Thermal power plant scr denitration control method, system, electronic equipment and storage medium

Abstract

This application relates to a method for SCR denitrification control in thermal power plants. The method includes: collecting unit operating data; calculating the theoretical urea demand and feedforward parameters based on the unit operating data; determining the feedforward control output based on the theoretical urea demand and feedforward parameters; analyzing the unit operating data to obtain the main controller output; determining the basic setpoint for urea flow rate based on the feedforward control output and the main controller output; performing lag compensation on the basic setpoint for urea flow rate using a Smith predictor external to the main controller to obtain the target setpoint for urea flow rate; using the urea flow rate setpoint as the setpoint for the secondary controller; using the actual urea solution flow rate as its process variable to obtain the output signal of the secondary controller; and controlling the SCR denitrification urea injection based on the output signal of the secondary controller. This application solves the problem of large lag in SCR denitrification control by using a Smith predictor to compensate for the lag in the basic setpoint for urea flow rate, effectively eliminating control deviations caused by lag.

Description

Technical Field

[0001] This application relates to the field of industrial intelligent control technology, and in particular to SCR denitrification control methods, systems, electronic devices and storage media for thermal power plants. Background Technology

[0002] Selective catalytic reduction (SCR) denitrification systems are widely used in thermal power plants to control nitrogen oxide (NOx) emissions through urea pyrolysis or direct injection. In the field of automatic control, the current mainstream solutions typically employ a single controller (PID) feedback loop based on the NOx concentration at the SCR outlet, combined with a simple static feedforward based on the inlet NOx concentration, flue gas flow rate, and a fixed molar ratio.

[0003] To further improve control quality, some improvement schemes have attempted to introduce multivariate feedforward and deviation correction mechanisms. For example, the setpoint of the SCR outlet is corrected by the deviation between the NOx at the SCR inlet / outlet and the NOx at the total discharge outlet, and advance compensation is achieved by combining the boiler oxygen change rate and pre-built functions to calculate the theoretical ammonia injection rate. However, such schemes still have significant limitations: First, the system lacks an effective model compensation mechanism for the large pure time lag (usually 8-15 minutes) from ammonia injection to the NOx response at the total discharge outlet. Relying solely on the "advance" of the change rate is insufficient to fundamentally avoid instantaneous NOx exceedances or control oscillations during load changes. Second, its feedback structure is essentially still an indirect correction around the SCR outlet, lacking sufficient dynamic tracking capability for the total discharge outlet, the final environmental assessment indicator. Third, feedforward control relies on fixed functional relationships established offline, making it difficult to adapt online to long-term operating condition drift caused by changes in coal quality and catalyst aging, resulting in weak adaptive capability, low automatic start-up rate in actual operation, and frequent manual intervention. Summary of the Invention

[0004] This application provides a method, system, electronic device, and storage medium for SCR denitrification control in thermal power plants, to at least solve the problem of large lag in SCR denitrification control in related technologies.

[0005] In a first aspect, embodiments of this application provide a method for SCR denitrification control in a thermal power plant, the method comprising: Collect unit operation data, calculate theoretical urea demand and feedforward parameters based on the unit operation data, and determine feedforward control output based on the theoretical urea demand and the feedforward parameters. The unit operation data includes flow parameters, concentration parameters and status parameters. The main controller output is obtained by analyzing the unit's operating data. Based on the feedforward control output and the main controller output, the basic setpoint for urea flow rate is determined. By using the Smith predictor attached to the main controller, the basic urea flow rate setpoint is lag-compensated to obtain the target urea flow rate setpoint. The urea flow rate setpoint is used as the setpoint of the secondary controller, and the actual urea solution flow rate is used as its process variable to obtain the output signal of the secondary controller. Based on the output signal of the secondary controller, the SCR denitrification urea injection is controlled.

[0006] In some embodiments, the flow parameters include total coal feed, total boiler air volume and furnace oxygen content; the concentration parameters include SCR inlet NOx concentration; the status parameters include unit load; and the feedforward parameters include load feedforward compensation and coal feedforward compensation. The calculation of theoretical urea demand and feedforward parameters based on the unit operating data, and the determination of feedforward control output based on the theoretical urea demand and the feedforward parameters, include: The theoretical urea requirement is calculated based on the total boiler air volume, the furnace oxygen content, and the SCR inlet NOx concentration. The load feedforward compensation is determined based on the unit load, the baseline load, and the load factor. The feedforward compensation is determined based on the total coal feed, the baseline coal feed, and the coal feed coefficient. The theoretical urea demand, the load feedforward compensation, and the coal feedforward compensation are added together to obtain the feedforward control output.

