Boiler thermal efficiency evaluation and prediction method based on data analysis

Abstract

The invention relates to the technical field of boiler data processing, in particular to a boiler thermal efficiency evaluation and prediction method based on data analysis. According to the hearth steady-state operation coefficients at different moments, the time required for the steady state of the hearth is obtained; obtaining boiler health factors of the real-time heating operation process according to the change characteristics of the time required for the steady state of the hearth in different heating operation processes in the process neighborhood range of the real-time heating operation process; a pipeline heat loss coefficient at the real-time moment is obtained according to the difference of the temperature data between the inside and outside of the water outlet pipeline at the real-time moment; according to the difference between the outlet water temperature data and the return water temperature data at the real-time moment, the pipeline aging degree at the real-time moment is obtained; further obtaining a pipeline heat loss factor; and obtaining a thermal efficiency correction factor, and adjusting the initial thermal efficiency coefficient to obtain an updated thermal efficiency coefficient. By obtaining the accurate heat efficiency of the boiler at different times, the prediction accuracy of the heat efficiency is improved.

Description

Technical Field

[0001] The invention relates to the technical field of boiler data processing, and in particular to a boiler thermal efficiency evaluation and prediction method based on data analysis. Background Art

[0002] The main function of a boiler is to convert the chemical energy of fuel into thermal energy through a combustion reaction to heat water or generate steam. The thermal efficiency of a boiler is a key indicator for evaluating its performance, reflecting the effective utilization of energy. High thermal efficiency means reduced fuel consumption and exhaust emissions. With the development of big data and machine learning, real-time data analysis has been widely used in the monitoring and prediction of thermal efficiency, which helps to identify efficiency declines and implement optimization measures to improve energy utilization.

[0003] In the prior art, temperature data from inside or on the surface of the boiler is obtained, and a linear relationship model is established by extracting key features to calculate the overall thermal efficiency. However, as time goes by, the boiler and related equipment are affected by factors such as aging and wear, which leads to reduced heat conduction and heat exchange efficiency, making the relationship between temperature and thermal efficiency nonlinear, and the thermal efficiency evaluation effect is poor. Summary of the invention

[0004] In order to solve the technical problem that the thermal efficiency evaluation is poor due to the wear and aging of boilers and related equipment over time, which affects the linear relationship between temperature and thermal efficiency, the purpose of the present invention is to provide a boiler thermal efficiency evaluation and prediction method based on data analysis. The technical solution adopted is as follows: The present invention proposes a boiler thermal efficiency evaluation and prediction method based on data analysis, the method comprising: Obtain the furnace temperature data, outlet water temperature data, return water temperature data and the temperature data inside and outside the outlet water pipe at each moment during each heating operation of the boiler system; For any heating operation process, the furnace steady-state operation coefficient at each moment is obtained according to the distribution of furnace temperature data within the time neighborhood of each moment; the furnace steady-state operation coefficient at different moments is obtained to obtain the time required for furnace steady-state; the boiler health factor of the real-time heating operation process is obtained according to the change characteristics of the furnace steady-state time required for different heating operation processes within the process neighborhood of the real-time heating operation process; According to the difference in temperature data between the inner and outer sides of the outlet water pipe at the real time, the heat loss coefficient of the pipe at the real time is obtained; according to the difference between the outlet water temperature data and the return water temperature data at the real time, the degree of pipe aging at the real time is obtained; according to the heat loss coefficient of the pipe at the real time and the degree of pipe aging at the real time, the heat loss factor of the pipe at the real time is obtained; According to the pipeline heat loss factor at real time and the corresponding boiler health factor, a thermal efficiency correction factor at real time is obtained; according to the thermal efficiency correction factor at real time, the initial thermal efficiency coefficient is adjusted to obtain an updated thermal efficiency coefficient.

[0005] Furthermore, the method for obtaining the furnace steady-state operation coefficient includes: For any heating operation process, the mean value of the furnace temperature data within the time neighborhood at each moment is obtained as the overall temperature level; the difference between the overall temperature level and the preset boiler operating temperature is obtained as the temperature difference coefficient; Fit the furnace temperature data within the neighborhood range of each moment to obtain a temperature fitting curve; obtain the fluctuation degree of the slope of the temperature fitting curve at different moments as the slope fluctuation coefficient; The product of the temperature difference coefficient and the slope fluctuation coefficient is obtained, and negative correlation mapping is performed as the steady-state operation coefficient of the furnace at each moment.

