In-boiler temperature intelligent management method and system for thermal power plant boiler

Abstract

The invention provides an in-furnace temperature intelligent management method and system for a thermal power plant boiler, and belongs to the field of industrial automation control, and the method comprises the steps: 1, installing a plurality of sensors of a preset type at in-furnace key parts of the thermal power plant boiler, analyzing the data of the sensors, and determining the combustion parameters of the thermal power plant boiler based on the analysis result; 2, the combustion efficiency of the thermal power plant boiler is determined based on the combustion parameters, and then a combustion efficiency curve is drawn; 3, analyzing the combustion efficiency curve, and determining a comprehensive efficiency deviation coefficient based on an analysis result; and 4, determining a combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determining an in-furnace temperature intelligent management strategy in combination with the initial in-furnace temperature. The boiler operation is optimized, and the combustion efficiency and the temperature control precision are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial automation control, and in particular to a method and system for intelligently managing the temperature inside a furnace of a thermal power plant boiler. Background Art

[0002] With the development of boiler technology in thermal power plants, the requirements for boiler combustion efficiency and temperature control are becoming increasingly higher.

[0003] Traditional temperature management methods usually rely on manual monitoring and adjustment, which is not only inefficient, but also difficult to ensure the accuracy and stability of control. In the existing technology, although there are some automatic control methods, most of them rely on simple control of fixed parameters and cannot optimize the combustion efficiency and temperature management of the boiler in real time according to the actual operating conditions.

[0004] Therefore, the present invention provides a method and system for intelligently managing the temperature inside a furnace of a thermal power plant boiler. Summary of the invention

[0005] The present invention provides a method and system for intelligent management of furnace temperature of a thermal power plant boiler, which is used to monitor the furnace temperature and combustion parameters in real time by installing sensors, and then draw a combustion efficiency curve, and determine the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and formulate an intelligent management strategy in combination with the initial furnace temperature, so as to optimize the boiler operation and improve the combustion efficiency and the accuracy of temperature control.

[0006] The present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, comprising:

[0007] Step 1: Install several preset types of sensors at key locations in the boiler of a thermal power plant, analyze the sensor data, and determine the combustion parameters of the thermal power plant boiler based on the analysis results;

[0008] Step 2: Determine the combustion efficiency of the thermal power plant boiler based on the combustion parameters, and then draw a combustion efficiency curve;

[0009] Step 3: Analyze the combustion efficiency curve and determine the comprehensive efficiency deviation coefficient based on the analysis results;

[0010] Step 4: Determine the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determine the furnace temperature intelligent management strategy based on the initial furnace temperature.

[0011] The present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, which analyzes sensor data and determines the combustion parameters of the thermal power plant boiler based on the analysis results, including:

[0012] Perform trend analysis on sensor data to obtain long-term trend coefficients and short-term fluctuation coefficients for each type of sensor data;

[0013] Determine the trend analysis coefficient of each type of sensor data based on the long-term trend coefficient and the short-term fluctuation coefficient of each type of sensor data, and screen each type of sensor data in combination with a preset trend coefficient threshold, obtain a plurality of first trend analysis coefficients that reach the preset trend coefficient threshold, and then determine the first sensor data corresponding to each first trend analysis coefficient;

[0014] Combustion parameters of the thermal power plant boiler are determined based on all first sensor data.

[0015] The present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, which determines the combustion parameters of the thermal power plant boiler based on all first sensor data, including:

[0016] Performing category analysis on all first sensor data, obtaining a plurality of sensor data categories of the first sensor data, and classifying the first sensor data into categories;

[0017] Determine the combustion parameters of the thermal power plant boiler based on all categories of first sensor data:

[0018]

[0019] Among them, FAR is the combustion parameter of the thermal power plant boiler, y j is the jth air-related sensor data, M is the number of air-related sensor data, a j is the preset weight corresponding to the jth air-related sensor data, d j is the preset initial data offset coefficient of the jth air-related sensor data, c j is the preset weight corresponding to the preset initial offset coefficient of the jth air-related sensor data, x i is the i-th fuel-related sensor data, k i is the preset weight corresponding to the i-th fuel-related sensor data, N is the number of fuel-related sensor data, q ij is the cross-term weight of the i-th fuel-related sensor data and the j-th fuel-related sensor data, r is the preset bias constant,

[0020] Where λ is the excess air index:

[0021]

[0022] Among them, k t is the preset correction factor for the air-related sensor data, k f is the preset correction factor for the fuel-related sensor data, T is the furnace temperature, T ref is the preset reference temperature, HVfuel is the calorific value of the fuel, ∝ΔT is the conversion coefficient corresponding to the difference between the furnace temperature and the preset reference temperature, ∝HV fuel is the conversion coefficient corresponding to the calorific value of the fuel, O 实测 is the oxygen percentage measured in the furnace, O 理论 is the oxygen percentage required for theoretical combustion, and 21 is the theoretical content of oxygen in the preset air.

[0023] The present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler. The sensor data categories include: air-related sensor data and fuel-related sensor data.