[0007] In some embodiments, calculating the theoretical urea requirement based on the total boiler air volume, the furnace oxygen content, and the SCR inlet NOx concentration includes: The flue gas volume is determined based on the total boiler air volume, the furnace oxygen content, and the excess air coefficient. Based on the flue gas volume, the SCR inlet NOx concentration, and the dynamic molar ratio, the theoretical urea demand is obtained, wherein the dynamic molar ratio is updated using an adaptive correction mechanism based on online fitting of historical unit operating data and / or current unit operating data.

[0008] In some embodiments, the concentration parameters include the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet; the analysis of the unit operating data to obtain the main controller output includes: The main controller feedback correction amount is obtained based on the actual NOx concentration at the total discharge outlet and the set NOx concentration. Calculate the SCR outlet rapid compensation based on the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet; The output of the main controller is obtained by superimposing the fast compensation of the SCR output with the feedback correction of the main controller.

[0009] In some embodiments, calculating the SCR outlet rapid compensation based on the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet includes: The average value of the difference between the actual NOx concentration at the SCR outlet and the actual NOx concentration at the corresponding total outlet is obtained within a historical preset time window. Based on the average value and the preset filtering coefficient, the dynamic deviation bias value is obtained. Based on the total NOx concentration setpoint, fixed bias value, and dynamic deviation bias value, the expected value of SCR outlet NOx concentration is determined; The SCR outlet NOx correction value is obtained by subtracting the expected value of the SCR outlet NOx concentration from the actual NOx concentration at the SCR outlet. Based on the SCR outlet NOx correction value and the compensation coefficient, the SCR outlet rapid compensation is determined.

[0010] In some embodiments, the concentration parameter includes the actual NOx concentration at the total discharge outlet; the step of using a Smith predictor external to the main controller to perform hysteresis compensation on the basic urea flow rate setpoint to obtain the target urea flow rate setpoint includes: Input the basic setpoint of urea flow rate into the Smith predictor to obtain the predicted value of NOx concentration at the total discharge outlet. The predicted NOx concentration at the total discharge outlet is compared with the actual NOx concentration at the total discharge outlet to obtain the model error. The model error is then fed back to the process variable input of the main controller for compensation, thereby obtaining the target setpoint for urea flow rate.

[0011] In some embodiments, the method further includes: Using the urea flow rate change in historical DCS data as input and the corresponding NOx concentration change response at the total discharge outlet as output, the model parameters of the Smith predictor are obtained by fitting the least squares method or correlation analysis method. The model parameters, including gain, time constant, and pure time delay, are updated online in the Smith predictor.

[0012] Secondly, embodiments of this application provide an SCR denitrification control system for a thermal power plant, the system comprising: The feedforward calculation module is used to collect unit operation data, calculate the theoretical urea demand and feedforward parameters based on the unit operation data, and determine the feedforward control output according to the theoretical urea demand and the feedforward parameters. The unit operation data includes flow parameters, concentration parameters and status parameters. The basic value determination module is used to analyze the unit's operating data to obtain the main controller output, and determine the basic set value of urea flow rate based on the feedforward control output and the main controller output. The target value determination module is used to perform hysteresis compensation on the basic urea flow rate setpoint through the Smith predictor connected to the main controller to obtain the target urea flow rate setpoint. The control module is used to take the urea flow rate setpoint as the setpoint of the secondary controller and the actual urea solution flow rate as its process variable to obtain the output signal of the secondary controller, and control the SCR denitrification urea injection based on the output signal of the secondary controller.

[0013] Thirdly, embodiments of this application provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the SCR denitrification control method for thermal power plants as described in the first aspect above.

[0014] Fourthly, embodiments of this application provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the SCR denitrification control method for thermal power plants as described in the first aspect above.

[0015] Compared to related technologies, the SCR denitrification control method for thermal power plants provided in this application determines the basic setpoint of urea flow rate through the coordinated output of feedforward control and the output of the main controller. It takes into account both the foresight of operating condition prediction and the feedback of real-time analysis of unit operating data. The two complement each other to eliminate the limitations of a single control mode, making the basic setpoint of urea flow rate more in line with the actual needs of the denitrification system and improving the accuracy of ammonia injection control.

[0016] The Smith predictor, externally connected to the main controller, is used to compensate for the lag in the basic setpoint of urea flow. The external design does not require modification of the original logic of the main controller, has strong compatibility, and the Smith predictor can accurately predict the output of the lag element and compensate and correct the setpoint in advance, effectively eliminating the control deviation caused by lag, avoiding NOx emission fluctuations and over / under-adjustment of ammonia injection caused by lag, and solving the problem of large lag in SCR denitrification control. Attached Figure Description

[0017] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart of a thermal power plant SCR denitrification control method according to an embodiment of this application; Figure 2This is a schematic diagram of a SCR denitrification control method for a thermal power plant according to an embodiment of this application; Figure 3 This is a structural block diagram of a thermal power plant SCR denitrification control system according to an embodiment of this application; Figure 4 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0019] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0020] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0021] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0022] This embodiment provides a method for controlling SCR denitrification in thermal power plants. Figure 1 This is a flowchart of a thermal power plant SCR denitrification control method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps: Step S101: Collect unit operation data, calculate the theoretical urea demand and feedforward parameters based on the unit operation data, and determine the feedforward control output based on the theoretical urea demand and feedforward parameters. The unit operation data includes flow parameters, concentration parameters and status parameters.

