Dirt cleaning judgment method and device for in-service plate heat exchanger based on operation data

Abstract

The invention discloses an in-service plate heat exchanger dirt cleaning judgment method and device based on operation data, and the method comprises the steps: obtaining the operation data, collected in a cleaning period and a current operation season, of a plate heat exchanger, and removing abnormal data to form a cleaning period effective data set and a current operation season effective data set; calculating the heat exchange amount and the logarithmic average temperature difference based on the effective data set, and executing steady-state working condition set screening to obtain steady-state working condition sets of the cleaning period and the current operation season; heat exchange thermal resistance is calculated based on the steady-state working condition set, linear fitting is carried out on the heat exchange thermal resistance and the sum of minus 0.8 power of flow of the two sides of the heat exchanger to obtain heat conduction thermal resistance, and the heat conduction thermal resistance in the cleaning period and the heat conduction thermal resistance in the current operation period are compared to calculate dirt thermal resistance in the current operation period; if the ratio of the dirt heat resistance in the current operation season to the heat conduction heat resistance in the cleaning period exceeds a preset threshold value, it is judged that dirt cleaning needs to be conducted on the plate heat exchanger. A method is provided for improving the efficiency of the plate heat exchanger and reducing carbon emission for heat exchange systems in the fields of heat supply, petrochemical engineering and the like.

Description

Technical Field

[0001] The present application relates to the technical field of plate heat exchangers, and in particular to a method and device for judging fouling cleaning of an in-service plate heat exchanger based on operating data. Background Art

[0002] With the acceleration of urbanization, the urban population and the number of buildings continue to grow, resulting in the continuous expansion of heating areas. As the main mode of urban heating, the energy consumption and carbon emission issues of the centralized heating system are becoming increasingly prominent.

[0003] Plate heat exchangers play a vital role in central heating systems, and their performance directly affects the efficiency of the entire heating system. However, in actual operation, plate heat exchangers are affected by salts such as calcium and magnesium and tiny particles contained in water. These substances will precipitate and adhere to the surface of the plate heat exchanger after being heated, forming dirt. These dirt will not only increase thermal resistance and reduce heat exchange efficiency, but also reduce the flow area of ​​the plate heat exchanger channel, resulting in reduced flow and increased pump work, thus causing energy waste.

[0004] In order to reduce the adverse effects of dirt on the heat transfer performance of plate heat exchangers, improve heating quality and reduce energy consumption, it is necessary to clean the plate heat exchangers regularly. However, since plate heat exchangers are usually in a sealed state, staff cannot directly observe the accumulation of dirt inside them with the naked eye. At present, heating companies generally adopt regular cleaning or decide whether to clean based on the complaint rate of residents. However, this method lacks specific basis, and not all plate heat exchangers need to be cleaned at a fixed time. Therefore, too frequent cleaning will cause a waste of resources and costs.

[0005] Therefore, how to accurately determine the cleaning time of the plate heat exchanger to improve the energy conversion rate of the heating system, reduce carbon emissions, and reduce unnecessary waste of resources and costs has become a problem to be solved. Based on this problem, the present invention has found a method for judging the fouling cleaning of the in-service plate heat exchanger based only on operating data. This method can be applied to other industries using plate heat exchangers, such as urban heating, chemical industry, food, textile, etc. Summary of the invention

[0006] In an embodiment of the present application, by providing a method for determining fouling cleaning of an in-service plate heat exchanger based on operating data, the problem of how to accurately determine the cleaning timing of the plate heat exchanger to improve the energy conversion rate of the heating system, reduce carbon emissions, and reduce unnecessary waste of resources and costs is solved.

[0007] In a first aspect, an embodiment of the present application provides a method for judging fouling cleaning of an in-service plate heat exchanger based on operating data, characterized in that it includes: obtaining a preset time period of a cleaning period and a current operating season, collecting each set of operating data of the plate heat exchanger at preset time intervals within the preset time period, and removing abnormal data to form valid data sets for the cleaning period and the current operating season respectively; each set of operating data of the plate heat exchanger includes the inlet temperature, outlet temperature of the primary side and the secondary side and the mass flow rate of the primary side; the heat exchange rate and logarithmic mean temperature difference of each set of operating data in the valid data sets of the cleaning period and the current operating season are calculated respectively; a steady-state operating condition set screening step is performed on the valid data sets of the cleaning period and the current operating season respectively to obtain the steady-state operating condition set of the cleaning period and the current operating season; the steady-state operating condition set screening step includes: dividing the data of the heat exchange rate and the logarithmic mean temperature difference into multiple sub-data sets respectively, and calculating the relative standard deviation of each sub-data set, and selecting the sub-data set with the smallest relative standard deviation The set is taken as the steady-state condition set; in the steady-state condition set of the cleaning period and the current operating season, the difference between adjacent data is calculated to find a set of data with the smallest difference. The heat transfer and logarithmic mean temperature difference of this set of data are the steady-state heat transfer and steady-state logarithmic mean temperature difference of the cleaning period and the current operating season, and it is taken as the steady-state condition; the heat transfer resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow on both sides are calculated using the steady-state condition data, and after linear fitting, the intercept is the plate heat exchange The thermal conductivity thermal resistance of the plate heat exchanger is used to calculate the fouling thermal resistance of the plate heat exchanger in the current operating season; whether the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds a preset threshold; if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds the preset threshold, the plate heat exchanger is cleaned before the start of the next operating season; if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period does not exceed the preset threshold, there is no need to clean the plate heat exchanger.

[0008] In a possible implementation, the cleaning period is the first operating season after the plate heat exchanger starts to operate or the first operating season after the plate heat exchanger is cleaned.

[0009] In a possible implementation, before respectively calculating the heat exchange amount and logarithmic mean temperature difference of each set of operating data in the valid data sets of the cleaning period and the current operating season, it also includes: removing the first month data and the last month data of the starting operating season in the valid data sets of the cleaning period and the current operating season.