[0006] Furthermore, the method for obtaining the time required for the furnace to reach a steady state includes: For any heating operation process, if the furnace steady-state operation coefficient at any moment is greater than the preset steady-state threshold, the corresponding moment will be regarded as the steady-state operation moment; The smallest value among the steady-state operation moments is selected, and the corresponding steady-state operation moment is used as the furnace steady-state time required for the corresponding heating operation process.

[0007] Furthermore, the method for obtaining the boiler health factor includes: Obtain the average time required for the furnace steady state of all other heating operation processes within the process neighborhood of the real-time heating operation process as the local time level; The difference between the furnace steady-state required time and the local time level in the real-time heating operation process is obtained, and a negative correlation normalization mapping is performed as the boiler health factor in the real-time heating operation process.

[0008] Furthermore, the method for obtaining the pipeline heat loss coefficient includes: Get the circumference of the boiler outlet pipe; The difference in temperature data between the inside and outside of the outlet pipe at the real time is obtained as the pipe temperature difference; the pipe temperature difference and the circumference are fused and normalized as the pipe heat loss coefficient at the real time.

[0009] Furthermore, the method for obtaining the pipeline aging degree includes: Get the water flow rate in the outlet pipe at real time; According to the difference between the outlet water temperature data and the return water temperature data in real time and the corresponding water flow rate, the pipeline aging degree in real time is obtained. The difference between the outlet water temperature data and the return water temperature data is positively correlated with the pipeline aging degree, and the water flow rate is negatively correlated with the pipeline aging degree.

[0010] Furthermore, the method for obtaining the pipeline heat loss factor includes: The product of the pipeline heat loss coefficient and the pipeline aging degree at the real time is obtained as the pipeline heat loss factor at the real time.

[0011] Furthermore, the method for obtaining the thermal efficiency correction factor includes: The boiler health factor is negatively correlated with the thermal efficiency correction factor, and the pipeline aging degree is positively correlated with the thermal efficiency correction factor.

[0012] Furthermore, the method for obtaining the updated thermal efficiency coefficient includes: The product of the real-time thermal efficiency correction factor and the initial thermal efficiency coefficient is obtained as the updated thermal efficiency coefficient.

[0013] Furthermore, the preset steady-state threshold is 0.7.

[0014] The present invention has the following beneficial effects: For any heating operation process, the present invention obtains the steady-state operation coefficient of the furnace at each moment according to the distribution of furnace temperature data within the time neighborhood of each moment, more accurately captures the slight changes and stability of the furnace temperature, and reflects the stable operation state of the furnace at different moments; obtains the time required for the furnace to be steady according to the steady-state operation coefficient of the furnace at different moments, which is helpful to evaluate the dynamic response performance and stability of the furnace; obtains the boiler health factor of the real-time heating operation process according to the change characteristics of the time required for the furnace to be steady in different heating operation processes within the process neighborhood of the real-time heating operation process, which is an important indicator reflecting the overall operation health of the boiler; obtains the pipeline heat loss at the real-time moment according to the difference in temperature data between the inside and outside of the water outlet pipeline at the real-time moment The heat loss coefficient is used to quantify the heat loss caused by the temperature difference between the inside and outside of the outlet water pipe of the boiler, which is helpful to evaluate the thermal insulation performance and energy efficiency level of the boiler; according to the difference between the outlet water temperature data and the return water temperature data at the real time, the pipeline aging degree at the real time is obtained, and the pipeline heat loss factor is obtained, which reflects the important indicators of pipeline materials and service life, and more comprehensively reflects the loss of water pipes during heat transmission; and then the thermal efficiency correction factor is obtained to more accurately evaluate the energy efficiency level of the boiler and reflect the actual thermal efficiency level of the boiler during the real-time heating operation; according to the thermal efficiency correction factor at the real time, the initial thermal efficiency coefficient is adjusted to obtain the updated thermal efficiency coefficient, which can more truly reflect the energy efficiency level of the boiler under the real-time heating operation process. The present invention improves the prediction accuracy of thermal efficiency by obtaining the accurate thermal efficiency of the boiler at different times. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A flow chart of a method for evaluating and predicting boiler thermal efficiency based on data analysis provided by one embodiment of the present invention; Figure 2 A flow chart of a method for obtaining a furnace steady-state operation coefficient provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0017] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a boiler thermal efficiency evaluation and prediction method based on data analysis proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0018] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0019] The following is a detailed description of a method for evaluating and predicting boiler thermal efficiency based on data analysis provided by the present invention in conjunction with the accompanying drawings.

