Boiler combustion optimization method

Abstract

The invention provides a boiler combustion optimization method. The boiler combustion optimization method comprises the steps that operation parameters of all key nodes in the combustion process are collected; determining an energy input item, an energy output item and a heat storage energy change item based on the collected operation parameters, establishing a load prediction model, and predicting a load change trend at a future preset moment; a fuel quantity optimization model based on the load change rate is established, the fuel conveying process is optimized, and the ideal fuel quantity is generated; an air quantity optimization model based on the flue gas oxygen content is established, the combustion process is optimized, and the ideal air quantity is generated; establishing a coal-water ratio optimization model based on the steam temperature, optimizing a steam generation process, and generating an ideal coal-water ratio; and controlling a corresponding execution mechanism to act according to an optimization result to realize optimization control of the combustion process of the boiler. By constructing the load prediction model and the combustion parameter optimization model, the dynamic optimization of the fuel quantity, the air quantity and the coal-water ratio is realized, the response speed of the boiler to the load change is improved, and the combustion efficiency and the steam quality are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of thermal power generation, and particularly to a method for optimizing boiler combustion. Background Art

[0002] In the early stage, the control of boilers in thermal power units mainly relied on simple manual operations and basic automatic regulation systems; operators adjusted parameters such as fuel quantity and air volume according to experience, and this method was difficult to accurately adapt to load changes, resulting in low and unstable combustion efficiency; with the development of automation technology, automatic control systems based on traditional control theories emerged, such as using conventional PID controllers to control the combustion process; however, these traditional control systems have many limitations; they are often based on fixed control parameters and have poor adaptability to the complex dynamic characteristics and changing operating conditions of boilers; when the load changes rapidly, due to the system lag, the combustion parameters cannot be adjusted in time, making it difficult for the boiler to quickly reach a new stable operating state; this not only reduces the combustion efficiency, increases fuel consumption, but also affects the steam quality and poses potential risks to the safe and stable operation of the unit;

[0003] In recent years, although some studies have been dedicated to introducing more advanced control algorithms, such as model predictive control (MPC), etc., many challenges still exist in practical applications; on the one hand, constructing an accurate mathematical model often requires a large amount of experimental data and a complex modeling process, and the accuracy and generality of the model still need to be improved; on the other hand, when these algorithms are used to process the boiler combustion process with high real-time requirements and strong multi-variable coupling, the computational complexity is too high, and it is difficult to achieve fast and effective control under the existing hardware conditions;

[0004] Therefore, there is an urgent need in this field for a method for optimizing boiler combustion to solve the above problems. Summary of the Invention

[0005] The present invention provides a method for optimizing boiler combustion, aiming to solve the problems existing in the above-mentioned prior art. By constructing a load prediction model and a combustion parameter optimization model, it realizes the dynamic optimization adjustment of fuel quantity, air volume, and coal-water ratio, improves the response speed of the boiler to load changes, enhances the combustion efficiency and steam quality, and ensures the efficient, stable, and environmental-friendly operation of the thermal power unit.

[0006] The present invention provides a method for optimizing boiler combustion, including:

[0007] Step 1: Real-time collect the operating parameters of each key node during the combustion process; the key nodes include fuel pipeline nodes, air pipeline nodes, steam pipeline nodes, and flue gas pipeline nodes;

[0008] Step 2: Determine the energy input term, energy output term, and change term of heat storage energy based on the collected operating parameters of each node; establish a load prediction model based on each term to predict the load change trend at a preset future time;

[0009] Step 3: Establish a fuel quantity optimization model based on the load change rate, optimize the fuel delivery process in combination with the load prediction result, and generate the ideal fuel quantity;

[0010] Step 4: Establish an air quantity optimization model based on the oxygen content in the flue gas, optimize the combustion process in combination with the ideal fuel quantity, and generate the ideal air quantity;

[0011] Step 5: Establish a coal-water ratio optimization model based on the steam temperature, optimize the steam generation process in combination with the ideal fuel quantity, and generate the ideal coal-water ratio;

[0012] Step 6: Control the actions of the corresponding actuators according to the optimized ideal fuel quantity, ideal air quantity, and ideal coal-water ratio to achieve the optimized control of the boiler combustion process.

[0013] According to a boiler combustion optimization method provided by the present invention, in Step 1, the operating parameters of each key node include:

[0014] The fuel pipeline node parameters include the mass flow rate of the fuel and the lower calorific value of the fuel;

[0015] The air pipeline node parameters include the volume flow rate of the air, the air density, the specific heat capacity of the air, and the air temperature;

[0016] The steam pipeline node parameters include the mass flow rate of the steam, the steam density, the steam enthalpy value, the steam temperature, and the mass flow rate of the feed water;

[0017] The flue gas pipeline node parameters include the volume flow rate of the flue gas, the oxygen content in the flue gas, the flue gas temperature, the average specific heat capacity of the flue gas, and the flue gas density.

[0018] According to a boiler combustion optimization method provided by the present invention, in Step 2, the energy input term is:

[0019]

[0020] Among them, Q in (u) is the energy input term, representing the total heat released by fuel combustion during the time period [t - τ, t]; t is the current time, τ is the preset time interval; Q f (u) is the mass flow rate of the fuel, q net (u) is the lower calorific value of the fuel, Q a (u) is the volume flow rate of the air, ρ a is the air density, C P,a is the specific heat capacity of the air, Ta (u) is the air temperature and u is the time index.