[0023] Flow parameters include, but are not limited to, total coal feed (t / h), total boiler air volume (Nm³ / h), furnace oxygen content (%), and urea solution flow rate (m³ / h); concentration parameters include, but are not limited to, SCR inlet NOx concentration (mg / Nm³), SCR outlet actual NOx concentration (mg / Nm³), and total post-desulfurization discharge actual NOx concentration (mg / Nm³); status parameters include, but are not limited to, unit load (MW) and regulating valve opening (%). Optionally, the sampling interval is 1 second, and the data is stored in a historical database via the DCS interface.

[0024] In some embodiments, the flow parameters include total coal feed, total boiler air volume, and furnace oxygen content; the concentration parameters include SCR inlet NOx concentration; the status parameters include unit load; and the feedforward parameters include load feedforward compensation and coal feedforward compensation. In step S101, the theoretical urea demand and feedforward parameters are calculated based on the unit operating data. The feedforward control output is determined based on the theoretical urea demand and feedforward parameters, including: Step S1011: Calculate the theoretical urea requirement based on the total boiler air volume, furnace oxygen content, and SCR inlet NOx concentration.

[0025] In the multi-feedforward calculation stage, the theoretical urea demand is calculated as an open-loop compensation.

[0026] In some embodiments, step S1011 specifically includes: Step S201: Determine the flue gas volume based on the total boiler air volume, furnace oxygen content, and excess air coefficient.

[0027] Step S202: Based on the flue gas volume, SCR inlet NOx concentration, and dynamic molar ratio, the theoretical urea demand is obtained. The dynamic molar ratio is updated using an adaptive correction mechanism that is based on online fitting of historical unit operating data and / or current unit operating data.

[0028] Flue gas volume = total boiler air volume × (21% / (21% - furnace oxygen content %)) × excess air coefficient.

[0029] Preferably, the excess air coefficient is set in the range of 1.1-1.2.

[0030] Theoretical requirement = SCR inlet NOx concentration × flue gas volume (10,000 Nm³ / h) × dynamic molar ratio.

[0031] Among them, the dynamic molar ratio dynamic adaptive correction specifically adopts an online adaptive correction mechanism, which automatically fits the correction curve with historical / current data (e.g., ammonia slip, NOx deviation, load) (molar ratio data-driven mainly adopts least squares online fitting + step to achieve multivariate fitting and adaptation) to solve the impact of coal quality / catalyst aging; at the same time, the integration with Smith prediction / dual feedback realizes the updating of Smith model parameters to achieve adaptive predictive control.

[0032] Step S1012: Determine the load feedforward compensation based on the unit load, the reference load, and the load factor.

[0033] Load feedforward compensation = (current unit load - reference load) × load factor (m³ / h / MW).

[0034] Step S1013: Determine the feedforward compensation for coal feeding based on the total coal feed, the benchmark coal feed, and the coal feeding coefficient.

[0035] Coal feedforward compensation = (current total coal feed - benchmark coal feed) × coal feed coefficient (m³ / h / t / h).

[0036] Step S1014: Add the theoretical urea demand, load feedforward compensation, and coal feedforward compensation to obtain the feedforward control output.

[0037] Feedforward control output = theoretical demand + load feedforward compensation + coal feedforward compensation.

[0038] It should be noted that the reference load and reference coal feed rate are automatically selected based on historical stable operating conditions (e.g., the average value under a rated load of 300MW). The load feedforward compensation and coal feedforward compensation are quantitatively compensated for the unit load and total coal feed rate, respectively, to achieve targeted correction for disturbances under different operating conditions.

[0039] Step S102: Analyze the unit operation data to obtain the main controller output, and determine the basic set value of urea flow rate based on the feedforward control output and the main controller output.

[0040] In some embodiments, the concentration parameters include the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet; the main controller output obtained by analyzing the unit operating data in step S102 includes: Step S1021: Based on the actual NOx concentration at the main discharge outlet and the set NOx concentration, obtain the feedback correction amount from the main controller.

