An online monitoring and early warning system for scale formation state of plate evaporator

Abstract

The present application belongs to the technical field of industrial equipment state monitoring and fault diagnosis, and specifically relates to a plate evaporator fouling state online monitoring and early warning system, which comprises a data acquisition module, a data processing module, an abnormal reason judgment module and a risk early warning module. Through preset logical rules, the present application can effectively distinguish different performance abnormal causes of fouling, non-condensable gas accumulation and mechanical changes. The present application has the abilities of localized abnormal positioning and multi-factor intelligent tracing, and can significantly reduce misjudgment. The actual heat transfer efficiency and the resistance along the way are calculated, a comprehensive abnormal value is constructed through the super-threshold degree, linear weighted fusion is carried out, the time sequence change trend is combined, visual support for the fouling degree classification and development trend is provided, and the operator can scientifically plan the shutdown cleaning time according to the fouling development trend.

Description

An online monitoring and early warning system for scaling conditions in plate evaporators Technical Field

[0001] This invention belongs to the field of industrial equipment condition monitoring and fault diagnosis technology, specifically an online monitoring and early warning system for scaling conditions in plate evaporators. Background Technology

[0002] Plate evaporators are a new type of energy-saving evaporation equipment that uses metal plates as the main heat transfer elements. Their core structure consists of heat exchange plate assemblies, a pressing device, and a separation chamber. This equipment achieves counter-current heat transfer through alternating material and heating channels, significantly reducing the amount of heating steam required. It is suitable for material concentration processing in industries such as food, pharmaceuticals, and environmental protection.

[0003] However, during operation, scaling can occur on the inner walls of the material channels in plate evaporators. Scale deposits, such as inorganic salt crystals and organic polymers, adhere to the inner surface of the plates, forming a thermal resistance layer that severely hinders heat transfer, leading to a significant decrease in heat transfer efficiency and a substantial increase in heating steam consumption.

[0004] Currently, monitoring the scaling status of plate evaporators mainly relies on offline inspections or threshold alarms based on a single operating parameter. Although the above solutions offer some solutions, existing technologies still have the following limitations: 1. Relying solely on inlet and outlet total parameters such as the decrease or exceedance of total temperature difference or total heat transfer coefficient to trigger alarms cannot identify local anomalies and their causes. Traditional methods are difficult to effectively distinguish between different factors such as scaling, accumulation of non-condensable gases, and mechanical changes, which can easily lead to misjudgments or delayed treatment.

[0005] 2. Alarms based on fixed thresholds lack the ability to dynamically perceive the scaling process and cannot reflect the severity and development trend of scaling. This makes it difficult for operators to scientifically plan the timing of cleaning. Cleaning too early wastes resources, while cleaning too late increases the risk. Summary of the Invention

[0006] To overcome the shortcomings of the prior art, embodiments of the present invention provide an online monitoring and early warning system for scaling conditions in plate evaporators, which can effectively solve the problems involved in the prior art.

[0007] The objective of this invention can be achieved through the following technical solution: an online monitoring and early warning system for scaling status of plate evaporators, comprising: a data acquisition module, a data processing module, an anomaly cause judgment module, and a risk early warning module.

[0008] The data acquisition module is connected to the data processing module, the data processing module is connected to the anomaly cause judgment module, the anomaly cause judgment module is connected to the risk warning module, and the anomaly cause judgment module is connected to the diagnostic notification module.

[0009] The data acquisition module acquires temperature data on the outer surface of the plate and fluid velocity data on the inner surface of the plate through multiple distributed temperature sensors and flow rate sensors arranged in a preset spatial pattern on the surface of the plate evaporator.

[0010] The data processing module analyzes the heat transfer and flow resistance of the plate evaporator plates using the temperature and flow rate data to quantify the abnormal performance characteristics of the plate evaporator.

[0011] The abnormal cause judgment module collects steam pressure data and combines it with the changes in the heat transfer efficiency and flow resistance of the current plate evaporator plates. It then uses preset logic rules to check whether the current evaporator performance abnormality is affected by non-scaling factors. These non-scaling factors include non-condensable gases or changes in mechanical structure. If the abnormality is due to non-scaling factors, the module locates the actual non-scaling factors and executes the diagnostic notification module. Otherwise, the module executes the risk warning module.

[0012] The risk warning module analyzes the degree of scaling risk based on the time-series change trend of the abnormal performance characteristic values ​​of the evaporator and triggers corresponding warning prompts.

[0013] The diagnostic notification module triggers a notification operation based on the identified non-scaling factors.