[0021] According to a boiler combustion optimization method provided by the present invention, in step two, the energy output term is:

[0022]

[0023] Where Q out (u) is the energy output term, representing the energy carried out by steam in the time period [t - τ, t]; Q s (u) is the mass flow rate of steam, ρ s (u) is the steam density, h s (u) is the steam enthalpy value.

[0024] According to a boiler combustion optimization method provided by the present invention, in step two, the heat storage energy change term is:

[0025] ΔE b (t) = C b *ΔT b (t)

[0026] Where ΔE b (t) is the heat storage energy change term, representing the change in the heat storage energy of the boiler at time t; C b is the heat capacity of the boiler metal structure, which is calculated and determined according to the material, mass and specific heat capacity of the boiler; ΔT b (t) is the temperature change of the boiler metal structure at time t relative to time t - τ, which is calculated from the measured values of the temperature sensors arranged on the boiler.

[0027] According to a boiler combustion optimization method provided by the present invention, in step three, the fuel quantity optimization model is:

[0028]

[0029] Where P(t + Δt) is the load prediction result, representing the load value at the future time t + Δt; Δt is the predicted time interval.

[0030] According to a boiler combustion optimization method provided by the present invention, in step three, the fuel quantity optimization model is:

[0031]

[0032] [[ID=S57]]Where Q <s f,opt (t) is the ideal fuel quantity, P(t + Δt) is the load prediction result, and q net (u) is the lower calorific value of the fuel;

[0033] β(t) is a correction coefficient related to the load change rate and steam pipeline node parameters;

[0034] When then

[0035] When then

[0036] Among them, the values of the weights α1 and α2 are determined according to the thermal response characteristics of the boiler during load increase and load decrease;

[0037] η b (t) is the boiler thermal efficiency at time t;

[0038]

[0039] Among them, Q g (t) is the volume flow rate of the flue gas, ρ g is the flue gas density, C P,g is the specific heat capacity of the flue gas, T g (t) is the flue gas temperature.

[0040] According to a boiler combustion optimization method provided by the present invention, in step four, the air quantity optimization model is:

[0041]

[0042] Among them, Q a,opt (t) is the ideal air quantity, Q f,opt (t) is the ideal fuel quantity, γ (t) is a coefficient related to the fuel type and combustion condition, and the value of the coefficient γ (t) is calculated based on the elemental analysis of the fuel and the combustion reaction equation; O2(t) is the oxygen content in the flue gas, O 2,set is the preset standard value of the oxygen content in the flue gas at the flue gas pipeline node;

[0043] δ (t) is a coefficient related to the deviation of the flue gas oxygen content;

[0044] When O2(t) > O 2,set then

[0045] When O2(t) < O 2,set then

[0046] Among them, the values of the weights ∈1 and ∈2 are determined according to the requirements of combustion stability and economy.

[0047] According to a boiler combustion optimization method provided by the present invention, in step five, the coal-water ratio optimization model is as follows:

[0048]

[0049] Wherein, R opt (t) is the ideal coal-water ratio, Q f,opt (t) is the ideal fuel quantity; M c (t) is the fixed carbon content of the fuel, M v (t) is the volatile content of the fuel, both determined based on a preset experimental process; Q w (t) is the mass flow rate of feed water at the steam pipeline node; T s (t) is the steam temperature, T s,set is the preset standard value of the steam temperature at the steam pipeline node; λ is a coefficient related to the steam temperature deviation, determined according to the unit characteristics and operation experience.

[0050] According to a boiler combustion optimization method provided by the present invention, it further includes: generating a feedback signal after the actuator completes the adjustment action, and repeating steps one to six based on the feedback signal.

[0051] Compared with the prior art, the beneficial effects of the present application are as follows:

[0052] Through the fuel quantity optimization model constructed based on the load change rate and combined with the accurate load prediction results, the present application can adjust the fuel delivery amount in real time to accurately match the actual load demand; at the same time, considering factors such as boiler thermal efficiency, it can maintain a high thermal efficiency while ensuring load supply, reduce the ineffective consumption of fuel, and thus reduce the power generation cost;

[0053] The air quantity optimization model based on the flue gas oxygen content of the present application ensures the best ratio of air quantity to fuel quantity; the appropriate air quantity can make the fuel burn fully and reduce the generation of incomplete combustion products (such as carbon monoxide, etc.); it avoids heat loss caused by excessive air (such as too much heat carried away by flue gas), improves the energy utilization rate of the fuel, and further enhances the combustion efficiency; by dynamically adjusting the coal-water ratio, it can stabilize the steam generation process and ensure that parameters such as steam pressure and temperature meet the requirements;

[0054] The load prediction model of the present application comprehensively considers various factors such as energy input, output, and the change of thermal storage energy, and can accurately predict the load change trend at a future preset time; based on this prediction, it timely optimizes the fuel quantity, air quantity, and coal-water ratio, enabling the boiler combustion system to quickly respond to load changes, reducing system instability caused by load fluctuations; effectively shortening the time interval from load change to combustion parameter adjustment, reducing the adverse effects of hysteresis on unit operation, and improving the flexibility and adjustability of the unit.

[0055] Other features and advantages of the present invention will be described in the following specification, and part of them will become obvious from the specification or be understood by implementing the present invention. The objectives and other advantages of the present invention can be achieved and obtained through the structures specifically pointed out in the written specification and the accompanying drawings.