[0041] The actual NOx concentration at the total discharge outlet is used as the process variable (PV) of the main controller (PID). The NOx concentration setpoint is input by the operator (e.g., 5 mg / Nm³). The main PID calculates the deviation (actual NOx concentration at the total discharge outlet - NOx concentration setpoint) and outputs the correction amount as feedback to the main controller.

[0042] Step S1022: Calculate the SCR outlet rapid compensation based on the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet.

[0043] In some embodiments, step S1022 specifically includes: Step S301: Obtain the average value of the difference between the actual NOx concentration at the SCR outlet and the actual NOx concentration at the corresponding total outlet within a historical preset time window. Based on the average value and the preset filter coefficient, obtain the dynamic deviation bias value.

[0044] The dynamic deviation bias value = avg(actual NOx concentration at SCR outlet - actual NOx concentration at total outlet) × filter coefficient. Preferably, the historical preset time window is within 60 minutes before the current time, and the filter coefficient is selected in the range of 0.5-0.8.

[0045] Step S302: Based on the total NOx concentration setpoint, fixed bias value, and dynamic deviation bias value at the total discharge outlet, determine the expected value of the NOx concentration at the SCR outlet.

[0046] The expected value of NOx concentration at the SCR outlet = the set value of NOx concentration at the total outlet + the fixed bias value + the dynamic deviation bias value.

[0047] The fixed bias value (preferably 1-2 mg / Nm³) serves to prevent exceeding environmental standards. This formula is used to calculate the expected NOx concentration at the SCR outlet, thus avoiding measurement errors caused by uneven NOx distribution in the flue gas duct.

[0048] Step S303: Subtract the expected value of the NOx concentration at the SCR outlet from the actual NOx concentration at the SCR outlet to obtain the NOx correction value at the SCR outlet. Based on the NOx correction value at the SCR outlet and the compensation coefficient, determine the rapid compensation at the SCR outlet.

[0049] SCR outlet NOx correction value = Actual SCR outlet NOx concentration - Expected SCR outlet NOx concentration.

[0050] SCR Export Rapid Compensation = SCR Export NO X Correction value × compensation coefficient (calculated on-site).

[0051] The compensation factor is the SCR outlet NO X There are several methods to convert the correction value into urea flow rate: Engineering tuning method, where commissioning personnel adjust this parameter appropriately based on the response sensitivity of the main discharge port and SCR outlet; Static gain matching method, the most direct on-site calculation method, where the compensation coefficient is essentially a proportional gain used to convert the NOx concentration deviation (mg / Nm) into a correction amount for urea flow rate (m^3 / h); Dynamic identification and online fitting (least squares method), where the system utilizes historical big data stored in the DCS to analyze changes in urea flow rate commands and the corresponding NOx response at the SCR outlet. The static gain K of the process is automatically fitted using an algorithm, and the compensation coefficient is often the reciprocal of this gain (or its associated proportionality coefficient), and is updated online based on operating conditions such as catalyst activity aging and coal quality changes.

[0052] Step S1023: The SCR output fast compensation is superimposed with the main controller feedback correction to obtain the main controller output.

[0053] Introducing a dynamic deviation bias value, based on the SCR outlet and total discharge NO within a historical preset time window. x The average concentration difference, combined with the filter coefficient, can accurately capture NO in the flue. x Long-term systematic measurement bias caused by uneven concentration distribution and insufficient flue gas mixing.

[0054] Dynamic bias and fixed bias work synergistically. The former dynamically corrects systematic errors caused by uneven flue gas distribution, while the latter remains constant regardless of operating conditions and is specifically designed to offset random measurement errors at measuring points. Together, they achieve comprehensive elimination of all types of measurement deviations between the SCR outlet and the main exhaust outlet, resolving NOx issues caused by differences in measuring point locations and uneven flue gas distribution. x Inaccurate concentration measurement issues caused SCR outlet NO to increase. x The expected concentration values ​​are more in line with the actual operating conditions of the total discharge outlets in environmental protection assessments.

[0055] Step S103: The urea flow rate target setpoint is obtained by performing hysteresis compensation on the basic setpoint of urea flow rate through the Smith predictor connected to the main controller.

[0056] In some embodiments, the concentration parameter includes the actual NOx concentration at the total emission outlet; step S103 includes: Step S1031: Input the basic set value of urea flow rate into the Smith predictor to obtain the predicted value of NOx concentration at the total discharge outlet.

[0057] Step S1032: Compare the predicted NOx concentration at the total discharge outlet with the actual NOx concentration at the total discharge outlet to obtain the model error, and feed the model error back to the process variable input of the main controller for compensation to obtain the urea flow target setpoint.

