Ultrasonic total cross-section SCR flue resistance dynamic monitoring and early warning method and device

Abstract

The invention belongs to the technical field of flue gas treatment online monitoring, and relates to an SCR inlet flue resistance dynamic monitoring and grading early warning method and device. The core of the method is that the total cross-section flow is measured by adopting an ultrasonic multi-channel time difference method, a static pressure measuring point is arranged at a stable section of a flow field, and an equivalent resistance characterization quantity is calculated as a core monitoring parameter based on the measured flue pressure drop delta P and flue gas volume flow Q, so that the influence of flow change is eliminated; then, on the basis of a historical R value sequence in the sliding time window, a dynamic baseline Rbase is constructed and updated online; and finally, by calculating the relative deviation between the current R value and the dynamic baseline, i.e., an abnormal index epsilon and a change rate g, graded early warning is realized. According to the method, the technical problems that traditional differential pressure monitoring representativeness is insufficient, blockage drifting is prone to occurring, and false alarm and missing alarm of a fixed threshold value exist in a traditional method are effectively solved, the accuracy and reliability of early warning are remarkably improved, and technical support is provided for predictive maintenance of an SCR system.

Description

Technical Field

[0001] This invention belongs to the field of online monitoring technology for thermal energy and power engineering and flue gas treatment of coal-fired units, and relates to a method and device for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance. Background Technology

[0002] With increasingly stringent environmental protection requirements, SCR (Selective Catalytic Reduction) denitrification systems have become core equipment for flue gas purification in coal-fired power units. The resistance of the SCR inlet flue is a key parameter reflecting the system's operating status; abnormal changes in this resistance can lead to a surge in induced draft fan energy consumption and, in severe cases, even trigger unit tripping, posing a direct threat to the safe and stable operation of the unit. Currently, SCR inlet flue resistance monitoring mainly employs single-point differential pressure measurement technology. This method calculates the overall cross-sectional resistance by measuring the local pressure difference using a differential pressure transmitter installed at a specific location in the flue. However, due to the extremely uneven flow field distribution in the SCR inlet flue and the presence of harsh conditions such as high temperature, high dust, and ash accumulation, single-point measurement data cannot accurately reflect the average flow field state across the entire cross-section, resulting in large resistance calculation errors and a high false alarm rate. Furthermore, existing technologies suffer from serious environmental adaptability issues. Under harsh conditions of high temperature and high dust, the pressure tapping pipeline is prone to blockage, leading to signal distortion or complete failure; simultaneously, the differential pressure transmitter is susceptible to signal drift during long-term operation, resulting in a decline in the long-term reliability of the monitoring data and making it difficult to meet the requirements for long-term online operation. Furthermore, existing alarm technologies typically use fixed thresholds for alarm judgment, which cannot overcome the significant impact of flow fluctuations caused by changes in unit load on pressure drop signals, leading to frequent false alarms and missed alarms. More importantly, they lack normalized characterization of resistance characteristics and dynamic baseline tracking capabilities, making it difficult to distinguish between normal operating condition fluctuations and actual resistance deterioration, thus failing to achieve early warning and tiered response.

[0003] Therefore, existing technologies cannot achieve the system engineering problem of accurately sensing and intelligently warning the resistance of the SCR inlet flue in complex environments with high temperature, high dust, and variable operating conditions. Summary of the Invention

[0004] The purpose of this invention is to provide a method and device for dynamic monitoring and early warning of SCR flue resistance across the entire cross section using ultrasonic technology, in order to solve the problems of inaccurate sensing and unintelligent early warning of SCR inlet flue resistance caused by unrepresentative monitoring, unreliable measurement, and rigid early warning logic in existing technologies.

[0005] To achieve the above objectives, the present invention provides a specific technical solution for a dynamic monitoring and early warning method for ultrasonic full-section SCR flue resistance, as follows: S1. Select a measurement interval in the straight section of the SCR inlet flue, and set up upstream and downstream static pressure measuring points within the measurement interval to measure and obtain the upstream static pressure. Pup With downstream static pressure P down The selection of the measurement interval satisfies the condition of flow field stability. S2. The ultrasonic measurement section within the measurement interval is measured using the ultrasonic multi-channel time-of-flight method to obtain the flue gas volumetric flow rate flowing through the measurement interval. Q ; S3, Based on the upstream static pressure P up With the downstream static pressure P down The difference is used to determine the pressure drop Δ between the measuring points. P ; S4, based on the voltage drop Δ P With the volumetric flow rate Q， Calculate the equivalent resistance characterization at the current moment. R =Δ P / Q ²; S5. Within the sliding time window, based on the equivalent resistance characterization quantity R Historical data sequences are used to calculate dynamic baselines. R base And update online; The sliding time window refers to a data interval consisting of N historical moments traced back from the current moment, where N is a positive integer greater than 1. S6. Equivalent resistance characterization based on the current moment R and the dynamic baseline R base Calculate abnormal indicators e and rate of change g ,in, e =( R - R base ) / R base , ; S7. The abnormal indicators e Each with a preset first threshold e 1 and second threshold e 2. Compare and change rates g With the rate of change threshold g th Comparison; among which e 2> e 1; S8. Based on the comparison results, output a warning signal according to the following rules; If the second warning condition is met, a level-two warning signal will be output. If the second warning condition is not met but the first warning condition is met, then a first-level warning signal will be output.

[0006] Preferably, the ultrasonic multichannel time difference method adopts a 4-channel structure, which consists of ultrasonic transducers installed on opposite sidewalls of the flue and arranged in two layers along the cross-sectional height direction. The two channels in the upper layer form a cross X structure, and the two channels in the lower layer form a cross X structure, thus forming a symmetrical multipath measurement structure of upper X + lower X.

[0007] Preferably, the ultrasonic full-section SCR flue resistance dynamic monitoring and early warning method further includes: The ultrasonic measurement section within the measurement interval was measured using the ultrasonic multi-channel time-of-flight method to obtain the average velocity of the flue gas section. ; Based on the average velocity of the flue gas cross section and smoke density Calculate the drag coefficient ; Among them, smoke density It is calculated using a simplified form of the ideal gas law, based on real-time measured flue gas temperature T and known standard density of state.

[0008] Preferably, the equivalent resistance characterization quantity is... R Historical data sequences are used to calculate dynamic baselines. R base And online updates include: Based on a pre-defined cleanliness state characterization strategy, the historical resistance characterization quantity within the sliding time window is... R The process generates baseline candidate values ​​representing low resistance levels; the cleanliness state characterization strategy is low quantile statistics or lower envelope extraction. The candidate baseline values ​​are fused into the historical baseline using an exponential smoothing or recursive filtering algorithm to output the updated dynamic baseline. R base .

[0009] Preferably, the step of outputting a warning signal according to the comparison result and the following rules specifically includes: Level 2 warning conditions: A level 2 warning signal is output when one of the following conditions is met, and the second debouncing condition is also met: A:e ≥ e 2; B:e ≥ e 1 and g ≥ g th ; Level 1 warning conditions: When the conditions for Level 2 warning are not met, but the conditions are met. e ≥ e 1. When the first debouncing condition is met, a first-level warning signal is output; The first debouncing condition is: within a preset duration window. T Within 1, abnormal state e ≥ e The number of times 1 appears reaches the preset consecutive number of times. N 1; The second debouncing condition is: within a preset duration window. T Within 2 days, the number of times an abnormal state meeting one of the following conditions occurs reaches a preset consecutive number. N 2: A:e ≥ e 2; B:e ≥ e 1 and g ≥ g th .

[0010] Preferably, it further includes: when a secondary warning signal is output, generating and outputting a soot blowing linkage signal; the linkage signal is used to control the start of the soot blowing equipment; The output of the soot blowing linkage signal must satisfy one or more interlocking conditions, including: a: The time interval between adjacent soot blowing actions performed according to the soot blowing linkage signal reaches the preset minimum soot blowing interval time; b: The soot blowing system is in a ready-to-blow state; c: The number of blowing actions per unit time has not reached the upper limit; d: The aforementioned abnormal indicators e The alert level has fallen back to the threshold for lifting the alert since it was triggered at Level 2. e 1-δ and below for a set stabilization time , where δ is a hysteresis parameter that is greater than zero.