[0056] The technical solutions of the present invention will be further described in detail below through the accompanying drawings and embodiments. Description of the Drawings

[0057] The accompanying drawings are used to provide a further understanding of the present invention, and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation to the present invention. In the accompanying drawings:

[0058] Figure 1 is a schematic flow chart of a boiler combustion optimization method provided by an embodiment of the present invention. Detailed Embodiments

[0059] The following describes the preferred embodiments of the present invention with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention and are not used to limit the present invention.

[0060] Embodiment 1:

[0061] An embodiment of the present invention provides a boiler combustion optimization method. Please refer to Figure 1 , including:

[0062] Step 1: Collect the operating parameters of each key node in real time during the combustion process; the key nodes include fuel pipeline nodes, air pipeline nodes, steam pipeline nodes, and flue gas pipeline nodes;

[0063] Step 2: Determine the energy input item, energy output item, and change item of the heat storage energy based on the collected operating parameters of each node; establish a load prediction model based on each item to predict the load change trend at a future preset time;

[0064] Step 3: Establish a fuel quantity optimization model based on the load change rate, and optimize the fuel delivery process in combination with the load prediction result to generate an ideal fuel quantity;

[0065] Step 4: Establish an air quantity optimization model based on the oxygen content in the flue gas, and optimize the combustion process in combination with the ideal fuel quantity to generate an ideal air quantity;

[0066] Step 5: Establish a coal-water ratio optimization model based on the steam temperature, and optimize the steam generation process in combination with the ideal fuel quantity to generate an ideal coal-water ratio;

[0067] Step 6: Control the corresponding actuator actions according to the optimized ideal fuel quantity, ideal air quantity, and ideal coal-water ratio to achieve optimized control of the boiler combustion process.

[0068] The principle and beneficial effects of this embodiment are as follows: By precisely controlling the ratios of fuel, air, and water, the combustion process can be made more complete and efficient, thereby improving energy utilization efficiency; optimizing the combustion conditions can reduce the emissions of pollutants such as SOx and NOx, which is beneficial to environmental protection; the optimized system can utilize energy more effectively, reduce unnecessary energy consumption, and lower operating costs; by predicting and responding to future loads, the system can better cope with load fluctuations and maintain stable operation; at the same time, optimizing the operation reduces the excessive work and wear of equipment, which helps to extend the service life of the boiler and related equipment.

[0069] To further optimize the above embodiment, in Step 1, the operating parameters of each key node include:

[0070] The fuel pipeline node parameters include the mass flow rate of the fuel and the lower calorific value of the fuel;

[0071] The air pipeline node parameters include the volume flow rate of air, air density, specific heat capacity of air, and air temperature;

[0072] The steam pipeline node parameters include the mass flow rate of steam, steam density, steam enthalpy value, steam temperature, and the mass flow rate of feed water;

[0073] The flue gas pipeline node parameters include the volume flow rate of flue gas, oxygen content in flue gas, flue gas temperature, average specific heat capacity of flue gas, and flue gas density.

[0074] It should be noted that a Coriolis mass flowmeter is used to measure the mass flow rate of the fuel; its working principle is based on the Coriolis force. When the fluid flows through the vibrating tube, a Coriolis force proportional to the mass flow rate will be generated. The mass flow rate is determined by measuring the phase difference or frequency change of the vibrating tube caused by this force; this type of flowmeter has high accuracy, can directly measure the mass flow rate, and is not affected by changes in parameters such as fluid temperature, pressure, and viscosity, and is suitable for the flow measurement of various fuels (such as coal slurry, fuel oil, etc.);

[0075] The installation position should be as close as possible to the inlet of the fuel entering the boiler burner to ensure that the measured fuel flow rate is the one about to participate in combustion, and reduce the interference of pipeline resistance and other factors on the measurement results;

[0076] An on-line calorific value analyzer can be used to measure the net calorific value of fuels in real time. It usually adopts the combustion heat method, where a certain amount of fuel is completely burned under specific conditions, and the heat released during the combustion process is measured. For solid fuels (such as coal), the fuel sample can be sent into the analyzer for detection through a sampling device. For liquid or gaseous fuels, they can be directly introduced into the analyzer for combustion analysis.

[0077] The analyzer should be installed in the stable section of the fuel supply system to ensure that the fuel sample is representative, and it needs to be calibrated and maintained regularly to ensure the accuracy of the measured calorific value.

[0078] A vortex shedding flowmeter is used to measure the volumetric flow rate of air. Its principle is that when air flows through the vortex shedding body, alternating vortices will be generated behind it. The frequency of the vortices is proportional to the air flow velocity, and the volumetric flow rate is calculated by measuring the vortex frequency. At the same time, a temperature sensor (such as a platinum resistance thermometer) and a pressure sensor (such as a capacitive pressure sensor) are installed on the air pipeline to measure the temperature and pressure of the air respectively.

[0079] According to the ideal gas state equation, the volumetric flow rate is compensated and calculated using the measured temperature and pressure values to obtain the volumetric flow rate under standard conditions. Then, combined with the air density, the mass flow rate of air is calculated to meet the subsequent calculation requirements.

[0080] The installation position of the sensor should be selected in a straight pipe section with a stable air flow field, no vortices and turbulence, and it is necessary to ensure that the measuring probe of the sensor can accurately sense the air flow parameters and avoid being affected by factors such as the roughness of the pipeline wall and local resistance.

[0081] The specific heat capacity of air can be calculated based on the composition of air (mainly the ratio of oxygen and nitrogen) and temperature. Generally, within a certain temperature range, empirical formulas can be used to calculate the specific heat capacity. However, to improve the accuracy, the calculation model can be calibrated regularly using standard gas samples.