[0020] See also Figure 1 , which shows a flow chart of a boiler thermal efficiency evaluation and prediction method based on data analysis provided by an embodiment of the present invention, and the specific method includes: Step S1: Obtain furnace temperature data, outlet water temperature data, return water temperature data, and temperature data of the inside and outside of the outlet water pipe at each moment during each heating operation of the boiler system.

[0021] In the embodiment of the present invention, in order to accurately evaluate the performance of the boiler system, timely discover problems in the operation of the system, and reduce energy consumption, it is necessary to evaluate and predict the thermal efficiency of the boiler. The thermal efficiency of the boiler is the ratio of the useful heat output by the boiler to the input fuel heat, reflecting the effective utilization of energy. First, in the boiler system, insulated thermocouple sensors are arranged at key positions such as the outlet position where the hot water produced by the boiler enters the outlet pipe, the return water position from the outlet pipe to the terminal area, the boiler furnace position, and multiple equally spaced positions inside and outside the outlet pipe for real-time monitoring. In order to avoid errors caused by vibration or thermal expansion and contraction, ensure that the sensor is firmly installed, and obtain the furnace temperature data, outlet water temperature data, return water temperature data, and temperature data inside and outside the outlet pipe at each moment of the boiler system during each heating operation. Among them, the one-time heating operation process refers to the process in which the boiler enters the steady-state operation stage after reaching the predetermined working temperature for one-time heating operation and completes the heat transfer stage.

[0022] It should be noted that, in one embodiment of the present invention, the sensor outside the water outlet pipe is installed at a distance of 0.5m from the water outlet pipe, the position interval is 0.25m, the time interval is 1s, and the average value of the sensor data of a type of temperature data at all positions is obtained at each moment to obtain the temperature data at the corresponding moment. In other embodiments of the present invention, the position interval and the time interval can be set according to the specific situation, which will not be limited or elaborated here.

[0023] It should be noted that in the embodiments of the present invention, in order to facilitate the subsequent data processing, after the acquired data is cleaned and denoised, the data can also be standardized to eliminate the dimension of the data, so that indicators of different units or orders of magnitude can be comprehensively analyzed and compared to ensure the accuracy of subsequent analysis. Standardization can adopt existing methods such as Z-score standardization and maximum and minimum standardization. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0024] Step S2: For any heating operation process, the furnace steady-state operation coefficient at each moment is obtained according to the distribution of furnace temperature data within the time neighborhood of each moment; the time required for furnace steady-state is obtained according to the furnace steady-state operation coefficient at different moments; the boiler health factor of the real-time heating operation process is obtained according to the changing characteristics of the time required for furnace steady-state of different heating operation processes within the process neighborhood of the real-time heating operation process.

[0025] Furnace temperature is an important parameter that reflects the combustion state in the furnace. After the boiler furnace temperature reaches the predetermined operating temperature, it enters a stable operating state. By considering the distribution of temperature data within the time neighborhood, these transient changes can be captured, thereby more accurately evaluating the steady-state operation of the furnace; for any heating operation process, the steady-state operation coefficient of the furnace at each moment is obtained based on the distribution of furnace temperature data within the time neighborhood at each moment.

[0026] Preferably, in one embodiment of the present invention, the furnace steady-state operation coefficient is Figure 2 , which shows a flow chart of a method for obtaining a furnace steady-state operation coefficient, including: Step S201: For any heating operation process, the mean value of the furnace temperature data within the time neighborhood at each moment is obtained as the overall temperature level; the difference between the overall temperature level and the preset boiler operating temperature is obtained as the temperature difference coefficient.