[0011] This invention provides an ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device, the specific technical solution of which is as follows: Data acquisition module: Used to select a measurement interval in the straight section of the SCR inlet flue, and set up upstream and downstream static pressure measuring points within the measurement interval to measure and obtain the upstream static pressure. P up With downstream static pressure P down ; Ultrasonic flow measurement module: used to measure the ultrasonic measurement section within the measurement interval using the ultrasonic multi-channel time-of-flight method, to obtain the volumetric flow rate of flue gas flowing through the measurement interval. Q ; First data processing module: used for processing data based on the upstream static pressure. P up With the downstream static pressure P down The difference is used to determine the pressure drop Δ between the measuring points. P ; It is also used based on the voltage drop Δ P With the volumetric flow rate Q， Calculate the equivalent resistance characterization at the current moment. R =Δ P / Q ²; Resistance modeling module: used to model resistance based on equivalent resistance characteristics within a sliding time window. R Historical data sequences are used to calculate dynamic baselines. R base And update online; Second data processing module: used for the equivalent resistance characterization quantity at the current moment. R and the dynamic baseline R base Calculate abnormal indicators e and rate of change g ,in, e =( R - R base ) / R base ; ; Third data processing module: used to process the abnormal indicators e Each with a preset first threshold e 1 and second threshold e 2. Compare and change rates g With the rate of change threshold g th Comparison; among which e 2> e 1; The graded early warning determination module is used to output early warning signals according to the following rules based on the comparison results; If the second warning condition is met, a level-two warning signal will be output. If the second warning condition is not met but the first warning condition is met, then a first-level warning signal will be output.

[0012] Preferably, the device further includes: a linkage module: The linkage module is used to trigger a soot blowing command when a level 2 alarm signal is received.

[0013] Preferably, the resistance modeling module further includes: First resistance modeling unit: used to characterize historical resistance quantities within a sliding time window based on a preset cleanliness state characterization strategy. R The process generates baseline candidate values ​​representing low resistance levels; the cleanliness state characterization strategy is low quantile statistics or lower envelope extraction. The second resistance modeling unit is used to fuse the baseline candidate values ​​into the historical baseline using exponential smoothing or recursive filtering algorithms, and output the updated dynamic baseline. R base .

[0014] Preferably, the graded early warning determination module includes: First warning unit: Used to output a secondary warning signal when one of the following conditions is met, and the second debouncing condition is also met: A:e ≥ e 2; B:e ≥ e 1 and g ≥ g th ; Second early warning unit: When the conditions for a Level II early warning are not met, but the conditions are met... e ≥ e 1. When the first debouncing condition is met, a first-level warning signal is output; Condition storage unit: used to store the first debouncing condition and the second debouncing condition; The first debouncing condition is: within a preset duration window. T Within 1, abnormal state e ≥ e The number of times 1 appears reaches the preset consecutive number of times. N 1; The second debouncing condition is: within a preset duration window. T Within 2 days, the number of times an abnormal state meeting one of the following conditions occurs reaches a preset consecutive number. N 2: A:e ≥ e 2; B:e ≥ e 1 and g ≥ g th .

[0015] The ultrasonic full-section SCR flue resistance dynamic monitoring and early warning method and device of the present invention has the following advantages: This invention measures the full-section flow rate using the ultrasonic multi-channel time-of-flight method and sets up static pressure measuring points in the stable flow field section, thus ensuring the original measurement data from the source. Q , The representativeness and accuracy of the differential pressure measurement points are improved, effectively overcoming the shortcomings of traditional differential pressure measurement points that are prone to clogging and drifting in high-temperature and high-dust environments; then, by introducing an equivalent resistance characterization quantity... R =Δ P / Q ² As a core monitoring parameter, this core criterion R It has unique physical significance, its molecular Δ P It increases with increasing flow rate, and the denominator increases. Q ² also increases with increasing flow rate, and the two cancel each other out. When the actual resistance of the flue remains constant, its value can remain basically stable, thus effectively eliminating the direct impact of flue gas flow rate changes on the monitoring results, making the monitoring values ​​comparable under different load conditions, and laying a solid foundation for achieving accurate early warning; furthermore, by constructing and updating a dynamic baseline online... R base As an early warning benchmark, this system can automatically track and adapt to normal operating condition changes of the unit, fundamentally solving the problem of high false alarm and missed alarm rates under varying operating conditions with the fixed threshold method, and significantly improving the reliability of early warning; finally, by calculating abnormal indicators... e =( R - R base ) / R base and its rate of change g This system shifts the monitoring focus from the "absolute value" of resistance to its "relative rate of change" relative to its normal baseline, making it extremely sensitive to small, slow trends in resistance deterioration, thus enabling timely early warning of early failures and gradual blockages; furthermore, by setting a first threshold... e 1 and second threshold e 2. By combining the rate of change g to output the first and second early warning signals, it is possible to distinguish between early anomalies that "require attention" and serious anomalies that "require action," providing operators with clear, graded operation and maintenance guidance and avoiding overreaction or underreaction. In summary, this invention ultimately achieves accurate perception and intelligent early warning of the resistance status of the SCR inlet flue. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the system architecture of a method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to the present invention; Figure 2 This is a schematic diagram of the ultrasonic measurement cross-section channel arrangement of the present invention; Figure 3 This is a flowchart illustrating a method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to the present invention. Figure 4 This is a flowchart illustrating an exemplary method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance. Figure 5 This is a block diagram of an ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device according to the present invention; Figure 6 This is a block diagram illustrating a resistance modeling module according to an exemplary embodiment; Figure 7 This is a block diagram illustrating a graded early warning determination module according to an exemplary embodiment.

[0017] Figure label: 1. SCR inlet flue; 2. Ultrasonic measurement section; 3. Ultrasonic transducer; 4-1. Upstream pressure measurement point; 4-2. Downstream pressure measurement point; 5. SCR denitrification reactor; 6. Communication optical fiber; 7. Server. Detailed Implementation

[0018] The technical solutions of this application will now be described clearly and in detail with reference to the accompanying drawings. In the description of the embodiments of this application, unless otherwise stated, " / " indicates "or," for example, A / B can mean A or B. "And / or" in the text is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Furthermore, in the description of the embodiments of this application, "multiple" refers to two or more. The terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of indicated technical features. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include one or more of that feature.

[0019] like Figure 1 This is a schematic diagram of the system architecture of a dynamic monitoring and early warning method for SCR inlet flue resistance provided in an embodiment of the present invention. Figure 3 For the corresponding Figure 1 The flowchart illustrates the monitoring and early warning method of the system shown. The following will combine... Figure 1 and Figure 3 This paper provides a detailed description of a method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance provided by an embodiment of the present invention.

[0020] In addition, the system architecture includes an SCR inlet flue 1, an ultrasonic measurement section 2, an ultrasonic transducer 3, a pressure measuring point 4, an SCR denitrification reactor 5, and a communication fiber optic cable 6. To achieve real-time monitoring, the present invention is laid out at the data source. The ultrasonic transducer (3), as a flow sensing element, is directly installed at the measurement point in the SCR inlet flue. Its function is to directly sense the flue gas flow state and send the acquired raw acoustic signal to a remote server (7) via a communication network (6) for processing. The ultrasonic transducer 3 includes eight transducers (3-1 to 3-8); the pressure measuring point 4 includes an upstream pressure measuring point 4-1 and a downstream pressure measuring point 4-2; the communication fiber optic cable 6 includes three transducers (6-1 to 6-3). The server 7, located in the remote control room, measures the obtained pressure signal. P up , P down and flow signal Q The data is transmitted to server 7 via optical fiber 6, where the software program executes subsequent steps S3 to S8, i.e., Δ. P It also includes R-value calculation, dynamic baseline update, anomaly detection, and early warning output.