[0014] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects: 1. The present invention, through the preset logical rules in the abnormal cause judgment module, combined with the abrupt change analysis of the friction resistance time sequence, the judgment of temperature uniformity, the analysis of the synergistic change of heat transfer efficiency and friction resistance, and the characteristics of steam pressure distribution, can not only identify the location of local performance abnormalities, but also effectively distinguish different performance abnormality causes such as scaling, accumulation of non-condensable gases, and mechanical changes. Compared with the traditional monitoring method that only relies on the total parameters of inlet and outlet, the present invention has the ability of localized abnormality location and multi-factor intelligent tracing, which can significantly reduce misjudgment and provide accurate basis for maintenance decisions.

[0015] 2. This invention collects plate temperature and fluid flow rate data in real time through a distributed sensor network, calculates the actual heat transfer efficiency and friction loss through a data processing module, constructs comprehensive outlier values ​​and linear weighted fusion based on the degree of exceeding thresholds, and analyzes the degree of scaling risk by combining time-series change trends. It reflects the dynamic evolution of scaling from the initial stage to the severe stage in real time, and provides visualization support for scaling degree classification and development trend.

[0016] 3. The risk warning module of this invention triggers targeted prompts based on the level of scaling risk, establishing a dynamically adjustable warning mechanism. This enables operators to scientifically plan downtime for cleaning based on the scaling development trend, avoiding resource waste or operational risks caused by cleaning too early or too late, maximizing the effective operating cycle of the equipment, and reducing heating steam consumption and maintenance costs. Attached Figure Description

[0017] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0018] Figure 1 is a schematic diagram of the module connection of the present invention.

[0019] Figure 2 is a flowchart of the preset logic rules of the present invention.

[0020] Figure 3 is a logic diagram of the operation corresponding to the early warning prompt of the present invention. Detailed Implementation

[0021] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Referring to Figure 1, the present invention provides an online monitoring and early warning system for scaling conditions in a plate evaporator, comprising: a data acquisition module, a data processing module, an anomaly cause judgment module, and a risk early warning module.

[0023] The data acquisition module is connected to the data processing module, the data processing module is connected to the anomaly cause judgment module, the anomaly cause judgment module is connected to the risk warning module, and the anomaly cause judgment module is connected to the diagnostic notification module.

[0024] The data acquisition module uses multiple distributed temperature sensors and flow rate sensors arranged in a preset spatial layout on the surface of the plate evaporator plates to acquire temperature data on the outer surface of the plates and fluid flow rate data on the inner surface.

[0025] It should be added that thin-film platinum resistance temperature sensors with corrosion resistance and anti-scaling properties are fixedly installed on the outer surface of the heating channel of the plate evaporator in a manner with equally spaced feature points. These sensors are used to collect temperature distribution data on the outer surface of the plates. At the same time, miniature electromagnetic flow velocity sensors are embedded inside the corresponding plate channels to measure the flow velocity distribution data of the fluid on the inner surface of the plates in real time.

[0026] The data processing module analyzes the heat transfer and flow resistance of the plate evaporator plates using the temperature and flow rate data to quantify the abnormal performance characteristics of the plate evaporator.

[0027] In a preferred embodiment of the present invention, the analysis of the heat transfer status of the current plate evaporator plates specifically includes: installing temperature sensors at the inlet position of the flow channel formed by the plate evaporator plates to obtain the initial temperature of the fluid at the inlet position, and simultaneously acquiring temperature distribution data of the outer surface of the plates; calculating the absolute value of the temperature difference between each monitoring point and the initial temperature; extracting the permissible maximum plate heat transfer temperature difference and its standard heat transfer efficiency value calibrated in the experiment under the non-scaling working condition of the plate evaporator from the cloud database; correcting the standard heat transfer efficiency value according to the degree of deviation between the absolute value of the current monitoring point temperature difference and the permissible maximum plate heat transfer temperature difference; the corrected heat transfer efficiency value is the current actual heat transfer efficiency; and using the current actual heat transfer efficiency of each monitoring point as the characterization parameter of the heat transfer status of the current plate evaporator plates.

[0028] It should be noted that the correction is achieved by introducing a direct calculation method for scaling thermal resistance. Specifically, the deviation is calculated by taking the difference between the absolute value of the temperature difference at the current monitoring point and the maximum permissible plate heat transfer temperature difference, and then dividing the difference by the design heat flux density at the monitoring point to directly calculate the additional thermal resistance caused by scaling. The standard heat transfer efficiency value is then multiplied by the ratio of the base thermal resistance to the sum of the additional thermal resistance and the base thermal resistance to calculate the current actual heat transfer efficiency.