[0010] In a possible implementation, the definition standard of the abnormal data is: if the primary side inlet temperature, primary side outlet temperature, secondary side inlet temperature, secondary side outlet temperature or primary side mass flow in any group of operating data is zero or a negative number, the group of data is regarded as abnormal data; if the calculation result of the logarithmic mean temperature difference is negative, the group of data is regarded as abnormal data; if the temperature or primary side mass flow of two or more adjacent groups of data is the same, the group of data is regarded as abnormal data.

[0011] In one possible implementation, the heat exchange amount is calculated as follows: calculate the temperature difference from the primary side inlet temperature to the primary side outlet temperature in the effective data set of the cleaning period and the current operating season; multiply the temperature difference by the mass flow rate of the primary side in the effective data set of the cleaning period and the current operating season to obtain an intermediate value; multiply this intermediate value by the specific heat capacity of the fluid to obtain the heat exchange amount of the effective data set of the cleaning period and the current operating season.

[0012] In one possible implementation, the logarithmic mean temperature difference is calculated as follows: for the effective data sets of the cleaning period and the current operating season, two sets of temperature differences are calculated respectively; wherein the first temperature difference is the temperature from the primary side inlet temperature to the secondary side inlet temperature, and the second temperature difference is the temperature from the primary side outlet temperature to the secondary side outlet temperature; the ratio of the first temperature difference to the second temperature difference is calculated, and the ratio is used as the base of the logarithm to perform a logarithmic operation to obtain a logarithmic value; the logarithmic value is used as the denominator and the difference between the first temperature difference and the second temperature difference as the numerator to perform a division operation to obtain the logarithmic mean temperature difference of the effective data sets of the cleaning period and the current operating season.

[0013] In one possible implementation, the relative standard deviation of each sub-data set is calculated as follows: each heat exchange value or logarithmic mean temperature difference value in the sub-data set is added to obtain a total, and the total is divided by the total number of data to obtain an average value; the difference between the heat exchange value or logarithmic mean temperature difference in the sub-data set and the average value is calculated; and the square difference is obtained; all square differences are added to obtain the sum of the square differences, and the sum of the square differences is divided by the total number of data in each sub-data set, and the square root is taken to calculate the standard deviation; the standard deviation is divided by the average value to obtain the relative standard deviation.

[0014] In a possible implementation, the heat transfer resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow on both sides are calculated using the steady-state operating data, and after linear fitting, the intercept is the thermal resistance of the plate heat exchanger to calculate the fouling resistance of the plate heat exchanger, including: for the cleaning period and the current operating season, the corresponding heat transfer capacity UA is calculated respectively, UA = Φ / LMTD; wherein the heat transfer capacity UA is the product of the total heat transfer coefficient of the plate heat exchanger and the heat transfer area, Φ is the heat transfer amount, and LMTD is the logarithmic mean temperature difference; when UA is expressed and calculated using the thermal resistance of the plate heat exchanger, the cleaning period Current season Where h1 and h2 are the convective heat transfer coefficients on the primary and secondary sides of the plate heat exchanger, A1 and A2 are the heat transfer areas on the primary and secondary sides of the plate heat exchanger, rw is the thermal conductivity coefficient of the plate of the plate heat exchanger, rf is the fouling coefficient, A0 is the heat transfer area of ​​the plate of the plate heat exchanger, Ai is the heat transfer area of ​​the fouling, and Nu=cRe n Pr m , where Re is the Reynolds number, Pr is the Prandtl number, n is 0.8, and m is 0.3-0.4. Where ρ is density, u is flow velocity, l is characteristic length, and μ is dynamic viscosity. Simplifying the calculation formula of UA, we get And h is proportional to the 0.8 power of flow rate u, h is proportional to the 0.8 power of flow rate G, simplifying the calculation formula of UA, the total heat transfer thermal resistance in G2=G1(T1-T2) / (T3-T4), G1 and G2 are the primary and secondary flow rates; It can be expressed as a function of y=kx+b, where y=1 / UA, x=(G1 -0.8 +G2 -0.8 ), and perform linear fitting, and the goodness of fit R 2 Not less than 0.8; After the fitting curves of the cleaning period and the current period are obtained, the intercept b is the thermal resistance of the plate heat exchanger. By comparing the intercepts b of the two periods, the fouling thermal resistance of the heat exchanger in the current operating season is obtained. Ratio of thermal resistance during dirt to thermal resistance during cleaning bu is the intercept of the current period, and bc is the intercept of the cleaning period.

[0015] In a second aspect, an embodiment of the present application provides a fouling cleaning judgment device for an in-service plate heat exchanger based on operating data, the device comprising: an acquisition module, used to acquire a preset time period of a cleaning period and a current operating season, collect each set of operating data of the plate heat exchanger at preset time intervals within the preset time period, and remove abnormal data to form valid data sets for the cleaning period and the current operating season respectively; each set of operating data of the plate heat exchanger comprises the inlet temperature, outlet temperature of the primary side and the secondary side and the mass flow rate of the primary side; an unknown quantity calculation module, used to respectively calculate the heat exchange rate and logarithmic mean temperature difference of each set of operating data in the valid data sets of the cleaning period and the current operating season; a screening step execution module, used to respectively execute a steady-state operating condition set screening step on the valid data sets of the cleaning period and the current operating season, so as to obtain a steady-state operating condition set for the cleaning period and the current operating season; the steady-state operating condition set screening step comprises: dividing the data of the heat exchange rate and the logarithmic mean temperature difference into a plurality of sub-data sets respectively, and calculating the relative standard deviation of each sub-data set, and selecting the sub-data set with the smallest relative standard deviation as the steady-state operating condition set. The module for obtaining steady-state operating conditions is used to find a set of data with the smallest difference in the steady-state operating conditions of the cleaning period and the current operating season by calculating the difference between adjacent data. The heat transfer and logarithmic mean temperature difference of this set of data are the steady-state heat transfer and steady-state logarithmic mean temperature difference of the cleaning period and the current operating season, and they are used as steady-state operating conditions; the module for calculating the fouling thermal resistance of the plate heat exchanger is used to calculate the heat transfer thermal resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow on both sides using the steady-state operating condition data, and perform linear fitting on them. , the intercept is the thermal resistance of the plate heat exchanger, so as to calculate the fouling thermal resistance of the plate heat exchanger in the current operating season; the judgment module is used to judge whether the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal resistance of the cleaning period exceeds the preset threshold; if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal resistance of the cleaning period exceeds the preset threshold, the plate heat exchanger is cleaned before the next operating season begins; if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal resistance of the cleaning period does not exceed the preset threshold, there is no need to clean the plate heat exchanger.