[0024] The present invention provides a method for intelligently managing the furnace temperature of a thermal power plant boiler, which determines the combustion efficiency of the thermal power plant boiler based on combustion parameters and then draws a combustion efficiency curve, including:

[0025]

[0026] Among them, FAR i is the combustion parameter of the thermal power plant boiler at the i-th moment, is the combustion efficiency of the thermal power plant boiler at the i-th moment, is the preset ideal maximum combustion efficiency at the i-th moment, AFR stoich is the preset theoretical complete combustion air-fuel ratio, δ(T i ,P i ) is the preset temperature and pressure related adjustment function at the i-th moment, E is the preset activation energy, which represents the energy barrier that the fuel needs to overcome for chemical reaction, R0 is the preset gas constant, T0 is the preset absolute temperature, α is the preset pressure adjustment coefficient, β is the preset adjustment coefficient corresponding to the pressure fluctuation, P i is the internal pressure of the boiler at the i-th moment, T i is the internal temperature of the boiler at the i-th moment, sin is a trigonometric function;

[0027] A combustion efficiency curve is drawn based on the boiler combustion efficiency at all times.

[0028] The present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, which analyzes a combustion efficiency curve and determines a comprehensive efficiency deviation coefficient based on the analysis result, including:

[0029] Analyze the combustion efficiency curve to determine the overall efficiency and optimal efficiency inside the boiler, and then determine the first efficiency deviation coefficient:

[0030]

[0031] Where R1 is the first efficiency deviation coefficient, C fis the preset fuel mass correction factor, C a is the excess air coefficient, C o is the preset correction factor for external factors, is the combustion efficiency of the thermal power plant boiler at the i-th moment, μ2 is the preset weight corresponding to the weighted combustion efficiency of the thermal power plant boiler, ω i is the time weight corresponding to the i-th moment, n is the preset time-weighted number of moments, k1 is the preset influence coefficient corresponding to the correction term, AUC is the overall efficiency inside the boiler, μ1 is the preset weight corresponding to the total combustion efficiency, η 最优 is the optimal efficiency, μ3 is the preset weight corresponding to the optimal efficiency, η 目标 The preset target combustion efficiency;

[0032] Performing an abnormality analysis on the combustion efficiency curve, and determining a second efficiency deviation coefficient based on the abnormality analysis result;

[0033] The comprehensive efficiency deviation coefficient is determined based on the first efficiency deviation coefficient and the second efficiency deviation coefficient:

[0034]

[0035] Where c is the comprehensive efficiency deviation coefficient, R2 is the second efficiency deviation coefficient, R0 is the preset deviation upper limit standard value, R t It is the preset deviation lower limit standard value.

[0036] The present invention provides a furnace temperature intelligent management method for a thermal power plant boiler, which determines a combustion adjustment coefficient based on a comprehensive efficiency deviation coefficient and determines a furnace temperature intelligent management strategy in combination with an initial furnace temperature, including:

[0037] According to the comprehensive efficiency deviation coefficient, determine the required combustion adjustment amount:

[0038]

[0039] Among them, K 调整 is the combustion adjustment amount, K 初始 is the basic combustion parameter, ε1 is the direct influence coefficient of combustion efficiency deviation on the adjustment coefficient, c is the comprehensive efficiency deviation coefficient, ε2 is the temperature difference adjustment coefficient, ΔT is the difference between the actual temperature in the furnace and the target temperature, and is the feedback adjustment coefficient. is the deviation influence coefficient, c v is the deviation of the boiler combustion efficiency in the vth measurement cycle from the expected target, t v is the duration of the deviation in the ith cycle, H is the number of measurement cycles, θ is the influence coefficient of the improvement ratio, and τ is the efficiency improvement ratio caused by the previous adjustment;

[0040] Obtain the current temperature in the furnace from the sensor data, and analyze the temperature data to determine the current heat distribution state in the furnace;

[0041] The adjustment scheme is determined based on the combustion adjustment coefficient and the preset adjustment coefficient-adjustment scheme database, and the intelligent management strategy of the furnace temperature is determined in combination with the current heat distribution state in the furnace.

[0042] The present invention provides an intelligent management system for the temperature in a furnace of a thermal power plant boiler, comprising:

[0043] Parameter acquisition module: Several preset sensors are installed at key locations in the boiler of a thermal power plant, the sensor data is analyzed, and the combustion parameters of the thermal power plant boiler are determined based on the analysis results;

[0044] Curve drawing module: determines the combustion efficiency of the thermal power plant boiler based on the combustion parameters, and then draws the combustion efficiency curve;

[0045] Coefficient determination module: Analyze the combustion efficiency curve and determine the comprehensive efficiency deviation coefficient based on the analysis results;

[0046] Strategy determination module: Determines the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determines the furnace temperature intelligent management strategy based on the initial furnace temperature.

[0047] The present invention provides a method and system for intelligent management of furnace temperature in a thermal power plant boiler, which is used to monitor the furnace temperature and combustion parameters in real time by installing sensors, and then draw a combustion efficiency curve, and determine the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and formulate an intelligent management strategy in combination with the initial furnace temperature, so as to optimize boiler operation and improve combustion efficiency and temperature control accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the technical solutions in the present invention or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0049] Figure 1 It is a flow chart of a method for intelligently managing the temperature in a furnace of a thermal power plant boiler provided by an embodiment of the present invention;

[0050] Figure 2 The present invention is a schematic diagram of a structure of an intelligent management system for the temperature in a furnace of a thermal power plant boiler provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the drawings of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0052] Example 1

[0053] like Figure 1 As shown, an embodiment of the present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, comprising:

[0054] Step 1: Install several preset types of sensors at key locations in the boiler of a thermal power plant, analyze the sensor data, and determine the combustion parameters of the thermal power plant boiler based on the analysis results;

[0055] Step 2: Determine the combustion efficiency of the thermal power plant boiler based on the combustion parameters, and then draw a combustion efficiency curve;

[0056] Step 3: Analyze the combustion efficiency curve and determine the comprehensive efficiency deviation coefficient based on the analysis results;

[0057] Step 4: Determine the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determine the furnace temperature intelligent management strategy based on the initial furnace temperature.