[0082] During the calibration process, standard gases with known specific heat capacities are passed through the measurement system at different flow rates and temperatures, and the differences between the measured parameters and the standard values are compared. The coefficients in the specific heat capacity calculation model are adjusted to make the calculation results more accurately reflect the specific heat capacity characteristics of the actual air.

[0083] Differential pressure flowmeters (such as orifice plate flowmeters or venturi flowmeters) are used to measure the mass flow rate of steam. Based on Bernoulli's equation, the flow rate is calculated by measuring the pressure difference generated when steam flows through the throttling device. At the same time, a steam densitometer (such as a vibrating densitometer) is installed on the steam pipeline to measure the density of steam in real time.

[0084] The installation of flow meters and density meters should follow relevant standards and specifications to ensure measurement accuracy. For example, sufficient straight pipe lengths should be ensured upstream and downstream of differential pressure flow meters to form a stable flow field. The density meter should be installed at a location with relatively stable temperature and pressure to avoid affecting the measurement results due to local temperature changes or pressure fluctuations.

[0085] The enthalpy value of steam can be calculated based on the pressure and temperature of steam through a pre-established enthalpy calculation model. This model is constructed based on the thermodynamic properties of water and water vapor, using polynomial fitting or other mathematical methods. At the same time, to verify the accuracy of the calculation results, a standard steam table (such as the IAPWS-IF97 International Standard Table of the Properties of Water and Steam) can be regularly referred to.

[0086] During actual operation, the measured steam pressure and temperature are substituted into the calculation model to obtain the enthalpy value, and then it is compared with the enthalpy value corresponding to the pressure and temperature in the steam table. If the deviation exceeds the allowable range, the calculation model is corrected. For example, the polynomial coefficients are adjusted or a more accurate calculation method is adopted to ensure the accuracy of enthalpy calculation, thereby providing a reliable basis for subsequent energy calculation and control.

[0087] The mass flow rate of feed water can be measured using an electromagnetic flow meter or a turbine flow meter. An electromagnetic flow meter is suitable for measuring conductive liquids. Its principle is based on Faraday's law of electromagnetic induction. When a conductive liquid flows through a magnetic field, an induced electromotive force is generated, which is proportional to the liquid flow rate, thus measuring the flow rate. A turbine flow meter, on the other hand, uses the water flow to impact the turbine blades to make them rotate, and calculates the flow rate by measuring the rotation speed of the turbine.

[0088] A temperature sensor (such as a thermocouple thermometer) is installed on the feed water pipeline to measure the feed water temperature. The temperature sensor should be inserted deep enough into the pipeline to ensure that the real temperature of the water flow is measured, and protective measures for the sensor should be taken to prevent factors such as scaling and corrosion from affecting the measurement accuracy.

[0089] A thermal mass flow meter is used to measure the volume flow rate of flue gas. Its working principle is based on the heat exchange principle between the fluid and the heating element. When the flue gas flows through the heating element, it will carry away heat, causing the temperature of the heating element to drop. The mass flow rate of the flue gas is calculated by measuring the temperature change of the heating element or the power required to maintain its temperature, and then the volume flow rate is calculated in combination with the flue gas density. At the same time, a flue gas composition analyzer (such as an infrared flue gas analyzer or an electrochemical flue gas analyzer) is used to measure the composition of the flue gas such as oxygen content and carbon dioxide content, as well as the flue gas temperature.

[0090] For the measurement of the oxygen content in flue gas, an electrochemical flue gas analyzer utilizes the electrochemical properties of an oxygen sensor to determine the oxygen content by measuring the reaction current of oxygen molecules on the electrode; an infrared flue gas analyzer analyzes the flue gas components based on the absorption characteristics of specific wavelength infrared light by different gas components.

[0091] The sampling probes of the flowmeter and analyzer should be inserted into the center of the flue gas pipeline, and regular cleaning and maintenance should be carried out to prevent impurities such as dust and particulate matter in the flue gas from blocking the sampling channel or contaminating the sensor surface, affecting the measurement accuracy.

[0092] The average specific heat capacity of flue gas can be calculated based on the composition of flue gas (the main components include carbon dioxide, nitrogen, oxygen, water vapor, etc.) and temperature; first, determine the proportion of each component through a flue gas composition analyzer, and then calculate the average specific heat capacity of flue gas by using the weighted average method according to the specific heat capacity data of each component at different temperatures.

[0093] The flue gas density can be estimated based on the ideal gas state equation in combination with the flue gas composition, temperature, and pressure; in practical applications, an empirical estimation formula for flue gas density can be established according to the type of fuel burned in the boiler and the combustion conditions, and verified and corrected through actual measurement data to improve the accuracy of density estimation and provide reliable parameters for the energy calculation related to flue gas.

[0094] To further optimize the above embodiments, in step two, the energy input term is:

[0095]

[0096] Among them, Q in (u) is the energy input term, representing the total heat released by fuel combustion within the time period [t - τ, t]; t is the current time, τ is the preset time interval; Q f (u) is the mass flow rate of the fuel, q net (u) is the lower calorific value of the fuel, Q a (u) is the volume flow rate of the air, ρ a is the air density, C P,a is the specific heat capacity of the air, T a (u) is the air temperature, and u is the time index.

