Quantitative monitoring method for soot deposition degree of heating surface based on big data analysis technology
By using big data analytics to monitor the ash level on the boiler's heating surface, the problem of excessive soot blowing in existing technologies has been solved, enabling a precise soot blowing system that reduces energy consumption and extends equipment lifespan.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- HARBIN BOILER CO LTD
- Filing Date
- 2024-08-20
- Publication Date
- 2026-07-31
AI Technical Summary
Existing technologies lack tools or methods to monitor the actual degree of ash accumulation on boiler heating surfaces, leading to excessive operation of soot blowers, resulting in heat source waste, damage to heating surfaces, and reduced soot blower lifespan.
A quantitative monitoring method for the degree of ash accumulation on the heated surface is adopted based on big data analysis technology. By calculating the polynomial expression of the heat transfer coefficient and combining it with the Bézier curve, the degree of ash accumulation is predicted, and the soot blower is activated when necessary.
It enables real-time monitoring and quantitative calculation of the degree of soot on the heated surface, optimizes the soot blowing system, reduces soot blowing energy consumption, extends the service life of the soot blower, and reduces equipment damage and operating costs.
Smart Images
Figure CN119046348B_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of visual monitoring technology for the degree of ash on the heated surface of boiler equipment in thermal power plants. Background Technology
[0002] Ash from coal combustion in boilers deposits on various heating surfaces, forming ash or coke buildup. This not only reduces heat transfer efficiency and affects boiler economics but also easily leads to corrosion, impacting boiler safety and stability. Typically, sootblowers are installed on each heating surface area of the boiler to remove ash and coke buildup. However, currently, there is a lack of tools or methods to monitor the actual degree of ash accumulation on the heating surfaces. It is impossible to directly observe the ash accumulation information at each heating surface level, and therefore, it is impossible to implement a logic for activating sootblowers based on the actual ash accumulation level. Sootblower activation is limited to fixed cycles or manual judgment. According to relevant data from the thermal power industry, to ensure effective ash removal on the boiler's heating surfaces, thermal power plants commonly over-operate sootblowers, resulting in a series of problems such as heat source waste, heating surface damage, and reduced sootblower lifespan, leading to economic losses. Summary of the Invention
[0003] The purpose of this invention is to solve the current problem of lacking monitoring of the actual degree of ash accumulation on heated surfaces; this invention provides a quantitative monitoring method for the degree of ash accumulation on heated surfaces based on big data analysis technology.
[0004] A method for quantitative monitoring of ash content on heated surfaces based on big data analytics, comprising the following steps:
[0005] Based on the historical operating data of the power plant boiler during the current monitoring period, calculate the heat transfer coefficient of each heating surface of the power plant boiler at each sampling time during the current monitoring period.
[0006] The heat transfer coefficients of each heated surface are smoothed and filtered during the current monitoring period.
[0007] The periodic variation distribution of the heat transfer coefficient of each heated surface in the current monitoring cycle is obtained by using Bézier curves after smoothing and filtering. Based on this periodic variation distribution, a polynomial expression of the heat transfer coefficient of the corresponding heated surface is fitted. The polynomial expression of the heat transfer coefficient of each heated surface in the current monitoring cycle is used as the polynomial expression of the heat transfer coefficient of each heated surface in the next monitoring cycle.
[0008] The polynomial expression of the heat transfer coefficient of each heated surface in the next monitoring cycle is fitted into a heat transfer coefficient curve. The degree of ash accumulation on the heated surface at each monitoring time in the next monitoring cycle is predicted by the heat transfer coefficient curve of each heated surface in the next monitoring cycle. The horizontal axis of the heat transfer coefficient curve represents time, and the vertical axis represents the heat transfer coefficient.
[0009] The method for predicting the degree of ash accumulation on each heated surface at each monitoring time is as follows: based on the ratio of the area between the curve of the heat transfer coefficient curve of the heated surface from time 0 to the monitoring time and the horizontal axis of time to the area between the curve of the minimum value of the heat transfer coefficient curve of the heated surface and time 0.