[0058] The Smith predictor compensation stage addresses the large time lag problem in the system through the FSMITH module (a standard or custom functional module implementing the Smith predictor control algorithm). Internally, the FSMITH module constructs a dynamic model representing the controlled process, consisting of a first / second-order inertial element (gain K≈-0.4, time constant τ≈4-6 min) and a pure time lag element (θ≈10-12 min) connected in series. Its working principle is as follows: the urea flow command output by the main PID controller is simultaneously fed into the actual physical process and the FSMITH model. The inertial element in the FSMITH model immediately calculates the time-free output, i.e., the instantaneous change in NOx at the total discharge outlet when transmission delay is ignored; simultaneously, the model, after superimposing the pure time lag, generates a predicted value with time lag, which is compared with the actual measured value received in the future. When the actual process variable (PV, i.e., the measured value of NOx at the total discharge outlet) arrives, the module calculates the model error (actual PV - predicted value with time lag). The key compensation step involves adding this real-time error value to the previously calculated hysteresis-free output to obtain the compensated PV, which is then immediately sent back to the main PID controller as its process variable input. This allows the main PID controller to seemingly anticipate the effect of its actions 10-12 minutes later, thus operating in a virtual hysteresis-free system and fundamentally overcoming the control oscillation and overshoot problems caused by large hysteresis.

[0059] In some embodiments, the method further includes: Using the urea flow rate change in historical DCS data as input and the corresponding NOx concentration change response at the total discharge outlet as output, the model parameters of the Smith predictor are obtained by fitting the least squares method or correlation analysis method. The model parameters, including gain, time constant, and pure time delay, are updated online to the Smith predictor.

[0060] The online adaptive identification phase is the core self-learning stage for the system to achieve long-term precise control. This phase is initiated periodically (e.g., hourly) or automatically when the load changes significantly. It utilizes historical production data stored in the DCS to analyze the dynamic relationship between changes in urea flow rate and the NOx concentration response at the main discharge outlet. Through mathematical methods such as least squares or correlation analysis, key model parameters characterizing the controlled object's properties are automatically fitted from the actual operating data: including the inertial gain (K), time constant (τ), and pure time delay (θ) required by the Smith predictor. After identification, the system updates these online parameters, which better reflect the current operating conditions, in real time to the dynamic model of the FSMITH module and coefficients such as the dynamic molar ratio in the feedforward calculation. This process enables the control system to automatically track and adapt to drifts in object characteristics caused by factors such as coal quality changes and catalyst aging, ensuring that the predictive model always matches the actual process, thereby maintaining the efficiency and robustness of the control strategy.

[0061] Step S104: Use the urea flow rate setpoint as the setpoint of the secondary controller and the actual urea solution flow rate as its process variable to obtain the output signal of the secondary controller, and control the SCR denitrification urea injection based on the output signal of the secondary controller.

[0062] The secondary loop control and execution stage is the key link in translating control commands into precise physical actions. The secondary controller receives the urea flow setpoint from the primary loop and compensates for it with Smith. Using the real-time collected actual urea solution flow rate as its process variable (PV), it quickly calculates the control output through PID calculation. This output directly corresponds to the opening command of the regulating valve or the frequency conversion command of the urea pump, thus forming an inner-loop fast flow tracking loop to ensure that the actual flow rate accurately and without lag follows the changes in the setpoint.

[0063] Subsequently, the frequency conversion command is sent to the field hardware actuator (regulating valve or frequency conversion pump). Before execution, it will pass through the amplitude limiting protection to prevent the equipment from exceeding the limit. At the same time, the system continuously monitors the ammonia escape concentration. If it exceeds the threshold, it will automatically bias the dynamic molar ratio in the feedforward calculation to achieve safety constraints.

[0064] The entire process relies on the coordinated operation of hardware. Each NOx analyzer provides real-time concentration data, while the DCS (Distributed Control System) acts as the core processing unit, running all control logic and generating instructions, which are ultimately executed by the urea pump or regulating valve. The entire control software is implemented on the DCS platform and includes manual / automatic switching functionality, ensuring system safety and operational flexibility. Figure 2 This is a schematic diagram of a SCR denitrification control method for a thermal power plant according to an embodiment of this application.

[0065] Through the above steps, the feedforward control output and the main controller output work together to determine the basic setpoint of urea flow rate. This takes into account both the foresight of operating condition prediction and the feedback of real-time analysis of unit operating data. The two complement each other to eliminate the limitations of a single control mode, making the basic setpoint of urea flow rate more in line with the actual needs of the denitrification system and improving the accuracy of ammonia injection control.

[0066] An external Smith predictor, integrated with the main controller, is used to compensate for lag in the urea flow rate setpoint. This external design requires no modification to the main controller's original logic, offering strong compatibility. Furthermore, the Smith predictor accurately predicts the output of the lag component, proactively compensating for and correcting the setpoint, effectively eliminating control deviations caused by lag and preventing NO emissions due to lag. x Emission fluctuations and over / under-adjustment of ammonia injection have resolved the issue of significant lag in SCR denitrification control.