[0097] It should be noted that Q f (u) reflects the amount of fuel participating in combustion per unit time; the greater the fuel mass flow rate, the more heat is released during combustion under the same calorific value; q net(u) is the lower calorific value of the fuel, which is an inherent property of the fuel and represents the heat released when a unit mass of fuel is completely burned (deducting the heat released when the water vapor in the combustion products condenses into liquid water); multiplying the two gives the instantaneous heat released by fuel combustion at time u; integrating this value means accumulating the heat released by fuel combustion at each instant during this period, thus obtaining the total heat released by fuel combustion during this time period; this is because in the actual boiler combustion process, fuel is continuously supplied and burned, and integration can comprehensively consider the energy release situation of fuel combustion throughout the time period, rather than just focusing on the value at a single instant;

[0098] Q a (u)*ρ a *C P,a *T a (u) This term is used to calculate the heat brought in by the air; Q a (u) is the air volume flow rate at time u, which represents the amount of air entering the boiler per unit time; air density and air specific heat capacity are physical property parameters of air, which determine the heat that can be carried or released by a unit volume of air when its temperature changes; T a (u) is the air temperature at time u. The higher the air temperature, the greater its internal energy and the more heat it brings into the boiler;

[0099] The calculation of the heat brought in by the air is different from that of the heat released by fuel combustion because air mainly participates in the combustion process as an oxidizer, and its own heat is also part of the energy input during the combustion process; similarly, integrating it is to calculate the total heat brought in by the air during this time period; although this part of the heat does not directly generate high temperature like the heat released by fuel combustion, it has an important impact on the stability of the combustion process and energy transfer. For example, appropriate air temperature and flow rate contribute to the full combustion of fuel and the uniform distribution of heat;

[0100] The calculation of the entire energy input term is in the form of integration, comprehensively considering the heat released by fuel combustion and the heat brought in by the air during the time period; the value of τ determines the time range for considering energy input, which reflects the time characteristics of the boiler system in the energy transfer and heat storage processes; through such a calculation method, the total energy input situation entering the boiler system within a certain time range can be more accurately reflected, providing a reliable basis for the calculation of energy balance in the subsequent load prediction model, thereby more accurately predicting the load change trend at future moments because the load change is closely related to the energy input entering the boiler system; if only the energy input at a certain moment is considered, the dynamic changes and energy accumulation effects during the combustion process may be ignored, resulting in inaccurate load prediction and further affecting the effect of the entire boiler combustion optimization control.

[0101] To further optimize the above embodiments, in step two, the energy output term is:

[0102]

[0103] where Q out (u) is the energy output term, representing the energy carried out by steam during the time period [t - τ, t]; Q s (u) is the mass flow rate of steam, ρ s (u) is the steam density, h s (u) is the steam enthalpy value.

[0104] It should be noted that the purpose of integrating {Q s (u) * ρ s (u) * h s (u)} is to calculate the total energy carried out by steam during this time period; similar to the energy input term, through integration, the dynamic changes in the steam energy output throughout the time period can be comprehensively considered; during the operation of the boiler, steam is continuously generated and output, and the energy it carries out is the main part of the energy output of the boiler system; accurately calculating the energy output term is crucial for load prediction; because the load of the boiler actually refers to its ability to output energy to meet external demands (such as power generation, heating, etc.), and the energy carried out by steam is a direct manifestation of the load; if the energy output term cannot be accurately calculated, the current energy output state of the boiler cannot be accurately grasped, which will in turn affect the prediction accuracy of the load prediction model for future load change trends; for example, if the energy carried out by steam is underestimated, it may lead to the incorrect belief during load prediction that the boiler still has more energy available for output, thus unable to adjust the combustion parameters in a timely manner when the actual load demand increases, affecting the normal operation of the unit.

[0105] To further optimize the above embodiments, in step two, the change term of the heat storage energy is:

[0106] ΔE b (t) = C b * ΔT b (t)

[0107] where ΔE b (t) is the change term of the heat storage energy, representing the change amount of the heat storage energy of the boiler at time t; C b is the heat capacity of the boiler metal structure, which is calculated and determined according to the material, mass, and specific heat capacity of the boiler; ΔT b (t) is the temperature change amount of the boiler metal structure at time t relative to time t - τ, which is calculated from the measured values of the temperature sensors arranged on the boiler.

[0108] It should be noted that for the heat capacity of the boiler metal structure, the material of the boiler determines its specific heat capacity characteristics. Different metal materials (such as carbon steel, alloy steel, etc.) have different specific heat capacity values. These specific heat capacity data can be obtained from material manuals or relevant standards. The heat capacity test experiment can be carried out during the non-operation period of the boiler. Arrange heating devices inside the boiler to uniformly heat the boiler metal structure, and at the same time measure the input heat and the temperature rise value of the boiler metal structure. According to the formula (where Q is the input heat and ΔT is the temperature rise value) to calculate the heat capacity.

[0109] For the temperature change amount, multiple temperature sensors can be arranged at key parts of the boiler (such as the furnace wall, superheater tube wall, economizer tube wall, etc.). These sensors should be evenly distributed to comprehensively and accurately monitor the temperature change of the boiler metal structure. For example, a circle of temperature sensors can be arranged on the furnace wall at a certain interval (such as every 1 m), and multiple layers can be arranged along the height direction of the furnace.

[0110] To further optimize the above embodiment, in step two, the load prediction model is:

[0111]

[0112] where P(t + Δt) is the load prediction result, representing the load value at the future time t + Δt; Δt is the prediction time interval.

