Air pre-heater blocking risk early warning method, device and system and storage medium

Abstract

The invention discloses an air pre-heater blocking risk early warning method, device and system and a storage medium. The method, device and system are used for accurately conducting air pre-heater early warning according to different working conditions. The method comprises the following steps: determining the working condition of the air pre-heater at each moment according to each preset parameter value corresponding to each moment of the air pre-heater; according to the working conditions of the air pre-heater at all moments, the optimal blocking rate corresponding to all the working conditions is determined; substituting the optimal blocking rate corresponding to each working condition into a pre-constructed model to determine the predicted differential pressure of the air pre-heater at the current moment; whether the air pre-heater has the blocking risk or not is judged according to the predicted differential pressure of the air pre-heater at the current moment; and when it is determined that the air pre-heater has the blocking risk, air pre-heater blocking early warning is carried out. According to the scheme, accurate early warning can be carried out on the blocking risk of the air pre-heater according to different working conditions.

Description

Technical Field

[0001] The present application relates to the field of industrial technology, and in particular to an air preheater blockage risk warning method, device, system and storage medium. Background Art

[0002] The air preheater (APH) is a critical component of the furnace-side system of coal-fired power plants. Located downstream of the denitrification system, it utilizes flue gas waste heat to heat primary and secondary air, improving boiler efficiency. However, during operation, the adhesion of sticky, corrosive byproducts and low-temperature corrosion ash accumulation can cause blockage, reducing unit efficiency and load capacity. In severe cases, this can threaten unit safety and lead to unplanned shutdowns.

[0003] Currently, blockage is primarily determined by the differential pressure between the flue gas inlet and outlet of the air preheater. An alarm is triggered when the differential pressure exceeds a set value. However, the magnitude of the air preheater differential pressure is closely related to the unit's operating conditions. Even under different operating conditions, the differential pressure values ​​may vary significantly, even if the air preheater has the same degree of blockage. Therefore, relying solely on the air preheater differential pressure to determine the degree of blockage fails to effectively differentiate between different operating conditions, making it difficult for staff to intuitively and accurately understand the actual blockage of the air preheater. For example, when the unit is operating at high load, the air preheater differential pressure will be high due to changes in parameters such as air volume and flue gas volume. At this time, relying solely on the differential pressure to determine the air preheater's blockage may lead to an erroneous judgment that the air preheater's blockage has worsened. During low-load operation, the differential pressure is relatively low. Even if the air preheater has already experienced a certain degree of blockage, it may not be discovered in time because the differential pressure has not reached the set limit.

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

[0005] The present application provides an air preheater blockage risk warning method, device, system and storage medium for accurately warning of the blockage risk of the air preheater according to different operating conditions.

[0006] This application provides an air preheater blockage risk early warning method, comprising:

[0007] Determine the operating conditions of the air preheater at each moment according to the preset parameter values ​​corresponding to each moment of the air preheater;

[0008] Determining the optimal blocking rate corresponding to each operating condition according to the operating condition of the air preheater at each moment;

[0009] Substituting the optimal blockage rate corresponding to each operating condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment;

[0010] Determine whether the air preheater has a blockage risk 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 blockage in the air preheater, an air preheater blockage warning will be issued.

[0012] The beneficial effects of the present application are: determining the working conditions of the air preheater at each moment according to the preset parameter values ​​corresponding to each moment; determining the optimal blocking rate corresponding to each working condition according to the working conditions of the air preheater at each moment; substituting the optimal blocking rate corresponding to each working condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment; judging whether the air preheater has a blocking risk according to the predicted differential pressure of the air preheater at the current moment; and issuing an air preheater blocking warning when it is determined that the air preheater has a blocking risk. Since the present application determines the corresponding optimal blocking rate according to the operating conditions of the air preheater, and determines the predicted differential pressure of the air preheater at the current moment according to the corresponding optimal blocking rate, the predicted differential pressure of the air preheater is determined in combination with different working conditions, and then judges in real time whether the air preheater has a blocking risk according to the predicted differential pressure of the air preheater at the current moment, and issues a timely warning when there is a risk. The purpose of accurately warning the blocking risk of the air preheater according to different working conditions is achieved.