[0016] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects:

[0017] The embodiment of the present application provides a method for judging the fouling cleaning of an in-service plate heat exchanger based on operation data. By collecting multiple sets of operation data of the plate heat exchanger in the cleaning period and the current operation season, the effective data sets of the cleaning period and the current operation season are formed after the abnormal data are removed, and then the accuracy and representativeness of the data used to calculate the fouling thermal resistance and the ratio of the fouling thermal resistance in the current operation season to the thermal conductivity thermal resistance in the cleaning period are ensured. This greatly improves the accuracy of judging the fouling of the plate heat exchanger. The ratio of the fouling thermal resistance in the current operation season to the thermal conductivity thermal resistance in the cleaning period is calculated based on the steady-state condition, and the performance change of the plate heat exchanger can be objectively evaluated. Only when the ratio of the fouling thermal resistance in the current operation season to the thermal conductivity thermal resistance in the cleaning period exceeds the preset threshold, the cleaning is performed, which not only ensures the timeliness of cleaning, but also avoids unnecessary cleaning and cost waste. By timely cleaning the plate heat exchanger, dirt can be effectively removed, thermal resistance can be reduced, and heat exchange efficiency can be improved, thereby saving energy consumption. It is of great significance to improve the overall energy efficiency of the central heating system and reduce carbon emissions. Regular cleaning of plate heat exchangers can also reduce the impact of dirt on the flow area of ​​the plate heat exchanger channel, avoid problems such as reduced flow and increased pump work, thereby enhancing the stability and reliability of the operating system. Frequent cleaning operations are avoided and maintenance costs are reduced. At the same time, due to the accurate grasp of the cleaning timing, it can ensure that each cleaning can achieve the best effect and extend the service life of the plate heat exchanger. It solves the problem of how to accurately judge the cleaning time of the plate heat exchanger to improve the energy conversion rate of the heating system, reduce carbon emissions, and reduce unnecessary waste of resources and costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments of the present application or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 A flow chart of a method for determining fouling cleaning of an in-service plate heat exchanger based on operating data provided in an embodiment of the present application;

[0020] Figure 2 A schematic diagram of steady-state operating condition screening provided in an embodiment of the present application;

[0021] Figure 3 A schematic diagram of a linear relationship provided in an embodiment of the present application;

[0022] Figure 4 A schematic diagram of a fouling cleaning judgment device for an in-service plate heat exchanger based on operation data provided in an embodiment of the present application;

[0023] Figure 5 A schematic diagram of a server for determining fouling cleaning of an in-service plate heat exchanger based on operating data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0025] The following describes some of the techniques involved in the embodiments of the present application to facilitate understanding, and they should be considered as merely exemplary. Therefore, it should be appreciated by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope and spirit of the present application. Similarly, for the sake of clarity and conciseness, some descriptions of well-known functions and structures are omitted in the following description.

[0026] The present application embodiment provides a method for judging the fouling cleaning of an in-service plate heat exchanger based on operation data, such as Figure 1 As shown, the method includes steps S101 to S108. Among them, Figure 1 This is only an execution sequence shown in the embodiment of the present application, and does not represent the only execution sequence of a fouling cleaning judgment method for an in-service plate heat exchanger based on operating data. In the case where the final result can be achieved, Figure 1 The steps shown may be performed in parallel or reversed.

[0027] S101: Obtain a preset time period of the cleaning period and the current operating season, collect each set of operating data of the plate heat exchanger at a preset time interval within the preset time period, remove abnormal data, and form valid data sets for the cleaning period and the current operating season. Each set of operating data of the plate heat exchanger includes the inlet temperature and outlet temperature of the primary side and the secondary side, and the mass flow rate of the primary side.

[0028] The cleaning period is the first operating season when the plate heat exchanger starts to operate or the first operating season after the plate heat exchanger is cleaned.

[0029] Specifically, the effective data set of the cleaning period includes all the operating data during the cleaning period, which is used as a benchmark for evaluating the performance of the current operating season. The effective data set of the current operating season includes all the operating data in the current operating season, which is used to compare with the data of the cleaning period to evaluate the performance changes of the plate heat exchanger.

[0030] Furthermore, the preset time period can be set to one day, which means that we are concerned about the operating data of the plate heat exchanger within one day. In order to fully and carefully understand the performance of the plate heat exchanger, data can be collected every hour. Specifically, from the beginning of the day (for example, midnight 0 o'clock) to the end (that is, 24 hours later), data will be collected at every hour (such as 1 o'clock, 2 o'clock,..., 23 o'clock). In the data sets of the cleaning period and the current operating season, data acquisition is carried out in chronological order, that is, the data collected first is ranked first in the data set, and the data collected later is ranked at the back. This data arrangement method is conducive to the subsequent analysis and processing of the data.