[0027] In order to facilitate data comparison, the furnace temperature data within the time neighborhood are quantified and counted by averaging, and compared by the overall temperature level in the subsequent calculation process; The preset boiler operating temperature is an important parameter to ensure the normal operation of the boiler. The difference between the overall temperature level and the preset boiler operating temperature reflects the operating efficiency of the boiler and whether the system operation is stable. If the difference is larger, the combustion in the furnace is more unstable and the combustion efficiency is lower. It should be noted that the preset boiler operating temperature can be obtained in advance by the implementation personnel based on relevant professional reference materials, which will not be elaborated here.

[0028] It should be noted that, in one embodiment of the present invention, the size of the time neighborhood range is based on each moment and is composed of a preset number of historical moments, and the preset number is 19; in other embodiments of the present invention, the size of the time neighborhood range can be set according to specific circumstances, and is not limited or elaborated here.

[0029] Step S202: fitting the furnace temperature data within the time neighborhood of each moment to obtain a temperature fitting curve; obtaining the fluctuation degree of the slope of the temperature fitting curve at different moments as the slope fluctuation coefficient.

[0030] The slope at each moment reflects the rate of change of temperature over time. The greater the fluctuation of the slope, the more uneven the slope distribution, the more unstable the temperature change rate, the larger the slope fluctuation coefficient, the smaller the fluctuation of the slope, the more uniform the slope distribution, the more stable the temperature change rate, and the smaller the slope fluctuation coefficient. The slope is obtained by taking the derivative of the temperature fitting curve at each moment to obtain the slope at the corresponding moment.

[0031] It should be noted that in some embodiments of the present invention, existing fitting methods such as least squares method or polynomial fitting can be used for fitting to obtain a temperature fitting curve. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0032] It should be noted that, in one embodiment of the present invention, the degree of fluctuation is expressed by calculating the variance. The larger the variance, the greater the degree of fluctuation, and the smaller the variance, the smaller the degree of fluctuation. In other embodiments of the present invention, standard deviation, range and other methods can also be used to express the degree of fluctuation. The specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0033] Step S203: Obtain the product of the temperature difference coefficient and the slope fluctuation coefficient, and perform negative correlation mapping as the furnace steady-state operation coefficient at each moment.

[0034] In one embodiment of the present invention, for any heating operation process, the formula of the furnace steady-state operation coefficient is expressed as: ; in, Indicates The furnace steady-state operation coefficient at the moment; Indicates the preset boiler operating temperature; Indicates The mean value of furnace temperature data in the neighborhood of time The overall temperature level at the moment; Indicates The fluctuation degree of the slope of the temperature fitting curve at different times, that is, The slope fluctuation coefficient at time.

[0035] In the formula of the furnace steady-state operation coefficient, It indicates the difference between the overall temperature level and the preset boiler operating temperature, that is, the temperature difference coefficient. The larger the temperature difference coefficient, the greater the difference between the overall temperature level and the preset boiler operating temperature, and the more unstable the temperature in the furnace; the smaller the temperature difference coefficient, the smaller the difference between the overall temperature level and the preset boiler operating temperature, the smaller the temperature change in the furnace, and the more stable it tends to be; The greater the slope fluctuation coefficient at each moment, the greater the fluctuation degree of the slope of the temperature fitting curve at different moments, the more uneven the slope distribution, the more unstable the temperature change rate, and the smaller the steady-state operation coefficient of the furnace.

[0036] By monitoring the steady-state operation coefficient of the furnace at different times, the stability of the furnace in different time periods can be evaluated, which helps to determine the time required for the furnace to reach a steady state; based on the steady-state operation coefficient of the furnace at different times, the time required for the furnace to reach a steady state can be obtained.

[0037] Preferably, in one embodiment of the present invention, the method for obtaining the time required for the furnace to reach a steady state includes: For any heating operation process, if the furnace steady-state operation coefficient at any moment is greater than the preset steady-state threshold, the corresponding moment will be regarded as the steady-state operation moment; The smallest value among the steady-state operation moments is selected, and the corresponding steady-state operation moment is used as the furnace steady-state time required for the corresponding heating operation process.

[0038] It should be noted that, in one embodiment of the present invention, the size of the preset steady-state threshold is 0.7; in other embodiments of the present invention, the size of the preset steady-state threshold can be set according to specific circumstances, which will not be elaborated here.