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

[0014] The preset parameter values ​​of the air preheater at each moment are matched with various preset cluster operating conditions. When the preset parameter value at the target moment falls within the value range specified by the target preset cluster operating condition, the operating condition at the target moment is determined to be the target preset cluster operating condition.

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

[0016] Obtain historical operation data of air preheater;

[0017] The historical operation data of the two cleaning rooms of the air preheater were selected as the dimension of cluster analysis, and the factors affecting the blockage of the air preheater were used as the clustering basis to construct multiple clustering conditions.

[0018] In one embodiment, determining the optimal blocking rate corresponding to each operating condition according to the operating condition of the air preheater at each moment 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, D r (t) is the actual differential pressure of the air preheater at time t, D p (t) is the predicted differential pressure of the air preheater at time t;

[0022] Substitute the following pre-built model into the objective function to optimize the blocking rate of the air preheater at each operating time:

[0023]

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

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

[0026] Substitute the time parameters corresponding to each moment of the air preheater and the optimal blockage rate corresponding to the operating conditions at each moment into the following pre-built model to determine the predicted differential pressure of the air preheater at the current moment:

[0027]

[0028] Among them, D p (t) is the predicted differential pressure corresponding to the final air preheater blocking process estimation model at the current moment, D1 is the differential pressure corresponding to the moment the model is activated, l io (t) is the optimal blocking rate corresponding to working condition i at time t.

[0029] In one embodiment, judging whether the air preheater has a blockage risk 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 of the air preheater and the actual differential pressure value is greater than a preset threshold, it is determined that there is a risk of blockage in the air preheater.

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

[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 real differential pressure of the air preheater to the air preheater differential pressure corresponding to the moment when the start signal is sent;

[0036] At the next moment, the air preheater differential pressure corresponding to the moment the start signal is sent is used as the initial true differential pressure of the air preheater to predict the air preheater blocking risk.

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

[0038] The first determining module is used to determine the operating conditions of the air preheater at each moment according to the preset parameter values ​​corresponding to the air preheater at each moment;

[0039] A second determining module is used to determine the optimal blocking rate corresponding to each operating condition according to the operating condition of the air preheater at each moment;

[0040] A third determination module is used to substitute the optimal blockage rate corresponding to each operating condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment;

[0041] A judgment module is used to judge whether there is a blockage risk of 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 issue an air preheater blockage warning when it is determined that there is a blockage risk 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 each moment are matched with various preset cluster operating conditions. When the preset parameter value at the target moment falls within the value range specified by the target preset cluster operating condition, the operating condition at the target moment is determined to be the target preset cluster operating condition.

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

[0046] Obtain historical operation data of air preheater;

[0047] The historical operation data of the two cleaning rooms of the air preheater were selected as the dimension of cluster analysis, and the factors affecting the blockage of the air preheater were used as the clustering basis to construct multiple clustering conditions.

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

[0049] Construct submodules to construct 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, D r (t) is the actual differential pressure of the air preheater at time t, D p (t) is 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 operating conditions at each moment:

[0053]

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

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

[0056] Substitute the time parameters corresponding to each moment of the air preheater and the optimal blockage rate corresponding to the operating conditions at each moment into the following pre-built model to determine the predicted differential pressure of the air preheater at the current moment:

[0057]

[0058] Among them, D p (t) is the predicted differential pressure corresponding to the final air preheater blocking process estimation model at the current moment, D1 is the differential pressure corresponding to the moment the model is activated, l io (t) is the optimal blocking rate corresponding to working condition i at time t.

[0059] In one embodiment, the judgment 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 of the air preheater and the actual differential pressure value is greater than a preset threshold.

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

[0063] Monitoring module, used to detect the start-up signal of the air preheater soot blower;

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

[0065] A reset module, used to reset the initial real differential pressure of the air preheater to the air preheater differential pressure corresponding to the moment when the start signal is sent;

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

[0067] The present application also provides an air preheater blockage risk warning system, comprising:

[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, and the instructions are executed by the at least one processor to implement the air preheater blockage risk warning method recorded in any of the above embodiments.