[0010] Preferably, the smoothing filtering process is implemented using the Savitzky-Golay method.
[0011] Preferably, the historical operating data of the power plant boiler includes the main steam flow rate D, the actual fuel quantity B, and the working fluid inlet temperature t of the heating surface. in Inlet working fluid pressure of the heating surface, outlet working fluid temperature of the heating surface t out Working fluid pressure at the outlet of the heating surface, desuperheating water temperature, desuperheating water pressure, and flue gas temperature T at the outlet of the heating surface. out and ambient air temperature.
[0012] Preferably, the methods for obtaining the heat transfer coefficients of each heating surface of the power plant boiler at each sampling time within the current monitoring period, based on historical operating data of the power plant boiler during the current monitoring period, include:
[0013] S11. During the operation of the power plant boiler, the heat transfer Q of the heating surface... dc The working fluid side absorbs heat Q dx Flue gas side heat release Q df To reach equilibrium, Q dc =Q dx =Q df ;
[0014] S12, based on the working fluid inlet temperature t of the heated surface in The enthalpy h′ of the working fluid at the inlet of the heating surface is obtained from the working fluid pressure at the inlet of the heating surface; based on the working fluid outlet temperature t of the heating surface... out The enthalpy h″ of the working fluid at the outlet of the heating surface is obtained from the working fluid pressure at the outlet of the heating surface; the enthalpy Δh of the desuperheating water is obtained from the temperature and pressure of the desuperheating water. jw ;
[0015] S13. Based on D, h″, h′, and Δh jw And B, calculate the heat absorbed by the working fluid side Q. dx ;
[0016] S14. Based on the flue gas temperature T at the outlet of the heated surface out The enthalpy of the flue gas at the outlet of the heated surface, H″, is obtained; based on the ambient air temperature, the enthalpy of the ambient air, H′, is obtained. lk ;
[0017] S15, based on a given heat retention coefficient And the air leakage coefficient Δα, according to Q dx =Qdf Calculate the enthalpy H' of the flue gas at the inlet of the heating surface;
[0018] S16. Based on the enthalpy H' of the flue gas at the inlet of the heating surface, obtain the flue gas temperature T at the inlet of the heating surface. in ;
[0019] S17, according to T in T out t in and t out Calculate the heat transfer temperature and pressure Δt;
[0020] S18. Based on the heat transfer temperature and pressure Δt, actual fuel quantity B, heating surface area F, and heat transfer Q of the heating surface. dc The heat transfer coefficient K is obtained.
[0021] Preferably, in S12, based on the working fluid inlet temperature t of the heated surface... in Given the working fluid pressure at the inlet of the heating surface, find the enthalpy h′ of the working fluid at the inlet of the heating surface from the steam parameter table;
[0022] Based on the working fluid outlet temperature t of the heated surface out Given the working fluid pressure at the outlet of the heating surface, find the enthalpy h″ of the working fluid at the outlet of the heating surface in the steam parameter table;
[0023] Based on the temperature and pressure of the desuperheating water, the enthalpy value Δh of the desuperheating water can be obtained by referring to the steam parameter table. jw .
[0024] Preferably, in S14, the enthalpy value H'' of the flue gas at the outlet of the heating surface is obtained by using the standard calculation method of flue gas physical properties to calculate the temperature T of the flue gas at the outlet of the heating surface. out The process is performed to obtain the enthalpy value H'' of the flue gas at the outlet of the heating surface;
[0025] In S14, the ambient air enthalpy value H′ is obtained. lk The implementation method is as follows: the ambient air temperature is processed using the standard calculation method of flue gas physical properties to obtain the ambient air enthalpy H′. lk .
[0026] Preferably, in S16, the inlet flue gas temperature T of the heating surface is obtained. in The implementation method is as follows: the enthalpy H' of the flue gas at the inlet of the heating surface is processed using the standard calculation method of flue gas physical properties to obtain the flue gas temperature T at the inlet of the heating surface. in .
