An air preheater blockage risk early warning method, device, system and storage medium

Abstract

The application discloses a kind of air preheater blocking risk early warning method, device, system and storage medium, to accurately carry out air preheater early warning according to different working conditions.The method comprises: determining the working condition of each time of air preheater according to each preset parameter value corresponding to each time of air preheater;Determine the optimal blocking rate corresponding to each working condition according to the working condition of each time of the air preheater;The optimal blocking rate corresponding to each working condition is substituted into the model constructed in advance to determine the predicted differential pressure of the air preheater at the current time;According to the predicted differential pressure of the air preheater at the current time, judge whether the air preheater exists blocking risk;When determining that the air preheater exists blocking risk, air preheater blocking early warning is carried out.The scheme can accurately warn the blocking risk of air preheater according to different working conditions.

Description

Technical Field

[0001] This application relates to the field of industrial technology, and in particular to a method, device, system and storage medium for early warning of air preheater blockage risk. Background Technology

[0002] The air preheater is an important part of the boiler-side system of a coal-fired power unit. It is located downstream of the denitrification system and uses the waste heat of flue gas to heat the primary and secondary air to improve boiler operating efficiency. However, during operation, the adhesion of sticky corrosive byproducts and low-temperature corrosion ash accumulation can cause blockages, reducing the unit's operating efficiency and load capacity, and in severe cases, threatening the unit's safety and causing unscheduled shutdowns.

[0003] Currently, blockage is mainly determined by the differential pressure at the inlet and outlet of the air preheater (APU), with an alarm triggered when the differential pressure exceeds a set value. However, the APU differential pressure is closely related to the unit's operating conditions. Under different operating conditions, even if the degree of blockage is the same, the differential pressure value may vary significantly. Therefore, relying solely on the APU differential pressure to determine the degree of blockage cannot effectively differentiate between different operating conditions, making it difficult for operators to intuitively and accurately understand the actual blockage situation. For example, when the unit is operating at high load, the APU differential pressure will naturally be at a higher level due to changes in parameters such as air volume and flue gas volume. In this case, judging solely by the differential pressure may lead to a misjudgment that the blockage has worsened. Conversely, when operating at low load, the differential pressure is relatively low, and even if the APU has experienced some degree of blockage, it may not be detected in time because the differential pressure has not reached the set limit.

[0004] Therefore, how to provide a method for early warning of air preheater blockage risk, so as to accurately warn of the blockage risk of air preheater according to different operating conditions, has become an urgent technical problem to be solved. Summary of the Invention

[0005] This application provides a method, device, system, and storage medium for early warning of air preheater blockage risk, which can accurately predict the blockage risk of air preheater according to different operating conditions.

[0006] This application provides a method for early warning of air preheater blockage risk, including:

[0007] The operating conditions of the air preheater at each time are determined based on the preset parameter values ​​corresponding to each time.

[0008] The optimal blocking rate corresponding to each operating condition is determined based on the operating conditions of the air preheater at each time.

[0009] Substitute the optimal blocking rate corresponding to each operating condition into the pre-built model to determine the air preheater predicted differential pressure at the current moment.

[0010] Determine whether there is a risk of blockage in the air preheater based on the predicted differential pressure of the air preheater at the current moment;

[0011] When it is determined that there is a risk of air preheater blockage, an air preheater blockage warning will be issued.

[0012] The beneficial effects of this application are as follows: The operating conditions of the air preheater at each moment are determined based on the preset parameter values ​​corresponding to each moment; the optimal blocking rate corresponding to each operating condition is determined based on the operating conditions of the air preheater at each moment; the optimal blocking rate corresponding to each operating condition is substituted into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment; the predicted differential pressure of the air preheater at the current moment is used to determine whether there is a blocking risk in the air preheater; and when it is determined that there is a blocking risk in the air preheater, an air preheater blocking warning is issued. Because this application determines the corresponding optimal blocking rate based on the operating conditions of the air preheater, and determines the predicted differential pressure of the air preheater at the current moment based on the corresponding optimal blocking rate, it combines different operating conditions to determine the predicted differential pressure of the air preheater, and then uses the predicted differential pressure of the air preheater at the current moment to judge whether there is a blocking risk in the air preheater in real time, and issues a timely warning when a risk exists. This achieves the goal of accurately warning of the blocking risk of the air preheater based on different operating conditions.

[0013] In one embodiment, determining the operating condition of the air preheater at each time step based on preset parameter values ​​corresponding to each time step includes:

[0014] The preset parameter values ​​of the air preheater at various times are matched with various preset clustering conditions. When the preset parameter value at the target time falls within the value range specified by the target preset clustering condition, the condition at the target time is determined as the target preset clustering condition.