[0071] The present application also provides a computer-readable storage medium. 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 can implement the air preheater blockage risk warning method recorded in any of the above embodiments.

[0072] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

[0073] The technical solution of the present application is further described in detail below through the accompanying drawings and examples. BRIEF DESCRIPTION OF THE DRAWINGS

[0074] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0075] Figure 1 This is a flow chart of an air preheater blockage risk warning method in one embodiment of the present application;

[0076] Figure 2 This is a structural diagram of an air preheater blockage risk warning device in one embodiment of the present application;

[0077] Figure 3 This is a hardware structure diagram of an air preheater blockage risk warning system in one embodiment of the present application. DETAILED DESCRIPTION

[0078] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application.

[0079] Figure 1 This is a flow chart of an air preheater blockage risk warning method in one embodiment of the present application. Figure 1 As shown, the method can be implemented as the following steps S101-S105:

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

[0081] In step S102, the optimal blocking rate corresponding to each operating condition is determined according to the operating condition of the air preheater at each moment;

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

[0083] In step S104, it is determined whether there is a risk of blockage of the air preheater based on the predicted differential pressure of the air preheater at the current moment;

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

[0085] In the present application, the operating conditions of the air preheater at each moment are determined based on the preset parameter values ​​corresponding to each moment of the air preheater. In order to scientifically determine the operating conditions of the air preheater, the present application is based on the historical operating data of the air preheater, and selects multiple air preheater blockage influencing factors as preset parameters to cluster the operating conditions. For example, the historical operating data of the two cleaning rooms of the air preheater are selected, and the air preheater flue gas side inlet and outlet temperatures, primary air side inlet and outlet temperatures, secondary air side inlet and outlet temperatures, ammonia slip, absorption tower inlet SO2 concentration, SCR reactor inlet and outlet NOX concentration, electrostatic precipitator inlet dust concentration, and air preheater inlet O2 amount are selected as air preheater blockage influencing factors, and cluster analysis is performed to obtain N types of clustered operating conditions. For another example, the judgment characteristics of the operating conditions are constructed based on the preset parameters; then, the judgment characteristics are clustered to obtain multiple clustered operating conditions. The judgment characteristics include at least the dynamic resistance coefficient K = differential pressure value / flue gas flow rate, temperature gradient ▽T = inlet temperature Th - outlet temperature Tc, etc. When determining the operating conditions at each moment, the preset parameter values ​​of the air preheater at each moment are matched with various preset cluster conditions. When the preset parameter values ​​at the target moment fall within the value range specified by the target preset cluster condition, the operating condition at the target moment is determined to be the target preset cluster condition.

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

[0087]

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

[0089] For the above model, the core needs to determine the blocking rate corresponding to different working conditions. Therefore, in order to determine the blocking rate corresponding to each working 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, D r (t) is the actual differential pressure of the air preheater at time t, D p (t) is 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 with the smallest cumulative deviation is determined as the optimal blocking rate corresponding to each operating condition, thereby constructing the corresponding relationship between each operating condition and the optimal blocking rate.

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

[0094]

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

[0096] For each operating condition in the above model, the blocking rate is usually a constant.

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

[0098] (1) When dynamic function is selected When the second air preheater blocking process prediction model is constructed as follows, the theoretical air preheater predicted differential pressure corresponding to each working condition is determined:

[0099]

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

[0101] Accordingly, after the optimal blocking rate is determined through optimization, when predicting the predicted differential pressure at the current moment, the optimal blocking rate corresponding to each working condition at the current moment is substituted into the second air preheater blocking process prediction model to determine the predicted differential pressure of the air preheater at the current moment:

[0102]

[0103] Among them, D p (t) is the predicted differential pressure of the air preheater, D1 is the differential pressure corresponding to the time when the model is activated, l ic1o is the optimal blocking rate corresponding to working condition i at time t, D r (t) is the actual differential pressure of the air preheater at time t.