[0031] The definition standard of abnormal data is: if the primary inlet temperature, primary outlet temperature, secondary inlet temperature, secondary outlet temperature or primary mass flow rate in any set of operating data is zero or negative, the set of data is considered abnormal data. Because in the actual physical process, the temperature and flow rate are usually impossible to be zero or negative, this situation is caused by data recording errors or sensor failures. If the calculation result of the logarithmic mean temperature difference is negative, the set of data is considered abnormal data. The logarithmic mean temperature difference is an important parameter for measuring the efficiency of plate heat exchangers, and its value should be positive. If it is negative, it usually means that the data is wrong or the measurement conditions do not conform to physical laws. If the temperature or primary mass flow rate of two or more adjacent sets of data is the same, the set of data is considered abnormal data. In normal data recording, the data at adjacent time points usually have certain fluctuations or changes. If the data is exactly the same, it may be caused by data recording errors or data duplication.

[0032] S102: Calculate the heat exchange amount and logarithmic mean temperature difference of each set of operating data in the effective data set during the cleaning period and the current operating season respectively.

[0033] The heat transfer rate is calculated by calculating the temperature difference between the primary side inlet temperature and the primary side outlet temperature in the effective data set of the cleaning period and the current operating season. Multiply the temperature difference by the mass flow rate of the primary side in the effective data set of the cleaning period and the current operating season to obtain the intermediate value. Multiply this intermediate value by the specific heat capacity of the fluid to obtain the heat transfer rate of the effective data set of the cleaning period and the current operating season.

[0034] Specifically, the calculation formula for the heat exchange of the effective data set during the cleaning period is: Φ i1 =C p ·G·(T1-T2). Among them, Φ i1 is the heat transfer of the effective data set of the cleaning period, that is, the heat transferred by the fluid in the heat exchange process described by the data set. T1 is the primary side inlet temperature of the effective data set of the cleaning period, T2 is the primary side outlet temperature of the effective data set of the cleaning period, G is the primary side mass flow rate of the effective data set of the cleaning period, Cp The unit of temperature in this application is ℃, the mass flow rate is kg / s, the specific heat capacity of the fluid is the specific heat capacity of water, the unit is kJ / (kg·℃), and 4.2kJ / (kg·℃) is taken.

[0035] Specifically, the calculation formula for the heat transfer of the effective data set in the current operating season is: Φ i2 =C p ·g·(t1-t2). Among them, Φ i2 is the heat transfer of the effective data set of the current operating season, that is, the heat transferred by the fluid in the heat exchange process described by the data set. t1 is the primary side inlet temperature of the effective data set of the current operating season, t2 is the primary side outlet temperature of the effective data set of the current operating season, g is the primary side mass flow rate of the effective data set of the current operating season, C p The unit of temperature in this application is ℃, the mass flow rate is kg / s, the specific heat capacity of the fluid is the specific heat capacity of water, the unit is kJ / (kg·℃), and 4.2kJ / (kg·℃) is taken.

[0036] The logarithmic mean temperature difference is calculated as follows: for the effective data sets of the cleaning period and the current operating season, two sets of temperature differences are calculated respectively. Among them, the first temperature difference is the temperature from the primary side inlet temperature to the secondary side inlet temperature, and the second temperature difference is the temperature from the primary side outlet temperature to the secondary side outlet temperature. Calculate the ratio of the first temperature difference to the second temperature difference, and use the ratio as the base of the logarithm to perform logarithmic operations to obtain the logarithmic value. Use the logarithmic value as the denominator and the difference between the first temperature difference and the second temperature difference as the numerator to perform a division operation to obtain the logarithmic mean temperature difference of the effective data sets of the cleaning period and the current operating season.

[0037] Specifically, the calculation formula for the average temperature difference of the effective data set during the cleaning period is: Among them, LMTD i1 is the average temperature difference of the effective data set during the cleaning period, T1 is the primary inlet temperature of the effective data set during the cleaning period, T3 is the secondary inlet temperature of the effective data set during the cleaning period, T2 is the primary outlet temperature of the effective data set during the cleaning period, T4 is the secondary outlet temperature of the effective data set during the cleaning period, and ln is the natural logarithm, that is, the logarithm with base e.

[0038] Specifically, the calculation formula for the average temperature difference of the valid data set in the current operating season is: Among them, LMTD i2is the average temperature difference of the valid data set of the current operating season, t1 is the primary inlet temperature of the valid data set of the current operating season, t3 is the secondary inlet temperature of the valid data set of the current operating season, t2 is the primary outlet temperature of the valid data set of the current operating season, t4 is the secondary outlet temperature of the valid data set of the current operating season, and ln is the natural logarithm, that is, the logarithm with base e.

[0039] Before respectively calculating the heat exchange amount and logarithmic mean temperature difference of each set of operating data in the effective data sets of the cleaning period and the current operating season, it also includes: removing the first month data and the last month data of the starting operating season in the effective data sets of the cleaning period and the current operating season.

[0040] Specifically, during the initial period of operation, the operating data of the plate heat exchanger may not be stable enough and may fluctuate greatly due to factors such as system startup, debugging, and changes in user heating habits. These data cannot truly reflect the long-term operating performance of the plate heat exchanger, so they need to be removed when calculating the heat exchange rate and logarithmic mean temperature difference. In the heating industry, in the last month of the heating season, as the temperature gradually rises and the demand for heating decreases, the operating data of the plate heat exchanger may also be affected to a certain extent. In addition, due to equipment aging, insufficient maintenance and other reasons, the performance of the plate heat exchanger may decay at this stage. Therefore, these data also need to be removed to avoid interference with the calculation results. Other industries can effectively process the data according to the particularities of their own industries.

[0041] S103: Perform steady-state operating condition set screening steps on the valid data sets of the cleaning period and the current operating season respectively to obtain steady-state operating condition sets of the cleaning period and the current operating season.

[0042] The steady-state operating condition set screening step includes: dividing the data of heat exchange amount and logarithmic mean temperature difference into multiple sub-data sets, calculating the relative standard deviation of each sub-data set, and selecting the sub-data set with the smallest relative standard deviation as the steady-state operating condition set.