[0021] like Figure 1 As shown, a straight pipe section satisfying the flow field stability condition is provided on the upstream flue adjacent to the inlet flange of the SCR denitrification reactor 5). That is, a measurement section is selected at the straight section of the SCR inlet flue 1, and an ultrasonic measurement section 2 is set within the measurement section. To reduce the influence of disturbing components such as elbows, baffles, and expansion / contraction sections on the velocity and static pressure fields, the ultrasonic measurement section is located within the measurement range along the flue axis, and the axial distance from the ultrasonic measurement section 2 to the adjacent upstream disturbing component is defined as... L u The axial distance to the downstream adjacent disturbance component is L d The axial distance from the upstream pressure measurement point 4-1 to the ultrasonic measurement section 2 is: L 1 The axial distance from the ultrasonic measurement section 2 to the downstream pressure measurement point 4-2 is L 2 Used to set the conditions that must be met for ultrasonic measurement sections.

[0022] To accommodate different flue sizes, this embodiment uses a hydraulic diameter... Distance normalization is performed for the description. For a rectangular flue (cross-sectional width is...) W Height is H ), the optimal formula for water conservancy diameter (1):

[0023] (1) The arrangement of the ultrasonic measurement section 2, the upstream pressure measurement point 4-1, and the downstream pressure measurement point 4-2 simultaneously satisfies the following conditions: L u ≥5 More preferably L u ≥10 ; L d ≥3 More preferably L d ≥5 ; L 1 =0.5~3 1 to 2 are preferred ; L 2 =0.5~3 1 to 2 are preferred ; Total span length L = L 1 + L 2 Preferably 1 to 6 More preferably 2 to 4 .

[0024] Under the condition of satisfying the above range, it is preferable to adopt an approximately symmetrical arrangement. L 1 ≈ L 2 This is to improve the robustness and representativeness of cross-segment voltage drop to local disturbances.

[0025] It should be noted that the flow field stability condition satisfied by the selection of this measurement interval is: the axial distance between the upstream boundary of the measurement interval and the adjacent upstream disturbance component. ≥5 The axial distance between its downstream boundary and the adjacent downstream disturbance component ≥3 .

[0026] Furthermore, pressure measuring points 4-1 and 4-2 can be in the form of wall-mounted pressure taps; in a preferred embodiment, the upstream pressure measuring point 4-1 and the downstream pressure measuring point 4-2 are respectively composed of multi-point pressure taps and pressure equalization chambers / manifolds, each outputting the upstream average static pressure. P up With downstream average static pressure P down This improves the representativeness of static pressure sampling and reduces measurement deviation caused by flow deviation.

[0027] The number of pressure taps at the above-mentioned multi-points is preferably 4 to 8, which are distributed along the circumference / four walls of the flue and converge into the pressure equalization cavity.

[0028] It should be noted that the number of multi-point pressure taps is preferably 4 to 8, but this is only for illustrative purposes and does not constitute a limitation on the present invention.

[0029] like Figure 2 As shown, the ultrasonic measurement section 2 is a rectangular section with a width of [missing information]. W The height is H Ultrasonic transducers (3-1) to (3-8) are installed on opposite sidewalls of the flue, forming four ultrasonic channels. They are divided into upper and lower layers along the cross-sectional height: the two upper channels form an intersecting "X" structure, and the two lower channels form an intersecting "X" structure, thus forming a symmetrical multipath measurement structure of "upper layer X + lower layer X" to improve the representativeness of the cross-sectional average velocity or volumetric flow rate under conditions of flow deviation and velocity distortion.

[0030] Preferably, to reduce the impact of near-wall boundary layer and dust accumulation on sound channel propagation, the ultrasonic transducer is installed away from the near-wall area, and the distance from the transducer's beam endpoint to the upper and lower walls is preferably not less than 0.10. H More preferably not less than 0.15 H The channel arrangement is symmetrical about the center of the cross section, so that the upper and lower channels respectively cover the main flow areas of the upper and lower halves of the cross section.

[0031] like Figure 1 As shown, to meet the measurement requirements of the axial velocity component in the ultrasonic time-of-flight method, the transducers at both ends of the same channel are axially offset in the direction of the main flue gas flow (axial direction). S This causes the sound beam to form an angle with the axis of the flue. i Preferably, the included angle i The angle is 30° to 60°, more preferably about 45°. Axial misalignment distance. S According to the transverse distance of the sound channel within the cross section D It is determined that the preferred option satisfies formula (2):

[0032] (2) Among them, for the oblique channels within the rectangular cross-section, D It can be taken as the span width W and the height difference Δ at the end of the vocal tract y The composite distance, i.e. When θ≈45°, the preferred option is... S ≈ D It takes into account both time zone sensitivity and propagation attenuation.

[0033] Figure 3This is a flowchart illustrating a method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to an exemplary embodiment. See also Figure 3 The method includes the following steps.

[0034] S1. Select a measurement section in the straight section of the SCR inlet flue, and set up upstream and downstream static pressure measuring points within this measurement section to measure and obtain the upstream static pressure. P up With downstream static pressure P down .

[0035] The selection of the measurement interval satisfies the condition of flow field stability.

[0036] In addition, to obtain representative measurement values ​​in the SCR inlet flue, a stable flow field section was selected as the measurement interval, and upstream and downstream static pressure measuring holes were respectively opened on the wall of this interval. The upstream static pressure was obtained by installing at least one first pressure sensor on the upstream static pressure measuring hole. P up The downstream static pressure is obtained by installing at least one second pressure sensor on the downstream static pressure measuring hole. P down .

[0037] This embodiment can also utilize the upstream static pressure. P up With downstream static pressure P down The pressure is collected in real time by pressure transmitters installed at upstream pressure measuring point 4-1 and downstream pressure measuring point 4-2 (or connected to a differential pressure transmitter via pressure taps). The pressure transmitters convert the physical static pressure signal into a standard electrical signal (e.g., 4-20mA). This electrical signal is input to a data acquisition system (e.g., DCS or PLC) via an analog input module or signal conditioner. After analog-to-digital conversion, the upstream static pressure value is obtained in digital form. P up With downstream static pressure value P down .

[0038] Therefore, this embodiment specifies the upstream static pressure value. P up With downstream static pressure value P down There are no specific restrictions on how to obtain it.

[0039] S2. The ultrasonic measurement section within the measurement interval is measured using the ultrasonic multi-channel time-of-flight method to obtain the flue gas volumetric flow rate flowing through the measurement interval. Q .

[0040] This embodiment employs the ultrasonic multi-channel time-of-flight method for measurement. Its core principle is that in a fluid (fluid), the propagation speed of ultrasonic waves is faster in the downstream direction and slower in the upstream direction. By accurately measuring the time difference of ultrasonic wave propagation in the downstream and upstream directions along a channel at a known distance, the linear average velocity of the flue gas along the channel direction can be calculated. Integrating the measurement results from multiple channels yields the average velocity of the entire measurement cross-section, which, combined with the cross-sectional area, allows for the calculation of the volumetric flow rate.

[0041] like Figure 1 As shown, on the ultrasonic measurement section 2 set within the measurement range, multiple pairs of ultrasonic transducers (3-1 / 3-2, 3-3 / 3-4, 3-5 / 3-6, 3-7 / 3-8) are arranged in layers on opposite side walls of the flue 1, forming a multi-channel (e.g., 4-channel) measurement array. Each pair of transducers constitutes an independent measurement channel, and the ultrasonic transducers are responsible for transmitting and receiving ultrasonic waves.

[0042] Each ultrasonic transducer is connected to a transmitter installed next to the flue. The transmitter has a built-in high-frequency clock circuit for accurately measuring the flight time of the ultrasonic waves.

[0043] Timing control: The transmitter cyclically controls the operation of each pair of ultrasonic transducers according to a preset cycle. For example, ultrasonic transducer 3-1 first transmits, and ultrasonic transducer 3-2 receives, measuring the downstream time. The ultrasonic transducer 3-2 then transmits the signal, and the ultrasonic transducer 3-1 receives it, measuring the reverse flow time. .

[0044] For any measurement channel, the formula (3) for calculating its flow velocity v is: (3) in, The length of the channel is the straight-line distance between the two transducers that make up the channel.