[0039] The time required for the furnace to reach steady-state in a healthy boiler system under different heating operation processes is stable. However, due to the accumulation of a large amount of deposits in the furnace area due to aging and wear of the boiler, the fuel may not be completely burned, thereby reducing the combustion efficiency and increasing the time required for the furnace area to reach steady-state operation during the heating process. Therefore, by analyzing the changing characteristics of the time required for the furnace to reach steady-state in different heating operation processes within the process neighborhood, the stability of the boiler in different heating operation processes can be understood and the boiler health factor can be quantified; according to the changing characteristics of the time required for the furnace to reach steady-state in different heating operation processes within the process neighborhood of the real-time heating operation process, the boiler health factor of the real-time heating operation process is obtained.

[0040] Preferably, in one embodiment of the present invention, the method for obtaining the boiler health factor includes: Obtain the average time required for the furnace steady state of all other heating operation processes within the process neighborhood of the real-time heating operation process as the local time level; The difference between the furnace steady-state required time and the local time level in the real-time heating operation process is obtained, and a negative correlation normalization mapping is performed as the boiler health factor in the real-time heating operation process.

[0041] In one embodiment of the present invention, a method for obtaining a boiler influencing factor includes: ; in, Indicates the real-time heating operation process The boiler health factor; Indicates the real-time heating operation process The time required for the furnace to reach steady state; Indicates the real-time heating operation process The process neighborhood The time required for the furnace to remain in steady state during other heating operations; Indicates the number of other heating processes in the process neighborhood; represents the normalization function; Indicates taking the absolute value.

[0042] In the formula of the iron furnace influence factor, It represents the average time required for the furnace steady state of all other heating operation processes within the neighborhood of the calculation process, that is, the local time level. The larger the local time level, the longer the time required for the furnace steady state of other heating operation processes; the smaller the local time level, the shorter the time required for the furnace steady state of other heating operation processes; It indicates the difference between the time required for calculating the steady-state of the furnace during the real-time heating operation and the local time level. The larger the difference, the less close the steady-state time between the real-time heating operation and the local time level, the greater the fluctuation in the operating state of the boiler, the more unstable the heat generated, and the smaller the boiler health factor.

[0043] It should be noted that, in the embodiment of the present invention, the normalization function may adopt a minimum-maximum normalization method, and the specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0044] It should be noted that, in one embodiment of the present invention, the method for obtaining the process neighborhood range is based on the real-time heating operation process and the range formed by a preset number of historical heating operation processes, and the preset number of times is 50 times; in other embodiments of the present invention, the size of the process neighborhood range can be set according to the specific circumstances, and is not limited or elaborated here.

[0045] Step S3: According to the difference in temperature data between the inner and outer sides of the outlet water pipe at the real time, the heat loss coefficient of the pipe at the real time is obtained; according to the difference between the outlet water temperature data and the return water temperature data at the real time, the aging degree of the pipe at the real time is obtained; according to the heat loss coefficient of the pipe at the real time and the aging degree of the pipe at the real time, the heat loss factor of the pipe at the real time is obtained.

[0046] In a boiler system, the material of the boiler outlet pipe usually has a certain thermal conductivity. When hot water flows in the pipe, the heat will be conducted to the external environment through the pipe wall, causing the temperature of the hot water to drop. Especially when the temperature difference is large, the heat conduction rate will increase significantly, thereby accelerating the heat loss. The larger the heat loss coefficient of the pipe, the greater the difference in temperature data between the inside and outside of the outlet pipe at real time. The heat loss coefficient of the pipe at real time is obtained.

[0047] Preferably, in one embodiment of the present invention, the method for obtaining the pipeline heat loss coefficient includes: Considering that the heat loss of the pipeline is related to the size of the pipeline, the larger the pipeline size, the more heat will be lost to the external environment during the transmission process. The circumference of the boiler outlet pipe is obtained; The difference in temperature data between the inside and outside of the outlet pipe at the real time is obtained as the pipe temperature difference; the pipe temperature difference and the circumference are fused and normalized as the pipe heat loss coefficient at the real time.

[0048] It should be noted that the circumference can be obtained in advance by the implementer according to the specific situation. In some embodiments of the present invention, the pipeline temperature difference and the circumference can be merged by addition or multiplication. The specific means are technical means well known to those skilled in the art and will not be described in detail here.