[0043] Specifically, the data of heat exchange and logarithmic mean temperature difference are divided into multiple sub-datasets according to certain standards (such as time, operating conditions, etc.). Each sub-dataset contains multiple data points of heat exchange and logarithmic mean temperature difference, which represent the operating conditions of the plate heat exchanger under different conditions. For each sub-dataset, the relative standard deviation of the heat exchange data and logarithmic mean temperature difference is calculated respectively. The relative standard deviation is an indicator to measure the degree of dispersion of the data set. A smaller relative standard deviation means that the data points in the data set are more concentrated, that is, the data is more stable. After calculating the relative standard deviation of each sub-dataset, the sub-dataset with the smallest relative standard deviation is selected as the steady-state operating condition set. The data points in this steady-state operating condition set have high stability and representativeness, and can better reflect the operating conditions of the plate heat exchanger in a steady state.

[0044] The relative standard deviation of each sub-dataset is calculated by adding up the values ​​of each heat transfer or logarithmic mean temperature difference in the sub-dataset to obtain the total, and dividing the total by the total number of data to obtain the average value. Calculate the difference between the heat transfer or logarithmic mean temperature difference values ​​in the sub-dataset and the average value. And obtain the square difference value. Add all the square differences to obtain the sum of the square differences, divide the sum of the square differences by the total number of data in each sub-dataset, and take the square root to calculate the standard deviation. Divide the standard deviation by the average to obtain the relative standard deviation.

[0045] Specifically, the calculation formula for the relative standard deviation is: Among them, x i For each individual measurement value, it can be the value of heat transfer or logarithmic mean temperature difference, n is the total number of data in the sub-dataset, is the mean, SD is the standard deviation, and RSD is the relative standard deviation.

[0046] S104: In the steady-state operating condition set of the cleaning period and the current operating season, by calculating the difference between adjacent data, find a group of data with the smallest difference. The heat exchange and logarithmic mean temperature difference of this group of data are the steady-state heat exchange and steady-state logarithmic mean temperature difference of the cleaning period and the current operating season, and use them as the steady-state operating condition.

[0047] Specifically, for the data points in the steady-state condition set, the differences are calculated in chronological order, that is, the difference between the first data and the second data, the difference between the second data and the third data, and so on. These differences reflect the degree of change between the data points. The two data points (a set of data) with the smallest difference are selected as the steady-state point. This steady-state point represents the most stable data point in the steady-state condition set, and its corresponding heat transfer and logarithmic mean temperature difference will be used as the steady-state condition for subsequent calculations.

[0048] S105: The heat transfer resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow rates on both sides are calculated using the steady-state operating data, and after linear fitting, the intercept is the thermal resistance of the plate heat exchanger to calculate the fouling resistance of the plate heat exchanger in the current operating season.

[0049] For the cleaning period and the current operating season, the corresponding heat exchange capacity UA is calculated respectively, UA = Φ / LMTD. Among them, the heat exchange capacity UA is the product of the total heat exchange coefficient of the plate heat exchanger and the heat exchange area, Φ is the heat exchange amount, and LMTD is the logarithmic mean temperature difference.

[0050] When the heat transfer capacity UA is expressed and calculated using the thermal resistance of the heat exchanger, the cleaning period Current season Among them, h1 and h2 are the convection heat transfer coefficients on the primary and secondary sides of the plate heat exchanger, A1 and A2 are the heat transfer areas on the primary and secondary sides of the plate heat exchanger, rw is the thermal conductivity coefficient of the plate of the plate heat exchanger, rf is the thermal resistance coefficient of the dirt, A0 is the heat transfer area of ​​the plate of the plate heat exchanger, and Ai is the heat transfer area of ​​the dirt. Nu=cRe n Pr m , where Re is the Reynolds number, Pr is the Prandtl number, n is 0.8, and m is 0.3-0.4. Where ρ is density, u is flow velocity, l is characteristic length, and μ is dynamic viscosity.

[0051] For cleaning the dirt of the heat exchanger, there is no need to make a very accurate calculation, and it is assumed that the heat exchange area on both sides of the plate heat exchanger is the same, so the calculation formula of UA is simplified to obtain And h is proportional to the 0.8 power of the flow rate u. Because the same plate heat exchanger has unchanged structural parameters, h is proportional to the 0.8 power of the flow rate G. The convective heat transfer coefficients on both sides are not much different, so only the flow rate G is retained, and the calculation formula of UA is further simplified, and the heat transfer thermal resistance in G2=G1(T1-T2) / (T3-T4), G1 and G2 are the primary and secondary side flow rates.

[0052] During the cleaning period, the plate heat exchanger is not scaled, only the thermal resistance of the plate is increased, and the thermal resistance of the plate heat exchanger remains unchanged before and after cleaning. However, due to different flow configurations in different operating seasons, It can be expressed as a function of y=kx+b, where y=1 / UA, x=G1 -0.8 +G2 -0.8After obtaining the fitting curves of the cleaning period and the current period, the intercept b of the two periods is compared. The intercept b is the thermal resistance of the plate heat exchanger, and its fouling thermal resistance can be obtained. Ratio of thermal resistance during dirt to thermal resistance during cleaning bu is the intercept of the current period, and bc is the intercept of the cleaning period.

[0053] S106: Determine whether the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds a preset threshold.

[0054] If the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds the preset threshold, the judgment result is yes, and S107 is executed: the plate heat exchanger is cleaned before the next operating season starts.

[0055] Specifically, if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds a preset threshold, the plate heat exchanger needs to be cleaned before the next operating season begins.

[0056] If the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period does not exceed the preset threshold, the judgment result is no, and S108 is executed: there is no need to clean the plate heat exchanger.

[0057] Specifically, if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period does not exceed the preset threshold, it means that the fouling degree of the plate heat exchanger is still within an acceptable range and there is no need to clean it immediately. However, it is recommended to continue to monitor the growth of the thermal conductivity thermal resistance of the plate heat exchanger so that timely measures can be taken when necessary.

[0058] The following is a case in which a heating company uses real data to evaluate the performance of a plate heat exchanger to illustrate this application.

