Coal quality monitoring method, device and equipment for pulverized coal furnace and storage medium

Abstract

The invention discloses a pulverized coal furnace coal quality monitoring method, device and equipment and a storage medium, which are applied to the field of coal quality monitoring and are used for determining process parameters related to the coal quality of a pulverized coal furnace. Determining furnace uniform temperature based on the furnace outlet smoke temperature, and determining a first numerical item based on the furnace uniform temperature and the boiler evaporation capacity; determining the total desuperheating water amount based on the desuperheating water amount; determining a second numerical item based on the total desuperheating water amount, the coal mill current and the boiler evaporation amount; determining a smoke amount coefficient based on the smoke exhaust oxygen amount, determining smoke exhaust uniform temperature based on the smoke exhaust temperature and the current of the coal mill, and determining a third numerical item based on the smoke amount coefficient, the smoke exhaust uniform temperature and the inlet air temperature of the air feeder; and constructing a coal quality detection model based on the first numerical item, the second numerical item and the third numerical item, and determining a coal quality detection result based on the monitoring data and the coal quality detection model. According to the boiler combustion principle, monitoring data of coal quality related process parameters are collected for multi-parameter correlation analysis, and real-time monitoring of the coal quality of the pulverized coal furnace is rapidly and accurately achieved at low cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of coal quality monitoring, in particular to a pulverized coal furnace coal quality monitoring method, a pulverized coal furnace coal quality monitoring device, an electronic device and a computer readable storage medium. BACKGROUND

[0002] The pulverized coal furnace coal quality refers to the comprehensive index of the quality characteristics and combustion performance of coal suitable for combustion in a pulverized coal furnace. The pulverized coal furnace coal quality monitoring faces higher requirements as the pulverized coal boiler is a core device of thermal power. The combustion efficiency and pollutant emission of the pulverized coal furnace are directly affected by the coal quality characteristics. However, the current differences between the coal entering the plant and the coal entering the furnace, the uneven mixing of the coal during transportation, and other problems lead to frequent fluctuations in the combustion conditions, and sometimes affect the safety indicators of the boiler. The traditional laboratory detection has defects such as lagging, sampling deviation, and the contradiction between cost and real-time performance. Although the existing online monitoring technology can achieve minute-level coal quality analysis, it is difficult to be applied on a large scale due to the complexity of equipment installation, high maintenance cost, and poor adaptability to harsh environments. SUMMARY

[0003] The purpose of the present application is to provide a pulverized coal furnace coal quality monitoring method, a pulverized coal furnace coal quality monitoring device, an electronic device and a computer readable storage medium, which are applied to the field of coal quality monitoring. According to the boiler combustion principle, the method collects monitoring data of coal quality related process parameters for multi-parameter correlation analysis, and realizes real-time monitoring of the pulverized coal furnace coal quality quickly, accurately and at low cost.

[0004] To solve the above technical problems, the present application provides a pulverized coal furnace coal quality monitoring method, comprising: determining process parameters related to the coal quality of the pulverized coal furnace; the process parameters include: boiler evaporation capacity, feed water temperature, furnace outlet flue gas temperature, coal mill current, desuperheating water quantity, exhaust gas temperature, inlet air temperature of the air supply fan, and exhaust gas oxygen content; determining the furnace uniform temperature based on the furnace outlet flue gas temperature, and determining a first numerical term based on the furnace uniform temperature and the boiler evaporation capacity; determining the total desuperheating water quantity based on the desuperheating water quantity, and determining a second numerical term based on the total desuperheating water quantity, the coal mill current and the boiler evaporation capacity; determining a flue gas quantity coefficient based on the exhaust gas oxygen content, determining an exhaust gas uniform temperature based on the exhaust gas temperature and the coal mill current, and determining a third numerical term based on the flue gas quantity coefficient, the exhaust gas uniform temperature and the inlet air temperature of the air supply fan; constructing a coal quality detection model based on the first numerical term, the second numerical term and the third numerical term, acquiring monitoring data of the process parameters, and determining a coal quality detection result based on the monitoring data and the coal quality detection model.