[0029] The cloud database contains a set of benchmark parameters calibrated by standard experimental conditions for plate evaporators operating in a non-scaling state. It includes a large amount of plate surface temperature distribution data and corresponding initial fluid temperatures measured by the data acquisition module. The plate heat transfer temperature difference and heat transfer efficiency under various operating conditions are calculated, and the maximum permissible plate heat transfer temperature difference and its standard heat transfer efficiency value are determined through statistical analysis. The design heat flux density is the measured heat flux density value at the corresponding monitoring point under rated operating conditions. It is a constant predetermined and stored in the cloud database based on the evaporator design parameters through heat transfer calculations. The basic thermal resistance is calculated by the reciprocal of the total heat transfer coefficient measured in standard experiments for the plate evaporator operating in a non-scaling state, and is also stored in the cloud database as a known benchmark constant.

[0030] For example, if the standard heat transfer efficiency is 0.8 and the overall heat transfer coefficient is 2, then the basic thermal resistance is 0.5 and the calculated additional thermal resistance is 0.2. Therefore, the current actual heat transfer efficiency is 0.571. All the above calculations are dimensionless and their numerical values ​​are used for logical analysis.

[0031] In a preferred embodiment of the present invention, the analysis of the flow resistance status of the current plate evaporator plates specifically includes: using a distributed flow velocity sensor to obtain the flow velocity difference between adjacent monitoring points, and the interval between adjacent monitoring points is a predetermined value; performing a ratio calculation between the flow velocity difference and the predetermined value to obtain the flow velocity attenuation rate along the path; substituting the flow velocity attenuation rate along the path into a preset formula to calculate the flow resistance of each monitoring point interval; the flow resistance along the path is the flow resistance status of each monitoring point interval; and using the current flow resistance of each monitoring point interval as a characterization parameter of the current plate evaporator plate flow resistance status.

[0032] It should be noted that the distributed flow velocity sensors are deployed along the flow path within the plate evaporator, with adjacent monitoring points serving as monitoring points. and monitoring points Then the velocity decay rate along the flow path The calculation formula is: ,in For monitoring points Instantaneous flow velocity, For monitoring points Instantaneous flow velocity, The friction velocity attenuation rate is a predetermined value between adjacent monitoring points. Used to reflect the velocity gradient and combined with the Reynolds number to calculate the friction coefficient. In turbulent flow, empirical formulas are combined with The revised formula, based on an extension of Blasius's formula, is as follows: ,in This is the basic drag coefficient for turbulence. The average flow velocity at adjacent monitoring points is the fluid in the flow channel of the plate evaporator, which is mostly turbulent. The convective heat transfer coefficient in turbulent flow is much higher than that in laminar flow. Therefore, in design and operation, turbulent flow is usually actively promoted through parameter optimization.

[0033] The preset formula is a friction resistance calculation model based on the Darcy-Weisbach formula in fluid mechanics, and its expression is: ,in For friction resistance, This is the friction coefficient. The equivalent diameter of the flow channel. For fluid density, The average flow velocity at adjacent monitoring points, and the equivalent diameter of the flow channel. Fluid density and the predetermined distance All of these are constants that are predetermined based on the evaporator flow channel design parameters and stored in the cloud database.

[0034] For example, the equivalent diameter of the flow channel The value is 0.1, representing the predetermined distance. The fluid density is 0.5. The average flow rate is 1000. The Reynolds number is 1.5. The flow rate attenuation rate is 15000. If it is 0.05, then the friction loss is... All the above calculations are performed by removing dimensions and taking the numerical values ​​to perform logical analysis.

[0035] In a preferred embodiment of the present invention, the specific method for quantifying the abnormal characteristic value of the current plate evaporator performance is as follows: the current actual heat transfer efficiency is compared with a preset permissible heat transfer efficiency threshold, and the friction resistance is compared with a preset permissible resistance threshold. If the current actual heat transfer efficiency is less than the preset permissible heat transfer efficiency threshold, the monitoring point is marked as a heat transfer efficiency abnormal point, and if the friction resistance is greater than the preset permissible resistance threshold, the interval is marked as a resistance abnormal point.

[0036] The locations where heat transfer efficiency and resistance anomalies occur together are marked as performance anomaly points. If a performance anomaly point appears, the current plate evaporator is determined to be in abnormal performance. Combining the actual heat transfer efficiency and the degree of friction resistance exceeding the threshold at each performance anomaly point, the characteristic value of the current plate evaporator performance anomaly is quantified.

[0037] It should be noted that the preset permissible heat transfer efficiency threshold and the preset permissible resistance threshold are both experimentally calibrated. Specifically, on an experimental prototype with the same design parameters and materials as the target plate evaporator, a large amount of heat transfer efficiency and friction resistance data are collected by the data acquisition module under non-scaling healthy conditions and different load conditions. The lower limit of the normal fluctuation range of heat transfer efficiency is determined as the permissible heat transfer efficiency threshold, and the upper limit of the normal fluctuation range of friction resistance is determined as the permissible resistance threshold, using the normal distribution method. Both the preset permissible heat transfer efficiency threshold and the preset permissible resistance threshold are stored in the cloud database.