[0058] In this embodiment, the key parts of the furnace refer to those areas that have a significant impact on combustion efficiency, temperature control and overall safety, usually including combustion chambers, superheaters and reheaters. For example, sensors are installed in the combustion chamber because this is the main area where fuel combustion generates high temperatures, which directly affects combustion efficiency and emissions.

[0059] In this embodiment, the preset type of sensor refers to a sensor type predefined according to specific application requirements, such as temperature, pressure or flow sensors, which are designed to measure and monitor specific parameters in boiler operation, for example, a thermocouple sensor is installed to measure the temperature in the furnace, and a pressure sensor is installed to monitor the steam pressure;

[0060] In this embodiment, the combustion parameters of the thermal power plant boiler include all variables that affect the combustion process, such as oxygen supply, fuel flow, fuel type and temperature, etc. For example, measuring the mixing ratio of oxygen and coal powder is crucial for optimizing combustion and reducing emissions.

[0061] In this embodiment, the combustion efficiency of the boiler refers to the efficiency of the boiler in generating energy using fuel. High combustion efficiency means less energy loss and more efficient fuel utilization. If the amount of heat extracted from each unit of coal by the boiler increases, it means that the combustion efficiency is improved;

[0062] In this embodiment, the combustion efficiency curve is a graphical representation showing the trend of combustion efficiency changing with time or combustion conditions, and the curve shows the change of boiler combustion efficiency under different air excess coefficients.

[0063] In this embodiment, the comprehensive efficiency deviation coefficient is a coefficient that measures the difference between the actual combustion efficiency and the expected or ideal efficiency. A high deviation coefficient indicates that the efficiency is lower than expected and needs to be adjusted. If the deviation coefficient is greater than 1.05, it may indicate that the fuel supply or air ratio needs to be adjusted to improve the efficiency.

[0064] In this embodiment, the combustion adjustment coefficient is an adjustment factor calculated based on the deviation coefficient and is used to adjust the combustion parameters to optimize the combustion efficiency. If the comprehensive efficiency deviation coefficient shows a decrease in efficiency, the combustion adjustment coefficient may indicate an increase in fuel flow or an adjustment in air volume;

[0065] In this embodiment, the temperature intelligent management strategy is a set of strategies for automatically adjusting the temperature inside the boiler furnace based on real-time data and algorithms to maintain optimal combustion efficiency and safe operation, including reducing the fuel supply when the data shows that the temperature is too high, or increasing the fuel supply when the temperature is detected to be too low.

[0066] The beneficial effects of the above technical solution are: by installing sensors to monitor the furnace temperature and combustion parameters in real time, the combustion efficiency curve is drawn, and the combustion adjustment coefficient is determined based on the comprehensive efficiency deviation coefficient. An intelligent management strategy is formulated in combination with the initial furnace temperature, thereby optimizing boiler operation and improving combustion efficiency and temperature control accuracy.

[0067] Example 2

[0068] An embodiment of the present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, which analyzes sensor data and determines the combustion parameters of the thermal power plant boiler based on the analysis results, including:

[0069] Perform trend analysis on sensor data to obtain long-term trend coefficients and short-term fluctuation coefficients for each type of sensor data;

[0070] Determine the trend analysis coefficient of each type of sensor data based on the long-term trend coefficient and the short-term fluctuation coefficient of each type of sensor data, and screen each type of sensor data in combination with a preset trend coefficient threshold, obtain a plurality of first trend analysis coefficients that reach the preset trend coefficient threshold, and then determine the first sensor data corresponding to each first trend analysis coefficient;

[0071] Combustion parameters of the thermal power plant boiler are determined based on all first sensor data.

[0072] In this embodiment, trend analysis is a statistical method used to identify and measure long-term performance or behavior trends in a data set. In this embodiment, this involves time series analysis of boiler sensor data to observe and predict the changing trends of boiler operating parameters, for example, trend analysis of historical data of boiler temperature sensors to determine whether there is a continuous temperature increase, which may indicate a decrease in combustion efficiency or maintenance needs;

[0073] In this embodiment, the long-term trend coefficient is a measurement value based on the data change over a longer period of time, and is used to indicate the long-term development direction and strength of the data. For example, by analyzing the flue gas temperature data of the past year, a long-term trend coefficient indicating a gradual increase or decrease in temperature is calculated;

[0074] In this embodiment, the short-term fluctuation coefficient refers to the fluctuation of data in a shorter period of time, reflecting the short-term stability or volatility of the data. For example, after a specific energy-saving measure is implemented, the fluctuation of the combustion air flow rate is observed within a week to evaluate the immediate response of the control system.

[0075] In this embodiment, the trend analysis coefficient is a comprehensive index obtained by combining the long-term trend coefficient and the short-term fluctuation coefficient, which is used to comprehensively evaluate the trend of the data. For example, the trend analysis coefficient of the boiler pressure is obtained by combining the long-term trend coefficient and the short-term fluctuation coefficient, thereby providing a basis for pressure adjustment;

[0076] In this embodiment, the preset trend coefficient threshold is a pre-set standard used to determine whether the trend analysis coefficient has reached a level of concern. If this threshold is exceeded, action may need to be taken. If the trend analysis coefficient exceeds 0.8, it is considered that the trend shown by the sensor data requires special attention and possible adjustment.

