A method, system and medium for predicting high-temperature corrosion rate of a water-cooled wall of a pulverized coal boiler

Abstract

The present application relates to the field of thermal power generation, and particularly relates to a coal-fired boiler water wall high-temperature corrosion rate prediction method, system and medium. The method comprises: establishing a five-factor coupling mathematical model comprising temperature field distribution, flue gas atmosphere, ash deposition and coking, material science and mechanical factors; collecting wall temperature, ash thickness, H2S / O2 / HCl / CO concentration in flue gas, alkali metal / chlorine / sulfur content in ash, material chromium content, coating thickness, mechanical stress and thermal stress; based on the collected parameters, using exponential, power law, linear and exponential coordination, negative exponential and power function models to calculate five corrosion rate components; the five components are comprehensively calculated by a product coupling model to obtain the total corrosion rate; based on the total corrosion rate, the remaining life is evaluated and optimization suggestions are output. The present application solves the technical problems of incomplete factor coverage, ignoring coupling effect, inability to represent nonlinear corrosion law and insufficient prediction accuracy of traditional methods through multi-factor coupling modeling.

Description

Technical Field

[0001] This invention relates to the field of thermal power generation, and in particular to a method, system and medium for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers. Background Technology

[0002] As the core heating surface of pulverized coal boilers in thermal power plants, water-cooled walls are subjected to harsh conditions such as high-temperature flue gas scouring, corrosive atmosphere erosion, ash and coking accumulation, and complex stress coupling. High-temperature corrosion is frequent, easily leading to wall thinning, leakage, and even tube rupture, directly affecting the safe and stable operation and service life of the boiler. This is a common technical challenge that urgently needs to be overcome in the coal-fired power generation field. Current methods for predicting and assessing the high-temperature corrosion rate of water-cooled walls have significant limitations. Mainstream methods generally fail to comprehensively cover the core influencing factors of corrosion, focusing only on single or a few factors such as flue gas composition and wall temperature. They fail to systematically integrate the five major categories of key influencing factors: temperature field distribution, flue gas atmosphere, ash and coking accumulation, material properties, and mechanical stress. The coupling effects of mutual influence and interaction between these factors are completely ignored. For example, the amplification effect of ash thermal resistance on wall temperature, the weakening effect of stress state on material corrosion resistance, and the synergistic corrosion effect of various corrosive gases and ash chemical components are not effectively reflected. Meanwhile, existing prediction models mostly employ simple linear models or empirical formulas, failing to accurately describe the complex nonlinear laws governing corrosion reactions under the combined effects of multiple factors. This results in significant discrepancies between predicted results and actual field corrosion conditions, making it difficult to truly reflect the corrosion state of water-cooled walls. Furthermore, traditional methods cannot achieve precise quantitative calculations of corrosion rates under multi-factor coupling. They cannot comprehensively characterize the combined impact of various factors on the corrosion process, nor can they provide scientific and accurate numerical basis for optimizing water-cooled wall operation and corrosion prevention control. Consequently, their engineering application value and on-site guidance capabilities are severely lacking. Against this backdrop, how to construct a coupled mathematical model encompassing five core factors—temperature field, flue gas atmosphere, ash and coking, materials, and mechanics—to achieve multi-factor coupled quantitative prediction of high-temperature corrosion in water-cooled walls, accurately characterize the nonlinear interactions between various influencing factors, and fundamentally improve the accuracy and engineering practicality of corrosion rate prediction has become a core problem urgently needing to be solved in this field. Summary of the Invention

[0003] This invention provides a method, system, and medium for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers. The purpose is to solve the core technical problems of traditional methods, such as incomplete factor coverage, neglect of multi-factor coupling effects, inability to characterize nonlinear corrosion laws, and insufficient prediction accuracy.

[0004] To achieve the above objectives, the following technical solution is adopted.