[0038] In a preferred embodiment of the present invention, the specific method for quantifying the current abnormal performance characteristic value of the plate evaporator includes: calculating the absolute value of the difference between the actual heat transfer efficiency at the abnormal performance point and a preset permissible heat transfer efficiency threshold, which is taken as the degree to which the actual heat transfer efficiency exceeds the threshold; calculating the absolute value of the difference between the friction resistance at the abnormal performance point and a preset permissible resistance threshold, which is taken as the degree to which the friction resistance exceeds the threshold; summing the degree to which the actual heat transfer efficiency exceeds the threshold and the degree to which the friction resistance exceeds the threshold, which is taken as the comprehensive abnormal value; assigning weight values ​​to the location of each abnormal performance point; and obtaining the current abnormal performance characteristic value of the plate evaporator through linear weighted fusion.

[0039] It should be noted that the determination of the weight values ​​requires combining multiple sets of operating data samples with known evaporator performance in normal condition. By quantitatively analyzing the impact of anomalies at each location on the overall performance, firstly, in the normal condition samples, standardized performance anomalies are simulated and introduced at different locations on the plates. The standardized performance anomaly can be exemplified by covering a designated location with a standard fouling material of known thickness and thermal conductivity. The changes in the overall heat transfer efficiency and flow resistance of the evaporator are collected and calculated after each simulation. Secondly, the ratio of the overall performance changes is used as the quantitative basis for the degree of influence at each location. Finally, the ratio is normalized to obtain the weight values ​​of the core heat exchange area and the edge area. Among them, performance anomaly points located in the core heat exchange area of ​​the plate are assigned higher weight values, and performance anomaly points located in the edge area of ​​the plate are assigned lower weight values.

[0040] The specific process of the linear weighted fusion is as follows: multiply the comprehensive abnormal value of each performance abnormal point by its corresponding position weight value to obtain the weighted abnormal value of that point, and sum the weighted abnormal values ​​of all performance abnormal points. The summation result is used as the performance abnormality feature value of the current plate evaporator. All of the above are dimensionless and numerical values ​​are used for logical analysis.

[0041] The abnormality cause judgment module collects steam pressure data and combines it with the changes in the heat transfer and flow resistance of the current plate evaporator plates. It then uses preset logic rules to check whether the current evaporator performance abnormality is affected by non-scaling factors. These non-scaling factors include non-condensable gases or changes in mechanical structure. If the abnormality is caused by non-scaling factors, the module locates the actual non-scaling factors and executes the diagnostic notification module. Otherwise, the module executes the risk warning module.

[0042] Referring to Figure 2, in a preferred embodiment of the present invention, the preset logic rule includes: S1. Obtaining the curve fitted by the time series change of the friction resistance at the performance anomaly point, calculating the instantaneous slope of the curve fitted by the time series change of the friction resistance at each time point, and calculating the linear regression slope of the overall time series, comparing the instantaneous slope with the linear regression slope to determine whether there is a sudden change phenomenon. If there is a sudden change phenomenon, proceed to step S2; otherwise, proceed to step S3.

[0043] S2. Obtain the temperature data set of the outer surface of the plate of the current plate evaporator, perform statistical analysis on the temperature data set, calculate the skewness and standard deviation of the temperature data set. If the absolute value of the skewness is less than or equal to the preset skewness threshold and the standard deviation of the temperature exceeds the preset multiple of the standard deviation under normal operating conditions, it is determined that the temperature distribution is asymmetrical and there is an uneven temperature phenomenon. Then, it is determined that the current evaporator performance abnormality is caused by mechanical changes in non-scaling factors.

[0044] It should be noted that the preset multiple of the standard deviation under normal operating conditions is calibrated experimentally. Specifically, on an experimental prototype with parameters consistent with the target plate evaporator, under normal operating conditions without scaling or mechanical failure, multiple sets of plate outer surface temperature data samples under different load conditions are collected. The temperature standard deviation of each set of data is calculated, and its average value is taken as the normal standard deviation benchmark value. Subsequently, by artificially simulating typical mechanical changes, the temperature data at this time is collected and the standard deviation is calculated. The range of the multiple of the standard deviation under mechanical change conditions relative to the normal standard benchmark value is statistically analyzed, and the minimum value of this range is taken as the preset multiple of the standard deviation under normal operating conditions.

[0045] The formula for calculating skewness is: ,in, Indicates the monitoring point number. , Indicates the number of monitoring points. Indicates standard deviation, This indicates the temperature value at the monitoring point. This represents the arithmetic mean of a sample of temperature data. If the skewness is less than or equal to the preset skewness threshold and the temperature standard deviation does not exceed a preset multiple of the standard deviation under normal operating conditions, then the data distribution is approximately symmetrical and the temperature distribution is uniform. If the temperature standard deviation is greater than or less than the preset skewness threshold and exceeds a preset multiple of the standard deviation under normal operating conditions, the data distribution will be asymmetrical and the temperature distribution will be uneven.