[0005] Optionally, a coal quality detection model is constructed based on the first numerical term, the second numerical term and the third numerical term, including: When the boiler evaporation capacity deviates from the preset evaporation capacity, a correction term is determined based on the boiler evaporation capacity; A coal quality detection model is constructed based on the first numerical term, the second numerical term, the third numerical term and the correction term. The expression of the correction term is: ; In the formula, U is the correction term, and C is the boiler evaporation capacity.

[0006] Optionally, the expression of the first numerical term is: ; In the formula, W is the first numerical term, R is the furnace uniform temperature, and C is the boiler evaporation capacity.

[0007] Optionally, the expression of the second numerical term is: ; In the formula, X is the second numerical term, Q is the total amount of desuperheating water, G is the mill current of the first coal mill, H is the mill current of the second coal mill, and C is the boiler evaporation capacity.

[0008] Optionally, the expression of the third numerical term is: ; The expression of the flue gas amount coefficient is: ; The expression of the flue gas uniform temperature is: ; In the formula, Y is the third numerical term, V is the flue gas amount coefficient, P is the flue gas oxygen amount, S is the flue gas uniform temperature, M is the left side flue gas temperature, N is the right side flue gas temperature, G is the mill current of the first coal mill, H is the mill current of the second coal mill, and O is the inlet air temperature of the air supply fan; the inlet air temperature of the air supply fan is the inlet air temperature of any air supply fan, or is the inlet uniform temperature of the air supply fan.

[0009] Optionally, the monitoring data of the process parameters is obtained, including: The monitoring data of the process parameters is obtained, and it is determined whether the monitoring data is abnormal data; When it is determined that the monitoring data is the abnormal data, the historical monitoring data mean of the process parameters is determined as the monitoring data.

[0010] Optionally, the method further includes: An alarm threshold is set; when the coal quality detection result is lower than the alarm threshold, an alarm instruction is generated to control the monitoring interface to alarm, and an alarm information is pushed to the terminal, and a diagnosis report is generated based on the monitoring data in a preset time period.

[0011] To solve the above technical problems, the present application provides a pulverized coal furnace coal quality monitoring device, comprising: A first module is used for determining process parameters related to the coal quality of the pulverized coal furnace; the process parameters include: boiler evaporation capacity, feed water temperature, furnace outlet flue gas temperature, coal mill current, desuperheating water quantity, exhaust gas temperature, inlet air temperature of the air supply fan and exhaust gas oxygen content; A second module is used for determining the furnace uniform temperature based on the furnace outlet flue gas temperature, and determining a first numerical item based on the furnace uniform temperature and the boiler evaporation capacity; A third module is used for determining the total desuperheating water quantity based on the desuperheating water quantity, and determining a second numerical item based on the total desuperheating water quantity, the coal mill current and the boiler evaporation capacity; A fourth module is used for determining a flue gas quantity coefficient based on the exhaust gas oxygen content, determining the exhaust gas uniform temperature based on the exhaust gas temperature and the coal mill current, and determining a third numerical item based on the flue gas quantity coefficient, the exhaust gas uniform temperature and the inlet air temperature of the air supply fan; A fifth module is used for constructing a coal quality detection model based on the first numerical item, the second numerical item and the third numerical item, obtaining monitoring data of the process parameters, and determining a coal quality detection result based on the monitoring data and the coal quality detection model.

[0012] To solve the above technical problems, the present application provides an electronic device, comprising: A memory is used for storing a computer program; A processor is used for executing the computer program to realize the above-mentioned pulverized coal furnace coal quality monitoring method.

[0013] To solve the above technical problems, the present application provides a computer readable storage medium, characterized in that the computer readable storage medium stores computer executable instructions, and the computer executable instructions are executed by a processor to realize the above-mentioned pulverized coal furnace coal quality monitoring method.