[0005] A method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers includes the following steps: Step 1: Establish a five-factor coupled mathematical model for predicting the high-temperature corrosion rate of water-cooled walls. The five factors include temperature field distribution, flue gas atmosphere, ash accumulation and coking, materials science, and mechanical factors. Step 2: Collect the operating status parameters of the water-cooled wall, including the wall temperature T. w Ash accumulation thickness δ ash Concentrations of H2S, O2, HCl, and CO in flue gas; and alkali metal content (C) in ash. KNa Chlorine content C in ash Cl Sulfur content C in ash S Chromium content of materials C Cr Coating thickness t, mechanical stress σ mech and thermal stress σ thermal ; Step 3: Based on the operating status parameters collected in Step 2, calculate the five corrosion rate components according to the five-factor coupled mathematical model: calculate the temperature field corrosion component R1 using the exponential relationship model, calculate the flue gas atmosphere corrosion component R2 using the power law relationship model, calculate the ash and coking corrosion component R3 using the linear and exponential synergistic relationship model, calculate the material effect coefficient R4 using the negative exponential relationship model, and calculate the stress effect coefficient R5 using the power function relationship model. Step 4: Combine the five corrosion rate components obtained in Step 3 using a product coupling model to obtain the total predicted corrosion rate value R. total The product coupling model is R total =(R1+R2+R3)×R4×R5; Step 5: Based on the predicted total corrosion rate R obtained in Step 4 total The remaining life of the water-cooled wall is assessed, and optimized operation recommendations are provided.

[0006] Optionally, the temperature field corrosion component R1 mentioned in step 3 is calculated using an exponential relationship model, specifically according to the following formula: R1=A1×exp[-E a1 / (K×T w ]×exp(α×δ ash ); Where A1 is the pre-exponential factor, and its value ranges from 1 × 10⁻⁶. 5 Up to 1×10 7 E a1 The activation energy has a range of 8 × 10⁻⁶. 4 Up to 1.2×10 5 J / mol; K is Boltzmann constant; T wThe wall temperature is α, ranging from 773 to 1073 K; α is the dust accumulation influence coefficient, ranging from 50 to 150; δ ash The thickness of the ash accumulation ranges from 0 to 0.01 m; exp(α×δ) in the formula ash The term is used to characterize the amplification effect of the thermal resistance of ash accumulation on the wall temperature. This amplification effect accelerates the corrosion reaction by increasing the effective value of the wall temperature.

[0007] Optionally, the flue gas atmosphere corrosion component R2 mentioned in step 3 is calculated using a power-law model, specifically according to the following formula: R2=K2×[H2S] m ×[O2] n ×[HCl] p +C CO ×[CO]; Where K2 is the flue gas corrosion coefficient, ranging from 0.05 to 0.15; m is the H2S concentration index, ranging from 0.7 to 0.9; n is the O2 concentration index, ranging from -0.6 to -0.4, used to reflect the inhibitory effect of oxygen on reducing atmospheres; p is the HCl concentration index, ranging from 0.2 to 0.4; C CO [CO] represents the CO influence coefficient, ranging from 0.03 to 0.07; [H2S] represents the H2S concentration in the flue gas, ranging from 50 to 1000 ppm; [O2] represents the O2 concentration in the flue gas, ranging from 1% to 5%; [HCl] represents the HCl concentration in the flue gas, ranging from 10 to 100 ppm; [CO] represents the CO concentration in the flue gas, ranging from 0.5% to 2.0%. Through the above power-law relationship, the synergistic effect of multiple corrosive gases and the inhibitory effect of oxygen are quantified and included in the flue gas atmosphere corrosion component.

[0008] Optionally, the ash and coking corrosion component R3 mentioned in step 3 is calculated using a linear and exponential synergistic relationship model, specifically according to the following formula: R3=(β1×C KNa +β2×C Cl +β3×C S )×δ ash ×exp(γ×T w ); Where β1 is the alkali metal influence coefficient, ranging from 0.015 to 0.025; β2 is the chlorine influence coefficient, ranging from 0.04 to 0.06; β3 is the sulfur influence coefficient, ranging from 0.008 to 0.012; γ is the temperature synergy coefficient, ranging from 0.004 to 0.006; C KNa The content of alkali metal elements in the ash is taken as 2% to 8%; C Cl The chlorine content in the ash is measured from 0.2% to 0.8%; CS The sulfur content in the ash is 1% to 5%; the linear combination in the formula (β1×C) KNa +β2×C Cl +β3×C S The contribution of the chemical composition of ash to corrosion is characterized by exp(γ×T). w The term characterizes the synergistic enhancing effect of temperature on ash corrosion.