[0046] The preset skewness threshold is determined based on the equipment specifications and the general requirements for temperature distribution symmetry in industry standards. Then, on an experimental prototype with parameters consistent with the target plate evaporator, under normal operating conditions without scaling or mechanical failure, multiple sets of plate outer surface temperature data samples under different load conditions are collected. The skewness value of each set of data is calculated and its distribution characteristics are statistically analyzed. The maximum value of the skewness distribution under normal conditions is used as the benchmark reference for the preset skewness threshold. Subsequently, by artificially simulating typical mechanical change faults, the temperature data at this time is collected and the skewness value is calculated. The distribution range of the skewness value under mechanical change conditions is statistically analyzed, and the minimum value of this range is used as the final value of the preset skewness threshold, which is stored in the cloud database for real-time retrieval.

[0047] S3. Obtain the friction resistance data and actual heat transfer efficiency data of the same time series during the operation of the plate evaporator, quantify the degree of coordinated change of friction resistance and actual heat transfer efficiency. If the degree of coordinated change indicates that the two are strongly negatively correlated, then the current evaporator performance abnormality is ruled out as being caused by scaling. If the degree of coordinated change indicates that the two are not correlated, then it is determined to be non-condensable gas among the suspected non-scaling factors, and step S4 is executed.

[0048] S4. Obtain the steam pressure data set at each key location of the evaporator, perform statistical analysis on the steam pressure data set, and if there is uneven steam pressure distribution, then investigate whether the current evaporator performance abnormality is caused by non-condensable gases among non-scaling factors.

[0049] It should be noted that the accumulation of non-condensable gases will occupy the effective space of the steam flow channel. According to the principle of gas partial pressure, its partial pressure will cause the steam partial pressure to differ at different locations. Specifically, the steam pressure is lower near the accumulation area and relatively higher in the area further away, which ultimately results in uneven steam pressure distribution.

[0050] The system collects steam pressure data from key locations on the evaporator, including the main steam inlet pipe, the inlet section of the plate steam channel, the middle section of the plate steam channel, and the end of the plate steam channel. The sampling frequency is consistent with the parameter calibration stage, and the collection time is a complete diagnostic time window. The same statistical logic as that used to determine uneven temperature distribution is employed for analysis. If uneven steam pressure distribution is determined, and the temperature distribution has been determined to be uniform based on plate temperature analysis, mechanical changes are prioritized for elimination. After eliminating mechanical faults, the system operating conditions are verified. If the steam main valve opening, inlet main pipe pressure, and system load meet the standards within the diagnostic time window, and the inlet pressure fluctuation is stable, the influence of operating parameter fluctuations is eliminated. If the above conditions are met, the current evaporator performance abnormality is investigated to be caused by non-condensable gases among non-scaling factors.

[0051] The diagnostic notification module triggers a notification operation based on the location of actual non-scaling factors.

[0052] It should be noted that the notification operation includes generating a notification message containing the device identifier, the type of non-scaling factor, the time of the abnormality, and the recommended handling measures, and sending the message to the operator's terminal via at least one communication method, such as SMS notification.

[0053] This invention, through the pre-set logical rules in the anomaly cause judgment module, combined with the abrupt change analysis of friction resistance over time, temperature uniformity judgment, synergistic change analysis of heat transfer efficiency and friction resistance, and steam pressure distribution characteristics, can not only identify the location of local performance anomalies, but also effectively distinguish different performance anomaly causes such as scaling, accumulation of non-condensable gases, and mechanical changes. Compared with the traditional monitoring method that only relies on the total inlet and outlet parameters, this invention has the ability to locate local anomalies and trace multiple factors intelligently, which can significantly reduce misjudgments and provide accurate basis for maintenance decisions.

[0054] In a preferred embodiment of the present invention, the mutation phenomenon is specifically defined as follows: if there are multiple consecutive time points where the instantaneous slope is significantly greater than the linear regression slope, it is determined to be a mutation phenomenon. The significant greater than is determined by comparing the instantaneous slope with a preset multiple of the linear regression slope. If the instantaneous slope is greater than the preset multiple of the linear regression slope, it is marked as significantly greater.

[0055] It should be noted that the multiple consecutive time points refer to the instantaneous slope that consistently and consistently deviates significantly from the linear regression slope within a preset time window, thus forming an identifiable abnormal change phase in the overall flat trend.

[0056] The logic for determining the preset linear regression slope multiple is consistent with the logic for determining the preset standard deviation multiple under normal operating conditions.