[0014] It can be seen that the present application determines the process parameters related to the coal quality of the pulverized coal furnace; the process parameters include: the boiler evaporation capacity, the feed water temperature, the flue gas temperature at the furnace outlet, the mill current, the desuperheating water quantity, the exhaust gas temperature, the air temperature at the inlet of the air supply fan and the exhaust gas oxygen content; the furnace uniform temperature is determined based on the flue gas temperature at the furnace outlet, the first numerical item is determined based on the furnace uniform temperature and the boiler evaporation capacity; the total desuperheating water quantity is determined based on the desuperheating water quantity; the second numerical item is determined based on the total desuperheating water quantity, the mill current and the boiler evaporation capacity; the flue gas quantity coefficient is determined based on the exhaust gas oxygen content, the exhaust gas uniform temperature is determined based on the exhaust gas temperature and the mill current, the third numerical item is determined based on the flue gas quantity coefficient, the exhaust gas uniform temperature and the air temperature at the inlet of the air supply fan; the coal quality detection model is constructed based on the first numerical item, the second numerical item and the third numerical item, the monitoring data of the process parameters are acquired, and the coal quality detection result is determined based on the monitoring data and the coal quality detection model. According to the boiler combustion principle, the present application collects the monitoring data of the coal quality related process parameters to perform multi-parameter correlation analysis, and realizes the real-time monitoring of the coal quality of the pulverized coal furnace quickly, accurately and at low cost. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute a part of the embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor based on the provided drawings.

[0016] Figure 1 A flow chart of a coal quality monitoring method of a pulverized coal furnace provided by the embodiments of the present application; Figure 2 A structural block diagram of a coal quality monitoring device of a pulverized coal furnace provided by the embodiments of the present application. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments only constitute a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0018] As a core piece of equipment in thermal power plants, pulverized coal boilers face higher requirements for real-time coal quality monitoring. The combustion efficiency and pollutant emissions of pulverized coal boilers are directly affected by coal characteristics (volatile matter, ash, sulfur, etc.). However, current issues such as differences in coal quality between the plant and the boiler, and uneven coal blending, lead to frequent fluctuations in combustion conditions, sometimes even affecting boiler safety indicators. Traditional laboratory testing, such as LIMS (Laboratory Information Management System), has three major drawbacks: First, a lag effect—daily testing cannot capture dynamic changes in coal quality (such as a sudden drop in volatile matter causing ignition delay), leading to delayed air-coal ratio adjustments and exacerbating efficiency losses and NOx emissions; second, sampling bias—single-point sampling cannot reflect the unevenness of coal flow (especially when blending with inferior coal), misleading combustion optimization decisions; and third, a conflict between cost and real-time performance—high-frequency laboratory analysis increases operational burden, while low-frequency testing cannot provide early warning of sudden inferior coal quality (high sulfur causing corrosion, high moisture leading to a surge in flue gas losses). Furthermore, the disconnect between offline data and the DCS (Distributed Control System) hinders the closed-loop optimization of coal quality and combustion.

[0019] While existing online monitoring technologies (such as infrared spectroscopy and microwave methods) can achieve minute-level coal quality analysis, their large-scale application is limited by the complexity of equipment installation, high maintenance costs, and insufficient adaptability to harsh environments. This invention integrates multiple parameters, including furnace flue gas temperature, desuperheating water, and exhaust oxygen content, to construct a combustion condition scoring model. This enables real-time online scoring and early warning of coal quality, helping operators implement a series of control measures based on real-time coal quality detection results.

[0020] The technical challenges that this invention aims to solve include the following: the low frequency of LIMS system sampling (single-day sampling); the instability of LIMS system sampling; the high level of professional expertise required of personnel and the complexity of analysis; and the isolation of test results and process control applications.

[0021] Technical challenges of real-time monitoring methods for coal quality inversion from thermal parameters of pulverized coal boilers: There are currently few domestic cases on real-time coal quality monitoring; the combustion conditions of pulverized coal boilers are complex, with strong coupling and multiple variables, and the experience level of the implementation team is tested through the combustion inversion coal quality model.

[0022] To address the shortcomings of existing technologies, this invention deeply explores the inherent patterns and correlations of key process parameter data through thermodynamics, combines advanced algorithm models, and comprehensively considers the dynamic operating conditions of boilers under different loads to establish a precise and efficient real-time coal quality monitoring model for pulverized coal boilers in thermal power plants.