[0027] Preferably,
[0028] Preferably,
[0029] Where, Δt lar =T in -t out , Δt sma =T out -t in ;Δt lar and Δt sma All are intermediate variables.
[0030] Preferably, the polynomial expression for the heat transfer coefficient of the heated surface, K(t), is:
[0031] Among them, C i P is the calculated coefficient for the i-th term. i Let t be the correction coefficient for the i-th term, t be time, and n be an integer.
[0032] The beneficial effects of this invention are:
[0033] This invention provides a method for quantitative monitoring of ash level on heated surfaces based on big data analysis technology. It can realize real-time monitoring and quantitative calculation of ash level on heated surfaces, fill the gap in ash level monitoring information on heated surfaces during boiler operation, and issue soot blower activation prompts for heated surfaces with high ash levels.
[0034] This invention utilizes historical operating data of power plant boilers within the current monitoring period under the current soot blowing method to predict the degree of ash accumulation on each heated surface in the next monitoring period without changing the soot blowing method. Based on the ash accumulation monitoring information, and comprehensively considering the soot blower operation requirements of all heated surfaces of the boiler, a more accurate and reasonable soot blowing system can be formulated. This achieves the goals of reducing soot blowing energy consumption, reducing heated surface damage, extending the service life of soot blowers, and reducing maintenance workload, bringing significant economic benefits to the operation of thermal power plants.
[0035] This invention proposes a quantitative monitoring method for the degree of ash on heated surfaces based on big data analysis technology. This method enables quantitative monitoring of the degree of contamination on heated surfaces at all levels of the boiler, providing a reliable means to improve the digitalization and visualization of the heated surface contamination monitoring process. Based on this, a more accurate and reasonable soot blowing system can be formulated. The soot blower operation mode is more in line with the boiler operation characteristics and the ash characteristics of the heated surfaces. The soot blowing frequency, steam consumption, and electricity consumption are significantly reduced, extending the service life of the soot blowing equipment, saving corresponding depreciation and maintenance costs, significantly reducing the occurrence of soot blowing on heated surfaces, extending the service life of equipment, reducing boiler flue gas heat loss, and improving boiler efficiency. Attached Figure Description
[0036] Figure 1 This is a flowchart of a method for quantitatively monitoring the degree of ash on heated surfaces based on big data analytics.
[0037] Figure 2This is a schematic diagram illustrating the smoothing filtering process applied to the heat transfer coefficients of each heated surface using the Savitzky-Golay filtering method; and Figure 2 The blue undulating curve represents the curve corresponding to the time series of the heat transfer coefficient K calculated based on historical operating data, while the yellow undulating curve represents the curve corresponding to the time series of the heat transfer coefficient K after processing by the Savitzky-Golay method.
[0038] Figure 3 A schematic diagram illustrating the periodic variation pattern of the heat transfer coefficient when using Bézier curves to solve the problem. Figure 3 In the diagram, the blue undulating curve represents the time series data of the heat transfer coefficient K after processing by the Savitzky-Golay method, and the red curve represents the periodic variation curve of the heat transfer coefficient K explored using the Bezier curve.
[0039] Figure 4 This is a heat transfer coefficient curve. Detailed Implementation
[0040] 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.
[0041] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other.
[0042] This invention provides a method for quantitatively monitoring the degree of ash accumulation on heated surfaces based on big data analysis technology. It calculates and analyzes the characteristic value of ash accumulation (i.e., heat transfer coefficient), explores the periodicity and fluctuation patterns of the characteristic value data through big data analysis, and then uses the characteristic value to quantify the degree of ash accumulation on heated surfaces. Based on boiler operating data, it enables real-time monitoring of the degree of ash accumulation on each level of heated surface, prompts sootblower operation information, reduces steam consumption, decreases the frequency of sootblower operation, and improves the economic efficiency of boiler operation.