[0104] (2) When dynamic function is selected When the third air preheater blocking process prediction model is constructed as follows, the theoretical air preheater predicted differential pressure corresponding to each working condition is determined:

[0105]

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

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

[0108] Accordingly, after the optimal blocking rate is determined through optimization, the optimal blocking rate corresponding to each working condition at the current moment is substituted into the third air preheater blocking process prediction model to determine the predicted differential pressure of the air preheater at the current moment when the model is applied:

[0109]

[0110] Among them, D p (t) is the predicted differential pressure of the air preheater, D1 is the differential pressure corresponding to the time when the model is activated, l ic2o is the optimal blocking rate corresponding to working condition i at time t, D r (t) is the actual differential pressure of the air preheater at time t.

[0111] (3) When dynamic function is selected When the fourth air preheater blocking process prediction model is constructed as follows, the theoretical air preheater predicted differential pressure corresponding to each working condition is determined:

[0112]

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

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

[0115] Accordingly, after the optimal blocking rate is determined through optimization, the optimal blocking rate corresponding to each working condition at the current moment is substituted into the fourth air preheater blocking process prediction model to determine the predicted differential pressure of the air preheater at the current moment:

[0116]

[0117] Among them, D p (t) is the predicted differential pressure of the air preheater, D1 is the differential pressure corresponding to the time when the model is activated, l ic3o is the optimal blocking rate corresponding to working condition i at time t, D r (t) is the actual differential pressure of the air preheater at time t.

[0118] (4) Select dynamic function The following fifth air preheater blocking process prediction model is constructed to determine the theoretical air preheater predicted differential pressure corresponding to each operating condition:

[0119]

[0120] Among them, D p (t) is the predicted differential pressure of the air preheater, D0 is the initial actual differential pressure of the air preheater, l ic4 is the blocking rate corresponding to working condition i at time t.

[0121] Accordingly, when the model is applied, the optimal blocking rate corresponding to each working 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) is the predicted differential pressure of the air preheater, D1 is the differential pressure corresponding to the time when the model is activated, l ic4o is the optimal blocking rate corresponding to working condition i at time t.

[0124] In addition, in another embodiment of the present application, the following sixth air preheater blockage process prediction model is constructed in combination with the flue gas temperature and flow rate to determine the theoretical air preheater predicted differential pressure corresponding to each operating condition:

[0125]

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

[0127] For α i (T(t)) and β i (v(t)) can be determined as follows:

[0128]

[0129] By introducing coefficients related to temperature and flow rate, the model can more accurately reflect the phase change dynamics of ammonium bisulfate, fly ash transport mechanism, etc., and provide a quantitative basis for blockage warning and soot blowing optimization. The temperature-related deposition coefficient can quantify the nonlinear effect of temperature on the deposition rate. In the low temperature section (T<150℃), ammonium bisulfate (ABS) is in the condensation stage, and the viscosity rises sharply. The viscosity can be more than 10 times that of conventional fly ash, resulting in a doubling of the fly ash adhesion rate. The law of viscosity decreasing with temperature rise is reflected by the exponential decay function. In the medium temperature section (150℃≤T≤230℃), ABS forms a composite deposit with fly ash, blocking the gaps in the corrugated plate. In the high temperature section (T>230℃), ABS decomposes into gaseous NH3 and The viscosity is significantly reduced. For the flue gas flow rate-related scouring coefficient, fly ash is easy to deposit at low flow rates (v<8m / s), and β(v) increases exponentially with decreasing flow rate; at the critical flow rate (8m / s≤v≤12m / s), scouring and deposition reach a dynamic balance; at high flow rates (v>12m / s), the kinetic energy of fly ash particles is enhanced, and the scouring effect is stronger. Similarly, based on the sixth air preheater blockage process prediction model, by optimizing the objective function described above, the optimal blockage rate corresponding to each operating condition can be obtained. Therefore, when predicting the predicted differential pressure at the current moment, the optimal blockage rate corresponding to each operating condition at the current moment is substituted into the sixth air preheater blockage process prediction model to determine the predicted differential pressure of the air preheater at the current moment:

[0130]

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

[0132] The air preheater blockage risk is determined based on the predicted air preheater differential pressure at the current moment. Since the predicted air preheater differential pressure is the theoretical differential pressure value of the air preheater under the current operating conditions at the current moment, in one embodiment of the present application, the predicted air preheater differential pressure is compared with the actual differential pressure value; when the difference between the predicted and actual air preheater differential pressure values ​​is greater than a preset threshold, the air preheater blockage risk is determined. In another embodiment, when the difference between the predicted and actual air preheater differential pressure values ​​accounts for a greater than preset ratio of the air preheater predicted difference, the air preheater blockage risk is determined.