[0015] In one embodiment, the construction process for the various preset clustering conditions is as follows:

[0016] Obtain historical operating data of the air preheater;

[0017] Historical operating data from the two cleaning cycles of the air preheater were selected as the dimension for cluster analysis. Based on the factors affecting air preheater blockage, various clustering conditions were constructed.

[0018] In one embodiment, determining the optimal blocking rate corresponding to each operating condition based on the operating conditions of the air preheater at each time step includes:

[0019] Construct the following objective function:

[0020]

[0021] Where O is the cumulative deviation between the actual differential pressure of the air preheater and the predicted differential pressure of the air preheater, and D... r (t) represents the actual differential pressure of the air preheater at time t, D p (t) represents the predicted differential pressure of the air preheater at time t;

[0022] Substituting the pre-built model into the objective function, the blocking rate of the air preheater at various time points is optimized:

[0023]

[0024] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l i (t) represents the blocking rate corresponding to operating condition i at time t.

[0025] In one embodiment, substituting the optimal blocking rate corresponding to each operating condition into a pre-built model to determine the air preheater predicted differential pressure at the current moment includes:

[0026] Substituting the time parameters corresponding to each moment of the air preheater and the optimal blocking rate corresponding to the operating conditions at each moment into the following pre-built model, the predicted differential pressure of the air preheater at the current moment is determined:

[0027]

[0028] Among them, D p (t) represents the predicted differential pressure corresponding to the final air preheater blocking process prediction model at the current time, and D1 represents the differential pressure corresponding to the time when the model is enabled. io (t) represents the optimal blocking rate corresponding to operating condition i at time t.

[0029] In one embodiment, determining whether there is a risk of blockage in the air preheater based on the predicted differential pressure of the air preheater at the current moment includes:

[0030] Compare the predicted differential pressure of the air preheater with the actual differential pressure value;

[0031] When the difference between the predicted differential pressure and the actual differential pressure of the air preheater is greater than a preset threshold, it is determined that the air preheater is at risk of blockage.

[0032] In one embodiment, the method further includes:

[0033] Detect the start signal of the air preheater soot blower;

[0034] When the start signal of the air preheater soot blower is triggered, the model is reset;

[0035] Reset the initial true differential pressure of the air preheater to the air preheater differential pressure corresponding to the moment the start signal was sent;

[0036] And at the next moment, the air preheater differential pressure corresponding to the start signal transmission time will be used as the initial true differential pressure of the air preheater to predict the air preheater blockage risk.

[0037] This application also provides an air preheater blockage risk early warning device, comprising:

[0038] The first determining module is used to determine the operating conditions of the air preheater at each time based on the preset parameter values ​​corresponding to each time of the air preheater.

[0039] The second determining module is used to determine the optimal blocking rate corresponding to each operating condition based on the operating conditions of the air preheater at each time.

[0040] The third determining module is used to substitute the optimal blocking rate corresponding to each working condition into the pre-built model in order to determine the air preheater predicted differential pressure at the current moment.

[0041] The judgment module is used to determine whether there is a risk of blockage in the air preheater based on the predicted differential pressure of the air preheater at the current moment.

[0042] The fourth determination module is used to provide an air preheater blockage warning when it is determined that there is a risk of blockage in the air preheater.

[0043] In one embodiment, the first determining module is further configured to:

[0044] The preset parameter values ​​of the air preheater at various times are matched with various preset clustering conditions. When the preset parameter value at the target time falls within the value range specified by the target preset clustering condition, the condition at the target time is determined as the target preset clustering condition.

[0045] In one embodiment, the construction process for the various preset clustering conditions is as follows:

[0046] Obtain historical operating data of the air preheater;

[0047] Historical operating data from the two cleaning cycles of the air preheater were selected as the dimension for cluster analysis. Based on the factors affecting air preheater blockage, various clustering conditions were constructed.

[0048] In one embodiment, the second determining module includes:

[0049] Construct a submodule to build the following objective function:

[0050]

[0051] Where O is the cumulative deviation between the actual differential pressure of the air preheater and the predicted differential pressure of the air preheater, and D... r (t) represents the actual differential pressure of the air preheater at time t, D p (t) represents the predicted differential pressure of the air preheater at time t;

[0052] The substitution submodule is used to substitute the following pre-built model into the objective function to optimize the blocking rate of the air preheater at various time points:

[0053]

[0054] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l i (t) represents the blocking rate corresponding to operating condition i at time t.

[0055] In one embodiment, the third determining module includes:

[0056] Substituting the time parameters corresponding to each moment of the air preheater and the optimal blocking rate corresponding to the operating conditions at each moment into the following pre-built model, the predicted differential pressure of the air preheater at the current moment is determined:

[0057]

[0058] Among them, D p (t) represents the predicted differential pressure corresponding to the final air preheater blocking process prediction model at the current time, and D1 represents the differential pressure corresponding to the time when the model is enabled. io (t) represents the optimal blocking rate corresponding to operating condition i at time t.