[0059] The data comes from a data acquisition system installed by a heating company. The system collects data once an hour, and a total of 120 days of data have been collected. Due to the huge amount of data, the data of a certain day is used as an example for illustration. Table 1 shows the data collected every hour in a certain heat exchange station provided in an embodiment of the present application, and only part of it is selected for illustration. Including primary side inlet temperature, primary side outlet temperature, secondary side inlet temperature, secondary side outlet temperature and primary side mass flow rate. These data are the basis for evaluating the performance of plate heat exchangers. These data will be used to calculate key parameters such as the heat transfer rate and logarithmic mean temperature difference of the plate heat exchanger, and then evaluate the performance of the plate heat exchanger. Since the performance evaluation of the plate heat exchanger needs to be carried out under steady-state conditions, it is necessary to perform steady-state processing on the original data. Here, 5 hours of data are taken as a group, and steady-state processing calculations are performed one by one. The purpose of steady-state processing is to eliminate fluctuations and noise in the data and obtain more accurate and stable performance parameters. As Figure 2After steady-state processing, the data corresponding to 13:00 was selected as the representative of steady-state conditions. This means that around 13:00, the operating state of the plate heat exchanger is relatively stable and can be used for subsequent performance evaluation.

[0060] Table 1

[0061] time Φ / kW time Φ / kW 0:00 835.98 12:00 844.80 1:00 832.04 13:00 844.80 2:00 880.50 14:00 844.80 3:00 825.16 15:00 844.80 4:00 853.75 16:00 849.71 5:00 834.98 17:00 873.83 6:00 840.93 18:00 849.71 7:00 827.12 19:00 873.83 8:00 837.95 20:00 862.86 9:00 830.07 21:00 890.16 10:00 840.93 22:00 846.32 11:00 835.98 23:00 847.52

[0062] This application calculates and analyzes the data of a heating company for 120 days (one day's data has been given as an example in the previous article), and obtains detailed data on the heat exchange Φ during the cleaning period and the current heating season and the heat exchange capacity UA of the plate heat exchanger. Table 2 shows the G1 of the cleaning period and the current heating season provided by the embodiment of this application. -0.8 +G2 -0.8 In order to understand these data more intuitively, they were plotted on a graph, and a linear fit was performed on the data of the cleaning period and the current heating season to obtain a linear relationship. Figure 3 A schematic diagram of a linear relationship provided in an embodiment of the present application, such as Figure 3 As shown, the fitting straight line functions of the two periods were obtained at the same time. Figure 2 The cleaning period in is the cleaning period, and the current period is the current heating season. Cleaning period: yc = 0.06536x + 0.00127. Current heating season: yu = 0.19059x + 0.000172.

[0063] Table 2

[0064]

[0065] It can be obtained that the intercept b of the cleaning period is 0.01376, and the intercept b of the current period is 0.04419. The fouling thermal resistance of the plate heat exchanger in the current operating season is calculated to be 0.03043℃ / W. The ratio of the fouling thermal resistance in the current operating season to the thermal conductivity thermal resistance in the cleaning period is This is much higher than the maximum acceptable threshold set by the company based on economic cost considerations1. Therefore, the plate heat exchanger needs to be cleaned before the start of the next heating season.

[0066] At the same time, multiple plate heat exchangers were also analyzed. As shown in Table 3, the ratio of fouling thermal resistance to thermal conductivity thermal resistance in the current operating season of each plate heat exchanger is different. Among them, plate heat exchangers No. 1, 2, 3, and 5 have exceeded the set threshold and met the cleaning requirements. In addition, plate heat exchangers No. 4 and No. 6 have not exceeded the set threshold and do not need to be cleaned for the time being.

[0067] Table 3

[0068]

[0069] The embodiment of the present application also provides a fouling cleaning judgment device 400 for an in-service plate heat exchanger based on operation data, such as Figure 4 As shown, the device includes: an acquisition module 401, an unknown quantity calculation module 402, a screening step execution module 403, a steady-state condition acquisition module 404, a plate heat exchanger fouling thermal resistance calculation module 405 and a judgment module 406.

[0070] The acquisition module 401 is used to acquire the preset time period of the cleaning period and the current operating season, collect each set of operating data of the plate heat exchanger at preset intervals within the preset time period, and remove abnormal data to form valid data sets for the cleaning period and the current operating season. Each set of operating data of the plate heat exchanger includes the inlet temperature and outlet temperature of the primary side and the secondary side, and the mass flow rate of the primary side.

[0071] The unknown quantity calculation module 402 is used to calculate the heat exchange amount and logarithmic mean temperature difference of each set of operating data in the effective data set of the cleaning period and the current operating season respectively.

[0072] The screening step execution module 403 is used to execute the steady-state operating condition set screening step for the effective data sets of the cleaning period and the current operating season, respectively, to obtain the steady-state operating condition sets of the cleaning period and the current operating season. The steady-state operating condition set screening step includes: dividing the data of the heat exchange amount and the logarithmic mean temperature difference into a plurality of sub-data sets, respectively, and calculating the relative standard deviation of each sub-data set, and selecting the sub-data set with the smallest relative standard deviation as the steady-state operating condition set.

[0073] The steady-state operating condition acquisition module 404 is used to find a group of data with the smallest difference in the steady-state operating condition set during the cleaning period and the current operating season by calculating the difference between adjacent data. The heat exchange rate and logarithmic mean temperature difference of this group of data are the steady-state heat exchange rate and steady-state logarithmic mean temperature difference of the cleaning period and the current operating season, and they are used as the steady-state operating condition.

[0074] The plate heat exchanger fouling thermal resistance calculation module 405 is used to calculate the heat transfer thermal resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow on both sides using the steady-state operating condition data, and after linear fitting, the intercept is the thermal resistance of the plate heat exchanger to calculate the fouling thermal resistance of the plate heat exchanger in the current operating season.