[0049] In one embodiment of the present invention, the formula for the pipeline heat loss coefficient is expressed as: ; in, Indicates real time The pipeline heat loss coefficient; Indicates the circumference of the boiler outlet pipe; Indicates real time Temperature data of the inner side of the outlet pipe; Indicates real time Temperature data of the outside of the outlet pipe; represents the normalization function; Indicates taking the absolute value.

[0050] In the formula of pipeline heat loss coefficient, when the circumference of the outlet pipe is larger, the contact area between the hot water and the external environment will also increase, which means that the hot water will be in contact with the environment for a longer time during the transportation process, and heat loss is more likely to occur; It means calculating the difference in temperature data between the inside and outside of the water pipe. The greater the difference, the more different the temperature is, and the greater the heat loss is.

[0051] As the running time increases, during the transportation process of the outlet water pipeline, in addition to the influence of the external environment on the temperature of the hot water in the pipeline, due to the wear and aging of the pipeline itself, a large amount of sediment exists inside, which reduces the heat transfer efficiency of the hot water. The temperature change trend is reflected by analyzing the difference between the boiler outlet water temperature data and the return water temperature data. The greater the temperature difference, the lower the heat transfer efficiency and thermal insulation performance may be, the worse the transportation performance, and the greater the degree of aging; according to the difference between the real-time outlet water temperature data and the return water temperature data, the real-time pipeline aging degree is obtained.

[0052] Preferably, in one embodiment of the present invention, the method for obtaining the pipeline aging degree includes: Obtain the water flow rate in the outlet pipe at the real time; wherein, the method for obtaining the water flow rate is: use a flow meter to perform real-time measurement at different positions of the outlet pipe, average the flow rate values ​​at all positions, and obtain the corresponding water flow rate; According to the difference between the outlet water temperature data and the return water temperature data in real time and the corresponding water flow rate, the pipeline aging degree in real time is obtained. The difference between the outlet water temperature data and the return water temperature data is positively correlated with the pipeline aging degree, and the water flow rate is negatively correlated with the pipeline aging degree.

[0053] Among them, the greater the difference between the outlet water temperature data and the return water temperature data, the lower the thermal conductivity of the pipeline, the greater the heat loss, and the greater the degree of pipeline aging; the smaller the water flow rate, the more sediment may accumulate in the pipeline, increasing the water flow resistance and the greater the degree of pipeline aging.

[0054] In one embodiment of the present invention, the formula for pipeline aging degree is expressed as: ; in, Indicates real time The degree of pipeline aging; Indicates real time Outlet water temperature data; Indicates real time Return water temperature data; Indicates real time Water flow rate; Represents the normalization function.

[0055] The pipeline heat loss coefficient can evaluate the thermal insulation performance and heat transfer efficiency of the outlet water pipeline. By considering the aging degree of the outlet water pipeline, the actual heat loss of the pipeline can be evaluated more accurately. The real-time pipeline heat loss factor is obtained based on the real-time pipeline heat loss coefficient and the pipeline aging degree.

[0056] Preferably, in one embodiment of the present invention, the method for obtaining the pipeline heat loss factor includes: The product of the pipeline heat loss coefficient and the pipeline aging degree at the real time is obtained as the pipeline heat loss factor at the real time.

[0057] Among them, the greater the pipeline heat loss coefficient, the greater the pipeline aging degree, the lower the heat transfer efficiency in the pipeline, and the greater the pipeline heat loss factor.

[0058] Step S4: According to the real-time pipeline heat loss factor and the corresponding boiler health factor, the real-time thermal efficiency correction factor is obtained; according to the real-time thermal efficiency correction factor, the initial thermal efficiency coefficient is adjusted to obtain an updated thermal efficiency coefficient.

[0059] The health status of the boiler affects its combustion efficiency and heat transfer efficiency. The pipeline heat loss factor can evaluate the magnitude of heat loss. By considering the boiler health factor and the pipeline heat loss factor, the thermal efficiency of the boiler can be evaluated more comprehensively. The larger the boiler health factor, the smaller the heat loss and the smaller the thermal efficiency loss factor. According to the pipeline heat loss factor at real time and the corresponding boiler health factor, the thermal efficiency correction factor at real time is obtained. According to the thermal efficiency correction factor at real time, the initial thermal efficiency coefficient is adjusted to obtain an updated thermal efficiency coefficient.

