring that the solution is fully dissolved and free from bubble interference; finally, compiling a data table of pure urea solution density at different concentrations and temperatures to form a pure urea density standard database; S12, the conductivity effect of typical inorganic salt impurities was screened and calibrated, including: identifying the mainstream adulterant inorganic salt impurities in denitrified urea as sodium sulfate, ammonium sulfate, ammonium chloride, and sodium chloride; preparing 50% concentration urea solutions containing the above four types of inorganic salts; controlling the ambient temperature at 25℃; preparing two groups of samples with impurity contents of 5.22 g / L and 3.82 g / L respectively; and measuring the conductivity values ​​of urea solutions with different inorganic salt contents in each group; comparing the conductivity response intensity of the four types of inorganic salts under the same contents and operating conditions; selecting sodium sulfate, which has the most significant conductivity effect, as the characteristic reference material for quantitative calculation of impurities; and compiling a comparative data table of conductivity of the four typical inorganic salts under standard operating conditions. S13, perform full-condition calibration of the conductivity-impurity content of the mixed solution, including: based on the concentration and temperature gradients in S11, and using sodium sulfate selected in S12 as the characteristic impurity, prepare urea mixed solutions with different base concentrations, temperatures, and sodium sulfate impurity contents; the sodium sulfate impurity content gradients are set to 0.25 g / L, 0.50 g / L, 0.75 g / L, 1.00 g / L, 1.5 g / L, 2.0 g / L, 3.0 g / L, 4.0 g / L, 5.0 g / L, and 6.0 g / L, with increased detection points for low impurity content ranges; the conductivity of the mixed solution is measured under each operating condition, and the correspondence between urea concentration, temperature, sodium sulfate content, and conductivity is recorded for all operating conditions, generating a conductivity-impurity content mapping data table and fitting curve, and constructing a baseline database of mixed solution conductivity; simultaneously, the detection interval of impurity concentration is dynamically adjusted according to the conductivity change range, with increased detection points in ranges of drastic conductivity changes and simplified detection points in ranges of gradual changes.

3. The method for real-time sensing and early warning of inorganic salt impurity content in urea according to claim 2, characterized in that, In step S2, the urea solution concentration is the real-time mass concentration of the urea solution; the solution temperature is the real-time temperature of the urea solution; the solution conductivity data is the real-time conductivity of the urea solution; and the real-time liquid level data of the urea dissolving tank is the internal liquid level height of the urea dissolving tank. The geometric parameters of the urea dissolving tank are the fixed radius dimensions of the urea dissolving tank; the synchronously acquired time-series running data includes millisecond-level time-series timestamp data.

4. The method for real-time sensing and early warning of inorganic salt impurity content in urea according to claim 3, characterized in that, S3 includes: S31, Calculate the concentration of impurities in the solution, including: retrieving the mixed solution conductivity benchmark database calibrated in S1; and calculating the equivalent concentration x of sodium sulfate in the current urea solution (unit: kg / m³) based on the real-time collected urea solution concentration, solution temperature, and solution conductivity using data table interpolation or a fitting function. 3 ; S32, calculate the total mass of impurities in the dissolving tank, including: calculating the total mass m of inorganic salt impurities in the dissolving tank based on formula (1): （1）； Where m represents the total mass of inorganic salt impurities in the dissolving tank, in kg; and x represents the concentration of inorganic salt impurity ions in the solution, in kg / m³. 3 ; r represents the radius of the urea dissolving tank, in meters; h represents the real-time liquid level in the urea dissolving tank, in meters.

5. The method for real-time sensing and early warning of inorganic salt impurity content in urea according to claim 4, characterized in that, S4 includes: extracting two adjacent time nodes. Corresponding inorganic salt impurity mass The total amount of urea dissolved in the storage tank per unit time is kept stable by default. The increment of impurities per unit time is calculated according to the differential formula (2). : （2）； A 1-minute time interval was selected as the standard statistical period, and the impurity increment data was continuously output every minute to form an impurity growth trend curve.

6. The method for real-time sensing and early warning of inorganic salt impurity content in urea according to claim 5, characterized in that, S5 includes: retrieving the on-site urea unloading electronic ledger to obtain the historical vehicle unloading total time, hourly urea unloading total amount, and historical batch impurity detection data; using the historical actual unloading volume as a benchmark, correcting the deviation of the impurity concentration, total impurity mass, and impurity increment per unit time calculated in real time, eliminating system errors caused by material dissolution rate fluctuations, sensor zero-point drift, and environmental interference; the calibration cycle is set to automatic calibration once a day, and manual forced calibration is performed after major overhaul or sensor replacement.