[0113] It should be noted that the load prediction model formula comprehensively considers the energy input, output and heat storage conditions of the boiler system, and calculates the future load value through the energy balance principle; for example, if the energy input has been increasing continuously, the energy output is relatively stable, and the heat storage energy has also changed to a certain extent in the past period of time, then according to this trend, it can be predicted that the future load will rise, and the specific load value can be calculated through the formula.

[0114] To further optimize the above embodiment, in step three, the fuel quantity optimization model is:

[0115]

[0116] where Q f,opt (t) is the ideal fuel quantity, P(t + Δt) is the load prediction result, and q net (u) is the lower calorific value of the fuel;

[0117] β (t) is a correction coefficient related to the load change rate and the steam pipeline node parameters;

[0118] When ,

[0119] When time

[0120] wherein, the values of the weights α1 and α2 are determined according to the thermal response characteristics of the boiler during load increase and load decrease;

[0121] η b (t) is the boiler thermal efficiency at time t;

[0122]

[0123] wherein, Q g (t) is the volumetric flow rate of the flue gas, ρ g is the flue gas density, C P,g is the specific heat capacity of the flue gas, T g (t) is the flue gas temperature.

[0124] It should be noted that during the process of increasing the boiler load, it is necessary to increase the fuel quantity to meet the load growth demand. However, the increase in fuel quantity cannot be too fast, otherwise it will lead to problems such as incomplete combustion and excessive furnace pressure fluctuations, affecting the safe and stable operation of the boiler. At the same time, it cannot be too slow, otherwise it cannot keep up with the load growth in time, resulting in insufficient steam production.

[0125] In order to determine the value of α1, a series of load increase tests need to be carried out. During the tests, the fuel quantity is gradually increased, and various parameters of the boiler are closely monitored, such as steam pressure, steam temperature, oxygen content in the flue gas, furnace temperature distribution, etc. When the fuel quantity increases, observe the rising rates of the steam pressure and temperature. If the steam pressure and temperature can rise smoothly and the combustion condition in the furnace is good (such as bright and stable flame, oxygen content in the flue gas within a reasonable range), it indicates that the increase rate of the fuel quantity is appropriate at this time.

[0126] Through multiple tests, the calculated values of α1 under stable operation at different load change rates are obtained, and then the average value is taken or appropriately adjusted according to actual operation experience to determine the final value range of α1. Generally speaking, if the thermal inertia of the boiler is large and the response to the increase in fuel quantity is relatively slow, the value of α1 will be relatively small, such as between 0.05 - 0.1; if the boiler is more sensitive to the change in fuel quantity, the value of α1 can be appropriately increased, such as between 0.1 - 0.15.

[0127] When the boiler reduces the load, the situation is different from that of increasing the load. When reducing the fuel quantity, if the speed is too fast, it may cause a sharp drop in the furnace temperature, resulting in unstable combustion and even flameout; if the speed is too slow, it will cause energy waste and reduce the operation efficiency; the principle of determining the value of α2 is the same as that of α1, and will not be described in detail.

[0128] To further optimize the above embodiments, in step four, the air volume optimization model is as follows:

[0129]

[0130] Wherein, Q a,opt (t) is the ideal air volume, Q f,opt (t) is the ideal fuel volume, γ (t) is a coefficient related to the fuel type and combustion conditions, and the value of the coefficient γ (t) is calculated based on the elemental analysis of the fuel and the combustion reaction equation; O2(t) is the oxygen content in the flue gas, and O 2,set is the preset standard value of the oxygen content in the flue gas at the flue gas pipeline node;

[0131] δ (t) is a coefficient related to the deviation of the flue gas oxygen content;

[0132] When O2(t) > O 2,set then,

[0133] When O2(t) < O 2,set then,

[0134] Wherein, the values of the weights ∈1 and ∈2 are determined according to the requirements of combustion stability and economy.

[0135] It should be noted that when the oxygen content in the flue gas O2(t) > O 2,set it indicates that the air volume is relatively excessive at this time. From the perspective of combustion stability, too much air volume may cause the flame to elongate and the gas flow disturbance in the furnace to intensify, thereby affecting the combustion stability, and phenomena such as flame flickering and flameout may occur.

[0136] To determine the value of ∈1, a series of combustion tests need to be carried out. In the test, gradually increase the air volume to make the oxygen content in the flue gas higher than the preset standard value, and observe the combustion condition. When it is found that the flame begins to show unstable signs (such as slight flickering), record the oxygen content in the flue gas, the air volume adjustment amount, and the corresponding fuel volume at this time, and inversely calculate the value of ∈1 through the model. Obtain the calculated values of ∈1 under different working conditions through multiple tests, and then comprehensively analyze to determine its value range. Generally speaking, in order to avoid the aggravation of combustion instability, the value of ∈1 cannot be too large, usually between 0.2 - 0.3. If the value of ∈1 is too large, it will cause the air volume adjustment to be too drastic, further destroying the combustion stability; if the value is too small, it may not be able to effectively correct the problem of excessive air, affecting the combustion efficiency.

[0137] The principle of obtaining the value of ∈2 is basically the same as that of ∈1. When obtaining the value of ∈2, while ensuring that it can effectively improve the incomplete combustion condition and increase the fuel utilization rate, the combustion stability should also be taken into account. Generally, it is between 0.3 and 0.4. This value range can balance the requirements of economy and combustion stability to a certain extent, enabling the boiler to reduce fuel waste and maintain stable operation during the operation process, thereby reducing the overall operation cost.