[0009] Optionally, the material effect coefficient R4 mentioned in step 3 is calculated using a negative exponential relationship model, specifically according to the following formula: R4=K4×exp(-λ×C Cr )×exp(-k×t); Where K4 is the material reference coefficient, ranging from 0.8 to 1.2; λ is the chromium protection coefficient, ranging from 0.25 to 0.35; k is the coating protection coefficient, ranging from 1.8 to 2.2; C Cr The chromium content of the material is 2% to 25%; t is the coating thickness, ranging from 0 to 1 mm; exp(-λ×C) in the formula Cr The term exp(-k×t) reflects the corrosion-inhibiting effect of chromium, while the term exp(-k×t) reflects the corrosion-mitigating effect of coating thickness. This negative exponential relationship quantifies the corrosion resistance of materials and coatings.

[0010] Optionally, the stress effect coefficient R5 mentioned in step 3 is calculated using a power function model, specifically according to the following formula: R5=[(σ mech 2 +σ thermal 2 ) 0.5 / σ0] q ; Where σ mech For mechanical stress, the value ranges from 30 to 70 MPa; σ thermal σ0 is the thermal stress, ranging from 20 to 40 MPa; σ0 is the reference stress, ranging from 70 to 90 MPa; q is the stress exponent, ranging from 1.1 to 1.3. The formula adopts the Mises equivalent stress form to comprehensively consider the combined effect of mechanical stress and thermal stress, and amplifies the accelerating effect of stress on corrosion rate through a power function.

[0011] Optionally, a sensitivity analysis step may be included after step 5: Step 6: Analyze the sensitivity of each input parameter to the total corrosion rate using the single-factor variation method to determine the key control parameters, including the wall temperature T. w H2S concentration, chromium content of materials (C) CrThe key control parameters will be used as the key indicators for operation monitoring.

[0012] Optionally, an optimization analysis step may be included after step 5: Step 7: Establish multiple sets of optimized operating condition parameter combinations. Repeat steps 2 to 4 for each set of optimized operating condition parameter combinations, calculate the reduction in corrosion rate under each optimized operating condition, quantify the synergistic effect of different anti-corrosion measures, including combustion adjustment, wall temperature control, material upgrade and coating protection, and output the optimal operating condition parameter combination.

[0013] A high-temperature corrosion rate prediction system for water-cooled walls of pulverized coal boilers includes: The parameter acquisition module is used to acquire real-time operating status parameters of the water-cooled wall, including the wall temperature T. w Ash accumulation thickness δ ash Concentrations of H2S, O2, HCl, and CO in flue gas; and alkali metal content (C) in ash. KNa Chlorine content C in ash Cl Sulfur content C in ash S Chromium content of materials C Cr Coating thickness t, mechanical stress σ mech and thermal stress σ thermal ; The model calculation module, connected to the parameter acquisition module, is used to receive the operating status parameters and execute the method for predicting the high-temperature corrosion rate of the water-cooled wall of a pulverized coal boiler, thereby calculating the predicted total corrosion rate value R. total ; The results display module is connected to the model calculation module and is used to display the predicted total corrosion rate and optimization operation suggestions in a visual manner. A data storage module, connected to the parameter acquisition module and the model calculation module, is used to store historical running data and model parameters; The system acquires operating status parameters in real time through the parameter acquisition module, calculates the total corrosion rate in real time through the model calculation module, and displays the results through the result display module, thereby realizing real-time prediction and trend analysis of the corrosion rate.

[0014] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting the high-temperature corrosion rate of a water-cooled wall in a pulverized coal boiler.

[0015] Compared with the prior art, the present invention has the following beneficial effects: This application constructs a mathematical model coupling five factors: temperature field distribution, flue gas atmosphere, ash and coking, materials science, and mechanics. It then uses a product-coupled approach to comprehensively calculate the total corrosion rate. This fundamentally solves the core technical problems of traditional methods, such as incomplete factor coverage, neglect of multi-factor coupling effects, inability to characterize nonlinear corrosion laws, and insufficient prediction accuracy. For the first time, it achieves multi-factor coupled quantitative prediction of high-temperature corrosion of water-cooled walls in pulverized coal boilers. This model can accurately reflect the nonlinear interactions between various influencing factors, ensuring that the prediction results are highly consistent with the actual operating conditions of the boiler, and significantly improving the accuracy and reliability of corrosion rate prediction. The scheme employs matched mathematical models to calculate corrosion components for different influencing factors. An exponential relationship model characterizes the accelerating effect of temperature field on corrosion, a power-law relationship model quantifies the synergistic corrosion of multiple gases in the flue gas atmosphere and the inhibitory effect of oxygen, a linear and exponential synergistic relationship model characterizes the combined corrosion effect of ash chemical composition and temperature, a negative exponential relationship model reflects the corrosion resistance of materials and coatings, and a power function relationship model integrates the corrosion accelerating effect of mechanical stress and thermal stress. The parameters of each model are set with scientific and reasonable value ranges, which can accurately fit the physical nature of corrosion reaction and further enhance the accuracy and stability of prediction results.