[0059] In one embodiment, the determining module includes:

[0060] The comparison submodule is used to compare the predicted differential pressure of the air preheater with the actual differential pressure value;

[0061] The determination submodule is used to determine that there is a risk of blockage in the air preheater when the difference between the predicted differential pressure and the actual differential pressure value of the air preheater is greater than a preset threshold.

[0062] In one embodiment, the apparatus further includes:

[0063] The monitoring module is used to detect the start signal of the air preheater soot blower;

[0064] A reset module is used to reset the model when the start signal of the air preheater soot blower is triggered;

[0065] The reset module is used to reset the initial true differential pressure of the air preheater to the air preheater differential pressure corresponding to the time when the start signal is sent.

[0066] The prediction module is used to predict the air preheater blockage risk at the next time step by using the air preheater differential pressure corresponding to the start signal transmission time as the initial true differential pressure of the air preheater.

[0067] This application also provides an air preheater blockage risk early warning system, including:

[0068] At least one processor; and,

[0069] A memory communicatively connected to the at least one processor; wherein,

[0070] The memory stores instructions that can be executed by the at least one processor to implement the air preheater blockage risk warning method described in any of the above embodiments.

[0071] This application also provides a computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor corresponding to the air preheater blockage risk warning system, enables the air preheater blockage risk warning system to implement the air preheater blockage risk warning method described in any of the above embodiments.

[0072] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings.

[0073] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0074] The accompanying drawings are provided to further illustrate the present application and form part of the specification. They are used together with the embodiments of the present application to explain the application and do not constitute a limitation thereof. In the drawings:

[0075] Figure 1 This is a flowchart of an air preheater blockage risk warning method according to an embodiment of this application;

[0076] Figure 2 This is a schematic diagram of the structure of an air preheater blockage risk warning device according to an embodiment of this application;

[0077] Figure 3 This is a schematic diagram of the hardware structure of an air preheater blockage risk early warning system according to an embodiment of this application. Detailed Implementation

[0078] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application.

[0079] Figure 1 This is a flowchart of an air preheater blockage risk warning method according to an embodiment of this application, such as... Figure 1 As shown, the method can be implemented as follows: S101-S105:

[0080] In step S101, the operating conditions of the air preheater at each time are determined according to the preset parameter values ​​corresponding to each time.

[0081] In step S102, the optimal blocking rate corresponding to each operating condition is determined based on the operating conditions of the air preheater at each time.

[0082] In step S103, the optimal blocking rate corresponding to each operating condition is substituted into the pre-built model to determine the air preheater predicted differential pressure at the current moment.

[0083] In step S104, the air preheater is judged to have a risk of blockage based on the air preheater predicted differential pressure at the current moment;

[0084] In step S105, when it is determined that there is a risk of blockage in the air preheater, an air preheater blockage warning is issued.

[0085] In this application, the operating conditions of the air preheater at each moment are determined based on the preset parameter values ​​corresponding to each moment. To scientifically determine the operating conditions of the air preheater, this application uses historical operating data of the air preheater as a basis and selects multiple factors affecting air preheater blockage as preset parameters for operating condition clustering. For example, historical operating data from the two cleaning chambers of the air preheater are selected, along with the inlet and outlet temperatures of the flue gas side, primary air side, and secondary air side, ammonia slip, SO2 concentration at the absorber inlet, NOx concentration at the SCR reactor inlet and outlet, dust concentration at the electrostatic precipitator inlet, and O2 concentration at the air preheater inlet as factors affecting air preheater blockage. Cluster analysis is then performed to obtain N clustered operating conditions. Alternatively, judgment features of the operating conditions are constructed based on each preset parameter; then, cluster analysis is performed on these judgment features to obtain multiple clustered operating conditions. These judgment features include at least the dynamic resistance coefficient K = differential pressure value / flue gas flow rate, and the temperature gradient ▽T = inlet temperature Th - outlet temperature Tc, etc. When determining the operating conditions at each time, the preset parameter values ​​of the air preheater at each time are matched with various preset clustering conditions. When the preset parameter value at the target time falls within the value range specified by the target preset clustering condition, the operating condition at the target time is determined as the target preset clustering condition.

[0086] The optimal blocking rate for each operating condition is determined based on the operating conditions of the air preheater at various times. In this application, the optimal blocking rate is pre-determined for each operating condition. Specifically, the following pre-constructed final air preheater blocking process prediction model is used to determine the theoretical predicted differential pressure of the air preheater for each operating condition:

[0087]

[0088] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l i(t) represents the blocking rate corresponding to operating condition i at time t.

[0089] For the above model, the core requirement is to determine the blocking rate corresponding to different operating conditions. Therefore, in order to determine the blocking rate corresponding to each operating condition, the following objective function is constructed:

[0090]

[0091] Where O is the cumulative deviation between the actual differential pressure of the air preheater and the predicted differential pressure of the air preheater, and D... r (t) represents the actual differential pressure of the air preheater at time t, D p (t) represents the predicted differential pressure of the air preheater at time t.