[0043] The historical operating data of the power plant boiler are different in different monitoring periods, which results in different distributions of the periodic variation law of the heat transfer coefficient, that is, different heat transfer coefficient curves.
[0044] See Figure 1 This embodiment describes a method for quantitatively monitoring the degree of ash on heated surfaces based on big data analytics. The method includes the following steps:
[0045] S1. Based on the historical operating data of the power plant boiler in the current monitoring period, calculate the heat transfer coefficient of each heating surface of the power plant boiler at each sampling time in the current monitoring period.
[0046] S2. Due to the slight fluctuations in the operating data, "noise" appears in the characterization data, that is, meaningless sawtooth data fluctuations, which adversely affect the subsequent analysis and exploration work. Therefore, it is necessary to perform smoothing filtering on the heat transfer coefficients corresponding to each heated surface within the current monitoring cycle.
[0047] As an example, the Savitzky-Golay method is used for smoothing filtering. Its principle is to select a sliding window of a certain width and, based on polynomial least squares fitting, perform an nth-order polynomial fit on the data within the window to create data that approximates the actual values. This approximation is then used to replace the actual values for subsequent data analysis, effectively reducing noise in the data while preserving important trends and periodicity. The implementation includes selecting a suitable window width, determining the order of the polynomial fit, and creating the Savitzky-Golay processed data.
[0048] The main formulas of the Savitzky-Golay method are as follows:
[0049]
[0050] Where: K i —Before processing The i-th data, —After processing The i-th Data, W j —Weight factor of window length (2r+1).
[0051] S3. Based on the principle and characteristics of ash accumulation on boiler heating surfaces, the degree of ash accumulation on heating surfaces increases continuously over time. Therefore, it is possible to attempt to establish a polynomial function relating the heat transfer coefficient K to time t. This invention provides a method that uses Bézier curves to establish this polynomial function. After processing the data using the Savitzky-Golay smoothing filter method, the periodic fluctuation pattern of the data is explored using Bézier curves, and the polynomial formula for solving the curve is obtained. Specifically, Bézier curves are used to solve for the periodic variation distribution of the heat transfer coefficients of each heating surface within the current monitoring period after smoothing and filtering, and a polynomial expression for the corresponding heat transfer coefficient of the heating surface is fitted based on this periodic variation distribution.
[0052] Specifically, the polynomial expression for the heat transfer coefficient of the heated surface, K(t), is:
[0053] Among them, C i P is the calculated coefficient for the i-th term. iLet be the correction coefficient for the i-th term, T be time, and n be an integer.
[0054] S4. Use the polynomial expression of the heat transfer coefficient of each heated surface in the current monitoring cycle as the polynomial expression of the heat transfer coefficient of each heated surface in the next monitoring cycle.
[0055] S5. The degree of ash accumulation on the heated surface is quantified as a percentage by the ratio of the actual area to the total area. Specifically, the polynomial expression of the heat transfer coefficient of each heated surface in the next monitoring cycle is fitted into a heat transfer coefficient curve. The degree of ash accumulation on the heated surface at each monitoring time in the next monitoring cycle is predicted by the heat transfer coefficient curve of each heated surface in the next monitoring cycle. The horizontal axis of the heat transfer coefficient curve represents time, and the vertical axis represents the heat transfer coefficient.
[0056] The method for predicting the degree of ash accumulation on each heated surface at each monitoring time is as follows: based on the ratio of the area between the curve of the heat transfer coefficient curve of the heated surface from time 0 to the monitoring time and the horizontal axis of time to the area between the curve of the minimum value of the heat transfer coefficient curve of the heated surface and time 0.
[0057] In this embodiment, the historical operating data of the power plant boiler includes the main steam flow rate D, the actual fuel quantity B, and the working fluid inlet temperature t of the heating surface. in Inlet working fluid pressure of the heating surface, outlet working fluid temperature of the heating surface t out Working fluid pressure at the outlet of the heating surface, desuperheating water temperature, desuperheating water pressure, and flue gas temperature T at the outlet of the heating surface. out and ambient air temperature.