[0060] Preferably, in one embodiment of the present invention, the method for obtaining the thermal efficiency correction factor includes: The boiler health factor is negatively correlated with the thermal efficiency correction factor, and the pipeline aging degree is positively correlated with the thermal efficiency correction factor. Among them, the larger the boiler health factor, the closer the time required for the furnace to steady state, the more stable the operating state, and the smaller the thermal efficiency correction factor; the greater the pipeline aging degree, the greater the heat consumption generated by hot water during transportation, the greater the impact on thermal efficiency, and the larger the thermal efficiency correction factor.

[0061] It should be noted that, in one embodiment of the present invention, the difference between the pipeline heat loss factor and the boiler health factor is calculated and normalized as a thermal efficiency correction factor, forming a correlation that the larger the pipeline heat loss factor, the smaller the boiler health factor, and the larger the thermal efficiency correction factor; in other embodiments of the present invention, the ratio between the pipeline heat loss factor and the boiler health factor can also be calculated as a thermal efficiency correction factor, forming a correlation that the larger the pipeline heat loss factor, the smaller the boiler health factor, and the larger the thermal efficiency correction factor, wherein, when calculating the ratio, in order to avoid the denominator of the formula being 0, it is necessary to add an artificially set parameter, such as 0.01; the specific means are technical means well known to those skilled in the art and will not be elaborated here.

[0062] Since the initial thermal efficiency coefficient may be affected by various factors such as equipment aging, resulting in a deviation between its actual value and the theoretical value, the initial thermal efficiency coefficient can be corrected according to actual conditions by introducing a thermal efficiency correction factor, thereby improving its accuracy and more truly reflecting the actual operating efficiency of the boiler or heat engine; the initial thermal efficiency coefficient is adjusted according to the thermal efficiency correction factor at real time to obtain an updated thermal efficiency coefficient.

[0063] Preferably, in one embodiment of the present invention, the method for obtaining the updated thermal efficiency coefficient includes: The product of the real-time thermal efficiency correction factor and the initial thermal efficiency coefficient is obtained as the updated thermal efficiency coefficient.

[0064] Among them, the larger the thermal efficiency correction factor, the larger the initial thermal efficiency coefficient, and the larger the updated thermal efficiency coefficient.

[0065] It should be noted that the method for obtaining the initial thermal efficiency coefficient is to calculate the ratio of the useful heat energy output by the boiler to the fuel heat input into the boiler system as the initial thermal efficiency coefficient. The specific technical means are well known to those skilled in the art and will not be elaborated here.

[0066] After obtaining the updated thermal efficiency coefficient, the current operating status of the boiler can be reflected more accurately, which serves as an important basis for analyzing the potential causes of boiler failure. Through continuous monitoring and evaluation of the boiler thermal efficiency, the prediction model is updated and optimized to improve the accuracy of subsequent boiler thermal efficiency predictions.

[0067] In summary, the present invention obtains the time required for the steady state of the furnace according to the steady-state operation coefficient of the furnace at different times; obtains the boiler health factor of the real-time heating operation process according to the change characteristics of the steady-state time required for the furnace of different heating operation processes within the process neighborhood of the real-time heating operation process; obtains the pipeline heat loss coefficient at the real-time moment according to the difference in temperature data between the inside and outside of the outlet pipe at the real-time moment; obtains the pipeline aging degree at the real-time moment according to the difference between the outlet water temperature data and the return water temperature data at the real-time moment; and then obtains the pipeline heat loss factor; obtains the thermal efficiency correction factor, adjusts the initial thermal efficiency coefficient, and obtains the updated thermal efficiency coefficient. The present invention improves the prediction accuracy of thermal efficiency by obtaining the accurate thermal efficiency of the boiler at different times.