[0057] In a preferred embodiment of the present invention, the degree of coordinated change specifically includes: obtaining the friction resistance and actual heat transfer efficiency at the same time series of performance anomalies, respectively denoted as the friction resistance sequence and the efficiency sequence; rounding up each data value in the friction resistance sequence and the efficiency sequence in sequence; and determining the degree of coordinated change between friction resistance and efficiency by calculating the Spearman coefficient and based on its magnitude and positive / negative direction.

[0058] It should be noted that the Spearman coefficient is used to measure the monotonic correlation between two sequences. The closer the absolute value of the coefficient is to 1, the stronger the degree of co-variation. A positive value indicates a positive correlation, and a negative value indicates a negative correlation.

[0059] Rounding the resistance and efficiency sequences upwards reduces the sensitivity of data fluctuations to rank calculation, thereby improving the robustness and reliability of the Spearman coefficients in engineering noise environments. The specific calculation method is existing technology and will not be elaborated here.

[0060] The judgment criterion is as follows: the Spearman coefficient value is compared with a preset synergy judgment threshold. If it is less than or equal to the threshold, it is determined that there is a strong negative correlation between resistance and efficiency. The synergy judgment threshold is obtained by: collecting multiple sets of resistance and efficiency benchmark sequences that have a known significant monotonic correlation, calculating the Spearman coefficient between each set of benchmark sequences to obtain a series of coefficient values, taking the average of the series of coefficient values, and using the average as the preset synergy judgment threshold. The preset synergy judgment threshold can be set to -0.8 for example.

[0061] In a preferred embodiment of the present invention, the analysis of the scaling risk level specifically includes: extracting the abnormal performance characteristic values ​​of the plate evaporator plates at different time stamps and integrating them into a time series in chronological order.

[0062] The least squares method was used to fit the time series to obtain a continuous performance anomaly characteristic-time fitting curve.

[0063] Linear regression analysis was performed on the fitted curve to calculate its overall slope, which represents the average rate of scale development.

[0064] The second derivative of the fitted curve is performed to obtain the slope change rate corresponding to each time stamp. The slope change rate characterizes the changing trend of the scaling development speed, such as the scaling development accelerating or decelerating.

[0065] Calculate the arithmetic mean of the rate of change of slope for all timestamps. The arithmetic mean can be used to quantify the overall trend of accelerated or decelerated scaling development.

[0066] By combining the overall slope with the statistically determined rate of change of the slope, the degree of scaling risk is obtained through linear weighted fusion.

[0067] Based on preset risk thresholds, the degree of scaling risk is divided into three levels: low, medium, and high.

[0068] Scaling in plate evaporators is a gradual process of impurities continuously depositing on the inner surface of the plates. The resulting abnormal performance characteristics change continuously over time, and this trend is positively correlated with the degree of scaling risk. In the early stage of scaling, the abnormal performance characteristics change slowly and the risk is low, while in the accelerated scaling stage, the abnormal performance characteristics change sharply and the risk is high. By using time series and curve fitting, the dynamic process of scaling development can be accurately captured, providing continuous data support for risk quantification.

[0069] The calculated overall slope reflects the average speed of scale development, while the rate of change of the slope obtained by taking the second derivative of the fitted curve reflects the acceleration of scale development. By analyzing whether the scaling speed has accelerated, it can be indicated whether the scale has entered a rapid deterioration stage. By combining the overall slope and the rate of change of the slope, the current development status and future deterioration trend of scale can be comprehensively quantified, avoiding the shortcomings of a single parameter that cannot reflect the risk of accelerated structural deterioration.

[0070] Under different operating conditions, the weight of the overall slope and the rate of change of slope on risk varies. For example, under high-load conditions, the rate of change of slope is more critical for early warning of accelerated scaling. Linear weighted fusion can adapt to actual operating conditions by adjusting the weight coefficients, ensuring the accuracy of scaling risk calculation and meeting the corresponding industrial equipment engineering requirements.

[0071] The weighting coefficients for the overall slope and the rate of change of slope are set by engineers according to the working conditions. For example, under low-load conditions, the overall trend is emphasized, and the weighting coefficient for the overall slope is larger than that for the rate of change of slope. For example, the weighting coefficient for the overall slope is 0.7 and the weighting coefficient for the rate of change of slope is 0.3. Under high-load conditions, the acceleration trend is emphasized, and the weighting coefficient for the rate of change of slope is larger than that for the overall slope. For example, the weighting coefficient for the overall slope is 0.4 and the weighting coefficient for the rate of change of slope is 0.6.

[0072] The scaling risk level is a dimensionless value. The higher the scaling risk level value, the higher the scaling risk, and it is used for subsequent risk level classification.