[0023] The following combination Figure 1 , Figure 1 A flowchart of a coal quality monitoring method for a pulverized coal boiler provided in an embodiment of the present invention, the method may include: S101: Determine the process parameters related to the coal quality of the pulverized coal furnace; the process parameters include: boiler evaporation capacity, feed water temperature, furnace outlet flue gas temperature, coal mill current, desuperheating water quantity, exhaust gas temperature, inlet air temperature of the air supply fan, and exhaust gas oxygen content.

[0024] The embodiment can monitor the working condition characteristics of the scene as needed, combine historical inspection and test data, analyze the change law of the main process parameters involved in the coal quality scoring, and determine the process parameters related to the coal quality of the pulverized coal furnace.

[0025] The embodiment is not limited to the specific types of process parameters. The embodiment can select the boiler evaporation capacity, feed water temperature, furnace outlet flue gas temperature, coal mill current, desuperheating water quantity, exhaust gas temperature, inlet air temperature of the air supply fan, and exhaust gas oxygen content for monitoring. As shown in Table 1, the determination of the coal quality detection result is performed through the detection data.

[0026] Table 1: Process parameter monitoring table

[0027] S102: Determine the furnace uniform temperature based on the furnace outlet flue gas temperature, and determine the first numerical item based on the furnace uniform temperature and the boiler evaporation capacity.

[0028] The embodiment can determine the furnace uniform temperature based on the furnace outlet flue gas temperature. As shown in the following formula, the expression of the furnace uniform temperature can be: ; In the formula, R is the furnace uniform temperature, representing the temperature level of the combustion zone, which is prone to cause slagging if too high; E is the left side flue gas temperature at the furnace outlet, and F is the right side flue gas temperature at the furnace outlet.

[0029] Further, the embodiment can determine the first numerical item based on the furnace uniform temperature and the boiler evaporation capacity. The expression of the first numerical item can be: ; In the formula, W is the first numerical item, R is the furnace uniform temperature, and C is the boiler evaporation capacity.

[0030] S103: Determine the total desuperheating water quantity based on the desuperheating water quantity; determine the second numerical item based on the total desuperheating water quantity, the coal mill current, and the boiler evaporation capacity.

[0031] The embodiment can determine the total desuperheating water quantity based on the desuperheating water quantity. As shown in the following formula, the expression of the total desuperheating water quantity can be: Q=I+J+K+L; In the formula, Q is the total amount of desuperheating water, that is, the flow rate of desuperheating water of each stage of the boiler, reflecting the heat load adjustment requirement of the superheater / reheater system, and the excess of desuperheating water will cause the enthalpy of steam to decrease; I is the left first-stage desuperheating water amount, J is the right first-stage desuperheating water amount, K is the left second-stage desuperheating water amount, and L is the right second-stage desuperheating water amount.

[0032] Further, the embodiment can determine a second numerical item based on the total amount of desuperheating water, the mill current and the boiler evaporation capacity, and the expression of the second numerical item can be: ; In the formula, X is the second numerical item, Q is the total amount of desuperheating water, G is the mill current of the first mill, H is the mill current of the second mill, and C is the boiler evaporation capacity.

[0033] In S104, the flue gas amount coefficient is determined based on the flue gas oxygen content, the flue gas average temperature is determined based on the flue gas temperature and the mill current, and the third numerical item is determined based on the flue gas amount coefficient, the flue gas average temperature and the air inlet temperature of the air supply fan.

[0034] The embodiment can determine the flue gas amount coefficient based on the flue gas oxygen content, as shown in the following formula, and the expression of the flue gas amount coefficient can be: ; In the formula, the flue gas amount coefficient V is the inverse function of the flue gas oxygen content P, reflecting the influence of the change of the excess flue gas amount coefficient on the flue gas amount.

[0035] The embodiment can determine the flue gas average temperature based on the flue gas temperature and the mill current, as shown in the following formula, and the expression of the flue gas average temperature can be: ; In the formula, S is the flue gas average temperature, G is the mill current of the first mill, H is the mill current of the second mill, the flue gas average temperature is corrected by adjusting the air-fuel ratio affected by the mill currents G and H, M is the left flue gas temperature, and N is the right flue gas temperature.