[0133] When it is determined that there is a risk of blockage in the air preheater, an air preheater blockage warning is issued to prompt the operator to perform air preheater sootblowing. In one embodiment of the present application, the start signal of the air preheater sootblower is detected; when the start signal of the air preheater sootblower 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 when the start signal is sent; at the next moment, the air preheater differential pressure corresponding to the time when the start signal is sent is used as the initial true differential pressure of the air preheater to predict the risk of air preheater blockage. For example, when the operator performs air preheater sootblowing in accordance with the warning instructions when the air preheater blockage warning occurs, the system monitors the start signal of the air preheater sootblower. When the start signal is triggered, the model is reset to the air preheater differential pressure corresponding to the time when the start signal is sent, and the air preheater differential pressure prediction is performed again according to the time axis based on the real-time input data.

[0134] The beneficial effects of the present application are: determining the working conditions of the air preheater at each moment according to the preset parameter values ​​corresponding to each moment; determining the optimal blocking rate corresponding to each working condition according to the working conditions of the air preheater at each moment; substituting the optimal blocking rate corresponding to each working condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment; judging whether the air preheater has a blocking risk according to the predicted differential pressure of the air preheater at the current moment; and issuing an air preheater blocking warning when it is determined that the air preheater has a blocking risk. Since the present application determines the corresponding optimal blocking rate according to the operating conditions of the air preheater, and determines the predicted differential pressure of the air preheater at the current moment according to the corresponding optimal blocking rate, the predicted differential pressure of the air preheater is determined in combination with different working conditions, and then judges in real time whether the air preheater has a blocking risk according to the predicted differential pressure of the air preheater at the current moment, and issues a timely warning when there is a risk. The purpose of accurately warning the blocking risk of the air preheater according to different working conditions is achieved.

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

[0136] The preset parameter values ​​of the air preheater at each moment are matched with various preset cluster operating conditions. When the preset parameter value at the target moment falls within the value range specified by the target preset cluster operating condition, the operating condition at the target moment is determined to be the target preset cluster operating condition.

[0137] In one embodiment, the process of constructing the various preset clustering working conditions may be implemented as follows: Steps A1-A2:

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

[0139] In step A2, the historical operating data between the two cleaning rooms of the air preheater are selected as the dimension of cluster analysis, and the factors affecting the blockage of the air preheater are used as the clustering basis to construct multiple clustering conditions.

[0140] In one embodiment, the above step S102 may be implemented as the following steps B1-B2:

[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, D r (t) is the actual differential pressure of the air preheater at time t, D p (t) is the predicted differential pressure of the air preheater at time t;

[0144] In step B2, the following pre-built model is substituted into the objective function to optimize the blocking rate of the air preheater operating conditions at each moment:

[0145]

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

[0147] In one embodiment, the above step S103 may be implemented as follows:

[0148] Substitute the time parameters corresponding to each moment of the air preheater and the optimal blockage rate corresponding to the operating conditions at each moment into the following pre-built model to determine the predicted differential pressure of the air preheater at the current moment:

[0149]

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

[0151] In one embodiment, the above step S104 may be implemented as the following steps C1-C2:

[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 of the air preheater and the actual differential pressure value is greater than a preset threshold, it is determined that there is a risk of blockage of the air preheater.

[0154] In one embodiment, the method may also be implemented as follows:

[0155] In step D1, a start signal of an 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 real 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 moment the start signal is sent is used as the initial true differential pressure of the air preheater to predict the air preheater blocking risk.