[0058] Specifically, based on the historical operating data of the power plant boiler within the current monitoring period, the methods for obtaining the heat transfer coefficients of each heating surface of the power plant boiler at each sampling time within the current monitoring period include:
[0059] S11. During the operation of the power plant boiler, the heat transfer Q of the heating surface... dc The working fluid side absorbs heat Q dx Flue gas side heat release Q df To reach equilibrium, Q dc =Q dx =Q df ;
[0060] S12, based on the working fluid inlet temperature t of the heated surface in The enthalpy of the working fluid at the inlet of the heating surface is obtained from the working fluid pressure at the inlet of the heating surface, g′; based on the working fluid outlet temperature t of the heating surface... out The enthalpy h″ of the working fluid at the outlet of the heating surface is obtained from the working fluid pressure at the outlet of the heating surface; the enthalpy Δh of the desuperheating water is obtained from the temperature and pressure of the desuperheating water. jwSpecifically, the enthalpy values obtained above are all obtained by consulting a steam parameter table. A steam parameter table is a commonly used tool; by inputting the temperature and pressure of the working fluid, the enthalpy value of the working fluid can be obtained. S13. Based on D, h″, h′, Δh jw And B, calculate the heat absorbed by the working fluid side Q. dx ;
[0061] S14. Based on the flue gas temperature T at the outlet of the heated surface out The enthalpy H'' of the flue gas exiting the heated surface is obtained; the enthalpy H′ of the ambient air is obtained based on the ambient air temperature. lk ;
[0062] S15, based on a given heat retention coefficient And the air leakage coefficient Δα, according to Q dx =Q df Calculate the enthalpy H' of the flue gas at the inlet of the heating surface;
[0063] S16. Based on the enthalpy H' of the flue gas at the inlet of the heating surface, obtain the flue gas temperature T at the inlet of the heating surface. in ;
[0064] S17, according to T in T out t in and t out Calculate the heat transfer temperature and pressure Δt; specifically,
[0065] Where, Δt lar =T in -t out , Δt sma =T out -t in ;Δt lar and Δt sma All are intermediate variables;
[0066] S18. Based on the heat transfer temperature and pressure Δt, actual fuel quantity B, heating surface area F, and heat transfer Q of the heating surface. dc The heat transfer coefficient K is obtained.
[0067] Verification test section:
[0068] The technical effects of the present invention are illustrated through the following verification experiments:
[0069] Figure 2 To collect boiler operation data over a 2500-minute cycle, Figure 2The blue undulating curve represents the time series of the heat transfer coefficient K calculated based on historical operating data, and is plotted as a curve. The heat transfer coefficient K over 2500 minutes (the blue portion of the curve) is filtered using the Savitzky-Golay method (the yellow portion of the curve). The data processed by the Savitzky-Golay method, while preserving the original data trend, reduces meaningless fluctuations in local data, making the data smoother and the trend clearer. For big data analytics, this significantly reduces the impact of noise in the data, making it more conducive to big data analytics techniques to uncover the periodicity and changing trend of the heat transfer coefficient K (i.e., the characteristic value K).
[0070] Figure 3 This diagram illustrates the periodic variation of the heat transfer coefficient on filtered data at a 2500-minute timescale, using the Bézier curve method. Figure 3 The blue curve represents the time-series data of the heat transfer coefficient K after processing by the Savitzky-Golay method, while the red curve represents the periodic variation curve of the heat transfer coefficient K explored using the Bézier curve method. The Bézier curve method fitted a curve that conforms to the variation trend and periodicity of most data over a period of 2500 minutes. The Bézier curve method is capable of fitting curves that conform to the variation trend and periodicity of most data.
[0071] Figure 4 This is a graph representing the heat transfer coefficient. Figure 4 The data shows the variation pattern of the heat transfer coefficient curve during the current monitoring period.