[0036] The embodiment can first determine the flue gas loss T based on the flue gas amount coefficient, the flue gas average temperature and the air inlet temperature of the air supply fan, as shown in the following formula: ; In the formula, T is the flue gas loss, V is the flue gas amount coefficient, S is the flue gas average temperature, O is the air inlet temperature of the air supply fan, and S-O is the temperature difference; in the embodiment, the air inlet temperature of the air supply fan is the air inlet temperature of any air supply fan, or is the average temperature of the air inlet temperatures of all air supply fans.

[0037] The embodiment can determine the third numerical item based on the flue gas loss, as shown in the following formula, and the expression of the third numerical item Y can be: .

[0038] S105: Construct a coal quality detection model based on the first numerical item, the second numerical item, and the third numerical item, obtain monitoring data of the process parameters, and determine a coal quality detection result based on the monitoring data and the coal quality detection model.

[0039] The coal quality of a pulverized coal furnace refers to the comprehensive index of the quality characteristics and combustion performance of coal suitable for combustion in a pulverized coal furnace, which mainly includes: basic coal quality parameters such as volatile matter, ash content, and sulfur content; combustion characteristic indexes such as calorific value, ash melting point, and grindability index; process adaptability such as coal fineness and moisture content. These comprehensive effects can significantly affect the combustion efficiency, and when the coal quality is not ideal, the operating cost can be increased by more than 15%, and the safe and stable operation of the boiler can be threatened.

[0040] The coal quality detection model in the embodiment can be constructed based on the first numerical item, the second numerical item, and the third numerical item. The coal quality detection result output by the coal quality detection model in the embodiment can be a percentage value. The larger the value, the higher the coal quality. Therefore, the expression of the coal quality detection model in the embodiment can be: Z = 100 - W - X - Y. In the formula, Z is the coal quality detection result, W is the first numerical item, X is the second numerical item, and Y is the third numerical item.

[0041] In the embodiment, the actual boiler evaporation capacity may deviate from the preset evaporation capacity. Therefore, the embodiment can introduce a correction term to correct the coal quality detection result when the boiler evaporation capacity deviates from the preset evaporation capacity.

[0042] Specifically, when the boiler evaporation capacity deviates from the preset evaporation capacity, a correction term is determined based on the boiler evaporation capacity. The expression of the correction term can be: ; In the formula, U is the correction term, and C is the boiler evaporation capacity.

[0043] Further, the coal quality detection model is constructed based on the first numerical item, the second numerical item, the third numerical item, and the correction term. The expression of the corrected coal quality detection model can be: Z = 100 - W - X - Y - U.

[0044] The embodiment can obtain monitoring data of each process parameter in real time. The monitoring data can be input into the coal quality detection model to obtain the coal quality detection result output by the model in real time.

[0045] During the DCS bottom layer data collection and transmission process, data may be abnormal due to instrument calibration, communication interruption, system environmental factors, and other factors, which may affect subsequent analysis and operation.

[0046] In the embodiment, the monitoring data can be detected to determine whether the monitoring data is abnormal data, so as to avoid that the coal quality detection result is greatly deviated from the actual value due to the abnormal monitoring data.

[0047] Specifically, the monitoring data of the process parameter is acquired, and it is determined whether the monitoring data is abnormal data. When it is determined that the monitoring data is abnormal data, the historical monitoring data mean value of the process parameter is determined as the monitoring data.

[0048] The embodiment is not limited to the specific way of determining whether the monitoring data is abnormal data. Generally, the abnormal data can be detected by comparing the data of the same type of equipment horizontally. Specifically, the same type of equipment monitoring data of the same type of equipment is acquired, and the same type of monitoring data mean value is determined. The mean value deviation between the monitoring data mean value and the same type of equipment monitoring data mean value is determined. When the mean value deviation is greater than a preset mean value deviation threshold, it can be determined that the monitoring data is abnormal data.

[0049] The embodiment can also detect abnormal data by tracing back the historical working condition curve. The threshold interval of the monitoring data is determined based on the historical working condition curve of the monitoring data. When the monitoring data exceeds the threshold interval, the mutation rate (such as the slope of the data curve) of the monitoring data currently acquired is determined. When the mutation rate of the monitoring data is greater than a mutation rate threshold, it can be determined that the monitoring data is abnormal data.