[0067] The main PID+SCR outlet rapid compensation at the main discharge port not only solves the problem of excessive lag at the main discharge port, but also makes advance adjustments by using the difference between the actual and expected SCR outlet values. This deeply decouples the final performance indicators at the far end from the dynamic response at the near end, while also compensating for each other.

[0068] This embodiment uses theoretical demand / load / coal feeding baseline deviation compensation instead of offline fixed value compensation. It compensates based on the increment of stable operating conditions. This method can automatically absorb system deviations in different load segments and has stronger robustness than fixed function feedforward.

[0069] In traditional methods, the Smith parameter is fixed. However, the catalyst activity of the denitrification system decreases over time, and changes in coal quality lead to drastic changes in flue gas composition. This embodiment solves the nonlinear control problem of the denitrification system throughout its entire life cycle by fitting K (gain), τ (time constant), and θ (pure time delay) in real time from big data production data using the least squares method.

[0070] By combining automatic molar ratio correction with factual data, data-driven parameter optimization is achieved. Combined with the Smith model, model parameters are updated in real time, providing more precise control. In traditional methods, the molar ratio is fixed, and the theoretically calculated urea flow rate ratio is also fixed. In this embodiment, the molar ratio is no longer a constant value but is automatically adjusted according to catalyst aging using the least squares method. This is key to solving urea waste and ammonia slip. The adaptive Smith parameter big data identification and calculation and the automatic molar ratio correction are interconnected. Once the Smith parameters are identified, they also affect the automatic calculation of the molar ratio, demonstrating the interconnectedness and real-time adaptability of the data.

[0071] It should be noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0072] This embodiment also provides an SCR denitrification control system for a thermal power plant. This system is used to implement the above embodiments and preferred embodiments, and details already described will not be repeated. As used below, the terms "module," "unit," "subunit," etc., can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0073] Figure 3 This is a structural block diagram of the SCR denitrification control system for a thermal power plant according to an embodiment of this application, such as... Figure 3 As shown, the system includes: The feedforward calculation module 31 is used to collect unit operation data, calculate the theoretical urea demand and feedforward parameters based on the unit operation data, and determine the feedforward control output based on the theoretical urea demand and feedforward parameters. The unit operation data includes flow parameters, concentration parameters and status parameters.

[0074] The basic value determination module 32 is used to analyze the unit operation data to obtain the output of the main controller, and determine the basic set value of urea flow rate based on the feedforward control output and the main controller output.

[0075] The target value determination module 33 is used to perform lag compensation on the basic setpoint of urea flow rate through the Smith predictor external to the main controller to obtain the target setpoint of urea flow rate.

[0076] The control module 34 is used to take the urea flow rate setpoint as the setpoint of the secondary controller and the actual urea solution flow rate as its process variable to obtain the output signal of the secondary controller, and control the SCR denitrification urea injection based on the output signal of the secondary controller.

[0077] In some embodiments, flow parameters include total coal feed, total boiler air volume, and furnace oxygen content; concentration parameters include SCR inlet NOx concentration; state parameters include unit load; and feedforward parameters include load feedforward compensation and coal feedforward compensation. The feedforward calculation module 31 includes: The theoretical demand calculation module is used to calculate the theoretical urea demand based on the total boiler air volume, furnace oxygen content, and SCR inlet NOx concentration.

[0078] The first compensation determination module is used to determine the load feedforward compensation based on the unit load, the reference load, and the load factor.

[0079] The second compensation determination module is used to determine the feedforward compensation based on the total coal feed, the benchmark coal feed, and the coal feed coefficient.

[0080] The feedforward control output module is used to add the theoretical urea demand, load feedforward compensation, and coal feedforward compensation to obtain the feedforward control output.

[0081] In some embodiments, the theoretical requirement calculation module includes: The flue gas volume calculation module is used to determine the flue gas volume based on the total boiler air volume, furnace oxygen content, and excess air coefficient.

[0082] The demand determination module is used to obtain the theoretical urea demand based on flue gas volume, SCR inlet NOx concentration and dynamic molar ratio. The dynamic molar ratio is updated using an adaptive correction mechanism based on online fitting of historical unit operating data and / or current unit operating data.

[0083] In some embodiments, the concentration parameters include the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet; the basic value determination module 32 includes: The correction amount determination module is used to obtain the correction amount fed back by the main controller based on the actual NOx concentration at the total discharge outlet and the set NOx concentration.

[0084] The compensation calculation module is used to calculate the rapid compensation at the SCR outlet based on the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet.

[0085] The output quantity determination module is used to superimpose the SCR output fast compensation with the main controller feedback correction to obtain the main controller output quantity.