[0075] The judgment module 406 is used to judge whether the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds a preset threshold. If the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds the preset threshold, the plate heat exchanger is cleaned before the start of the next operating season. If the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period does not exceed the preset threshold, the plate heat exchanger does not need to be cleaned.

[0076] Some modules in the apparatus described in the present application can be described in the general context of computer executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, classes, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0077] The devices or modules described in the above application embodiments can be implemented by computer chips or entities, or by products with certain functions. For the convenience of description, the above devices are described in various modules according to their functions. When implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and / or hardware. Of course, the module that implements a certain function can also be implemented by combining multiple sub-modules or sub-units.

[0078] The methods, devices or modules described in this application can be implemented in a computer-readable program code mode. The controller can be implemented in any appropriate manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) that can be executed by the (micro) processor, a logic gate, a switch, an application-specific integrated circuit (English: Application Specific Integrated Circuit, referred to as: ASIC), a programmable logic controller and an embedded microcontroller. Examples of controllers include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in a pure computer-readable program code mode, the controller can be completely implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller and an embedded microcontroller by logically programming the method steps. Therefore, this controller can be considered as a hardware component, and the devices included in it for implementing various functions can also be regarded as structures within the hardware component. Or even, the means for realizing various functions may be regarded as both a software module for realizing the method and a structure within a hardware component.

[0079] like Figure 5As shown, an embodiment of the present application also provides a fouling cleaning judgment server for an in-service plate heat exchanger based on operating data, comprising a memory 501 and a processor 502; the memory 501 is used to store computer executable instructions; the processor 502 is used to execute computer executable instructions to implement a fouling cleaning judgment method for an in-service plate heat exchanger based on operating data described above in an embodiment of the present application.

[0080] An embodiment of the present application also provides a computer-readable storage medium, which stores executable instructions. When a computer executes the executable instructions, it can implement the method for determining the cleaning of an in-service plate heat exchanger for heating based on operating data described above in the embodiment of the present application.

[0081] It can be known from the description of the above implementation mode that a person skilled in the art can clearly understand that the present application can be implemented by means of software plus necessary hardware. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, or it can be reflected in the implementation process of data migration. The computer software product can be stored in a storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a mobile terminal, a server, or a network device, etc.) to execute the method described in the embodiment of the present application.

[0082] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. All or part of this application can be used in many general or special computer system environments or configurations.

[0083] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, a person of ordinary skill in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some or all of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the present application.

Claims

1. A method for judging fouling cleaning of an in-service plate heat exchanger based on operation data, characterized in that: include: Obtain the preset time period of the cleaning period and the current operating season, collect each set of operating data of the plate heat exchanger at preset intervals within the preset time period, remove abnormal data, and form valid data sets for the cleaning period and the current operating season respectively; each set of operating data of the plate heat exchanger includes the inlet temperature and outlet temperature of the primary side and the secondary side, and the mass flow rate of the primary side; Calculate the heat exchange and logarithmic mean temperature difference of each set of operating data in the effective data set of the cleaning period and the current operating season respectively; Performing steady-state condition set screening steps on the effective data sets of the cleaning period and the current operating season respectively to obtain steady-state condition sets of the cleaning period and the current operating season; The steady-state operating condition set screening step includes: dividing the data of heat exchange amount and logarithmic mean temperature difference into a plurality of sub-data sets, calculating the relative standard deviation of each sub-data set, and selecting the sub-data set with the smallest relative standard deviation as the steady-state operating condition set; In the steady-state working condition set of the cleaning period and the current operating season, by calculating the difference between adjacent data, find a group of data with the smallest difference. The heat transfer and logarithmic mean temperature difference of this group of data are the steady-state heat transfer and steady-state logarithmic mean temperature difference of the cleaning period and the current operating season, and take it as the steady-state working condition; The heat transfer resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow rates on both sides are calculated using the steady-state operating data. After linear fitting, the intercept is the thermal resistance of the plate heat exchanger to calculate the fouling resistance of the plate heat exchanger in the current operating season. Determine whether the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds a preset threshold; If the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds the preset threshold, the plate heat exchanger is cleaned before the next operating season begins; If the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period does not exceed the preset threshold, there is no need to clean the plate heat exchanger.

2. The method for judging fouling cleaning of an in-service plate heat exchanger based on operation data according to claim 1, characterized in that: The cleaning period is the first operating season when the plate heat exchanger starts to operate and is used for operation or the first operating season after the plate heat exchanger is cleaned.

3. The method for judging fouling cleaning of an in-service plate heat exchanger based on operation data according to claim 1, characterized in that: Before respectively calculating the heat exchange amount and logarithmic mean temperature difference of each set of operating data in the effective data sets of the cleaning period and the current operating season, it also includes: removing the data that have not reached the stable operating period at the beginning of the operating season in the effective data sets of the cleaning period and the current operating season.

4. The method for judging fouling cleaning of an in-service plate heat exchanger based on operation data according to claim 1 is characterized in that: The definition standard of abnormal data is: if the primary side inlet temperature, primary side outlet temperature, secondary side inlet temperature, secondary side outlet temperature or primary side mass flow rate in any set of operating data is zero or negative, the set of data is regarded as abnormal data; If the calculated result of the logarithmic mean temperature difference is negative, the data set is considered abnormal data; If the temperature or primary-side mass flow rate of two or more adjacent groups of data are the same, the group of data is regarded as abnormal data.

5. The method for judging fouling cleaning of an in-service plate heat exchanger based on operation data according to claim 1, characterized in that: The calculation method of heat transfer is: Calculate the temperature difference between the primary side inlet temperature and the primary side outlet temperature in the effective data set during the cleaning period and the current operating season; Multiplying the temperature difference by the mass flow rate of the primary side of the effective data set during the cleaning period and the current operating season to obtain an intermediate value; This intermediate value is multiplied by the specific heat capacity of the fluid to obtain the heat transfer capacity for the effective data set for the cleaning period and the current operating season.