[0092] Furthermore, based on historical operating data, the above model is optimized, for example by applying particle swarm optimization and teaching optimization, to optimize the blocking rate, and the blocking rate corresponding to each operating condition that minimizes the cumulative deviation is determined as the optimal blocking rate for each operating condition, thereby constructing the correspondence between each operating condition and the optimal blocking rate.

[0093] By substituting the optimal blocking rate corresponding to each operating condition at the current moment into a pre-built final air preheater blocking process prediction model, the predicted differential pressure of the air preheater at the current moment can be determined. During operation, the initial true differential pressure of the air preheater can be obtained, and the operating conditions and corresponding optimal blocking rates at different times can be determined based on real-time acquired preset parameters. Therefore, the theoretical predicted differential pressure of the air preheater for each operating condition can be determined through the air preheater blocking process prediction model.

[0094]

[0095] Among them, D p (t) represents the predicted differential pressure of the air preheater, and D1 is the differential pressure at the time the model is activated. io (t) represents the optimal blocking rate corresponding to operating condition i at time t.

[0096] The blocking rate values ​​for each operating condition in the above model are usually constants.

[0097] Furthermore, a corresponding empty preheater blocking process prediction model can be constructed using dynamic functions.

[0098] (1) When selecting a dynamic function At that time, the following second air preheater blocking process prediction model is constructed to determine the theoretical predicted differential pressure of the air preheater for each operating condition:

[0099]

[0100] Among them, D p(t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l ic1 Let D be the blocking rate corresponding to operating condition i at time t. r (t) represents the actual differential pressure of the air preheater at time t.

[0101] Accordingly, after determining the optimal blocking rate, when predicting the differential pressure at the current moment, the optimal blocking rate corresponding to each operating condition at the current moment is substituted into the second air preheater blocking process prediction model to determine the air preheater's predicted differential pressure at the current moment:

[0102]

[0103] Among them, D p (t) represents the predicted differential pressure of the air preheater, and D1 is the differential pressure at the time the model is activated. ic1o Let D be the optimal blocking rate for operating condition i at time t. r (t) represents the actual differential pressure of the air preheater at time t.

[0104] (2) When selecting a dynamic function At that time, the following prediction model for the third air preheater blockage process is constructed to determine the theoretical predicted differential pressure of the air preheater for each operating condition:

[0105]

[0106] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l ic2 Let D be the blocking rate corresponding to operating condition i at time t. r (t) represents the actual differential pressure of the air preheater at time t.

[0107] It should be noted that when t=1,

[0108] Accordingly, after determining the optimal blocking rate, the predicted differential pressure of the air preheater at the current moment is determined by substituting the optimal blocking rate corresponding to each operating condition at the current moment into the prediction model of the blocking process of the third air preheater when applying the model:

[0109]

[0110] Among them, D p (t) represents the predicted differential pressure of the air preheater, and D1 is the differential pressure at the time the model is activated. ic2o Let D be the optimal blocking rate for operating condition i at time t. r (t) represents the actual differential pressure of the air preheater at time t.

[0111] (3) When selecting a dynamic function At that time, the following fourth air preheater blocking process prediction model is constructed to determine the theoretical predicted differential pressure of the air preheater for each operating condition:

[0112]

[0113] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l ic3 Let D be the blocking rate corresponding to operating condition i at time t. r (t) represents the actual differential pressure of the air preheater at time t.

[0114] It should be noted that when t=1,

[0115] Accordingly, after determining the optimal blocking rate, the predicted differential pressure of the air preheater at the current moment is determined by substituting the optimal blocking rate corresponding to each operating condition at the current moment into the prediction model of the blocking process of the fourth air preheater:

[0116]

[0117] Among them, D p (t) represents the predicted differential pressure of the air preheater, and D1 is the differential pressure at the time the model is activated. ic3o Let D be the optimal blocking rate for operating condition i at time t. r (t) represents the actual differential pressure of the air preheater at time t.

[0118] (4) Selecting a dynamic function The following model for predicting the blockage process of the fifth air preheater is constructed to determine the theoretical predicted differential pressure of the air preheater for each operating condition:

[0119]

[0120] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l ic4 Let be the blocking rate corresponding to operating condition i at time t.

[0121] Accordingly, when applying the model, the optimal blocking rate corresponding to each operating condition at the current moment is substituted into the fifth air preheater blocking process prediction model to determine the predicted differential pressure of the air preheater at the current moment:

[0122]

[0123] Among them, D p (t) represents the predicted differential pressure of the air preheater, and D1 is the differential pressure at the time the model is activated. ic4o Let be the optimal blocking rate corresponding to operating condition i at time t.