[0077] In this embodiment, the first trend analysis coefficient refers to the first group of coefficients that meet or exceed the preset threshold value, and among all trend analysis coefficients, the first coefficient that exceeds the preset threshold value is the trend analysis coefficient of the furnace wall temperature sensor;

[0078] In this embodiment, the first sensor data refers to the data of a specific sensor related to the first trend analysis coefficient. If the trend analysis coefficient of the furnace wall temperature sensor is the first to exceed the threshold, then the sensor data related thereto is the first sensor data, which will be used for further analysis and decision-making.

[0079] The beneficial effect of the above technical solution is: by accurately analyzing the boiler sensor data, the boiler combustion parameters are optimized to improve efficiency and safety. Specifically, the method includes trend analysis of various types of sensor data, and respectively calculating the long-term trend coefficient and the short-term fluctuation coefficient. The trend analysis coefficient is synthesized based on these coefficients, and the key sensor data, i.e. the sensor data corresponding to the first trend analysis coefficient, is screened out using a preset trend coefficient threshold, which can accurately capture the key changes that affect the boiler performance, thereby making the boiler operation more efficient and more responsive, while also reducing energy consumption and extending the service life of the equipment.

[0080] Example 3

[0081] An embodiment of the present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, characterized in that the combustion parameters of the thermal power plant boiler are determined based on all first sensor data, including:

[0082] Performing category analysis on all first sensor data, obtaining a plurality of sensor data categories of the first sensor data, and classifying the first sensor data into categories;

[0083] Determine the combustion parameters of the thermal power plant boiler based on all categories of first sensor data:

[0084]

[0085] Among them, FAR is the combustion parameter of the thermal power plant boiler, y j is the jth air-related sensor data, M is the number of air-related sensor data, a j is the preset weight corresponding to the jth air-related sensor data, d j is the preset initial data offset coefficient of the jth air-related sensor data, c j is the preset weight corresponding to the preset initial offset coefficient of the jth air-related sensor data, x i is the i-th fuel-related sensor data, k i is the preset weight corresponding to the i-th fuel-related sensor data, N is the number of fuel-related sensor data, q ij is the cross-term weight of the i-th fuel-related sensor data and the j-th fuel-related sensor data, r is the preset bias constant,

[0086] Where λ is the excess air index:

[0087]

[0088] Among them, k T is the preset correction factor for the air-related sensor data, kf is the preset correction factor for the fuel-related sensor data, T is the furnace temperature, T ref is the preset reference temperature, HV fuel is the calorific value of the fuel, ∝ΔT is the conversion coefficient corresponding to the difference between the furnace temperature and the preset reference temperature, ∝HV fuel is the conversion coefficient corresponding to the calorific value of the fuel, O 实测 is the oxygen percentage measured in the furnace, O 理论 is the oxygen percentage required for theoretical combustion, and 21 is the theoretical content of oxygen in the preset air.

[0089] In this embodiment, the preset initial data offset coefficient is a predefined value used to adjust the sensor data to reflect its initial system error or deviation. This helps to calibrate the sensor data and ensure the accuracy and reliability of the data. If a temperature sensor shows that it is 2°C higher than the actual temperature during calibration, its preset initial data offset coefficient may be set to -2 to correct this deviation;

[0090] In this embodiment, the preset bias constant is a fixed value added to the calculation model to adjust the final calculation result to ensure the accuracy and adaptability of the model output. This bias can be set based on historical data or empirical values. When calculating the combustion efficiency, if the model systematically underestimates the actual efficiency, a positive preset bias constant may be added to improve the calculated combustion efficiency value to be closer to the actual operating data.

[0091] In this embodiment, the preset correction coefficient is a coefficient used to adjust specific variables in the model to take into account uncertainties, changing operating conditions or other external influencing factors. These coefficients can be adjusted based on new research findings, technology updates or changes in actual operating experience. For example, when the ambient temperature has a significant impact on the efficiency of the boiler, different preset correction coefficients may be used to adjust the calculation of fuel consumption according to seasonal changes to ensure that the accuracy of the combustion parameters is not affected by seasonal changes.

[0092] The beneficial effects of the above technical solution are: through category analysis and weight calculation, the fusion of multiple sensor data is realized, and the accuracy and reliability of combustion parameters are improved. The calculation of the excess air index takes into account the air-related sensor data, fuel-related sensor data and cross-term weights, and realizes the detailed adjustment of the combustion process. At the same time, the use of preset correction coefficients, conversion coefficients and bias constants further improves the accuracy and stability of the calculation results. This method can effectively optimize the combustion parameters of thermal power plant boilers, improve efficiency, reduce emissions and extend equipment life.

[0093] Example 4

[0094] An embodiment of the present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler. The sensor data categories include: air-related sensor data and fuel-related sensor data.

[0095] In this embodiment, the air-related sensor data includes: oxygen concentration sensor data in the furnace, air flow sensor data in the furnace, and pressure sensor data in the furnace;

[0096] In this embodiment, the fuel-related sensor data includes: in-furnace fuel flow sensor data, in-furnace fuel temperature sensor data, and in-furnace fuel pressure sensor data.