[0045] In this embodiment, the collected flow velocity data from all channels are used to calculate the average flow velocity across the entire measurement cross-section using a specific mathematical algorithm (such as the Gaussian integral method). Then, the flue gas volume flow rate is calculated using formula (4). Q : Q = A (4) Where A is the cross-sectional area of ​​the flue, and is a known design parameter.

[0046] Therefore, this embodiment calculates the flow velocity by the time difference of sound waves traveling through the flue gas. This time difference is directly related only to the fluid velocity, while the influence of the absolute flight time of the sound waves (i.e., the speed of sound, which is affected by temperature and composition) is canceled out in the calculation. Thus, it has low sensitivity to changes in flue gas composition and temperature. Furthermore, the ultrasonic transducer is installed on the outer wall of the flue, avoiding direct contact with the high-temperature, high-dust flue gas, fundamentally eliminating measurement drift or failure caused by blockage, corrosion, or wear at the measuring point. Multi-channel measurement technology is employed to obtain the average flow velocity of multiple lines at different locations within the measurement cross-section, and then integrates to calculate the spatial average flow velocity of the entire cross-section. This method overcomes the inherent defect of insufficient representativeness in single-point measurements (such as single-point differential pressure) in large-size flues due to uneven flow field distribution, making the obtained flue gas volumetric flow rate Q value more accurately reflect the actual flow rate across the entire cross-section.

[0047] S3, based on the upstream static pressure P up With the downstream static pressure P down The difference is used to determine the pressure drop Δ between the measuring points. P .

[0048] S4, based on this pressure drop Δ P With this volumetric flow rate Q， Calculate the equivalent resistance characterization at the current moment. R =Δ P / Q ².

[0049] This step involves implementing the core algorithm. It reads the current voltage drop Δ from the cache after synchronization alignment. P With flue gas volume flow rate Q The sampled values. To eliminate the influence of flue gas flow fluctuations on the absolute value of resistance and achieve comparability across operating conditions, the formula is used. R =Δ P / Q ² Calculate the equivalent resistance characterization quantity R .

[0050] The specific calculation process is as follows: First, calculate the volumetric flow rate. R The square value Q ²; secondly, calculate the pressure drop Δ P and Q The ratio of 2. R This is the equivalent resistance after normalization.

[0051] The core advantage of this invention lies in the equivalent resistance characterization quantity used. R =Δ P / Q ² It possesses unique physical properties that can effectively distinguish between abnormal resistance and normal operating condition fluctuations, specifically manifested as: (5): Sensitivity to changes in actual resistance: When abnormal conditions such as ash accumulation or blockage in the flue cause an increase in actual resistance, the flue gas volume flow rate... Q Under the condition that remains basically unchanged, the flue gas pressure drop Δ P It will increase significantly, directly leading to a decrease in the equivalent resistance characterization value. R The calculation results increase synchronously, thus sensitively indicating abnormal states.

[0052] (2): Stability to flow fluctuations (normalization capability): When the unit load changes and flue gas volume flow rate is affected... Q When the pressure changes, according to the principles of fluid mechanics, the pressure drop Δ P Approximation and flow rate Q It changes in proportion to the square of (Δ) P ∝ Q ²). Therefore, in the equivalent resistance characterization quantity R Calculation formula R =Δ P / Q In ², Δ P The changes were Q The change in ² compensates for this, making R The value remains at a stable level corresponding to the actual health status of the flue, and will not generate false alarms due to normal operating condition fluctuations. Therefore, by normalizing the impact of flow rate changes, the indicator becomes comparable under different load conditions, fundamentally solving the technical problem of high false alarm and missed alarm rates under varying operating conditions with the fixed threshold method, and significantly improving the reliability and accuracy of monitoring.

[0053] S5. Within the sliding time window, based on this equivalent resistance characterization quantity R Historical data sequences are used to calculate dynamic baselines. R base And it is updated online.

[0054] In this invention, the method is executed based on the current sampling time. The current sampling time being processed is defined as T0, and the previous historical times are sequentially T-1, T-2, ..., Tk. The sliding time window refers to a data set that includes the previous N consecutive historical times (T-(N-1), ..., T-1, T0) ending at the current time T0.

[0055] It should be noted that the length of this sliding time window is N sampling periods, where N is a positive integer greater than 1. The sliding mechanism of this time window is as follows: each time new data for the current moment is obtained, the oldest historical data within the window is removed to maintain a constant amount of data within the window; for example, the window's endpoint is always aligned with the current processing time T0, and the window length is fixed at N sampling periods. Whenever a new sampling moment arrives, the current moment is updated to T0+1, and the time window slides accordingly, removing the oldest data from time T-(N-1) and incorporating the latest data from time T0+1, thus always maintaining the window containing only the most recent N historical data.

[0056] For example, when the time window length N=6 is set, the data sequence within this time window at time T0 is the data at times [T-5, T-4, T-3, T-2, T-1, T0].

[0057] Among them, the equivalent resistance characterization quantity R Historical data sequences are used to calculate dynamic baselines. R base The online update process includes the following steps: S51. Based on the preset cleanliness state characterization strategy, the historical resistance characterization quantity R within the sliding time window is processed to generate baseline candidate values ​​representing low resistance levels; the cleanliness state characterization strategy is low quantile statistics or lower envelope extraction.

[0058] The logic behind generating baseline candidate values ​​based on low quantile statistics (such as the 20th percentile) is as follows: within a sliding time window (such as the past 24 hours), the data with the lowest resistance (such as the lowest 20%) best represents the optimal state of the flue under current operating conditions (i.e., the state with the least ash accumulation). The specific construction steps are as follows.

[0059] Step S511 (Filtering): Automatically filter out all data within the pre-selected time period (past 24 hours). R The values ​​are sorted from smallest to largest.

[0060] Step S512 (Calculate candidate value): The system selects the one that is in the 20% position after sorting. R Value (i.e., 20th percentile), or calculate the lowest 20% of all values. R The average of the values.

[0061] The calculated 20th percentile is the constructed baseline candidate value. It is not the original data, but a new data point constructed from the original data according to a specific strategy (taking the lower quantile). This new data point is considered by the system to be the best candidate for a clean baseline.

[0062] In addition, the generation of baseline candidate values ​​is based on the lower envelope extraction, and the construction logic is as follows: by drawing a line that closely follows all... R The lower envelope is a smooth curve representing the lower edge of the data points. This line represents the lowest possible level of resistance, or the theoretically optimal state.

[0063] Furthermore, how does the lower envelope extraction construct baseline candidate values? Step S513 (Tracing the lower boundary): Continuously track the lower boundary of the R value fluctuation using a lower envelope algorithm (such as moving minimum).

[0064] Step S514 (Generate candidate value): Within the current sliding window, output a value representing the lower boundary.

[0065] Based on the above description, the value output by the lower envelope algorithm is the constructed baseline candidate value, which is also a new data point representing the ideal state constructed through a specific strategy (tracing the lower envelope).

[0066] S52. Using an exponential smoothing or recursive filtering algorithm, fuse the candidate baseline value into the historical baseline and output the updated dynamic baseline. R base .

[0067] Subsequently, the server reads the dynamic baseline value from the previous period. The updated dynamic baseline is calculated based on the baseline candidate values ​​obtained in this period according to formula (5). R base The data is then written to the database for use in the next cycle. This process is repeated continuously, enabling online and smooth updates of the dynamic baseline.

[0068] R base (5) in, The value is the low quantile of the current window, and α is the smoothing coefficient, preferably 0.8~0.98. Using this strategy allows the baseline to change smoothly with long-term slow drift, while preventing short-term anomalies from being "written into the baseline".

[0069] This embodiment details the dynamic baseline. R base The system's construction and update process. The system performs an update once per sampling time (e.g., per second). The initial dynamic baseline is set to... =1.0. The update strategy is as follows: select the minimum value of the data within the most recent 6 sampling points (sliding time window) as the baseline candidate value, and use an exponential smoothing algorithm with a smoothing coefficient α=0.8 to incorporate it into the current baseline.