[0124] Furthermore, in another embodiment of this application, a sixth air preheater blockage process prediction model is constructed by combining flue gas temperature and flow rate to determine the theoretical predicted differential pressure of the air preheater for each operating condition:

[0125]

[0126] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l i (t) represents the blocking rate corresponding to operating condition i at time t, α i (T(t)) is the temperature-dependent deposition coefficient corresponding to the temperature of condition i at time t, β i (v(t)) is the flue gas velocity-related scouring coefficient corresponding to operating condition i at time t.

[0127] For α i (T(t)) and β i (v(t)) can be determined in the following way:

[0128]

[0129] By introducing temperature- and flow rate-dependent coefficients, the model can more accurately reflect the phase transition kinetics of ammonium bisulfate and the fly ash transport mechanism, providing a quantitative basis for blockage early warning and soot blowing optimization. The temperature-dependent deposition coefficient can quantify the nonlinear effect of temperature on the deposition rate. In the low-temperature range (T < 150℃), ammonium bisulfate (ABS) is in the condensation stage, and its viscosity increases sharply, reaching more than 10 times that of conventional fly ash, leading to a doubling of the fly ash adhesion rate. The exponential decay function reflects the law of viscosity decreasing with increasing temperature. In the medium-temperature range (150℃ ≤ T ≤ 230℃), ABS and fly ash form composite deposits, clogging the gaps in the corrugated plates. In the high-temperature range (T > 230℃), ABS decomposes into gaseous NH3 and... Viscosity is significantly reduced. Regarding the scouring coefficient related to flue gas velocity, fly ash is easily deposited at low velocities (v < 8 m / s), and β(v) increases exponentially with decreasing velocity; at the critical velocity (8 m / s ≤ v ≤ 12 m / s), scouring and deposition reach a dynamic equilibrium; at high velocities (v > 12 m / s), the kinetic energy of fly ash particles increases, and the scouring effect is stronger. Similarly, based on the sixth air preheater blockage process prediction model, by optimizing the objective function mentioned above, the optimal blockage rate corresponding to each operating condition can be obtained. Therefore, when predicting the differential pressure at the current moment, the predicted differential pressure of the air preheater at the current moment is determined by substituting the optimal blockage rate corresponding to each operating condition at the current moment into the sixth air preheater blockage process prediction model.

[0130]

[0131] Among them, Dp (t) represents the predicted differential pressure of the air preheater, and D1 is the differential pressure at the time the model is activated. io Let α be the optimal blocking rate for operating condition i at time t. i (T(t)) is the temperature-dependent deposition coefficient corresponding to the temperature of condition i at time t, β i (v(t)) is the flue gas velocity-related scouring coefficient corresponding to operating condition i at time t.

[0132] The presence of air preheater blockage risk is determined based on the predicted differential pressure of the air preheater at the current moment. Since the predicted differential pressure is the theoretical differential pressure value of the air preheater under the current operating conditions, in one embodiment of this application, the predicted differential pressure is compared with the actual differential pressure value; when the difference between the predicted and actual differential pressure values ​​exceeds a preset threshold, it is determined that the air preheater has a blockage risk. In another embodiment, when the proportion of the difference between the predicted and actual differential pressure values ​​to the predicted difference exceeds a preset proportion, it is determined that the air preheater has a blockage risk.

[0133] When an air preheater is identified as having a risk of blockage, an air preheater blockage warning is issued, prompting operators to perform air preheater soot blowing. In one embodiment of this application, the start signal of the air preheater soot blower is detected; when the start signal of the air preheater soot blower is triggered, the model is reset; the initial true differential pressure of the air preheater is reset to the air preheater differential pressure corresponding to the time the start signal is sent; at the next moment, the air preheater differential pressure corresponding to the time the start signal is sent is used as the initial true differential pressure of the air preheater for air preheater blockage risk prediction. For example, when operators perform air preheater soot blowing according to the warning instruction after an air preheater blockage warning occurs, the system monitors the start signal of the air preheater soot blower. When the start signal is triggered, the model is reset to the air preheater differential pressure corresponding to the time the start signal is sent, and the air preheater differential pressure prediction is performed again according to the time axis based on real-time input data.

[0134] The beneficial effects of this application are as follows: The operating conditions of the air preheater at each moment are determined based on the preset parameter values ​​corresponding to each moment; the optimal blocking rate corresponding to each operating condition is determined based on the operating conditions of the air preheater at each moment; the optimal blocking rate corresponding to each operating condition is substituted into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment; the predicted differential pressure of the air preheater at the current moment is used to determine whether there is a blocking risk in the air preheater; and when it is determined that there is a blocking risk in the air preheater, an air preheater blocking warning is issued. Because this application determines the corresponding optimal blocking rate based on the operating conditions of the air preheater, and determines the predicted differential pressure of the air preheater at the current moment based on the corresponding optimal blocking rate, it combines different operating conditions to determine the predicted differential pressure of the air preheater, and then uses the predicted differential pressure of the air preheater at the current moment to judge whether there is a blocking risk in the air preheater in real time, and issues a timely warning when a risk exists. This achieves the goal of accurately warning of the blocking risk of the air preheater based on different operating conditions.