[0097] The beneficial effect of the above technical solution is: by classifying and processing the sensor data related to air and fuel, the temperature inside the furnace of the thermal power plant boiler can be intelligently managed. This method can improve the accuracy of temperature control, optimize combustion efficiency, reduce energy consumption and pollution emissions, and help improve the safety and economy of boiler operation.

[0098] Example 5

[0099] An embodiment of the present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, which determines the combustion efficiency of the thermal power plant boiler based on combustion parameters, and then draws a combustion efficiency curve, including:

[0100]

[0101] Among them, FAR i is the combustion parameter of the thermal power plant boiler at the i-th moment, is the combustion efficiency of the thermal power plant boiler at the i-th moment, is the preset ideal maximum combustion efficiency at the i-th moment, AFR stoich is the preset theoretical complete combustion air-fuel ratio, δ(T i ,P i ) is the preset temperature and pressure related adjustment function at the i-th moment, E is the preset activation energy, which represents the energy barrier that the fuel needs to overcome for chemical reaction, R0 is the preset gas constant, T0 is the preset absolute temperature, α is the preset pressure adjustment coefficient, β is the preset adjustment coefficient corresponding to the pressure fluctuation, P i is the internal pressure of the boiler at the i-th moment, T i is the internal temperature of the boiler at the i-th moment, sin is a trigonometric function;

[0102] A combustion efficiency curve is drawn based on the boiler combustion efficiency at all times.

[0103] In this embodiment, sin(β·P i ) represents the effect of periodic changes caused by pressure fluctuations on combustion efficiency;

[0104] In this embodiment, the preset ideal maximum combustion efficiency is the maximum combustion efficiency that can be achieved under the most ideal conditions (such as the best air-fuel ratio, no external interference, etc.), and is a theoretically defined parameter used to measure the difference in actual combustion efficiency. For example, the preset ideal maximum combustion efficiency may be set to 98%, which means that under the most ideal conditions, the fuel can be converted into 98% of effective energy;

[0105] In this embodiment, the preset theoretical complete combustion air-fuel ratio is the mass ratio of theoretical air to fuel required for complete combustion of the fuel, also known as the stoichiometric air-fuel ratio. This ratio can theoretically enable the fuel to be completely burned without producing excess oxygen or fuel. For example, for natural gas, the preset theoretical complete combustion air-fuel ratio is about 17.2:1, which means that about 17.2 kg of air is required for each kg of natural gas to be completely burned;

[0106] In this embodiment, the preset activation energy refers to the minimum energy required for the fuel combustion reaction. For example, for the combustion of coal, the preset activation energy may be 150 kJ / mol, which means that at least 150 kJ of energy is required to initiate a chemical reaction when the coal is burned;

[0107] In this embodiment, the preset gas constant refers to the ideal gas constant of the gas. When calculating the combustion reaction related to temperature and pressure, this constant is often used for correction. The preset gas constant is usually 8.314 J / (mol·K). It is a constant in the ideal gas equation and is used to calculate the gas state inside the boiler.

[0108] In this embodiment, the preset absolute temperature is used to standardize the temperature calculation of the combustion process in the boiler. If the operating temperature of the boiler is 200°C, the preset absolute temperature is 473.15K (200°C+273.15);

[0109] In this embodiment, the preset pressure adjustment coefficient is a coefficient used to adjust the combustion efficiency according to the internal pressure of the boiler to compensate for the influence of the actual pressure on the combustion reaction. For example, in a high-pressure boiler, the preset pressure adjustment coefficient may be set to 1.05, indicating that the calculated combustion efficiency needs to be adjusted when the pressure is higher than the standard value;

[0110] In this embodiment, the preset adjustment coefficient corresponding to the pressure fluctuation is a coefficient used to adjust the change in combustion efficiency caused by the pressure fluctuation inside the boiler. The fluctuation of the internal pressure of the boiler will affect the stability of combustion, so corresponding adjustments need to be made. If the internal pressure fluctuation of the boiler is large, the preset adjustment coefficient corresponding to the pressure fluctuation may be set to 0.95 to reduce the impact of the fluctuation on the combustion efficiency;

[0111] The beneficial effects of the above technical solution are: the combustion efficiency of the thermal power plant boiler is accurately calculated through an intelligent management method, and a combustion efficiency curve is drawn based on the combustion parameters, which comprehensively considers various factors such as temperature, pressure, fuel quality, etc., and achieves fine control of the combustion process by adjusting functions and correction coefficients, which can effectively improve the combustion efficiency of the boiler and the economy of the overall operation.

[0112] Example 6

[0113] An embodiment of the present invention provides a method for intelligently managing the temperature in a furnace of a thermal power plant boiler, which analyzes a combustion efficiency curve and determines a comprehensive efficiency deviation coefficient based on the analysis result, including:

[0114] Analyze the combustion efficiency curve to determine the overall efficiency and optimal efficiency inside the boiler, and then determine the first efficiency deviation coefficient:

[0115]

[0116] Where R1 is the first efficiency deviation coefficient, C f is the preset fuel mass correction factor, C a is the excess air coefficient, C o is the preset correction factor for external factors, is the combustion efficiency of the thermal power plant boiler at the i-th moment, μ2 is the preset weight corresponding to the weighted combustion efficiency of the thermal power plant boiler, ω i is the time weight corresponding to the i-th moment, n is the preset time-weighted number of moments, k1 is the preset influence coefficient corresponding to the correction term, AUC is the overall efficiency inside the boiler, μ1 is the preset weight corresponding to the total combustion efficiency, η 最优 is the optimal efficiency, μ3 is the preset weight corresponding to the optimal efficiency, η 目标 The preset target combustion efficiency;

[0117] Performing an abnormality analysis on the combustion efficiency curve, and determining a second efficiency deviation coefficient based on the abnormality analysis result;

[0118] The comprehensive efficiency deviation coefficient is determined based on the first efficiency deviation coefficient and the second efficiency deviation coefficient:

[0119]

[0120] Where c is the comprehensive efficiency deviation coefficient, R2 is the second efficiency deviation coefficient, R0 is the preset deviation upper limit standard value, R t It is the preset deviation lower limit standard value.