[0016] Sensitivity analysis using the single-factor variation method can accurately pinpoint key control parameters such as wall temperature, hydrogen sulfide concentration, and chromium content in materials, clarifying core indicators for water-cooled wall operation monitoring and improving the targeting and efficiency of operation and maintenance management. By constructing multiple sets of optimized operating condition parameter combinations and calculating the reduction in corrosion rate, the synergistic effect of anti-corrosion measures such as combustion adjustment, wall temperature control, material upgrades, and coating protection can be quantified, outputting the optimal operating condition parameter combination and providing precise numerical guidance for water-cooled wall anti-corrosion control and operation optimization. The supporting prediction system can realize real-time acquisition of operating status parameters, real-time calculation of corrosion rate, and visualization of prediction results and optimization suggestions. It also completes the storage and management of historical operating data and model parameters, realizing real-time prediction and trend analysis of corrosion rate. The supporting computer-readable storage medium ensures the programmatic implementation and large-scale application of the prediction method, comprehensively improving the engineering practicality of high-temperature corrosion prediction for water-cooled walls, providing scientific support for water-cooled wall remaining life assessment and safe operation control, and effectively ensuring the safe and economical operation of pulverized coal boilers in thermal power plants. Attached Figure Description

[0017] Figure 1 This is a schematic flowchart illustrating the steps of an embodiment of the method for predicting the high-temperature corrosion rate of a water-cooled wall in a pulverized coal boiler according to the present invention.

[0018] Figure 2 This is a structural block diagram of a five-factor coupled mathematical model for an embodiment of a method for predicting the high-temperature corrosion rate of a water-cooled wall in a pulverized coal boiler according to the present invention.

[0019] Figure 3 This is a schematic diagram illustrating the calculation principle of the product coupling model in an embodiment of the method for predicting the high-temperature corrosion rate of the water-cooled wall of a pulverized coal boiler according to the present invention.

[0020] Figure 4 This is a schematic diagram of the system modules of an embodiment of a high-temperature corrosion rate prediction system for water-cooled walls of pulverized coal boilers according to the present invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other.

[0022] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. The terminology used in this invention is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0023] like Figures 1-3 As shown, this invention provides a method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers. This method establishes a coupled mathematical model encompassing five major categories of influencing factors: temperature field distribution, flue gas atmosphere, ash and coking, materials science, and mechanical factors. It then uses a product-coupled approach to comprehensively calculate the corrosion rate, achieving multi-factor coupled quantitative prediction of the high-temperature corrosion rate of water-cooled walls. This method accurately reflects the nonlinear interactions between various influencing factors, with significantly higher prediction accuracy than traditional single-factor or simple linear superposition methods. Based on the prediction results, the remaining life of the water-cooled walls can be assessed, and operational optimization guidance can be provided, offering a scientific basis for the safe and economical operation of power plants. Accordingly, this invention also provides a prediction system for implementing the above method and a storage medium storing a computer program that executes the above method.