[0086] In some embodiments, the compensation calculation module includes: The deviation offset calculation module is used to obtain the average value of the difference between the actual NOx concentration at the SCR outlet and the actual NOx concentration at the corresponding total outlet within a historical preset time window. Based on the average value and the preset filtering coefficient, the dynamic deviation offset value is obtained.

[0087] The first expected value determination module is used to determine the expected value of the NOx concentration at the SCR outlet based on the total NOx concentration setpoint, fixed bias value, and dynamic deviation bias value.

[0088] The deviation compensation calculation module is used to subtract the expected value of the NOx concentration at the SCR outlet from the actual NOx concentration at the SCR outlet to obtain the NOx correction value at the SCR outlet. Based on the NOx correction value at the SCR outlet and the compensation coefficient, the rapid compensation at the SCR outlet is determined.

[0089] In some embodiments, the concentration parameter includes the actual NOx concentration at the total discharge outlet; the target value determination module 33 includes: The second expected value determination module is used to input the basic set value of urea flow rate into the Smith predictor to obtain the predicted value of NOx concentration at the total discharge outlet.

[0090] The error compensation module is used to compare the predicted NOx concentration at the total discharge outlet with the actual NOx concentration at the total discharge outlet, obtain the model error, and feed the model error back to the process variable input of the main controller for compensation, so as to obtain the urea flow target setpoint.

[0091] In some embodiments, the system further includes: The model parameter determination module uses the urea flow rate change in historical DCS data as input and the corresponding NOx concentration change response at the total discharge outlet as output. It then uses the least squares method or correlation analysis to fit and obtain the model parameters of the Smith predictor.

[0092] The model parameter update module is used to update the model parameters to the Smith predictor online. The model parameters include gain, time constant, and pure time delay.

[0093] Through the above system, the feedforward control output and the main controller output work together to determine the basic setpoint of urea flow rate, taking into account both the foresight of operating condition prediction and the feedback of real-time analysis of unit operating data. The two complement each other to eliminate the limitations of a single control mode, making the basic setpoint of urea flow rate more in line with the actual needs of the denitrification system and improving the accuracy of ammonia injection control.

[0094] An external Smith predictor, integrated with the main controller, is used to compensate for lag in the urea flow rate setpoint. This external design requires no modification to the main controller's original logic, offering strong compatibility. Furthermore, the Smith predictor accurately predicts the output of the lag component, proactively compensating for and correcting the setpoint, effectively eliminating control deviations caused by lag and preventing NO emissions due to lag. x Emission fluctuations and over / under-adjustment of ammonia injection have resolved the issue of significant lag in SCR denitrification control.

[0095] It should be noted that the above modules can be functional modules or program modules, and can be implemented through software or hardware. For modules implemented through hardware, the above modules can reside in the same processor; or the above modules can be located in different processors in any combination.

[0096] This embodiment also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0097] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0098] Optionally, in this embodiment, the processor can be configured to perform the following steps via a computer program: S1 collects unit operating data, calculates the theoretical urea demand and feedforward parameters based on the unit operating data, and determines the feedforward control output based on the theoretical urea demand and feedforward parameters. The unit operating data includes flow parameters, concentration parameters, and status parameters.

[0099] S2 analyzes the unit's operating data to obtain the main controller's output. Based on the feedforward control output and the main controller's output, the basic setpoint for urea flow is determined.

[0100] S3 uses the Smith predictor, which is external to the main controller, to perform lag compensation on the basic setpoint of urea flow rate and obtain the target setpoint of urea flow rate.

[0101] S4, the urea flow rate setpoint is used as the setpoint of the secondary controller, and the actual urea solution flow rate is used as its process variable to obtain the output signal of the secondary controller. Based on the output signal of the secondary controller, the SCR denitrification urea injection is controlled.

[0102] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0103] In one embodiment, Figure 4 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 4 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 4As shown, this electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a SCR denitrification control method for a thermal power plant.

[0104] Those skilled in the art will understand that Figure 4 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0105] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0106] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0107] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for controlling SCR denitration in a thermal power plant, characterized in that, The method includes: Collect unit operation data, calculate theoretical urea demand and feedforward parameters based on the unit operation data, and determine feedforward control output based on the theoretical urea demand and the feedforward parameters. The unit operation data includes flow parameters, concentration parameters and status parameters. The main controller output is obtained by analyzing the unit's operating data. Based on the feedforward control output and the main controller output, the basic setpoint for urea flow rate is determined. By using the Smith predictor attached to the main controller, the basic urea flow rate setpoint is lag-compensated to obtain the target urea flow rate setpoint. The urea flow rate setpoint is used as the setpoint of the secondary controller, and the actual urea solution flow rate is used as its process variable to obtain the output signal of the secondary controller. Based on the output signal of the secondary controller, the SCR denitrification urea injection is controlled.