[0050] The embodiment can also perform correlation analysis on the process parameter logical chain to ensure the authenticity and reliability of the data participating in system analysis. For example, the correlation between each process parameter is analyzed and determined. Whether the monitoring data is abnormal data is determined based on the correlation between the process parameters and the change trend between the monitoring data of the process parameters. For example, A data and B data are positively correlated, but the change trend of A data in the acquired monitoring data is increased, and the change trend of B data is decreased. Therefore, it does not conform to the process parameter logical chain. At this time, it can be determined that the corresponding monitoring data is abnormal data.

[0051] In the embodiment, for the monitoring data sampled at a certain time point, if it is abnormal data, the mean value or other effective value of the corresponding monitoring data in a preset time period can be used instead of the abnormal data.

[0052] The embodiment can perform real-time monitoring of data anomalies. When the monitoring data of the process parameter is restored to normal data from abnormal data, the coal quality detection result can be determined again by acquiring the monitoring data.

[0053] In this embodiment, real-time calculation of the coal quality detection result can be performed based on the monitoring data of the process parameters, and a real-time online analysis algorithm is used to achieve a sub-minute (≤30s) detection response. In this embodiment, an alarm threshold can be set, and when the coal quality detection result is lower than the alarm threshold, an alarm instruction is generated to control the monitoring interface to alarm, and alarm information is simultaneously pushed to the terminal, and a diagnosis report is generated based on the monitoring data in a preset time period, which effectively assists the operation personnel to perform corresponding processing operations.

[0054] Specifically, when the coal quality detection result is lower than the alarm threshold, the monitoring interface will appear corresponding alarm, the system manages the alarm information separately, lists each alarm record in chronological order, and the alarm record includes the associated device number, the specific monitoring object name, the alarm timestamp accurate to seconds, the preset safety parameter threshold range and the actual alarm value, etc., which assists the personnel to make accurate and rapid decisions and to perform subsequent tracing and offline analysis and other business requirements.

[0055] In this embodiment, by means of thermodynamics and boiler combustion principle, important associated process analysis parameters are selected to establish a coal powder furnace coal quality monitoring model, combined with data pre-processing, abnormal data is filtered to ensure the effectiveness of the data, and the coal powder furnace coal quality monitoring model is accurately established. According to the combustion conditions of the boiler under different loads, combined with a large amount of historical data analysis and training, the self-adaptive ability of the model under different conditions is improved, and the universality and robustness of the model are ensured. The monitoring system calculates the current coal quality score by monitoring various parameters in real time, and reminds the operator when reaching the early warning value. When the monitoring system detects that the coal quality is abnormal at the current time, the deviation from the early warning within 30 seconds is realized, a series of alarm contents such as associated device number and specific monitoring object name are included, so that the operator can discover problems in time, and the response speed and operation accuracy are improved.

[0056] Based on the above embodiment, according to the boiler combustion principle, the monitoring data of the coal quality related process parameters are collected for multi-parameter correlation analysis, and the real-time monitoring of the coal powder furnace coal quality is realized quickly, accurately and at low cost.

[0057] The following will be described in combination with Figure 2 , Figure 2 A structure block diagram of a coal powder furnace coal quality monitoring device provided by the embodiment of the present application, which can include: A first module 100 for determining process parameters related to the coal powder furnace coal quality; the process parameters include: boiler evaporation capacity, feed water temperature, furnace outlet flue gas temperature, coal mill current, desuperheating water quantity, exhaust gas temperature, inlet air temperature of the air supply fan, and exhaust gas oxygen content; A second module 200 for determining the furnace uniform temperature based on the furnace outlet flue gas temperature, and determining the first numerical item based on the furnace uniform temperature and the boiler evaporation capacity; The third module 300 is configured to determine a total desuperheating water amount based on the desuperheating water amount, and determine a second numerical item based on the total desuperheating water amount, the mill current and the boiler evaporation amount. The fourth module 400 is configured to determine a flue gas amount coefficient based on the flue gas oxygen amount, determine a flue gas average temperature based on the flue gas temperature and the mill current, and determine a third numerical item based on the flue gas amount coefficient, the flue gas average temperature and the air inlet temperature of the air blower. The fifth module 500 is configured to construct a coal quality detection model based on the first numerical item, the second numerical item and the third numerical item, acquire monitoring data of the process parameters, and determine a coal quality detection result based on the monitoring data and the coal quality detection model.