[0072] Figure 3 The red curve in the middle represents two periodic curves of the fitted heat transfer coefficient K. Taking the curve of one of the periods is... Figure 4 The red curve in the image. Figure 4 The horizontal axis represents the time series, and the vertical axis represents the heat transfer coefficient K value. Figure 4 The K value went through a cycle from maximum to minimum and back to maximum, corresponding to a dust level on the heated surface transitioning from clean to heavily contaminated to clean. The horizontal time series from 0 to 578.65 represents the gradual accumulation of dust on the heated surface. Sootblowers were deployed in the region around 578.65, but their deployment and operation required a considerable amount of time. Figure 4 The latter half of the curve represents the gradual elimination of surface ash from the heated area.
[0073] use Figure 4 The first half, from time 0 to 578.65, simulates the rate of ash accumulation, i.e., the degree of ash accumulation on the heated surface increases from 0 to 100%. The second half, from time 578.65 to the end, simulates the rate of ash elimination, i.e., the degree of ash accumulation on the heated surface gradually decreases from 100% to 0.
[0074] While the invention has been described herein with reference to specific embodiments, it should be understood that these embodiments are merely examples of the principles and applications of the invention. Therefore, it should be understood that many modifications can be made to the exemplary embodiments, and other arrangements can be designed without departing from the spirit and scope of the invention as defined by the appended claims. It should be understood that different dependent claims and features described herein can be combined in ways different from those described in the original claims. It is also understood that features described in conjunction with individual embodiments can be used in other described embodiments.
Claims
1. A method for quantitatively monitoring the degree of ash on heated surfaces based on big data analytics, characterized in that, The method includes the following steps: Based on the historical operating data of the power plant boiler during the current monitoring period, calculate the heat transfer coefficient of each heating surface of the power plant boiler at each sampling time during the current monitoring period. The heat transfer coefficients of each heated surface are smoothed and filtered during the current monitoring period. The periodic variation distribution of the heat transfer coefficient of each heated surface in the current monitoring cycle is obtained by using Bézier curves after smoothing and filtering. Based on this periodic variation distribution, a polynomial expression of the heat transfer coefficient of the corresponding heated surface is fitted. The polynomial expression of the heat transfer coefficient of each heated surface in the current monitoring cycle is used as the polynomial expression of the heat transfer coefficient of each heated surface in the next monitoring cycle. The polynomial expression of the heat transfer coefficient of each heated surface in the next monitoring cycle is fitted into a heat transfer coefficient curve. The degree of ash accumulation on the heated surface at each monitoring time in the next monitoring cycle is predicted by the heat transfer coefficient curve of each heated surface in the next monitoring cycle. The horizontal axis of the heat transfer coefficient curve represents time, and the vertical axis represents the heat transfer coefficient. The method for predicting the degree of ash accumulation on each heated surface at each monitoring time is as follows: based on the ratio of the area between the curve of the heat transfer coefficient curve of the heated surface from time 0 to the monitoring time and the horizontal axis of time to the area between the curve of the minimum value of the heat transfer coefficient curve of the heated surface and time 0.
2. The method for quantitative monitoring of ash content on heated surface area based on big data analysis technology according to claim 1, characterized in that, The smoothing filtering process is implemented using the Savitzky-Golay method.
3. The method for quantitative monitoring of ash content on heated surface area based on big data analysis technology according to claim 1, characterized in that, Historical operating data of the power plant boiler includes main steam flow rate D, actual fuel quantity B, and working fluid inlet temperature t at the heating surface. in Inlet working fluid pressure of the heating surface, outlet working fluid temperature of the heating surface t out Working fluid pressure at the outlet of the heating surface, desuperheating water temperature, desuperheating water pressure, and flue gas temperature T at the outlet of the heating surface. out and ambient air temperature.