[0138] To further optimize the above embodiments, in step five, the coal-water ratio optimization model is:

[0139]

[0140] Among them, R opt (t) is the ideal coal-water ratio, Q f,opt (t) is the ideal fuel quantity; M c (t) is the fixed carbon content of the fuel, M v (t) is the volatile content of the fuel, both determined based on a preset experimental process; Q w (t) is the mass flow rate of feed water at the steam pipeline node; T s (t) is the steam temperature, T s,set is the preset standard value of the steam temperature at the steam pipeline node; λ is a coefficient related to the steam temperature deviation, determined according to the unit characteristics and operation experience.

[0141] It should be noted that by collecting the historical data during the long-term operation of the boiler, including the actual measured values of steam temperature under different loads and different coal-water ratios. Statistical analysis is performed on these data to observe the relationship between the steam temperature deviation and the adjustment of the coal-water ratio. For example, when the steam temperature deviation is large, analyze the amplitude and effect of the previous coal-water ratio adjustment, and judge according to experience how large the value of λ is required to make the steam temperature approach the preset standard value faster. If it is found that in the past, when the steam temperature deviation was 10 degrees Celsius, adjusting the coal-water ratio by 0.1 could quickly restore the temperature to normal, then the value of λ can be determined based on this empirical relationship.

[0142] Meanwhile, the operators have accumulated rich experience in daily operations and have an intuitive understanding of the operating characteristics of the boiler under different conditions. Based on the feedback from the operators on the change in steam temperature during the adjustment of the coal-water ratio, such as whether the steam temperature can reach the preset value smoothly after adjustment, and whether overshoot or undershoot occurs during the adjustment process, the value of λ is optimized. If the operators report that in a certain adjustment, the value of λ is too small, resulting in slow adjustment of the steam temperature and affecting the unit operation efficiency, then the value of λ can be appropriately increased next time, and the adjustment effect can be continuously observed. By summarizing experience, the range of λ values most suitable for this unit is determined. Generally speaking, for units with high requirements for operating stability and low tolerance for steam temperature fluctuations, the value of λ will be relatively small to achieve more precise control; while for units with a certain tolerance for steam temperature fluctuations and more emphasis on response speed, the value of λ can be appropriately increased.

[0143] To further optimize the above embodiments, it further includes: generating a feedback signal after the actuator completes the adjustment action, and repeating Steps 1 to 6 based on the feedback signal.

[0144] It should be noted that after the actuator (such as fuel regulating valve, air damper, feed pump, etc.) completes the adjustment action, new operating parameters are collected through sensors arranged at key parts of the boiler. These sensors include the sensors mentioned above for measuring parameters such as fuel flow, air flow, steam flow, temperature, and pressure. For example, after the fuel regulating valve adjusts the fuel quantity, the mass flowmeter on the fuel pipeline immediately measures the new fuel mass flow; after the air damper adjusts the air quantity, the vortex flowmeter and temperature and pressure sensors on the air pipeline measure the new air flow, temperature, and pressure. These sensors transmit the real-time measured data back to the control system.

[0145] The control system processes the collected feedback signals, including operations such as data filtering and validity judgment, to ensure the accuracy and reliability of the data. For example, digital filtering algorithms are used to remove noise interference in the measured data, and reasonable parameter upper and lower limits are set to judge whether the data is valid. For invalid data, methods such as interpolation or replacement with the previous valid data are used for processing.

[0146] Calculate the key performance indicators of the boiler based on the new operating parameters, such as combustion efficiency, steam quality parameters (pressure, temperature, humidity, etc.), and load satisfaction. The combustion efficiency can be calculated according to relevant formulas by measuring data such as fuel input heat and steam output heat; the steam quality parameters are directly obtained from the measured values of the sensors on the steam pipeline and compared with the preset standards; the load satisfaction is evaluated by the difference between the actual load and the target load.

[0147] Compare the calculated performance indicators with the preset target values or ideal ranges to determine whether the operating state of the current boiler meets the optimization requirements. For example, if the combustion efficiency is lower than the preset minimum efficiency requirement, or the steam temperature deviation exceeds the allowable range, or the deviation between the load and the target load is large, it indicates that the current adjustment effect is not ideal and further optimization is needed.

[0148] When the performance indicator evaluation result shows that further optimization is needed, the control system triggers the operations of repeating Steps 1 to 6. Specific triggering conditions can set different thresholds according to different performance indicators. For example, when the combustion efficiency is lower than the preset threshold, or the absolute value of the steam temperature deviation is greater than the preset threshold, or the load deviation exceeds the preset threshold of the rated load, a new round of optimization cycle is started.

[0149] At the same time, during the process of repeating the steps, the control system will pay more attention to the links or parameters where problems occurred before. For example, if the previous repeated optimization was triggered by poor steam temperature control, then in the new round of cycle, the acquisition frequency and accuracy of the steam pipeline node parameters will be increased, and relevant parameters such as the coal-water ratio will be adjusted more precisely to ensure that the steam temperature can be stabilized near the preset standard value, improve the overall operating performance of the boiler, and achieve the goal of continuous optimization. By continuously repeating this feedback optimization process, the boiler combustion control system can adapt to various working condition changes and always maintain an efficient and stable operating state.