[0058] Based on the above embodiments, according to the boiler combustion principle, the monitoring data of the coal quality related process parameters are collected for multi-parameter correlation analysis, so that the real-time monitoring of the coal quality of the pulverized coal furnace is realized quickly, accurately and at low cost.

[0059] Based on the above embodiments, the fifth module 500 can include: The first unit is configured to determine a correction item based on the boiler evaporation amount when the boiler evaporation amount deviates from the preset evaporation amount. The second unit is configured to construct a coal quality detection model based on the first numerical item, the second numerical item, the third numerical item and the correction item. The expression of the correction item is: ; In the expression, U is the correction item, and C is the boiler evaporation amount.

[0060] Based on the above embodiments, the expression of the first numerical item is: ; In the expression, W is the first numerical item, R is the furnace average temperature, and C is the boiler evaporation amount.

[0061] Based on the above embodiments, the expression of the second numerical item is: ; In the expression, X is the second numerical item, Q is the total desuperheating water amount, G is the mill current of the first mill, H is the mill current of the second mill, and C is the boiler evaporation amount.

[0062] Based on the above embodiments, the expression of the third numerical item is: ; The expression of the flue gas amount coefficient is: ; The expression of the flue gas average temperature is: ; In the formula, Y is a third numerical term, V is a flue gas amount coefficient, P is an oxygen content of flue gas, S is a flue gas average temperature, M is a left flue gas temperature, N is a right flue gas temperature, G is a first mill current, H is a second mill current, and O is an air inlet temperature of an air supply fan; the air inlet temperature of the air supply fan is an air inlet temperature of any air supply fan or an air inlet average temperature of the air supply fan.

[0063] Based on the above embodiments, the fifth module 500 can include: The third unit is configured to acquire the monitoring data of the process parameter, and determine whether the monitoring data is abnormal data. The fourth unit is configured to determine the historical monitoring data mean of the process parameter as the monitoring data when the monitoring data is determined as abnormal data.

[0064] Based on the above embodiments, the device can further include: The sixth module is configured to set an alarm threshold, generate an alarm instruction to control the monitoring interface to alarm when the coal quality detection result is lower than the alarm threshold, push the alarm information to the terminal, and generate a diagnosis report based on the monitoring data in a preset time period.

[0065] Based on the above embodiments, the present application further provides an electronic device, which can include a memory and a processor, wherein the memory has a computer program stored therein, and the processor can implement the steps provided in the above embodiments when calling the computer program in the memory. Of course, the device can further include various necessary network interfaces, power supplies and other components.

[0066] The present application further provides a computer readable storage medium having a computer program stored thereon, and the computer program can implement the method provided in the embodiments of the present application when executed by a terminal or a processor; the storage medium can include a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0067] In this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but can include other elements not expressly listed or inherent to such process, method, article, or apparatus. An element proceeded by "comprises... a" does not, without more constraints, exclude the existence of additional elements of the same name in the process, method, article, or apparatus.

Claims

1. A method for monitoring coal quality in a pulverized coal boiler, characterized in that, include: Determine the process parameters related to the coal quality of the pulverized coal boiler; The process parameters include: boiler evaporation rate, feedwater temperature, furnace outlet flue gas temperature, coal mill current, desuperheating water volume, exhaust gas temperature, blower inlet air temperature, and exhaust gas oxygen content. The furnace average temperature is determined based on the furnace outlet flue gas temperature, and the first numerical item is determined based on the furnace average temperature and the boiler evaporation rate. The total amount of desuperheating water is determined based on the aforementioned desuperheating water volume; a second numerical item is determined based on the total amount of desuperheating water, the coal mill current, and the boiler evaporation rate. The flue gas volume coefficient is determined based on the flue gas oxygen content, the flue gas average temperature is determined based on the flue gas temperature and the coal mill current, and the third numerical item is determined based on the flue gas volume coefficient, the flue gas average temperature and the blower inlet air temperature. A coal quality testing model is constructed based on the first, second, and third numerical terms to obtain monitoring data of the process parameters, and the coal quality testing results are determined based on the monitoring data and the coal quality testing model.