[0070] The following uses the historical data sequence of the equivalent resistance characterization quantity R [1.05, 1.02, 0.98, 1.10, 1.01, 1.04] based on the current time T0 and the previous 5 historical times (T-5, T-4, T-3, T-2, T-1, T0) after system startup to construct dynamic baseline candidate values. First, construct the candidate values: find the minimum value within the window to obtain the baseline candidate values. =min(in-window data)=0.98. Update baseline: =0.8*1.0+0.2*0.98=0.996. The result at this moment: the baseline has been updated from the initial value of 1.0 to 0.996, reflecting the system's recent optimal state.

[0071] Based on the above description, by using the low quantile statistics / lower envelope extraction strategy, data points representing "health status" are always selected from historical data as candidates, effectively resisting the interference of outliers; the role of "update" is to ensure that the baseline does not jump drastically, but smoothly tracks the changes in the system's health status through algorithms such as exponential smoothing, thus ensuring both stability and a certain degree of adaptability.

[0072] S6. Equivalent resistance characterization based on the current moment R and the dynamic baseline R base Calculate abnormal indicators e and rate of change g .

[0073] in, e =( R - R base ) / R base ; .

[0074] In addition, molecules R - R base This represents the absolute deviation of the current resistance relative to the baseline state. Dividing by the denominator R_base and normalizing the result converts the absolute deviation into a relative rate of change. e This becomes a dimensionless percentage value. e It is comparable under different operating conditions (such as different loads and fuels), laying the foundation for achieving unified and accurate threshold judgment.

[0075] therefore, e This visually represents the degree of relative deterioration of resistance compared to its healthy level. For example, e =0.15 indicates that the resistance has increased by 15% compared to the normal level.

[0076] In order to monitor the trend and speed of resistance deterioration, the rate of change can also be calculated. g The derivative is approximated using the first-order finite difference method. g Used to assess the drastic degree of the deterioration trend of resistance, serving as an auxiliary criterion for early warning judgment.

[0077] S7, This abnormal indicator e Each with a preset first threshold e 1 and second threshold e 2. Compare and change rates g With the rate of change threshold g th Comparison; among which e 2> e 1.

[0078] S8. Based on the comparison results, output the warning signal according to the following rules.

[0079] If the second warning condition is met, a level-two warning signal will be output.

[0080] If the second warning condition is not met but the first warning condition is met, then a first-level warning signal will be output.

[0081] The determination and output of this early warning condition satisfy the following: Level 2 warning conditions: A level 2 warning signal is output when one of the following conditions is met, and the second debouncing condition is also met: A:e ≥ e 2; B:e ≥ e 1 and g ≥ g th ; Level 1 warning conditions: When the conditions for Level 2 warning are not met, but the conditions are met. e ≥ e 1. When the first debouncing condition is met, a first-level warning signal is output; In addition, the first debouncing condition is: within a preset duration window. T Within 1, this abnormal state e ≥ e The number of times 1 appears reaches the preset consecutive number of times. N 1; The second debouncing condition is: within a preset duration window T Within 2 days, the number of times an abnormal state meeting one of the following conditions occurs reaches a preset consecutive number. N 2: A:e ≥ e 2; B:e ≥ e 1 and g ≥ g th ; Furthermore, for ease of engineering application, the threshold and debouncing parameters are preferably within the following ranges: e 1 is preferably 0.05~0.15; e 2 is preferably 0.15~0.40; consecutive times N 1 and N 2. Preferably, 3 to 10 times; Duration T 1. T 2. The preferred time is 30~300s; It should be noted that the rate of change threshold g th The settings are based on scientific methods, not empirical values. This is related to the sampling period. Closely related: The smaller the value, the more frequently the system samples, in order to prevent false alarms due to instantaneous fluctuations. g th The value increases accordingly; conversely, it decreases. Furthermore, to improve alarm reliability, a time window Tg (e.g., 300 seconds) can be set. The first warning condition is optimized as follows: within a continuous duration Tg, the rate of change g continuously exceeds the threshold. g th This measure can effectively identify real and ongoing deterioration trends and avoid false alarms.

[0082] The advantage of this tiered early warning mechanism lies in: setting a relatively high second threshold. e 2. Once abnormal indicators are detected e Rise sharply and reach e 2. The system can immediately trigger a high-level (Level 2) alarm. This design ensures that in the event of a sudden and severe deterioration in resistance (such as catalyst module blockage, foreign object obstruction, or other unforeseen circumstances), the system can achieve a response within seconds, alerting operators to intervene immediately and preventing the accident from escalating; this is achieved by setting a relatively low first threshold. e 1, and combined with the rate of change threshold g th Combining judgment conditions (such as) e ≥ e 1 and g ≥ g th The system can keenly detect slow, gradual deterioration trends in resistance (such as early accumulation of dust). Simultaneously, this combined condition effectively avoids the pitfalls of a single [condition / condition]. eThe threshold is designed to mitigate false alarms that are prone to occur under normal operating conditions such as unit load fluctuations and soot blowing disturbances. This improves alarm sensitivity while greatly ensuring alarm accuracy and significantly reducing the false alarm rate.

[0083] The above mechanism ultimately transforms abstract resistance data into intuitive, tiered risk signals (such as "Attention / Level 1 Warning" and "Critical / Level 2 Warning"), providing operators with clear maintenance guidance and realizing the transformation from "passive response to alarms" to "proactive risk warning," thereby improving the intelligence level of the entire SCR system's operation and maintenance.

[0084] To achieve more accurate resistance state analysis, this embodiment calculates the equivalent resistance characterization quantity. R Simultaneously, the drag coefficient ζ is calculated in parallel. The specific implementation steps are as follows: S91: Obtain Calculation Required supplementary parameters.

[0085] The real-time flue gas temperature T is read. Based on this temperature T and the local atmospheric pressure (which can be set to the default value or approximated by the measured static pressure), the real-time flue gas density is calculated according to the ideal gas law (6). The calculation formula (6) is as follows:

[0086] (6) in, , , The values ​​represent the flue gas density, temperature, and pressure under standard conditions.

[0087] Alternatively, it can be measured directly or by flue gas volume flow rate. Q The average velocity vˉ ​​of the cross section is obtained by converting it to the cross-sectional area A. The drag coefficient ζ is calculated according to the following formula (7):

[0088] (7) Based on this, the calculated drag coefficient ζ achieves its function of "assisting in more accurate drag state analysis" in the following ways: providing a dimensionless benchmark for lateral comparison: on the human-machine interface of the DCS interface of the server (7) and the event log display unit, in addition to displaying the core parameters R and e In addition, a historical trend curve of the drag coefficient ζ is plotted. Since ζ is a dimensionless parameter, its value eliminates the influence of unit capacity and flue size, providing a unified benchmark for operators to compare different units or directly compare with the design value of their own unit. For example, operators can observe the ζ value slowly rising from 0.5 in the design state to 0.8, intuitively recognizing the degree of increased flow loss.

[0089] AsR Value verification and supplementation: The trend of ζ and R Correlation analysis is performed on the trends. When R When the trends of the ζ value and the ζ value are highly consistent, they can corroborate each other and enhance confidence in the judgment of worsening resistance. If a brief divergence occurs, it can trigger a diagnostic prompt, helping analysts determine whether the problem is caused by a flow measurement issue or an abnormality in the measurement of state parameters (such as temperature).

[0090] For in-depth diagnostic reports: When generating periodic analysis reports or event reports, the system will simultaneously list... R The values ​​and changes of ζ. Professional analysts can use the theoretical background of ζ to analyze the fluid dynamics reasons for the increase in resistance more deeply (such as whether local resistance or friction resistance is dominant).

[0091] ζ does not directly participate in the main process mentioned above, but provides auxiliary and enhanced judgments outside the main process through the following two methods, especially when a high-level warning (level 2 warning) is triggered, thereby increasing the reliability and foresight of the system.

[0092] Function 1: Serving as a "fast track" or "enhanced confirmation" for Level 2 early warnings. This is the most important early warning-related function of ζ. The triggering conditions for a Level II early warning, besides... e ≥ e 2. There is one more: when e ≥ e 1 and g ≥ g th A level-two warning is also triggered when the rate of change is too high.

[0093] The calculation of the rate of change g here depends on e ,and e Depends on R However, ζ can play an indirect enhancing role here:

[0094] Scenario: The system detected e Just over e 1 (Level 1 warning), but the resistance deteriorates very rapidly (g is very large).