[0024] First, the method for predicting the high-temperature corrosion rate of the water-cooled wall of a pulverized coal boiler according to the present invention will be described in detail. This method includes the following steps: The first step was to establish a five-factor coupled mathematical model for predicting the high-temperature corrosion rate of water-cooled walls. The core of this model lies in the first systematic integration of five major categories of key factors affecting the high-temperature corrosion of water-cooled walls. These five categories of factors include temperature field distribution, flue gas atmosphere, ash accumulation and coking, materials science, and mechanical factors. Temperature field distribution is a fundamental factor affecting corrosion rate; high-temperature environments accelerate corrosion chemical reactions. Flue gas atmosphere factors mainly refer to the concentrations and interactions of various corrosive gases such as hydrogen sulfide, hydrogen chloride, carbon monoxide, and oxygen in the near-wall region of the water-cooled wall. Reducing atmospheres generally exacerbate corrosion, while oxygen has a certain inhibitory effect. Ash accumulation and coking factors involve the deposition of ash on the wall surface and its chemical composition. Ash accumulation not only creates thermal resistance and alters wall temperature, but its alkali metals, chlorine, sulfur, and other elements also directly participate in the corrosion reaction. Materials science factors mainly refer to the corrosion resistance of the water-cooled wall pipes themselves, such as the chromium content in the substrate and the thickness of the protective coating. These factors directly determine the material's ability to resist corrosion. Mechanical factors include the mechanical and thermal stresses experienced by the pipe wall during operation. Stress can damage the protective oxide film on the material surface, thereby accelerating the corrosion process. This mathematical model provides a basic framework for subsequent quantitative calculations.

[0025] The second step is to collect the operating status parameters of the water-cooled wall. These parameters are the specific input values ​​of the five-factor coupled mathematical model mentioned above, and need to be accurately obtained through various monitoring methods. Specifically, the parameters that need to be collected include: wall temperature T. w The temperature can be obtained using an infrared thermometer or an embedded thermocouple, etc.; the dust accumulation thickness δ ash The concentrations of H2S, O2, HCl, and CO in the flue gas can be measured in real time using online monitoring equipment such as laser gas analyzers in the near-wall area of ​​the water-cooled wall. The chemical composition of the ash, specifically the content of alkali metal elements (C) in the ash, can also be measured. KNa Chlorine content C Cl and sulfur content C S These parameters need to be obtained by periodically sampling and chemically analyzing the accumulated ash; the chromium content (C) of the material. Cr The coating thickness (t) can be obtained directly from the boiler's equipment file or material quality certificate; if the water-cooled wall surface has a protective coating, it can be measured using equipment such as an ultrasonic thickness gauge; mechanical stress (σ) mech Stress can be acquired in real time by installing a stress monitoring system, such as strain gauges, on the pipe wall; thermal stress σ thermal The parameters then need to be obtained indirectly through thermodynamic calculations based on the temperature distribution of the pipe wall and the physical properties of the material. All these parameters together constitute a complete description of the current operating state of the water-cooled wall.

[0026] The third step involves establishing a five-factor coupled mathematical model based on the operational parameters collected in the second step, and calculating the five corrosion rate components. Each of these five components employs a different mathematical relationship model to accurately characterize the influence mechanism of different factors on the corrosion rate.

[0027] First, the corrosion component R1 of the temperature field is calculated. This component is calculated using an exponential relationship model, specifically: R1 = A1 × exp[-Ea1 / (K × T w )]×exp(α×δ ash In this formula, A1 is the pre-exponential factor, whose physical meaning is related to the material properties and the intrinsic frequency of the corrosion reaction. It can be determined through high-temperature corrosion tests, and its value is generally in the range of 1×10⁻⁶. 5 Up to 1×10 7 Between; Ea1 is the activation energy, measured in joules per mole. It reflects the energy barrier that needs to be overcome for a corrosion reaction to occur. It is also determined by high-temperature corrosion tests, and its value is usually in the range of 8 × 10⁻⁶. 4 Up to 1.2×10 5 J / mol; K is Boltzmann's constant, a known physical constant; T w The wall temperature collected in the first step is expressed in Kelvin, with a typical operating range between 773 K and 1073 K. The formula uses exp[-Ea1 / (K×T)] to represent the wall temperature. w The part )] is the classic Arrhenius form, accurately reflecting the exponential accelerating effect of temperature on the corrosion reaction rate. The exp(α×δ) part in the formula... ash The first item is a key innovation of this invention, used to characterize the amplification effect of ash accumulation thermal resistance on wall temperature. Specifically, the ash layer has an insulating effect, causing the actual temperature of the pipe wall metal below it to be higher than that without ash accumulation. This localized temperature rise further accelerates corrosion. α is the ash accumulation influence coefficient, its value is determined by combining high-temperature corrosion tests with the influence of ash accumulation thickness, and its value is typically between 50 and 150. δ ash The value represents the thickness of the ash accumulation, expressed in meters, and ranges from 0 to 0.01 m. The existence of this term allows the temperature field corrosion component to be coupled with the influence of ash accumulation, making it more closely approximated by the actual physical process.