[0150] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A boiler combustion optimization method, characterized in that, Including: Step 1: Collect the operating parameters of each key node during the combustion process in real time; the key nodes include fuel pipeline nodes, air pipeline nodes, steam pipeline nodes, and flue gas pipeline nodes. Step 2: Determine the energy input item, energy output item, and change item of heat storage energy based on the collected operating parameters of each node. Establish a load prediction model based on each item to predict the load change trend at a future preset time. Step 3: Establish a fuel quantity optimization model based on the load change rate, and optimize the fuel delivery process in combination with the load prediction result to generate the ideal fuel quantity. Step 4: Establish an air quantity optimization model based on the oxygen content in the flue gas, and optimize the combustion process in combination with the ideal fuel quantity to generate the ideal air quantity. Step 5: Establish a coal-water ratio optimization model based on the steam temperature, and optimize the steam generation process in combination with the ideal fuel quantity to generate the ideal coal-water ratio. Step 6: Control the actions of the corresponding actuators according to the optimized ideal fuel quantity, ideal air quantity, and ideal coal-water ratio to achieve the optimized control of the boiler combustion process.

2. The boiler combustion optimization method according to claim 1, wherein In Step 1, the operating parameters of each key node include: The fuel pipeline node parameters include the mass flow rate of the fuel and the lower calorific value of the fuel. The air pipeline node parameters include the volume flow rate of the air, air density, specific heat capacity of air, and air temperature. The steam pipeline node parameters include the mass flow rate of the steam, steam density, steam enthalpy value, steam temperature, and mass flow rate of the feed water. The flue gas pipeline node parameters include the volume flow rate of the flue gas, oxygen content in the flue gas, flue gas temperature, average specific heat capacity of the flue gas, and flue gas density.

3. The boiler combustion optimization method according to claim 2, wherein In Step 2, the energy input item is: Among them, Q in (u) is the energy input term, representing the total heat released by fuel combustion during the time period [t - τ, t]; t is the current time, τ is the preset time interval; Q f (u) is the mass flow rate of the fuel, q net (u) is the lower calorific value of the fuel, Q a (u) is the volume flow rate of the air, ρ a is the air density, C P,a is the specific heat capacity of the air, T a (u) is the air temperature, and u is the time index.

4. A boiler combustion optimization method according to claim 3, characterized in that, In Step 2, the energy output item is: Among them, Q out (u) is the energy output term, representing the energy carried out by the steam during the time period [t - τ, t]; Q s (u) is the mass flow rate of the steam, ρ s (u) is the steam density, h s (u) is the steam enthalpy value.

5. A boiler combustion optimization method according to claim 4, characterized in that, In Step 2, the change item of heat storage energy is: ΔE b (t) = C b *ΔT b (t) Among them, ΔE b (t) is the thermal energy storage change term, representing the change in the thermal energy storage of the boiler at time t; C b is the heat capacity of the boiler metal structure, which is determined by calculating based on the material, mass and specific heat capacity of the boiler; ΔT b (t) is the temperature change of the boiler metal structure at time t relative to time t-τ, which is calculated from the measured values of the temperature sensors arranged on the boiler.

6. A boiler combustion optimization method according to claim 5, characterized in that, In Step 2, the load prediction model is: Wherein, P(t + Δt) is the load prediction result, representing the load value at the future time t + Δt; Δt is the prediction time interval.

7. A boiler combustion optimization method according to claim 6, characterized in that, In Step 3, the fuel quantity optimization model is: Among them, Q f,opt (t) is the ideal fuel quantity, P(t + Δt) is the load prediction result, and q net (u) is the lower calorific value of the fuel; β (t) is a correction coefficient related to the load change rate and the steam pipeline node parameters; When , When , Wherein, the values of the weights α1 and α2 are determined according to the thermal response characteristics of the boiler during load increase and load decrease. η b (t) is the boiler thermal efficiency at time t; Among them, Q g (t) is the volumetric flow rate of the flue gas, ρ g is the flue gas density, C P,g is the specific heat capacity of the flue gas, T g (t) is the flue gas temperature.

8. A boiler combustion optimization method according to claim 7, characterized in that In Step 4, the air quantity optimization model is: Among them, Q a,opt (t) is the ideal air volume, Q f,opt (t) is the ideal fuel volume, γ (t) is a coefficient related to the fuel type and combustion conditions. The value of the coefficient γ (t) is obtained by calculating according to the elemental analysis of the fuel and the combustion reaction equation; O2(t) is the oxygen content in the flue gas, and O 2,set is the preset standard value of the oxygen content of the flue gas at the node of the flue gas pipeline; δ (t) is the coefficient related to the deviation of flue gas oxygen content; When O2(t) > O 2,set at this time When O2(t) <O 2,set hour, Wherein, the values of the weights ∈1 and ∈2 are determined according to the requirements of combustion stability and economy.

9. The boiler combustion optimization method according to claim 8, characterized in that, In Step 5, the coal-water ratio optimization model is: Among them, R opt (t) is the ideal coal-water ratio, Q f,opt (t) is the ideal fuel quantity; M c (t) is the fixed carbon content of the fuel, M v (t) is the volatile matter content of the fuel, which is determined based on a pre-set experimental process; Q w (t) is the mass flow rate of feed water at the steam pipe node; T s (t) is the steam temperature, T s,set is the preset standard value of the steam temperature at the steam pipeline node; λ is the coefficient related to the steam temperature deviation, which is determined according to the unit characteristics and operating experience.

10. A boiler combustion optimization method according to claim 9, characterized in that, It also includes: Generate a feedback signal after the complete adjustment action of the actuator, and repeat Steps 1 to 6 based on the feedback signal.

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