[0121] In this embodiment, the first efficiency deviation coefficient is a coefficient used to measure the deviation between the actual combustion efficiency and the ideal combustion efficiency. It takes into account multiple factors such as fuel quality, air supply, external environment, etc., and is calculated through a formula. If the calculated first efficiency deviation coefficient is 1.2, it means that the actual combustion efficiency is 20% lower than expected, and adjustments need to be made to optimize the combustion process;

[0122] In this embodiment, the overall efficiency inside the boiler refers to the overall thermal energy conversion efficiency of the boiler within a given period of time. It reflects the comprehensive ability of the boiler to convert fuel energy into effective thermal energy. For example, in a specific operation cycle, if the overall efficiency inside the boiler is 85%, it means that 85% of the fuel energy in the cycle is effectively used and the remaining 15% is wasted;

[0123] In this embodiment, the optimal efficiency refers to the highest efficiency that the boiler can achieve under optimal operating conditions (such as optimal air-fuel ratio, optimal temperature and pressure). It is usually used as a benchmark to evaluate the deviation during actual operation. For example, the optimal efficiency of a boiler under optimal conditions is 95%, that is, under ideal conditions, the boiler can convert 95% of the fuel energy into heat energy.

[0124] In this embodiment, the abnormal analysis results are obtained by statistically analyzing the combustion efficiency curve to identify abnormal conditions (such as sudden efficiency drop or instability) that occur during operation. These results are used to further correct and optimize boiler operation. For example, through abnormal analysis, it is found that the boiler has a sharp drop in combustion efficiency in certain time periods. These abnormal conditions may be caused by changes in fuel quality or equipment failures.

[0125] In this embodiment, the comprehensive efficiency deviation coefficient is a comprehensive indicator calculated by combining the first efficiency deviation coefficient and the abnormal analysis result, and is used to evaluate the overall deviation between the actual operating efficiency of the boiler and the ideal state. If the comprehensive efficiency deviation coefficient is 1.5, it indicates that the overall operating efficiency deviation is large and major adjustments or maintenance may be required.

[0126] In this embodiment, the preset upper limit standard value of deviation is the maximum allowable value of the deviation coefficient preset by the system. Exceeding this value may trigger an alarm or an automatic adjustment program to ensure the safe and efficient operation of the boiler. If the preset upper limit standard value of deviation is set to 2.0, once the comprehensive efficiency deviation coefficient exceeds 2.0, the system will issue an alarm to prompt the operator to take measures;

[0127] In this embodiment, the preset deviation lower limit standard value is the minimum allowable value of the deviation coefficient preset by the system. A value lower than this value indicates that the boiler operation may be too conservative and there may be room for optimization. For example, if the preset deviation lower limit standard value is set to 0.8, when the comprehensive efficiency deviation coefficient is lower than 0.8, the system may recommend increasing the boiler operating parameters to improve efficiency.

[0128] The beneficial effects of the above technical solution are: by conducting an in-depth analysis of the combustion efficiency curve, the overall efficiency and optimal efficiency inside the boiler are determined, and the first efficiency deviation coefficient is calculated. Combined with the abnormal analysis results, the comprehensive efficiency deviation coefficient is further determined, which can accurately identify the efficiency deviation in the operation of the boiler, provide a basis for optimization and adjustment, improve the stability of the boiler operation and the overall combustion efficiency, reduce energy consumption and enhance economic benefits.

[0129] Example 7

[0130] An embodiment of the present invention provides a furnace temperature intelligent management method for a thermal power plant boiler, which determines a combustion adjustment coefficient based on a comprehensive efficiency deviation coefficient, and determines a furnace temperature intelligent management strategy in combination with an initial furnace temperature, including:

[0131] According to the comprehensive efficiency deviation coefficient, determine the required combustion adjustment amount:

[0132]

[0133] Among them, K 调整 is the combustion adjustment amount, K 初始 is the basic combustion parameter, ε1 is the direct influence coefficient of combustion efficiency deviation on the adjustment coefficient, c is the comprehensive efficiency deviation coefficient, ε2 is the temperature difference adjustment coefficient, ΔT is the difference between the actual temperature in the furnace and the target temperature, is the feedback adjustment coefficient, φ is the deviation influence coefficient, and c v is the deviation of the boiler combustion efficiency in the vth measurement cycle from the expected target, t v is the duration of the deviation in the ith cycle, H is the number of measurement cycles, θ is the influence coefficient of the improvement ratio, and τ is the efficiency improvement ratio caused by the previous adjustment;

[0134] Obtain the current temperature in the furnace from the sensor data, and analyze the temperature data to determine the current heat distribution state in the furnace;

[0135] The adjustment scheme is determined based on the combustion adjustment coefficient and the preset adjustment coefficient-adjustment scheme database, and the intelligent management strategy of the furnace temperature is determined in combination with the current heat distribution state in the furnace.