[0095] Enhanced Judgment: The system can simultaneously check the value and trend of ζ. If ζ also shows the same severe and rapid deterioration trend, this provides additional evidence from different physical dimensions to support the decision of "triggering a secondary warning in advance due to rapid deterioration of resistance," making the warning judgment more reliable and reducing false alarms.

[0096] Function 2: Used for root cause analysis and state diagnosis (after early warning). Once the warning has been triggered, the value of ζ becomes even more apparent: Auxiliary diagnostics: Operators can view simultaneously R The historical trend of ζ.

[0097] if R The trend of change is highly consistent with that of ζ, indicating that the deterioration of resistance is real, and the problem may be due to the increase of general flow resistance such as dust accumulation and blockage.

[0098] if R A significant deterioration in flow resistance while ζ remains relatively unchanged may indicate that the problem is not simply an increase in flow resistance, but rather related to other factors such as inaccurate flow measurement or incorrect density calculation. This provides operators with more in-depth diagnostic clues.

[0099] To ensure the long-term reliable operation of the monitoring system in complex industrial environments and to avoid false alarms caused by momentary measurement anomalies, this invention adds data quality assurance and fault tolerance mechanisms to the core algorithm. The specific steps of data quality assurance and fault tolerance are combined... Figure 4 include:

[0100] S10, State correction (optional, used to improve accuracy).

[0101] To improve the accuracy of the drag coefficient ζ, the system performs real-time correction on the flue gas density ρ. This flue gas density ρ is calculated using real-time measured flue gas temperature T and pressure (optional), combined with a simplified form of the ideal gas law. This correction ensures the accuracy of ζ value calculation, especially when operating conditions fluctuate significantly.

[0102] S20, Signal quality inspection (core fault-tolerant step).

[0103] Calculating flow rate using ultrasonic measurement data Q Previously, the quality assessment module in the server performed real-time assessment of the quality of the ultrasonic measurement signal. Quality assessment indicators included, but were not limited to:

[0104] 1) Channel connectivity status: Check whether the transmission and reception of each channel are normal.

[0105] 2) Echo correlation coefficient: assesses the similarity between the received signal and the transmitted signal template. A correlation coefficient below a threshold (e.g., 0.8) indicates signal distortion.

[0106] 3) Signal-to-noise ratio (SNR): A signal-to-noise ratio below a threshold (e.g., 20dB) indicates that the signal is overwhelmed by noise.

[0107] 4) Flight time stability: Excessive fluctuations in flight time over multiple consecutive cycles indicate unstable flow field or unreliable measurements.

[0108] This quality indicator can be evaluated individually or in combination (such as by weighted average).

[0109] S30, Data Validity Determination and Branch Processing (Decision Core).

[0110] In one embodiment of the present invention, to prevent "bad data" caused by sensor drift, disconnection, or signal interference from entering the calculation process and causing the system to issue incorrect warnings or control commands, the validity of the data is checked before performing core calculations. See [reference needed]. Figure 4 When the logical judgment box "D101 data valid?" is entered, the system executes the following judgment process: - Validity criteria: Data in the current sampling period k is considered invalid if any of the following conditions are met: a) The number of effective channels (i.e., the channels that have passed the quality inspection) is less than the preset number (e.g., less than 4 of the 8 channels are effective). b) The overall quality index after fusion is lower than the preset requirements.

[0111] - Branch processing: 1) If the data is valid: the system process enters the core calculation step S104 (i.e., calculates the resistance characterization quantity R or ζ) and performs subsequent early warning judgment.

[0112] 2) If the data is invalid (No): The system process jumps to the fault tolerance processing step S109 and performs the following operations: I. Removal: Actively discard invalid raw data at the current time k. P up , P down , Q (This part is not involved in the current or subsequent resistance calculations and early warning determinations.)

[0113] II. Waiting: The system waits until the next sampling period (time k+1) arrives.

[0114] III. Return: Return to S102 (data acquisition step) and restart the cycle.

[0115] The beneficial effects of this embodiment are as follows: by introducing a multi-level quality inspection and invalid data removal mechanism, the system is made "immune" to transient interference, which fundamentally avoids false alarms caused by common field problems such as temporary sensor failure and signal interference, and significantly improves the long-term operational reliability and early warning accuracy of the entire monitoring system in actual industrial environments.

[0116] In one embodiment of the present invention, when a secondary warning signal is output, a soot blowing linkage signal can also be generated and output; see reference. Figure 4 .

[0117] The linkage signal is used to control the start of the soot blowing equipment.

[0118] Specifically, the output of the soot blowing linkage signal must satisfy one or more interlocking conditions, which include: a: The time interval between adjacent soot blowing actions performed according to the soot blowing linkage signal reaches the preset minimum soot blowing interval time; b: The soot blowing system is in a ready-to-blow state; c: The number of blowing actions per unit time has not reached the upper limit; d: The abnormal indicator ε has fallen back to below the release threshold ε1-δ after the triggering of the level 2 warning and has remained stable for a set period of time, where δ is a hysteresis parameter that is greater than zero.

[0119] Based on the aforementioned resistance monitoring and early warning methods, this embodiment further expands their engineering application value, realizing closed-loop control from intelligent diagnosis to automatic execution, thereby upgrading the system from a state monitoring platform to a proactive operation and maintenance control system.

[0120] The system pre-defines the mapping relationship between early warning signals and soot blowing equipment linkage commands. This mapping relationship defines the specific control actions triggered by different levels of early warning results: - The Level 1 warning signal (A1) is mapped to the "Recommended Soot Blowing" instruction (B1): This instruction is a prompting operation that can be uploaded to the background monitoring system, illuminate the "Recommended Soot Blowing" indicator on the control panel, or send a prompt message to the operator's workstation, recommending that the soot blowing operation be performed. The operator can then manually start the operation after making a judgment.

[0121] - The Level 2 warning signal (A2) is mapped to the "forced soot blowing" linkage command (B2): This command is an automatic control command. When the system generates this command, it will directly and automatically start the SCR soot blowing system or send a "start allowed" signal to the soot blowing control system.

[0122] Linked Judgment and Instruction Generation (Decision Core): After comparing the abnormal indicator ε with the threshold, the linked judgment logic is executed synchronously. -If the second warning condition is met ( A:e ≥ e 2; or B:e ≥ e 1 and g ≥ g th ; and satisfy the second debouncing condition), then: 1) Output a level 2 warning signal (A2).

[0123] 2) At the same time, generate and issue the corresponding "forced soot blowing" linkage command (B2).

[0124] -If the first warning condition is met (but the second warning condition is not met, but the condition is met) e ≥ e 1. If the first debouncing condition is met simultaneously, then: 1) Output a first-level warning signal (A1).

[0125] 2) At the same time, generate and issue the corresponding "suggested dust removal" instruction (B1).

[0126] - If no warning conditions are met, no linkage command will be output.

[0127] -Instruction execution and feedback (execution closed loop): - For the "Forced Soot Blowing" command (B2): This command is sent to the soot blowing control system via hardwired connection or communication protocol (such as Modbus TCP, OPCUA). Upon receiving the command, the soot blowing control system starts the corresponding soot blowers in a preset sequence without delay to powerfully purge the catalyst bed at the SCR reactor inlet and remove accumulated ash.

[0128] - For the "suggest soot blowing" instruction (B1): The instruction is sent to the upper-level monitoring information system (SIS / MIS), and an audible and visual alarm and text prompt are displayed on the operator interface. After confirmation by the operator, the soot blowing procedure is manually executed.

[0129] - Feedback loop: After the soot blowing operation is performed, the system continuously monitors the change in the resistance characteristic R. When R The value has fallen back to normal levels (e.g.) e < e 1) Once the system stabilizes, it can automatically stop blowing soot or indicate that blowing is complete, forming a complete closed-loop control loop of "monitoring-early warning-execution-verification".