4. The method for quantitative monitoring of ash content on heated surfaces based on big data analysis technology according to claim 1, characterized in that, Based on the historical operating data of the power plant boiler within the current monitoring period, the methods for obtaining the heat transfer coefficients of each heating surface of the power plant boiler at each sampling time within the current monitoring period include: S11. During the operation of the power plant boiler, the heat transfer Q of the heating surface... dc The working fluid side absorbs heat Q dx Flue gas side heat release Q df To reach equilibrium, Q dc =Q dx =Q df ; S12, based on the working fluid inlet temperature t of the heated surface in The enthalpy h′ of the working fluid at the inlet of the heating surface is obtained from the working fluid pressure at the inlet of the heating surface; based on the working fluid outlet temperature t of the heating surface... out The enthalpy h of the working fluid at the outlet of the heating surface is obtained from the working fluid pressure at the outlet of the heating surface; the enthalpy Δh of the desuperheating water is obtained from the temperature and pressure of the desuperheating water. jw ; S13. Based on D, h", h′, Δh jw And B, calculate the heat absorbed by the working fluid side Q. dx ; S14. Based on the flue gas temperature T at the outlet of the heated surface out The enthalpy H'' of the flue gas exiting the heated surface is obtained; the enthalpy H' of the ambient air is obtained based on the ambient air temperature. lk ; S15, based on a given heat retention coefficient And the air leakage coefficient Δα, according to Q dx =Q df Calculate the enthalpy H' of the flue gas at the inlet of the heating surface; S16. Based on the enthalpy H' of the flue gas at the inlet of the heating surface, obtain the flue gas temperature T at the inlet of the heating surface. in ; S17, according to T in T out t in and t out Calculate the heat transfer temperature and pressure Δt; S18. Based on the heat transfer temperature and pressure Δt, actual fuel quantity B, heating surface area F, and heat transfer Q of the heating surface. dc The heat transfer coefficient K is obtained.
5. The method for quantitative monitoring of ash content on heated surfaces based on big data analysis technology according to claim 4, characterized in that, In S12, based on the inlet temperature t of the working fluid at the heated surface... in Given the working fluid pressure at the inlet of the heating surface, find the enthalpy h′ of the working fluid at the inlet of the heating surface from the steam parameter table; Based on the working fluid outlet temperature t of the heated surface out Given the working fluid pressure at the outlet of the heating surface, find the enthalpy h" of the working fluid at the outlet of the heating surface in the steam parameter table; Based on the temperature and pressure of the desuperheating water, the enthalpy value Δh of the desuperheating water can be obtained by referring to the steam parameter table. jw .
6. The method for quantitative monitoring of ash content on heated surface area based on big data analysis technology according to claim 4, characterized in that, In S14, the enthalpy value H'' of the flue gas at the outlet of the heating surface is obtained by using the standard calculation method of flue gas physical properties to calculate the temperature T of the flue gas at the outlet of the heating surface. out The process is performed to obtain the enthalpy value H'' of the flue gas at the outlet of the heating surface; In S14, the ambient air enthalpy value H′ is obtained. lk The implementation method is as follows: the ambient air temperature is processed using the standard calculation method of flue gas physical properties to obtain the ambient air enthalpy H′. lk .
7. The method for quantitative monitoring of ash content on heated surface area based on big data analysis technology according to claim 4, characterized in that, In S16, the inlet flue gas temperature T of the heating surface is obtained. in The implementation method is as follows: the enthalpy H' of the flue gas at the inlet of the heating surface is processed using the standard calculation method of flue gas physical properties to obtain the flue gas temperature T at the inlet of the heating surface. in .
8. The method for quantitative monitoring of ash content on heated surface area based on big data analysis technology according to claim 4, characterized in that, 9. The method for quantitative monitoring of ash content on heated surface area based on big data analysis technology according to claim 4, characterized in that, Where, Δt lar =T in -t out , Δt sma =T out -t in ;Δt lar and Δt sma All of these are intermediate variables.
10. The method for quantitative monitoring of ash content on heated surface area based on big data analysis technology according to claim 1, characterized in that, The polynomial expression for the heat transfer coefficient of the heated surface, K(t), is: Among them, C i P is the calculated coefficient for the i-th term. i Let t be the correction coefficient for the i-th term, t be time, and n be an integer.