[0136] In this embodiment, the basic combustion parameter is the initial amount of air or fuel;

[0137] In this embodiment, the temperature difference adjustment coefficient is used to adjust the combustion adjustment caused by the temperature change in the furnace;

[0138] In this embodiment, the feedback adjustment coefficient is determined based on historical data and the adjustment effect of the previous cycle;

[0139] In this embodiment, the deviation influence coefficient is used to adjust the weight of the deviation on the feedback coefficient;

[0140] In this embodiment, the impact coefficient of the improvement ratio is a measure of the success rate of the adjustment measures of the previous cycle relative to the efficiency target. By calculating the product of the efficiency deviation and its duration in each cycle and averaging them, a weighted deviation can be obtained, which reflects the size of the deviation and the persistence of its impact. Consider the actual improvement of the boiler combustion efficiency after the adjustment of the previous cycle. If the last adjustment significantly improves the efficiency, the improvement ratio will increase positively, thereby increasing the feedback coefficient, and vice versa.

[0141] In this embodiment, the heat distribution state is used to determine whether the temperature is uniform and whether there is local overheating or overcooling.

[0142] The beneficial effects of the above technical solution are: accurately calculating the combustion adjustment amount through the comprehensive efficiency deviation coefficient, and formulating an intelligent management strategy in combination with the temperature and heat distribution state in the furnace, effectively optimizing the combustion process, improving the operating efficiency of the boiler, ensuring the stability of the temperature in the furnace, reducing energy consumption and reducing pollution emissions, thereby enhancing the overall economic benefits and environmental performance of the thermal power plant boiler.

[0143] Example 8

[0144] An embodiment of the present invention provides an intelligent management system for the temperature in a furnace of a thermal power plant boiler, comprising:

[0145] Parameter acquisition module: install several preset types of sensors at key locations in the boiler of a thermal power plant, analyze the sensor data, and determine the combustion parameters of the thermal power plant boiler based on the analysis results;

[0146] Curve drawing module: determines the combustion efficiency of the thermal power plant boiler based on the combustion parameters, and then draws the combustion efficiency curve;

[0147] Coefficient determination module: Analyze the combustion efficiency curve and determine the comprehensive efficiency deviation coefficient based on the analysis results;

[0148] Strategy determination module: Determines the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determines the furnace temperature intelligent management strategy based on the initial furnace temperature.

[0149] The beneficial effects of the above technical solution are: by installing sensors to monitor the furnace temperature and combustion parameters in real time, the combustion efficiency curve is drawn, and the combustion adjustment coefficient is determined based on the comprehensive efficiency deviation coefficient. An intelligent management strategy is formulated in combination with the initial furnace temperature, thereby optimizing boiler operation and improving combustion efficiency and temperature control accuracy.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent management of furnace temperature in a thermal power plant boiler, characterized in that: include: Step 1: Install several preset types of sensors at key locations in the boiler of a thermal power plant, analyze the sensor data, and determine the combustion parameters of the thermal power plant boiler based on the analysis results; Step 2: Determine the combustion efficiency of the thermal power plant boiler based on the combustion parameters, and then draw a combustion efficiency curve; Step 3: Analyze the combustion efficiency curve and determine the comprehensive efficiency deviation coefficient based on the analysis results; Step 4: Determine the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determine the furnace temperature intelligent management strategy based on the initial furnace temperature.

2. The method for intelligent management of furnace temperature in a thermal power plant boiler according to claim 1, characterized in that: Analyze the sensor data and determine the combustion parameters of the thermal power plant boiler based on the analysis results, including: Perform trend analysis on sensor data to obtain long-term trend coefficients and short-term fluctuation coefficients for each type of sensor data; Determine the trend analysis coefficient of each type of sensor data based on the long-term trend coefficient and the short-term fluctuation coefficient of each type of sensor data, and screen each type of sensor data in combination with a preset trend coefficient threshold, obtain a plurality of first trend analysis coefficients that reach the preset trend coefficient threshold, and then determine the first sensor data corresponding to each first trend analysis coefficient; Combustion parameters of the thermal power plant boiler are determined based on all first sensor data.

3. The method for intelligent management of furnace temperature in a thermal power plant boiler according to claim 2, characterized in that: The combustion parameters of the thermal power plant boiler are determined based on all the first sensor data, including: Performing category analysis on all first sensor data, obtaining a plurality of sensor data categories of the first sensor data, and classifying the first sensor data into categories; Determine the combustion parameters of the thermal power plant boiler based on all categories of first sensor data: Among them, FAR is the combustion parameter of the thermal power plant boiler, y j is the jth air-related sensor data, M is the number of air-related sensor data, a j is the preset weight corresponding to the jth air-related sensor data, d j is the preset initial data offset coefficient of the jth air-related sensor data, c j is the preset weight corresponding to the preset initial offset coefficient of the jth air-related sensor data, x i is the i-th fuel-related sensor data, k i is the preset weight corresponding to the i-th fuel-related sensor data, N is the number of fuel-related sensor data, q ij is the cross-term weight of the i-th fuel-related sensor data and the j-th fuel-related sensor data, r is the preset bias constant, Where λ is the excess air index: Among them, k T is the preset correction factor for the air-related sensor data, k f is the preset correction factor for the fuel-related sensor data, T is the furnace temperature, T ref is the preset reference temperature, HV fuel is the calorific value of the fuel, ∝ΔT is the conversion coefficient corresponding to the difference between the furnace temperature and the preset reference temperature, ∝HV fueI is the conversion coefficient corresponding to the calorific value of the fuel, O 实测 is the oxygen percentage measured in the furnace, O 理论 is the oxygen percentage required for theoretical combustion, and 21 is the theoretical content of oxygen in the preset air.