[0159] Figure 2 FIG. 1 is a structural diagram of an air preheater blockage risk warning device in one embodiment of the present application. Figure 2 Shown, including:

[0160] The first determining module 201 is used to determine the operating condition of the air preheater at each moment according to the preset parameter values ​​corresponding to each moment of the air preheater;

[0161] The second determining module 202 is configured to determine the optimal blocking rate corresponding to each operating condition according to the operating condition of the air preheater at each moment;

[0162] The third determination module 203 is configured to substitute the optimal blockage rate corresponding to each operating condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment;

[0163] A judgment module 204 is used to judge whether there is a blockage risk of the air preheater based on the predicted differential pressure of the air preheater at the current moment;

[0164] The fourth determining module 205 is configured to issue an air preheater blockage warning when it is determined that there is a blockage risk for 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 each moment are matched with various preset cluster operating conditions. When the preset parameter value at the target moment falls within the value range specified by the target preset cluster operating condition, the operating condition at the target moment is determined to be the target preset cluster operating condition.

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

[0168] Obtain historical operation data of air preheater;

[0169] The historical operation data of the two cleaning rooms of the air preheater were selected as the dimension of cluster analysis, and the factors affecting the blockage of the air preheater were used as the clustering basis to construct multiple clustering conditions.

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

[0171] Construct submodules to construct 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, D r (t) is the actual differential pressure of the air preheater at time t, D p (t) is 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 operating conditions at each moment:

[0175]

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

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

[0178] Substitute the time parameters corresponding to each moment of the air preheater and the optimal blockage rate corresponding to the operating conditions at each moment into the following pre-built model to determine the predicted differential pressure of the air preheater at the current moment:

[0179]

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

[0181] In one embodiment, the judgment 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 of the air preheater and the actual differential pressure value is greater than a preset threshold.

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

[0185] Monitoring module, used to detect the start-up signal of the air preheater soot blower;

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

[0187] A reset module, used to reset the initial real differential pressure of the air preheater to the air preheater differential pressure corresponding to the moment when the start signal is sent;

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

[0189] Figure 3FIG. 1 is a schematic diagram of the hardware structure of an air preheater blockage risk warning system in one embodiment of the present application. Figure 3 As shown, the air preheater blocking risk warning system includes:

[0190] at least one processor 320; and,

[0191] A memory 304 in communication with the at least one processor 320; wherein,

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

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

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

[0195] The memory 304 is configured to store various types of data to support the operation of the air preheater blockage risk warning system 300. Examples of such data include instructions for any application or method operating on the air preheater blockage risk warning system 300, such as text, pictures, videos, etc. The 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 memory, flash memory, magnetic disk, or optical disk.

[0196] The power supply component 306 provides power to various components of the air preheater blockage risk warning system 300. The 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 blockage risk warning system 300.

[0197] The multimedia component 308 includes a screen that provides an output interface between the air preheater blockage 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 touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, slides, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 may also include a front camera and / or a rear camera. When the air preheater blockage risk warning system 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have focal length and optical zoom capabilities.

[0198] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC) that is configured to receive external audio signals when the air preheater blockage risk warning system 300 is in an operating mode, such as an alarm mode, a recording mode, a voice recognition mode, and a voice output mode. The received audio signal can be further stored in the memory 304 or transmitted via the communication component 316. In some embodiments, the 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 a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.

[0200] The sensor assembly 314 includes one or more sensors for providing various status assessments of the air preheater blockage risk warning system 300. For example, the sensor assembly 314 may include an acoustic sensor. Additionally, the sensor assembly 314 may detect the open / closed state of the air preheater blockage risk warning system 300, the relative positioning of components, such as the display and keypad of the air preheater blockage risk warning system 300, the operating state of the air preheater blockage risk warning system 300 or a component thereof, the orientation or acceleration / deceleration of the air preheater blockage risk warning system 300, and temperature changes within the air preheater blockage risk warning system 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include a magnetic sensor, a pressure sensor, a material accumulation thickness sensor, or a temperature sensor.

[0201] The communication component 316 is configured to enable the air preheater blockage risk warning system 300 to provide the ability to communicate with other devices and cloud platforms in a wired or wireless manner. The air preheater blockage risk warning system 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can 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 blockage risk warning system 300 can 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 execute the air preheater blockage risk warning method described in any of the above embodiments.