[0068] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0069] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A boiler thermal efficiency evaluation and prediction method based on data analysis, characterized in that: The method comprises: Obtain the furnace temperature data, outlet water temperature data, return water temperature data and the temperature data inside and outside the outlet water pipe at each moment during each heating operation of the boiler system; For any heating operation process, the furnace steady-state operation coefficient at each moment is obtained according to the distribution of furnace temperature data within the time neighborhood of each moment; the furnace steady-state operation coefficient at different moments is obtained to obtain the time required for furnace steady-state; the boiler health factor of the real-time heating operation process is obtained according to the change characteristics of the furnace steady-state time required for different heating operation processes within the process neighborhood of the real-time heating operation process; According to the difference in temperature data between the inner and outer sides of the outlet water pipe at the real time, the heat loss coefficient of the pipe at the real time is obtained; according to the difference between the outlet water temperature data and the return water temperature data at the real time, the degree of pipe aging at the real time is obtained; according to the heat loss coefficient of the pipe at the real time and the degree of pipe aging at the real time, the heat loss factor of the pipe at the real time is obtained; According to the pipeline heat loss factor at real time and the corresponding boiler health factor, a thermal efficiency correction factor at real time is obtained; according to the thermal efficiency correction factor at real time, an initial thermal efficiency coefficient is adjusted to obtain an updated thermal efficiency coefficient; The method for obtaining the boiler health factor includes: Obtain the average time required for the furnace steady state of all other heating operation processes within the process neighborhood of the real-time heating operation process as the local time level; The difference between the furnace steady-state required time and the local time level in the real-time heating operation process is obtained, and a negative correlation normalization mapping is performed as the boiler health factor in the real-time heating operation process; The method for obtaining the pipeline heat loss factor includes: Obtaining the product of the pipeline heat loss coefficient and the pipeline aging degree at the real time as the pipeline heat loss factor at the real time; The method for obtaining the thermal efficiency correction factor includes: The boiler health factor is negatively correlated with the thermal efficiency correction factor, and the pipeline aging degree is positively correlated with the thermal efficiency correction factor.

2. A boiler thermal efficiency evaluation and prediction method based on data analysis according to claim 1, characterized in that: The method for obtaining the furnace steady-state operation coefficient includes: For any heating operation process, the mean value of the furnace temperature data within the time neighborhood at each moment is obtained as the overall temperature level; the difference between the overall temperature level and the preset boiler operating temperature is obtained as the temperature difference coefficient; Fit the furnace temperature data within the neighborhood range of each moment to obtain a temperature fitting curve; obtain the fluctuation degree of the slope of the temperature fitting curve at different moments as the slope fluctuation coefficient; The product of the temperature difference coefficient and the slope fluctuation coefficient is obtained, and negative correlation mapping is performed as the steady-state operation coefficient of the furnace at each moment.

3. The method for evaluating and predicting boiler thermal efficiency based on data analysis according to claim 1, characterized in that: The method for obtaining the time required for the furnace steady state includes: For any heating operation process, if the furnace steady-state operation coefficient at any moment is greater than the preset steady-state threshold, the corresponding moment will be regarded as the steady-state operation moment; The smallest value among the steady-state operation moments is selected, and the corresponding steady-state operation moment is used as the furnace steady-state time required for the corresponding heating operation process.

4. A boiler thermal efficiency evaluation and prediction method based on data analysis according to claim 1, characterized in that: The method for obtaining the pipeline heat loss coefficient includes: Get the circumference of the boiler outlet pipe; The difference in temperature data between the inside and outside of the outlet pipe at the real time is obtained as the pipe temperature difference; the pipe temperature difference and the circumference are fused and normalized as the pipe heat loss coefficient at the real time.

5. The method for evaluating and predicting boiler thermal efficiency based on data analysis according to claim 1, characterized in that: The method for obtaining the pipeline aging degree includes: Get the water flow rate in the outlet pipe at real time; According to the difference between the outlet water temperature data and the return water temperature data in real time and the corresponding water flow rate, the pipeline aging degree in real time is obtained. The difference between the outlet water temperature data and the return water temperature data is positively correlated with the pipeline aging degree, and the water flow rate is negatively correlated with the pipeline aging degree.

6. A boiler thermal efficiency evaluation and prediction method based on data analysis according to claim 1, characterized in that: The method for obtaining the updated thermal efficiency coefficient includes: The product of the real-time thermal efficiency correction factor and the initial thermal efficiency coefficient is obtained as the updated thermal efficiency coefficient.

7. A boiler thermal efficiency evaluation and prediction method based on data analysis according to claim 3, characterized in that: The preset steady-state threshold is 0.7.

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