7. The method for real-time sensing and early warning of inorganic salt impurity content in urea according to claim 6, characterized in that, S6 includes pre-setting four threshold standards, corresponding to four levels of control actions: (1) Reminder threshold: When the increase in inorganic salts per unit time accounts for 0.68% of the urea mass, i.e. 6.8g / kg, an audible and visual reminder is triggered to prompt the on-duty maintenance personnel to pay attention to the urea quality; (2) Level 1 warning threshold: When the total content of inorganic salts accounts for 0.9% of the urea mass, i.e. 9g / kg, a continuous audible and visual warning is triggered, and an abnormal data is recorded in a pop-up window and an abnormal log is pushed. (3) Level 2 alarm threshold: When the total content of inorganic salts accounts for 0.93% of the urea mass, i.e. 9.3g / kg, a high-intensity audible and visual alarm is triggered, the system locks the current batch of urea data and uploads it to the plant monitoring platform; (4) Emergency interlock alarm threshold: When the total inorganic salt content accounts for 1.2% of the urea mass, i.e. 12g / kg, the highest level alarm is triggered and an interlock signal is output, automatically stopping the operation of the urea unloading equipment.

8. A real-time sensing and early warning system for the content of inorganic salt impurities in urea, used to implement the method described in any one of claims 1-7, characterized in that, It includes a benchmark database module, a field parameter acquisition module, an impurity content calculation module, a dynamic incremental analysis module, a data calibration module, and a graded alarm and interlock control module. These modules are sequentially connected and work together to complete the entire process of real-time sensing, quantitative calculation, dynamic analysis, data calibration, graded alarm, and equipment interlocking for urea inorganic salt impurities. Among them: The benchmark database module (102) is used to calibrate the benchmark database, including: calibrating the density-temperature-concentration benchmark curve and data table of pure urea solution, calibrating the conductivity characteristics of typical inorganic salt impurities, and calibrating the conductivity-impurity content mapping relationship of mixed urea solution, thereby constructing three core benchmark databases. The field parameter acquisition module (102) is deployed in the urea dissolving tank and urea delivery pipeline to collect field data in real time, including: in the urea dissolving process, real-time data on urea solution concentration, solution temperature, solution conductivity, real-time liquid level data of the urea dissolving tank and geometric parameter data of the urea dissolving tank, and synchronously collecting time-series operation data; The impurity content calculation module (103) is connected to the benchmark database module and the field parameter acquisition module respectively, and is used to calculate the static content of inorganic salt impurities, including: quantitatively calculating the total concentration of inorganic salt impurities in the current urea solution based on the benchmark database calibrated by S1, combined with the real-time acquired solution temperature, urea solution concentration and solution conductivity data; and then calculating the total mass of inorganic salt impurities in the current dissolving tank by combining the total concentration of inorganic salt impurities in the current urea solution with the geometric parameters of the urea dissolving tank and the real-time liquid level data of the urea dissolving tank. The dynamic incremental analysis module (104) is connected to the impurity content calculation module and is used to dynamically calculate the impurity increment per unit time, including: calculating the increase of inorganic salt impurities in the urea dissolution system per unit time based on multiple sets of static impurity mass data collected in a continuous time series, and obtaining the impurity growth rate index. The data calibration module (105) is connected to the dynamic incremental analysis module and is used to perform dynamic calibration based on historical data. This includes: retrieving on-site urea unloading log, historical unloading time and historical urea consumption data; performing deviation calibration on the total concentration of inorganic salt impurities in the current urea solution, the total mass of inorganic salt impurities in the current dissolving tank and the increase in inorganic salt impurities in the urea dissolving system per unit time obtained in S3 and S4; and correcting the output results of the detection model to obtain the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data. The graded alarm and interlock control module (106) is connected to the data calibration module and has built-in multi-level impurity thresholds for graded pre-alarm and interlock control, including: pre-setting multi-level impurity content thresholds and unit time impurity increment thresholds, comparing the calibrated inorganic salt impurity content data and the calibrated inorganic salt impurity increment data with the preset thresholds, and executing graded reminders, early warnings, emergency alarms and urea unloading equipment interlock stop operations based on the comparison results.

9. The real-time sensing and early warning system for the content of inorganic salt impurities in urea according to claim 8, characterized in that, The system also includes a human-computer interaction and data storage module, which is connected to all functional modules and is used to realize parameter setting, data display, log storage, ledger retrieval and remote data upload functions.

10. A real-time sensing and early warning system for the content of inorganic salt impurities in urea according to claim 9, characterized in that, The on-site parameter acquisition module includes four types of online detection sensors: solution concentration sensor, temperature sensor, conductivity sensor, and liquid level sensor. The solution concentration sensor, temperature sensor, and conductivity sensor are integrated and installed inside the urea dissolving tank's outlet pipeline, directly contacting the urea solution for detection. The liquid level sensor is installed on the top of the urea dissolving tank and uses a non-contact radar level gauge to detect the liquid level height inside the tank.

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