[0130] The beneficial effects of this embodiment are as follows: It achieves automated operation and maintenance: upgrading manual experience-based judgment to data-driven automatic control reduces human delays and improves response speed, especially enabling immediate action in the event of severe blockage to prevent the accident from escalating. It achieves precise soot blowing: enabling "on-demand soot blowing" avoids energy waste (soot blowing medium consumption) and equipment wear associated with timed soot blowing, activating only when resistance abnormally increases, resulting in significant energy savings and consumption reduction. It enhances system safety: through automatic linkage, it provides fast and reliable anti-clogging protection for the SCR system, ensuring the safe and stable operation of the denitrification system, which is a crucial component in building a smart power plant.

[0131] Accordingly, this invention abandons the traditional method of directly using the voltage drop Δ P The approach of using absolute values ​​creatively introduces the equivalent resistance characterization quantity R=Δ P / Q² is used as the core monitoring parameter. This parameter is determined through its mathematical structure (numerator Δ). P(Since the denominator Q² is also related to the flow rate, it inherently offsets the influence of flue gas flow rate changes, making the monitored values ​​inherently comparable under different load conditions. This solves the industry problem of inaccurate monitoring values ​​caused by flow rate fluctuations in traditional methods.) This invention also provides a method based on the fluid dynamics standard drag coefficient ζ=Δ P / (0.5*ρ* ²) Parallel monitoring implementation. This scheme is more theoretically rigorous, further enriching the technical content of the invention and providing diverse options for scenarios with different accuracy requirements; it constructs and updates a dynamic baseline online by introducing historical data from a sliding time window. R base The warning threshold is no longer a fixed value, but a floating value that tracks the system's optimal health state. This adaptive mechanism enables the warning system to adapt to long-term, slow changes in unit performance, avoiding the dilemma of "either false alarms or missed alarms" inherent in fixed threshold methods; it also establishes hierarchical warning and trend prediction capabilities: by setting multi-level thresholds for the abnormal indicator ε ( e 1, e 2) Combined with the rate of change threshold g th The system enables early warning of "slow deterioration" (Level 1 warning) and immediate alert of "sudden serious failure" (Level 2 warning). This tiered warning mechanism not only accurately identifies the risk level but also provides operators with valuable buffer time to intervene, achieving a leap from "passive alarm" to "proactive warning".

[0132] In summary, through collaborative innovation in monitoring principles, early warning algorithms, and system design, this invention has successfully constructed a precise, reliable, and intelligent SCR inlet flue resistance status monitoring and early warning system. It provides a complete and efficient solution to the long-standing problem of flue blockage monitoring in the industry, and has significant engineering application value and promising prospects for promotion.

[0133] Figure 5 This is a block diagram of an ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device according to an exemplary embodiment. (Refer to...) Figure 5 The ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device includes:

[0134] Data acquisition module 501: used to select a measurement interval in the straight section of the SCR inlet flue, and set upstream and downstream static pressure measuring points in the measurement interval to measure and obtain the upstream static pressure Pup and downstream static pressure Pdown; Ultrasonic flow measurement module 502: used to measure the ultrasonic measurement section within the measurement interval using the ultrasonic multi-channel time difference method, and obtain the flue gas volume flow rate Q flowing through the measurement interval; First data processing module 503: used to determine the pressure drop ΔP between measuring points based on the difference between the upstream static pressure Pup and the downstream static pressure Pdown; It is also used to calculate the equivalent resistance characterization quantity R=ΔP / Q² at the current moment based on the pressure drop ΔP and the volumetric flow rate Q; Resistance modeling module 504: used to calculate the dynamic baseline Rbase and update it online based on the historical data sequence of the equivalent resistance characterization quantity R within a sliding time window; The second data processing module 505 is used to calculate the anomaly index ε and the rate of change g based on the equivalent resistance characterization quantity R and the dynamic baseline Rbase at the current moment, where ε=(R−Rbase) / Rbase; The third data processing module 506 is used to compare the abnormal indicator ε with the preset first threshold ε1 and second threshold ε2 respectively, and to compare the rate of change g with the rate of change threshold gth; where ε2>ε1; The graded early warning determination module 507 is used to output early warning signals according to the following rules based on the comparison results; If the second warning condition is met, a level-two warning signal will be output. If the second warning condition is not met but the first warning condition is met, then a first-level warning signal will be output.

[0135] Linkage module 508: used to trigger the soot blowing command when a level 2 alarm signal is received.

[0136] Reference Figure 6 The resistance modeling module 504 also includes: First resistance modeling unit 5041: Used to process the historical resistance representation quantity R within the sliding time window based on a preset clean state representation strategy, and generate baseline candidate values ​​representing low resistance levels; the clean state representation strategy is low quantile statistics or lower envelope extraction. The second resistance modeling unit 5042 is used to fuse the baseline candidate value into the historical baseline using an exponential smoothing or recursive filtering algorithm, and output the updated dynamic baseline Rbase.

[0137] Reference Figure 7 The graded early warning determination module 507 includes: First warning unit 5071: Used to output a secondary warning signal when one of the following conditions is met, and the second debouncing condition is met simultaneously: A: ε≥ε2; B: ε≥ε1 and g≥gth; Second early warning unit 5072: When the conditions for a level two early warning are not met, but the condition ε≥ε1 is met, and the first debouncing condition is met at the same time, a level one early warning signal is output; Condition storage unit 5073: used to store the first debouncing condition and the second debouncing condition; The first debouncing condition is: within a preset duration window. T Within 1, this abnormal state e ≥ e The number of times 1 appears reaches the preset consecutive number of times. N 1; The second debouncing condition is: within a preset duration window. T Within 2 days, the number of times an abnormal state meeting one of the following conditions occurs reaches a preset consecutive number. N 2: A:e ≥ e 2; B:e ≥ e 1 and g ≥ g th .

[0138] The present invention provides a method and device for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance, which has the following advantages: The present invention uses ultrasonic multi-channel time-of-flight method to measure the full-section flow rate and sets static pressure measuring points in the stable flow field section, thus ensuring the original measurement data from the source. Q , The representativeness and accuracy of the differential pressure measurement points are improved, effectively overcoming the shortcomings of traditional differential pressure measurement points that are prone to clogging and drifting in high-temperature and high-dust environments; then, by introducing an equivalent resistance characterization quantity... R =Δ P / Q ² As a core monitoring parameter, this core criterion R It has unique physical significance, its molecular Δ P It increases with increasing flow rate, and the denominator increases. Q ² also increases with increasing flow rate, and the two cancel each other out. When the actual resistance of the flue remains constant, its value can remain basically stable, thus effectively eliminating the direct impact of flue gas flow rate changes on the monitoring results, making the monitoring values ​​comparable under different load conditions, and laying a solid foundation for achieving accurate early warning; furthermore, by constructing and updating a dynamic baseline online... R base As an early warning benchmark, this system can automatically track and adapt to normal operating condition changes of the unit, fundamentally solving the problem of high false alarm and missed alarm rates under varying operating conditions with the fixed threshold method, and significantly improving the reliability of early warning; finally, by calculating abnormal indicators... e =( R - R base ) / R base and its rate of change gThis system shifts the monitoring focus from the "absolute value" of resistance to its "relative rate of change" relative to its normal baseline, making it extremely sensitive to small, slow trends in resistance deterioration, thus enabling timely early warning of early failures and gradual blockages; furthermore, by setting a first threshold... e 1 and second threshold e 2. By combining the rate of change g to output the first and second early warning signals, it is possible to distinguish between early anomalies that "require attention" and serious anomalies that "require action," providing operators with clear, graded maintenance guidance and avoiding overreaction or underreaction. In summary, this invention ultimately achieves accurate perception and intelligent early warning of the resistance status of the SCR inlet flue, providing a scientific basis for condition-based maintenance and optimized operation of equipment, and has extremely high engineering application value.

[0139] It is understood that the present invention has been described through some embodiments, and those skilled in the art will recognize that various changes or equivalent substitutions can be made to these features and embodiments without departing from the spirit and scope of the invention. Furthermore, under the teachings of the present invention, these features and embodiments can be modified to adapt to specific situations and materials without departing from the spirit and scope of the invention. Therefore, the present invention is not limited to the specific embodiments disclosed herein, and all embodiments falling within the scope of the claims of this application are within the protection scope of the present invention.