[0073] The preset risk threshold is determined experimentally. Specifically, on an experimental prototype with parameters consistent with the target plate evaporator, the prototype is operated stably in a non-scaling state. Multiple sets of fouling thermal resistance data samples under different load conditions are collected, and the normal fluctuation range is statistically analyzed, with the upper limit taken as the low-risk threshold. Subsequently, by simulating different degrees of fouling, fouling thermal resistance data under corresponding conditions are collected. Based on the degree of impact on heat transfer efficiency and system performance, the critical thresholds for medium and high risk levels are determined as preset risk thresholds, thereby classifying the fouling risk into three levels: low, medium, and high.

[0074] The risk warning module analyzes the degree of scaling risk and triggers corresponding warning prompts based on the time-series change trend of the abnormal performance characteristic values ​​of the evaporator.

[0075] Referring to Figure 3, in a preferred embodiment of the present invention, the warning prompt specifically includes: if the risk level is determined to be low, then an instruction to continue running is generated.

[0076] If the risk level is determined to be medium, an early warning message recommending a cleaning plan will be generated.

[0077] If the risk level is determined to be high, an early warning message recommending immediate cleaning will be generated.

[0078] The risk warning module of this invention triggers targeted prompts based on the level of scaling risk, establishing a dynamically adjustable early warning mechanism. This enables operators to scientifically plan downtime for cleaning according to the scaling development trend, avoiding resource waste or operational risks caused by cleaning too early or too late, maximizing the effective operating cycle of the equipment, and reducing heating steam consumption and maintenance costs.

[0079] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0080] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0081] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0082] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0083] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0084] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An online monitoring and early warning system for scaling conditions in a plate evaporator, characterized in that: include: The data acquisition module uses multiple distributed temperature and flow rate sensors arranged in a preset spatial pattern on the surface of the plate evaporator plates to acquire temperature data on the outer surface of the plates and fluid flow rate data on the inner surface. The data processing module uses the temperature and flow rate data to analyze the current heat transfer and flow resistance of the plate evaporator plates to quantify the abnormal performance characteristics of the plate evaporator. The abnormal cause judgment module collects steam pressure data and combines it with the changes in the heat transfer and flow resistance of the current plate evaporator plates. It then uses preset logic rules to check whether the current evaporator performance abnormality is affected by non-scaling factors. These non-scaling factors include non-condensable gases or changes in mechanical structure. If the abnormality is caused by non-scaling factors, the module locates the actual non-scaling factors and executes the diagnostic notification module. If not, the module executes the risk warning module. The risk warning module analyzes the degree of scaling risk based on the time-series change trend of the abnormal performance characteristic values ​​of the evaporator and triggers corresponding warning prompts. The diagnostic notification module triggers a notification operation based on the identified non-scaling factors.

2. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 1, characterized in that: The analysis of the current heat transfer status of the plate evaporator plates specifically includes: installing temperature sensors at the inlet position of the flow channel formed by the plate evaporator plates to obtain the initial temperature of the fluid at the inlet position, and simultaneously acquiring temperature distribution data of the outer surface of the plates; calculating the absolute value of the temperature difference between each monitoring point and the initial temperature; extracting the permissible maximum plate heat transfer temperature difference and its standard heat transfer efficiency value calibrated in the experiment under the non-scaling working condition of the plate evaporator from the cloud database; correcting the standard heat transfer efficiency value according to the degree of deviation between the current absolute value of the temperature difference at the monitoring point and the permissible maximum plate heat transfer temperature difference; the corrected heat transfer efficiency value is the current actual heat transfer efficiency; and using the current actual heat transfer efficiency at each monitoring point as the characterization parameter of the current heat transfer status of the plate evaporator plates.

3. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 2, characterized in that: The analysis of the flow resistance status of the current plate evaporator plates specifically includes: using distributed flow velocity sensors to obtain the flow velocity difference between adjacent monitoring points, with the interval between adjacent monitoring points being a predetermined value; calculating the ratio between the flow velocity difference and the predetermined value as the flow velocity attenuation rate along the path; substituting the flow velocity attenuation rate along the path into a preset formula to calculate the flow resistance of each monitoring point interval; the flow resistance along the path is the flow resistance status of each monitoring point interval; and using the current flow resistance of each monitoring point interval as a parameter characterizing the flow resistance status of the current plate evaporator plates.

4. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 3, characterized in that: The specific method for quantifying the current abnormal performance characteristics of the plate evaporator is as follows: compare the current actual heat transfer efficiency with the preset permissible heat transfer efficiency threshold and the friction resistance with the preset permissible resistance threshold. If the current actual heat transfer efficiency is less than the preset permissible heat transfer efficiency threshold, mark the monitoring point as an abnormal heat transfer efficiency point. If the friction resistance is greater than the preset permissible resistance threshold, mark the interval as a friction resistance point. Mark the positions where the heat transfer efficiency point and the friction resistance point coexist as performance abnormal points. If a performance abnormal point appears, determine that the current plate evaporator is abnormal. Combine the actual heat transfer efficiency and the degree of friction resistance exceeding the threshold at each performance abnormal point to quantify the current abnormal performance characteristics of the plate evaporator.

5. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 4, characterized in that: The specific method for quantifying the current abnormal performance characteristic value of the plate evaporator includes: calculating the absolute value of the difference between the actual heat transfer efficiency at the abnormal performance point and the preset permissible heat transfer efficiency threshold, which is taken as the degree to which the actual heat transfer efficiency exceeds the threshold; calculating the absolute value of the difference between the friction resistance at the abnormal performance point and the preset permissible resistance threshold, which is taken as the degree to which the friction resistance exceeds the threshold; summing the degree to which the actual heat transfer efficiency exceeds the threshold and the degree to which the friction resistance exceeds the threshold, which is taken as the comprehensive abnormal value; assigning weight values ​​to the location of each abnormal performance point; and obtaining the current abnormal performance characteristic value of the plate evaporator through linear weighted fusion.

6. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 4, characterized in that: The preset logic rules include: S1. Obtaining the curve fitted by the time series change of friction resistance at the performance anomaly point, calculating the instantaneous slope of the curve fitted by the time series change of friction resistance at each time point, and calculating the linear regression slope of the overall time series. Comparing the instantaneous slope with the linear regression slope to determine whether there is a sudden change. If there is a sudden change, proceed to step S2; otherwise, proceed to step S3. S2. Obtaining the temperature data set of the outer surface of the plate evaporator plates, performing statistical analysis on the temperature data set, calculating the skewness and standard deviation of the temperature data set. If the absolute value of the skewness is greater than or less than the preset skewness threshold and the standard deviation of the temperature exceeds a preset multiple of the standard deviation under normal operating conditions, it is determined that the temperature distribution is asymmetrical and there is a temperature unevenness. If the current evaporator performance abnormality is determined to be caused by mechanical changes in non-scaling factors, then the following steps are performed: S3. Obtain the friction resistance data and actual heat transfer efficiency data of the same time series during the operation of the plate evaporator, quantify the degree of coordinated change of friction resistance and actual heat transfer efficiency. If the degree of coordinated change indicates a strong negative correlation between the two, then the current evaporator performance abnormality is determined to be caused by scaling. If the degree of coordinated change indicates no correlation between the two, then it is determined to be a non-condensable gas in the suspected non-scaling factors, and step S4 is executed; S4. Obtain the steam pressure data set of each key location of the evaporator, perform statistical analysis on the steam pressure data set, and if there is uneven steam pressure distribution, then the current evaporator performance abnormality is determined to be caused by non-condensable gases in the non-scaling factors.

7. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 6, characterized in that: The mutation phenomenon is specifically defined as follows: if there are multiple consecutive time points where the instantaneous slope is significantly greater than the linear regression slope, it is determined to be a mutation phenomenon. The "significantly greater" means comparing the instantaneous slope with a preset multiple of the linear regression slope. If the instantaneous slope is greater than the preset multiple of the linear regression slope, it is marked as significantly greater.

8. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 6, characterized in that: The degree of coordinated change specifically includes: obtaining the friction resistance and actual heat transfer efficiency at the same time series of performance anomalies, denoted as the friction resistance sequence and efficiency sequence respectively; rounding up each data value in the friction resistance sequence and efficiency sequence in sequence; and determining the degree of coordinated change between friction resistance and efficiency by calculating the Spearman coefficient and based on its magnitude and direction.

9. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 1, characterized in that: The analysis of scaling risk specifically includes: extracting abnormal performance characteristic values ​​of plate evaporators at different time stamps and integrating them into a time series in chronological order; using the least squares method to fit the time series to obtain a continuous performance abnormality characteristic-time fitting curve; performing linear regression analysis on the fitted curve to calculate its overall slope, which represents the average speed of scaling development; performing second derivative on the fitted curve to obtain the slope change rate corresponding to each time stamp, which represents the changing trend of scaling development speed; calculating the arithmetic mean of the slope change rates of all time stamps; combining the overall slope and the statistically derived slope change rate, obtaining the scaling risk level through linear weighted fusion; and classifying the scaling risk level into three levels: low, medium, and high, based on a preset risk threshold.

10. The online monitoring and early warning system for scaling status of a plate evaporator according to claim 9, characterized in that: The warning prompts specifically include: if the risk level is determined to be low, an instruction to continue operation is generated; if the risk level is determined to be medium, a warning message suggesting planned cleaning is generated; if the risk level is determined to be high, a warning message suggesting immediate cleaning is generated.

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