4. The method for intelligent management of furnace temperature in a thermal power plant boiler according to claim 3, characterized in that: The sensor data category includes: air-related sensor data and fuel-related sensor data.

5. The method for intelligent management of furnace temperature in a thermal power plant boiler according to claim 1, characterized in that: The combustion efficiency of the thermal power plant boiler is determined based on the combustion parameters, and then the combustion efficiency curve is drawn, including: Among them, FAR i is the combustion parameter of the thermal power plant boiler at the i-th moment, is the combustion efficiency of the thermal power plant boiler at the i-th moment, is the preset ideal maximum combustion efficiency at the i-th moment, AFR stoich is the preset theoretical complete combustion air-fuel ratio, δ(T i ,P i ) is the preset temperature and pressure related adjustment function at the i-th moment, E is the preset activation energy, which represents the energy barrier that the fuel needs to overcome for chemical reaction, R0 is the preset gas constant, T0 is the preset absolute temperature, α is the preset pressure adjustment coefficient, β is the preset adjustment coefficient corresponding to the pressure fluctuation, P i is the internal pressure of the boiler at the i-th moment, T i is the internal temperature of the boiler at the i-th moment, sin is a trigonometric function; A combustion efficiency curve is drawn based on the boiler combustion efficiency at all times.

6. The method for intelligent management of furnace temperature in a thermal power plant boiler according to claim 1, characterized in that: Analyze the combustion efficiency curve and determine the comprehensive efficiency deviation coefficient based on the analysis results, including: Analyze the combustion efficiency curve to determine the overall efficiency and optimal efficiency inside the boiler, and then determine the first efficiency deviation coefficient: Where R1 is the first efficiency deviation coefficient, C f is the preset fuel mass correction factor, C a is the excess air coefficient, C o is the preset correction factor for external factors, is the combustion efficiency of the thermal power plant boiler at the i-th moment, μ2 is the preset weight corresponding to the weighted combustion efficiency of the thermal power plant boiler, ω i is the time weight corresponding to the i-th moment, n is the preset time-weighted number of moments, k1 is the preset influence coefficient corresponding to the correction term, AUC is the overall efficiency inside the boiler, μ1 is the preset weight corresponding to the total combustion efficiency, η 最优 is the optimal efficiency, μ3 is the preset weight corresponding to the optimal efficiency, η 目标 The preset target combustion efficiency; Performing an abnormality analysis on the combustion efficiency curve, and determining a second efficiency deviation coefficient based on the abnormality analysis result; The comprehensive efficiency deviation coefficient is determined based on the first efficiency deviation coefficient and the second efficiency deviation coefficient: Where c is the comprehensive efficiency deviation coefficient, R2 is the second efficiency deviation coefficient, R0 is the preset deviation upper limit standard value, R t It is the preset deviation lower limit standard value.

7. The method for intelligent management of furnace temperature in a thermal power plant boiler according to claim 1, characterized in that: The combustion adjustment coefficient is determined based on the comprehensive efficiency deviation coefficient, and the intelligent management strategy of the furnace temperature is determined in combination with the initial furnace temperature, including: According to the comprehensive efficiency deviation coefficient, determine the required combustion adjustment amount: Among them, K 调整 is the combustion adjustment amount, K 初始 is the basic combustion parameter, ε1 is the direct influence coefficient of combustion efficiency deviation on the adjustment coefficient, c is the comprehensive efficiency deviation coefficient, ε2 is the temperature difference adjustment coefficient, ΔT is the difference between the actual temperature in the furnace and the target temperature, and is the feedback adjustment coefficient. is the deviation influence coefficient, c v is the deviation of the boiler combustion efficiency in the vth measurement cycle from the expected target, t v is the duration of the deviation in the ith cycle, H is the number of measurement cycles, θ is the influence coefficient of the improvement ratio, and τ is the efficiency improvement ratio caused by the previous adjustment; Obtain the current temperature in the furnace from the sensor data, and analyze the temperature data to determine the current heat distribution state in the furnace; The adjustment scheme is determined based on the combustion adjustment coefficient and the preset adjustment coefficient-adjustment scheme database, and the intelligent management strategy of the furnace temperature is determined in combination with the current heat distribution state in the furnace.

8. An intelligent management system for the temperature in a furnace of a thermal power plant boiler, characterized in that: include: Parameter acquisition module: Several preset sensors are installed at key locations in the boiler of a thermal power plant, the sensor data is analyzed, and the combustion parameters of the thermal power plant boiler are determined based on the analysis results; Curve drawing module: determines the combustion efficiency of the thermal power plant boiler based on the combustion parameters, and then draws the combustion efficiency curve; Coefficient determination module: Analyze the combustion efficiency curve and determine the comprehensive efficiency deviation coefficient based on the analysis results; Strategy determination module: Determines the combustion adjustment coefficient based on the comprehensive efficiency deviation coefficient, and determines the furnace temperature intelligent management strategy based on the initial furnace temperature.

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