[0203] The present application also provides a computer-readable storage medium. 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 can implement the air preheater blockage risk warning method recorded in any of the above embodiments.

[0204] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) that contain computer-usable program code.

[0205] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0206] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0207] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0208] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. An air preheater blockage risk early warning method, characterized in that: include: Determine the operating conditions of the air preheater at each moment according to the preset parameter values ​​corresponding to each moment of the air preheater; Determining the optimal blocking rate corresponding to each operating condition according to the operating condition of the air preheater at each moment; Substituting the optimal blockage rate corresponding to each operating condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment; Determine whether the air preheater has a blockage risk based on the predicted differential pressure of the air preheater at the current moment; When it is determined that there is a risk of blockage in the air preheater, an air preheater blockage warning will be issued.

2. The method according to claim 1, wherein The operating conditions of the air preheater at each moment are determined according to the preset parameter values ​​corresponding to each moment of the air preheater, including: The preset parameter values ​​of the air preheater at each moment are matched with various preset cluster operating conditions. When the preset parameter value at the target moment falls within the value range specified by the target preset cluster operating condition, the operating condition at the target moment is determined to be the target preset cluster operating condition.

3. The method according to claim 2, wherein The construction process of the various preset clustering conditions is as follows: Obtain historical operation data of air preheater; The historical operation data of the two cleaning rooms of the air preheater were selected as the dimension of cluster analysis, and the factors affecting the blockage of the air preheater were used as the clustering basis to construct multiple clustering conditions.

4. The method according to claim 1, wherein Determining the optimal blocking rate corresponding to each operating condition according to the operating condition of the air preheater at each moment includes: Construct the following objective function: Where O is the cumulative deviation between the actual differential pressure of the air preheater and the predicted differential pressure of the air preheater, D r (t) is the actual differential pressure of the air preheater at time t, D p (t) is the predicted differential pressure of the air preheater at time t; Substitute the following pre-built model into the objective function to optimize the blocking rate of the air preheater at each operating time: Among them, D p (t) is the predicted differential pressure of the air preheater, D0 is the initial actual differential pressure of the air preheater, l i (t) is the blocking rate corresponding to working condition i at time t.

5. The method according to claim 1, wherein Substituting the optimal blockage rate corresponding to each operating condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment includes: Substitute the time parameters corresponding to each moment of the air preheater and the optimal blockage rate corresponding to the operating conditions at each moment into the following pre-built model to determine the predicted differential pressure of the air preheater at the current moment: Among them, D p (t) is the predicted differential pressure corresponding to the final air preheater blocking process estimation model at the current moment, D1 is the differential pressure corresponding to the moment the model is activated, l io (t) is the optimal blocking rate corresponding to working condition i at time t.

6. The method according to claim 1, wherein The determining whether there is a blockage risk for 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 of the air preheater and the actual differential pressure value is greater than a preset threshold, it is determined that there is a risk of blockage in the air preheater.

7. The method according to claim 1, wherein The method further comprises: 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 real differential pressure of the air preheater to the air preheater differential pressure corresponding to the moment when the start signal is sent; At the next moment, the air preheater differential pressure corresponding to the moment the start signal is sent is used as the initial true differential pressure of the air preheater to predict the air preheater blocking risk.

8. An air preheater blockage risk warning device, characterized in that: include: The first determining module is used to determine the operating conditions of the air preheater at each moment according to the preset parameter values ​​corresponding to the air preheater at each moment; A second determining module is used to determine the optimal blocking rate corresponding to each operating condition according to the operating condition of the air preheater at each moment; A third determination module is used to substitute the optimal blockage rate corresponding to each operating condition into a pre-built model to determine the predicted differential pressure of the air preheater at the current moment; A judgment module is used to judge whether there is a blockage risk of the air preheater based on the predicted differential pressure of the air preheater at the current moment; The fourth determination module is used to issue an air preheater blockage warning when it is determined that there is a blockage risk in the air preheater.

9. An air preheater blockage risk 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, and the instructions are executed by the at least one processor to implement the air preheater blockage risk warning method according to any one of claims 1 to 7.

10. 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 can implement the air preheater blockage risk warning method according to any one of claims 1 to 7.

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