[0135] In one embodiment, step S101 above can be implemented as follows:

[0136] The preset parameter values ​​of the air preheater at various times are matched with various preset clustering conditions. When the preset parameter value at the target time falls within the value range specified by the target preset clustering condition, the condition at the target time is determined as the target preset clustering condition.

[0137] In one embodiment, the construction process of the various preset clustering conditions can be implemented as follows: A1-A2:

[0138] In step A1, historical operating data of the air preheater is obtained;

[0139] In step A2, historical operating data between the two cleaning cycles of the air preheater are selected as the dimension for cluster analysis. Based on the factors affecting air preheater blockage, multiple clustering conditions are constructed.

[0140] In one embodiment, step S102 above can be implemented as steps B1-B2 as follows:

[0141] In step B1, the following objective function is constructed:

[0142]

[0143] Where O is the cumulative deviation between the actual differential pressure of the air preheater and the predicted differential pressure of the air preheater, and D... r (t) represents the actual differential pressure of the air preheater at time t, D p (t) represents the predicted differential pressure of the air preheater at time t;

[0144] In step B2, the following pre-constructed model is substituted into the objective function to optimize the blocking rate of the air preheater at various time points:

[0145]

[0146] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l i (t) represents the blocking rate corresponding to operating condition i at time t.

[0147] In one embodiment, step S103 can be implemented as follows:

[0148] Substituting the time parameters corresponding to each moment of the air preheater and the optimal blocking rate corresponding to the operating conditions at each moment into the following pre-built model, the predicted differential pressure of the air preheater at the current moment is determined:

[0149]

[0150] Among them, D p (t) represents the predicted differential pressure corresponding to the final air preheater blocking process prediction model, and D1 represents the differential pressure at the time the model is enabled. io (t) represents the optimal blocking rate corresponding to operating condition i at time t.

[0151] In one embodiment, step S104 above can be implemented as steps C1-C2 as follows:

[0152] In step C1, the predicted differential pressure of the air preheater is compared with the actual differential pressure value;

[0153] In step C2, when the difference between the predicted differential pressure and the actual differential pressure of the air preheater is greater than a preset threshold, it is determined that there is a risk of blockage in the air preheater.

[0154] In one embodiment, the method may also be implemented as steps D1-D4:

[0155] In step D1, the start signal of the air preheater soot blower is detected;

[0156] In step D2, when the start signal of the air preheater soot blower is triggered, the model is reset;

[0157] In step D3, the initial true differential pressure of the air preheater is reset to the air preheater differential pressure corresponding to the time when the start signal is sent;

[0158] In step D4, at the next moment, the air preheater differential pressure corresponding to the start signal transmission moment is used as the initial true differential pressure of the air preheater for air preheater blockage risk prediction.

[0159] Figure 2 This is a schematic diagram of the structure of an air preheater blockage risk early warning device according to an embodiment of this application, as shown below. Figure 2 As shown, it includes:

[0160] The first determining module 201 is used to determine the operating conditions of the air preheater at each time based on the preset parameter values ​​corresponding to each time of the air preheater.

[0161] The second determining module 202 is used to determine the optimal blocking rate corresponding to each operating condition based on the operating conditions of the air preheater at each time.

[0162] The third determining module 203 is used to substitute the optimal blocking rate corresponding to each working condition into the pre-built model in order to determine the air preheater predicted differential pressure at the current moment.

[0163] The judgment module 204 is used to determine whether there is a risk of blockage in the air preheater based on the predicted differential pressure of the air preheater at the current moment.

[0164] The fourth determination module 205 is used to provide an air preheater blockage warning when it is determined that there is a risk of blockage in the air preheater.

[0165] In one embodiment, the first determining module is further configured to:

[0166] The preset parameter values ​​of the air preheater at various times are matched with various preset clustering conditions. When the preset parameter value at the target time falls within the value range specified by the target preset clustering condition, the condition at the target time is determined as the target preset clustering condition.

[0167] In one embodiment, the construction process for the various preset clustering conditions is as follows:

[0168] Obtain historical operating data of the air preheater;

[0169] Historical operating data from the two cleaning cycles of the air preheater were selected as the dimension for cluster analysis. Based on the factors affecting air preheater blockage, various clustering conditions were constructed.