2. The method of claim 1, wherein, The flow parameters include total coal feed, total boiler air volume and furnace oxygen content; the concentration parameters include SCR inlet NOx concentration; the status parameters include unit load; and the feedforward parameters include load feedforward compensation and coal feedforward compensation. The calculation of theoretical urea demand and feedforward parameters based on the unit operating data, and the determination of feedforward control output based on the theoretical urea demand and the feedforward parameters, include: The theoretical urea requirement is calculated based on the total boiler air volume, the furnace oxygen content, and the SCR inlet NOx concentration. The load feedforward compensation is determined based on the unit load, the baseline load, and the load factor. The feedforward compensation is determined based on the total coal feed, the baseline coal feed, and the coal feed coefficient. The theoretical urea demand, the load feedforward compensation, and the coal feedforward compensation are added together to obtain the feedforward control output.

3. The method of claim 2, wherein, The calculation of the theoretical urea requirement based on the total boiler air volume, the furnace oxygen content, and the SCR inlet NOx concentration includes: The flue gas volume is determined based on the total boiler air volume, the furnace oxygen content, and the excess air coefficient. Based on the flue gas volume, the SCR inlet NOx concentration, and the dynamic molar ratio, the theoretical urea demand is obtained, wherein the dynamic molar ratio is updated using an adaptive correction mechanism based on online fitting of historical unit operating data and / or current unit operating data.

4. The method of claim 1, wherein, The concentration parameters include the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet; the main controller output obtained by analyzing the unit's operating data includes: The main controller feedback correction amount is obtained based on the actual NOx concentration at the total discharge outlet and the set NOx concentration. Calculate the SCR outlet rapid compensation based on the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet; The output of the main controller is obtained by superimposing the fast compensation of the SCR output with the feedback correction of the main controller.

5. The method of claim 4, wherein, The calculation of SCR outlet rapid compensation based on the actual NOx concentration at the total discharge outlet and the actual NOx concentration at the SCR outlet includes: The average value of the difference between the actual NOx concentration at the SCR outlet and the actual NOx concentration at the corresponding total outlet is obtained within a historical preset time window. Based on the average value and the preset filtering coefficient, the dynamic deviation bias value is obtained. Based on the total NOx concentration setpoint, fixed bias value, and dynamic deviation bias value, the expected value of SCR outlet NOx concentration is determined; The SCR outlet NOx correction value is obtained by subtracting the expected value of the SCR outlet NOx concentration from the actual NOx concentration at the SCR outlet. Based on the SCR outlet NOx correction value and the compensation coefficient, the SCR outlet rapid compensation is determined.

6. The method of claim 1, wherein, The concentration parameters include the actual NOx concentration at the total discharge outlet; the urea flow target setpoint obtained by performing hysteresis compensation on the basic urea flow setpoint using a Smith predictor external to the main controller includes: Input the basic setpoint of urea flow rate into the Smith predictor to obtain the predicted value of NOx concentration at the total discharge outlet. The predicted NOx concentration at the total discharge outlet is compared with the actual NOx concentration at the total discharge outlet to obtain the model error. The model error is then fed back to the process variable input of the main controller for compensation, thereby obtaining the target setpoint for urea flow rate.

7. The method according to claim 6, characterized in that, The method further includes: Using the urea flow rate change in historical DCS data as input and the corresponding NOx concentration change response at the total discharge outlet as output, the model parameters of the Smith predictor are obtained by fitting the least squares method or correlation analysis method. The model parameters, including gain, time constant, and pure time delay, are updated online in the Smith predictor.

8. A SCR denitrification control system for a thermal power plant, characterized in that, The system includes: The feedforward calculation module is used to collect unit operation data, calculate the theoretical urea demand and feedforward parameters based on the unit operation data, and determine the feedforward control output according to the theoretical urea demand and the feedforward parameters. The unit operation data includes flow parameters, concentration parameters and status parameters. The basic value determination module is used to analyze the unit's operating data to obtain the main controller output, and determine the basic set value of urea flow rate based on the feedforward control output and the main controller output. The target value determination module is used to perform hysteresis compensation on the basic urea flow rate setpoint through the Smith predictor connected to the main controller to obtain the target urea flow rate setpoint. The control module is used to take the urea flow rate setpoint as the setpoint of the secondary controller and the actual urea solution flow rate as its process variable to obtain the output signal of the secondary controller, and control the SCR denitrification urea injection based on the output signal of the secondary controller.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the SCR denitrification control method for thermal power plants as described in any one of claims 1 to 7.

10. A storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the SCR denitrification control method for thermal power plants as described in any one of claims 1 to 7.

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