Claims

1. A method for dynamic monitoring and early warning of SCR flue resistance using ultrasonic full-section technology, characterized in that, include: S1. Select a measurement interval in the straight section of the SCR inlet flue, and set up upstream and downstream static pressure measuring points within the measurement interval to measure and obtain the upstream static pressure. P up With downstream static pressure P down The selection of the measurement interval satisfies the condition of flow field stability. S2. The ultrasonic measurement section within the measurement interval is measured using the ultrasonic multi-channel time-of-flight method to obtain the flue gas volumetric flow rate flowing through the measurement interval. Q ; S3, Based on the upstream static pressure P up With the downstream static pressure P down The difference is used to determine the pressure drop Δ between the measuring points. P ; S4, based on the voltage drop Δ P With the volumetric flow rate Q， Calculate the equivalent resistance characterization at the current moment. R =Δ P / Q ²; S5. Within the sliding time window, based on the equivalent resistance characterization quantity R Historical data sequences are used to calculate dynamic baselines. R base And update online; The sliding time window refers to a data interval consisting of N historical moments traced back from the current moment, where N is a positive integer greater than 1. S6. Equivalent resistance characterization based on the current moment R and the dynamic baseline R base Calculate abnormal indicators ε and rate of change g ,in, ε =( R - R base ) / R base , ; S7. The abnormal indicators ε Each with a preset first threshold ε 1 and second threshold ε 2. Compare and change rates g With the rate of change threshold g th Comparison; among which ε 2> ε 1; S8. Based on the comparison results, output a warning signal according to the following rules; If the second warning condition is met, a level-two warning signal will be output. If the second warning condition is not met but the first warning condition is met, then a first-level warning signal will be output.

2. The method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to claim 1, characterized in that, The ultrasonic multichannel time difference method adopts a 4-channel structure, which consists of ultrasonic transducers installed on opposite sidewalls of the flue. It is divided into upper and lower layers arranged in a cross-shaped manner along the cross-sectional height direction. The two channels of the upper layer form a cross-X structure, and the two channels of the lower layer form a cross-X structure, thus forming a symmetrical multipath measurement structure of upper X + lower X.

3. The method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to claim 1, characterized in that, The ultrasonic full-section SCR flue resistance dynamic monitoring and early warning method also includes: The ultrasonic measurement section within the measurement interval was measured using the ultrasonic multi-channel time-of-flight method to obtain the average velocity of the flue gas section. ; Based on the average velocity of the flue gas cross section and smoke density Calculate the drag coefficient ; Among them, smoke density It is calculated using a simplified form of the ideal gas law, based on real-time measured flue gas temperature T and known standard density of state.

4. The method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to claim 1, characterized in that, The equivalent resistance characterization quantity R Historical data sequences are used to calculate dynamic baselines. R base And online updates include: Based on a pre-defined cleanliness state characterization strategy, the historical resistance characterization quantity within the sliding time window is... R The process generates baseline candidate values ​​representing low resistance levels; the cleanliness state characterization strategy is low quantile statistics or lower envelope extraction. The candidate baseline values ​​are fused into the historical baseline using an exponential smoothing or recursive filtering algorithm, and the updated dynamic baseline is output. R base .

5. The method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to claim 1, characterized in that, The specific steps for outputting a warning signal based on the comparison results according to the following rules include: Level 2 warning conditions: A level 2 warning signal is output when one of the following conditions is met, and the second debouncing condition is also met: A:ε ≥ ε 2； B:ε ≥ ε 1 and g ≥ g th ; Level 1 warning conditions: When the conditions for Level 2 warning are not met, but the conditions are met. ε ≥ ε 1. When the first debouncing condition is met, a first-level warning signal is output; The first debouncing condition is: within a preset duration window. T Within 1, abnormal state ε ≥ ε The number of times 1 appears reaches the preset consecutive number of times. N 1; The second debouncing condition is: within a preset duration window. T Within 2 days, the number of times an abnormal state meeting one of the following conditions occurs reaches a preset consecutive number. N 2: A:ε ≥ ε 2； B:ε ≥ ε 1 and g ≥ g th .

6. The method for dynamic monitoring and early warning of ultrasonic full-section SCR flue resistance according to claim 1, characterized in that, Also includes: When a level-two warning signal is output, a soot blowing linkage signal is generated and output. The linkage signal is used to control the start-up of the soot blowing equipment; The output of the soot blowing linkage signal must satisfy one or more interlocking conditions, including: a: The time interval between adjacent soot blowing actions performed according to the soot blowing linkage signal reaches the preset minimum soot blowing interval time; b: The soot blowing system is in a ready-to-blow state; c: The number of blowing actions per unit time has not reached the upper limit; d: The aforementioned abnormal indicators ε The alert level has fallen back to the threshold for lifting the alert since it was triggered at Level 2. ε 1-δ and below for a set stabilization time , where δ is a hysteresis parameter that is greater than zero.

7. An ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device, characterized in that, include: Data acquisition module: Used to select a measurement interval in the straight section of the SCR inlet flue, and set up upstream and downstream static pressure measuring points within the measurement interval to measure and obtain the upstream static pressure. P up With downstream static pressure P down ; Ultrasonic flow measurement module: used to measure the ultrasonic measurement section within the measurement interval using the ultrasonic multi-channel time-of-flight method, to obtain the volumetric flow rate of flue gas flowing through the measurement interval. Q ; First data processing module: used for processing data based on the upstream static pressure. P up With the downstream static pressure P down The difference is used to determine the pressure drop Δ between the measuring points. P ; It is also used based on the voltage drop Δ P With the volumetric flow rate Q， Calculate the equivalent resistance characterization at the current moment. R =Δ P / Q ²; Resistance modeling module: used to model resistance based on equivalent resistance characteristics within a sliding time window. R Historical data sequences are used to calculate dynamic baselines. R base And update online; Second data processing module: used for the equivalent resistance characterization quantity at the current moment. R and the dynamic baseline R base Calculate abnormal indicators ε and rate of change g ,in, ε =( R - R base ) / R base ; ; Third data processing module: used to process the abnormal indicators ε Each with a preset first threshold ε 1 and second threshold ε 2. Compare and change rates g With the rate of change threshold g th Comparison; among which ε 2> ε 1; The graded early warning determination module is used to output early warning signals according to the following rules based on the comparison results; If the second warning condition is met, a level-two warning signal will be output. If the second warning condition is not met but the first warning condition is met, then a first-level warning signal will be output.

8. The ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device according to claim 6, characterized in that, The device also includes: a linkage module. The linkage module is used to trigger a soot blowing command when a level 2 alarm signal is received.

9. The ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device according to claim 6, characterized in that, The resistance modeling module also includes: First resistance modeling unit: used to characterize historical resistance quantities within a sliding time window based on a preset cleanliness state characterization strategy. R The process generates baseline candidate values ​​representing low resistance levels; the cleanliness state characterization strategy is low quantile statistics or lower envelope extraction. The second resistance modeling unit is used to fuse the baseline candidate values ​​into the historical baseline using exponential smoothing or recursive filtering algorithms, and output the updated dynamic baseline. R base .

10. The ultrasonic full-section SCR flue resistance dynamic monitoring and early warning device according to claim 1, characterized in that, The graded early warning determination module includes: First warning unit: Used to output a secondary warning signal when one of the following conditions is met, and the second debouncing condition is also met: A:ε ≥ ε 2； B:ε ≥ ε 1 and g ≥ g th ; Second early warning unit: When the conditions for a Level II early warning are not met, but the conditions are met... ε ≥ ε 1. When the first debouncing condition is met, a first-level warning signal is output; Condition storage unit: used to store the first debouncing condition and the second debouncing condition; The first debouncing condition is: within a preset duration window. T Within 1, abnormal state ε ≥ ε The number of times 1 appears reaches the preset consecutive number of times. N 1; The second debouncing condition is: within a preset duration window. T Within 2 days, the number of times an abnormal state meeting one of the following conditions occurs reaches a preset consecutive number. N 2: A:ε ≥ ε 2； B:ε ≥ ε 1 and g ≥ g th .

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