[0170] In one embodiment, the second determining module includes:

[0171] Construct a submodule to build the following objective function:

[0172]

[0173] Where O is the cumulative deviation between the actual differential pressure of the air preheater and the predicted differential pressure of the air preheater, and D... r (t) represents the actual differential pressure of the air preheater at time t, D p (t) represents the predicted differential pressure of the air preheater at time t;

[0174] The substitution submodule is used to substitute the following pre-built model into the objective function to optimize the blocking rate of the air preheater at various time points:

[0175]

[0176] Among them, D p (t) represents the predicted differential pressure of the air preheater, D0 represents the initial true differential pressure of the air preheater, and l i (t) represents the blocking rate corresponding to operating condition i at time t.

[0177] In one embodiment, the third determining module includes:

[0178] Substituting the time parameters corresponding to each moment of the air preheater and the optimal blocking rate corresponding to the operating conditions at each moment into the following pre-built model, the predicted differential pressure of the air preheater at the current moment is determined:

[0179]

[0180] Among them, D p (t) represents the predicted differential pressure corresponding to the final air preheater blocking process prediction model, and D1 represents the differential pressure at the time the model is enabled. io (t) represents the optimal blocking rate corresponding to operating condition i at time t.

[0181] In one embodiment, the determining module includes:

[0182] The comparison submodule is used to compare the predicted differential pressure of the air preheater with the actual differential pressure value;

[0183] The determination submodule is used to determine that there is a risk of blockage in the air preheater when the difference between the predicted differential pressure and the actual differential pressure value of the air preheater is greater than a preset threshold.

[0184] In one embodiment, the apparatus further includes:

[0185] The monitoring module is used to detect the start signal of the air preheater soot blower;

[0186] A reset module is used to reset the model when the start signal of the air preheater soot blower is triggered;

[0187] The reset module is used to reset the initial true differential pressure of the air preheater to the air preheater differential pressure corresponding to the time when the start signal is sent.

[0188] The prediction module is used to predict the air preheater blockage risk at the next time step by using the air preheater differential pressure corresponding to the start signal transmission time as the initial true differential pressure of the air preheater.

[0189] Figure 3This is a schematic diagram of the hardware structure of an air preheater blockage risk early warning system according to an embodiment of this application, as shown below. Figure 3 As shown, the air preheater blockage risk early warning system includes:

[0190] At least one processor 320; and,

[0191] Memory 304 communicatively connected to the at least one processor 320; wherein,

[0192] The memory 304 stores instructions that can be executed by the at least one processor 320 to implement the air preheater blockage risk warning method described in any of the above embodiments.

[0193] Reference Figure 3 The air preheater blockage risk warning system 300 may include one or more of the following components: processing component 302, memory 304, power supply component 306, multimedia component 308, audio component 310, input / output (I / O) interface 312, sensor component 314, and communication component 316.

[0194] Processing component 302 typically controls the overall operation of the air preheater congestion risk warning system 300. Processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the method described above. Furthermore, processing component 302 may include one or more modules to facilitate interaction between processing component 302 and other components. For example, processing component 302 may include a multimedia module to facilitate interaction between multimedia component 308 and processing component 302.

[0195] Memory 304 is configured to store various types of data to support the operation of the air preheater congestion risk warning system 300. Examples of this data include instructions for any application or method operating on the air preheater congestion risk warning system 300, such as text, images, videos, etc. Memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0196] Power supply component 306 provides power to various components of the air preheater congestion risk warning system 300. Power supply component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the air preheater congestion risk warning system 300.

[0197] The multimedia component 308 includes a screen that provides an output interface between the air preheater congestion risk warning system 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, the multimedia component 308 may also include a front-facing camera and / or a rear-facing camera. When the air preheater congestion risk warning system 300 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0198] Audio component 310 is configured to output and / or input audio signals. For example, audio component 310 includes a microphone (MIC) configured to receive external audio signals when the air preheater congestion risk warning system 300 is in an operating mode, such as alarm mode, recording mode, voice recognition mode, and voice output mode. The received audio signals may be further stored in memory 304 or transmitted via communication component 316. In some embodiments, audio component 310 also includes a speaker for outputting audio signals.

[0199] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0200] Sensor assembly 314 includes one or more sensors for providing status assessments of various aspects of the air preheater congestion risk warning system 300. For example, sensor assembly 314 may include a sound sensor. Additionally, sensor assembly 314 may detect the on / off state of the air preheater congestion risk warning system 300, the relative positioning of components (e.g., the display and keypad of the air preheater congestion risk warning system 300), the operating status of the air preheater congestion risk warning system 300 or one of its components, the orientation or acceleration / deceleration of the air preheater congestion risk warning system 300, and temperature changes of the air preheater congestion risk warning system 300. Sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 314 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 314 may also include a magnetic sensor, a pressure sensor, a material buildup thickness sensor, or a temperature sensor.