6. The method for judging fouling cleaning of an in-service plate heat exchanger based on operation data according to claim 1, characterized in that: The log mean temperature difference is calculated as: For the effective data sets of the cleaning period and the current operating season, two sets of temperature differences are calculated respectively; the first temperature difference is the temperature from the primary side inlet temperature to the secondary side inlet temperature, and the second temperature difference is the temperature from the primary side outlet temperature to the secondary side outlet temperature; Calculating a ratio of the first temperature difference to the second temperature difference, and performing a logarithmic operation using the ratio as a base of a logarithm to obtain a logarithmic value; Using the logarithmic value as the denominator and the difference between the first temperature difference and the second temperature difference as the numerator, a division operation is performed to obtain the logarithmic mean temperature difference of the effective data set of the cleaning period and the current operating season.

7. The method for judging fouling cleaning of an in-service plate heat exchanger based on operation data according to claim 1, characterized in that: The relative standard deviation of each sub-dataset is calculated as: Add up the values ​​of each heat exchange or logarithmic mean temperature difference in the sub-dataset to obtain the total, and divide the total by the total number of data to obtain the average value; Calculate the difference between the heat transfer value or logarithmic mean temperature difference in the sub-dataset and the average value; And obtain the squared difference; Add all the squared differences to get the sum of the squared differences, divide the sum of the squared differences by the total number of data in each sub-dataset, and take the square root to calculate the standard deviation; Divide the standard deviation by the mean to obtain the relative standard deviation.

8. The method for judging fouling cleaning of an in-service plate heat exchanger based on operation data according to claim 1, characterized in that: The heat transfer resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow on both sides are calculated using the steady-state operating data. After linear fitting, the intercept is the thermal resistance of the plate heat exchanger to calculate the fouling resistance of the plate heat exchanger, including: For the cleaning period and the current operating season, the corresponding heat exchange capacity UA is calculated respectively, UA = Φ / LMTD; where the heat exchange capacity UA is the product of the total heat exchange coefficient of the plate heat exchanger and the heat exchange area, Φ is the heat exchange amount, and LMTD is the logarithmic mean temperature difference; When UA is expressed and calculated using the thermal resistance of the plate heat exchanger, the cleaning period Current season Where h1 and h2 are the convective heat transfer coefficients on the primary and secondary sides of the plate heat exchanger, A1 and A2 are the heat transfer areas on the primary and secondary sides of the plate heat exchanger, rw is the thermal conductivity coefficient of the plate of the plate heat exchanger, rf is the fouling coefficient, A0 is the heat transfer area of ​​the plate of the plate heat exchanger, Ai is the heat transfer area of ​​the fouling, and Nu=cRe n Pr m , where Re is the Reynolds number, Pr is the Prandtl number, n is 0.8, m is 0.3-0.4, Where ρ is density, u is flow velocity, l is characteristic length, and μ is dynamic viscosity; Simplify the calculation formula of UA, and we get And h is proportional to the 0.8 power of flow rate u, h is proportional to the 0.8 power of flow rate G, simplifying the calculation formula of UA, the total heat transfer thermal resistance in G2 = G1 (T1-T2) / (T3-T4), G1 and G2 are the primary and secondary flow rates; Will It can be expressed as a function of y=kx+b, where y=1 / UA, x=(G1 -0.8 +G2 -0.8 ), and perform linear fitting, the goodness of fit R 2 Not less than 0.8; After the fitting curves of the cleaning period and the current period are obtained, the intercept b is the thermal resistance of the plate heat exchanger. By comparing the intercepts b of the two periods, the fouling thermal resistance of the heat exchanger in the current operating season is obtained. Ratio of thermal resistance during dirt to thermal resistance during cleaning bu is the intercept of the current period, and bc is the intercept of the cleaning period.

9. A fouling cleaning judgment device for an in-service plate heat exchanger based on operation data, characterized in that: include: The acquisition module is used to acquire the preset time period of the cleaning period and the current operating season, collect each set of operating data of the plate heat exchanger at preset time intervals within the preset time period, and remove abnormal data to form valid data sets for the cleaning period and the current operating season respectively; each set of operating data of the plate heat exchanger includes the inlet temperature and outlet temperature of the primary side and the secondary side, and the mass flow rate of the primary side; An unknown quantity calculation module is used to calculate the heat exchange and logarithmic mean temperature difference of each set of operating data in the effective data set of the cleaning period and the current operating season respectively; An execution screening step module is used to execute a steady-state operating condition set screening step on the effective data sets of the cleaning period and the current operating season respectively, so as to obtain the steady-state operating condition sets of the cleaning period and the current operating season; The steady-state operating condition set screening step includes: dividing the data of heat exchange amount and logarithmic mean temperature difference into a plurality of sub-data sets, calculating the relative standard deviation of each sub-data set, and selecting the sub-data set with the smallest relative standard deviation as the steady-state operating condition set; The module for obtaining steady-state working conditions is used to find a group of data with the smallest difference by calculating the difference between adjacent data in the steady-state working condition set of the cleaning period and the current operating season. The heat exchange and logarithmic mean temperature difference of this group of data are the steady-state heat exchange and steady-state logarithmic mean temperature difference of the cleaning period and the current operating season, and they are used as the steady-state working conditions; The module for calculating the fouling thermal resistance of the plate heat exchanger is used to calculate the heat transfer thermal resistance of the cleaning period and the current operating season data and the sum of the negative 0.8 power of the flow on both sides using the steady-state operating data, and after linear fitting, the intercept is the thermal resistance of the plate heat exchanger to calculate the fouling thermal resistance of the plate heat exchanger in the current operating season; The judgment module is used to judge whether the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds a preset threshold; if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period exceeds the preset threshold, the plate heat exchanger is cleaned before the start of the next operating season; if the ratio of the fouling thermal resistance of the plate heat exchanger in the current operating season to the thermal conductivity thermal resistance in the cleaning period does not exceed the preset threshold, there is no need to clean the plate heat exchanger.

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