2. The coal quality monitoring method for pulverized coal boilers according to claim 1, characterized in that, A coal quality detection model is constructed based on the first numerical term, the second numerical term, and the third numerical term, including: When the boiler evaporation rate deviates from the preset evaporation rate, a correction term is determined based on the boiler evaporation rate; A coal quality detection model is constructed based on the first numerical term, the second numerical term, the third numerical term, and the correction term; The expression for the correction term is: ； In the formula, U is the correction term, and C is the boiler evaporation rate.

3. The method for monitoring coal quality in a pulverized coal boiler according to claim 1, characterized in that, The expression for the first numerical term is: ； In the formula, W is the first numerical term, R is the uniform temperature of the furnace, and C is the evaporation rate of the boiler.

4. The coal quality monitoring method for pulverized coal boilers according to claim 1, characterized in that, The expression for the second numerical term is: ； In the formula, X is the second numerical term, Q is the total amount of desuperheating water, G is the coal mill current of the first coal mill, H is the coal mill current of the second coal mill, and C is the boiler evaporation rate.

5. The method for monitoring coal quality in a pulverized coal boiler according to claim 1, characterized in that, The expression for the third numerical term is: ； The expression for the flue gas volume coefficient is as follows: ； The expression for the average temperature of the exhaust gas is: ； In the formula, Y is the third numerical term, V is the flue gas volume coefficient, P is the flue gas oxygen content, S is the flue gas average temperature, M is the left flue gas temperature, N is the right flue gas temperature, G is the coal mill current of the first coal mill, H is the coal mill current of the second coal mill, and O is the blower inlet air temperature; the blower inlet air temperature can be the blower inlet air temperature of any blower, or the blower inlet average temperature.

6. The method for monitoring coal quality in a pulverized coal boiler according to claim 1, characterized in that, Acquiring monitoring data for the process parameters includes: Obtain the monitoring data of the process parameters and determine whether the monitoring data is abnormal; When the monitoring data is determined to be abnormal data, the average historical monitoring data of the process parameter is determined as the monitoring data.

7. The method for monitoring coal quality in a pulverized coal boiler according to claim 1, characterized in that, Also includes: Set alarm thresholds; When the coal quality test result is lower than the alarm threshold, an alarm command is generated to control the monitoring interface to alarm, alarm information is generated and pushed to the terminal, and a diagnostic report is generated based on the monitoring data within a preset time period.

8. A coal quality monitoring device for a pulverized coal boiler, characterized in that, include: The first module is used to determine the process parameters related to the coal quality of the pulverized coal boiler; The process parameters include: boiler evaporation rate, feedwater temperature, furnace outlet flue gas temperature, coal mill current, desuperheating water volume, exhaust gas temperature, blower inlet air temperature, and exhaust gas oxygen content. The second module is used to determine the furnace average temperature based on the furnace outlet flue gas temperature, and to determine the first numerical item based on the furnace average temperature and the boiler evaporation rate. The third module is used to determine the total amount of desuperheating water based on the desuperheating water volume; and to determine the second numerical item based on the total amount of desuperheating water, the coal mill current, and the boiler evaporation rate. The fourth module is used to determine the flue gas volume coefficient based on the flue gas oxygen content, determine the flue gas average temperature based on the flue gas temperature and the coal mill current, and determine the third numerical item based on the flue gas volume coefficient, the flue gas average temperature and the blower inlet air temperature. The fifth module is used to construct a coal quality testing model based on the first, second, and third numerical items, obtain monitoring data of the process parameters, and determine the coal quality testing results based on the monitoring data and the coal quality testing model.

9. An electronic device, characterized in that, include: Memory, used to store computer programs; A processor is configured to implement the coal quality monitoring method for pulverized coal boilers as described in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, implement the coal quality monitoring method for pulverized coal boilers as described in any one of claims 1 to 7.

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