[0201] Communication component 316 is configured to enable air preheater congestion risk warning system 300 to provide wired or wireless communication capabilities with other devices and cloud platforms. Air preheater congestion risk warning system 300 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 316 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 316 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0202] In an exemplary embodiment, the air preheater congestion risk warning system 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the air preheater congestion risk warning method described in any of the above embodiments.

[0203] This application also provides a computer-readable storage medium, which, when the instructions in the storage medium are executed by the processor corresponding to the air preheater blockage risk warning system, enables the air preheater blockage risk warning system to implement the air preheater blockage risk warning method described in any of the above embodiments.

[0204] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0205] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0206] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0207] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0208] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for early warning of air preheater blockage risk, characterized in that, include: The operating conditions of the air preheater at each time are determined based on the preset parameter values ​​corresponding to each time. The optimal blocking rate corresponding to each operating condition is determined based on the operating conditions of the air preheater at each time. Substitute the optimal blocking rate corresponding to each operating condition into the pre-built model to determine the air preheater predicted differential pressure at the current moment. Determine whether there is a risk of blockage in the air preheater based on the predicted differential pressure of the air preheater at the current moment; When it is determined that there is a risk of air preheater blockage, an air preheater blockage warning will be issued; The step of determining the optimal blocking rate corresponding to each operating condition based on the operating conditions of the air preheater at each time includes: Construct the following objective function: ； in, This represents the cumulative deviation between the actual differential pressure of the air preheater and the predicted differential pressure of the air preheater. The actual differential pressure of the air preheater at time t. The air preheater predicts the differential pressure at time t; Substituting the pre-built model into the objective function, the blocking rate of the air preheater at various time points is optimized: ； in, Predict differential pressure for air preheater. This represents the initial true differential pressure of the air preheater. Let be the blocking rate corresponding to operating condition i at time t; The step of substituting the optimal blocking rate corresponding to each operating condition into a pre-built model to determine the air preheater predicted differential pressure at the current moment includes: Substituting the time parameters corresponding to each moment of the air preheater and the optimal blocking rate corresponding to the operating conditions at each moment into the following pre-built model, the predicted differential pressure of the air preheater at the current moment is determined: ； in, The predicted differential pressure corresponding to the model for the final air preheater blocking process at the current moment. The differential pressure corresponding to the moment the model is enabled. Let be the optimal blocking rate corresponding to operating condition i at time t; The method further includes: Detect the start signal of the air preheater soot blower; When the start signal of the air preheater soot blower is triggered, the model is reset; Reset the initial true differential pressure of the air preheater to the air preheater differential pressure corresponding to the moment the start signal was sent; And at the next moment, the air preheater differential pressure corresponding to the start signal transmission time will be used as the initial true differential pressure of the air preheater to predict the air preheater blockage risk.

2. The method as described in claim 1, characterized in that, The operating conditions of the air preheater at each time point are determined based on the preset parameter values ​​corresponding to each time point, including: The preset parameter values ​​of the air preheater at various times are matched with various preset clustering conditions. When the preset parameter value at the target time falls within the value range specified by the target preset clustering condition, the condition at the target time is determined as the target preset clustering condition.

3. The method as described in claim 2, characterized in that, The construction process for each of the preset clustering conditions is as follows: Obtain historical operating data of the air preheater; Historical operating data from the two cleaning cycles of the air preheater were selected as the dimension for cluster analysis. Based on the factors affecting air preheater blockage, various clustering conditions were constructed.

4. The method as described in claim 1, characterized in that, The step of determining whether there is a risk of blockage in the air preheater based on the predicted differential pressure of the air preheater at the current moment includes: Compare the predicted differential pressure of the air preheater with the actual differential pressure value; When the difference between the predicted differential pressure and the actual differential pressure of the air preheater is greater than a preset threshold, it is determined that the air preheater is at risk of blockage.

5. An air preheater blockage risk early warning device, used in the air preheater blockage risk early warning method as described in any one of claims 1-4, characterized in that, include: The first determining module is used to determine the operating conditions of the air preheater at each time based on the preset parameter values ​​corresponding to each time of the air preheater. The second determining module is used to determine the optimal blocking rate corresponding to each operating condition based on the operating conditions of the air preheater at each time. The third determining module is used to substitute the optimal blocking rate corresponding to each operating condition into the pre-built model in order to determine the air preheater predicted differential pressure at the current moment. The judgment module is used to determine whether there is a risk of blockage in the air preheater based on the predicted differential pressure of the air preheater at the current moment. The fourth determination module is used to provide an air preheater blocking warning when it is determined that there is a risk of air preheater blocking.

6. An air preheater blockage risk early warning system, characterized in that, include: At least one processor; as well as, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor to implement the air preheater blockage risk warning method as described in any one of claims 1-4.

7. A computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor corresponding to the air preheater blockage risk warning system, the air preheater blockage risk warning system is able to implement the air preheater blockage risk warning method as described in any one of claims 1-4.

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