[0028] Next, the corrosion component R2 of the flue gas atmosphere is calculated. This component is calculated using a power-law model, with the specific formula: R2 = K2 × [H2S]ᵐ × [O2] n ×[HCl]ᵖ+C CO×[CO]. In this formula, [H2S], [O2], [HCl], and [CO] represent the concentrations of hydrogen sulfide, oxygen, hydrogen chloride, and carbon monoxide in the flue gas collected in the second step, respectively. K2 is the flue gas corrosion coefficient, a comprehensive proportionality coefficient determined through flue gas corrosion experiments, with a value ranging from 0.05 to 0.15. m, n, and p are the indices of H2S, O2, and HCl concentrations, respectively. The H2S concentration index m ranges from 0.7 to 0.9, reflecting the significant promoting effect of H2S on corrosion; the O2 concentration index n ranges from -0.6 to -0.4, a negative index whose physical meaning is to reflect the inhibitory effect of oxygen on the reducing atmosphere, i.e., as the oxygen concentration increases, the corrosion rate decreases due to the inhibition of the reducing atmosphere; the HCl concentration index p ranges from 0.2 to 0.4, reflecting the promoting effect of HCl on corrosion. C CO The CO influence coefficient, ranging from 0.03 to 0.07, is used to quantify the contribution of carbon monoxide to corrosion. This power-law model quantifies the synergistic effect of multiple corrosive gases and the inhibitory effect of oxygen in the final corrosion component, accurately reflecting the corrosive impact of complex flue gas atmospheres on water-cooled walls.

[0029] Next, the ash and coking corrosion component R3 is calculated. This component is calculated using a linear and exponential synergistic model, with the specific formula: R3 = (β1 × C KNa +β2×C Cl +β3×C S )×δ ash ×exp(γ×T w In this formula, C KNa C Cl C S These represent the alkali metal content, chlorine content, and sulfur content in the ash obtained in the second step. β1 is the alkali metal influence coefficient, ranging from 0.015 to 0.025; β2 is the chlorine influence coefficient, ranging from 0.04 to 0.06; and β3 is the sulfur influence coefficient, ranging from 0.008 to 0.012. These coefficients were determined through specialized ash corrosion tests to quantify the contribution of different chemical elements to corrosion. The linear combination part in the formula (β1×C) KNa +β2×C Cl +β3×C S This characterizes the combined effects of multiple corrosive chemical components in the ash. The result of this linear combination is then multiplied by the ash thickness δ. ash This means that the greater the amount of dust accumulation, the more severe the corrosion caused by its chemical composition. Finally, the entire result is multiplied by an exponential term exp(γ×T). w), where γ is the temperature synergy coefficient, ranging from 0.004 to 0.006, determined through ash corrosion tests; T w The value represents the wall temperature. This exponential term characterizes the synergistic enhancing effect of temperature on ash corrosion; that is, the higher the temperature, the more significant the promoting effect of the chemical components in the ash on corrosion. Through this linear and exponential synergistic relationship model, the corrosion mechanism under the combined influence of the chemical composition of the ash, the amount of ash, and the temperature can be accurately described.

[0030] Then, the material effect coefficient R4 is calculated. This coefficient is calculated using a negative exponential relationship model, with the specific formula: R4 = K4 × exp(-λ × C Cr )×exp(-k×t). It should be noted that the material effect coefficient R4 here is a dimensionless correction coefficient; the smaller its value, the stronger the material's resistance to corrosion, and the lower the total corrosion rate. In the formula, C Cr Here, t represents the chromium content of the material obtained in the second step, and t represents the coating thickness. K4 is the material baseline coefficient, ranging from 0.8 to 1.2, determined through material corrosion testing, and used as the baseline level for calibrating the model. λ is the chromium protection coefficient, ranging from 0.25 to 0.35, determined through material corrosion testing. It quantifies the inhibitory effect of chromium content on corrosion; the higher the chromium content, the greater the effect of exp(-λ×C). Cr The smaller the value of the exp(-k×t) term, the smaller the material effect coefficient R4. k is the coating protection coefficient, ranging from 1.8 to 2.2, determined through material corrosion tests. It quantifies the mitigating effect of coating thickness on corrosion; the thicker the coating, the smaller the value of the exp(-k×t) term, and the smaller R4. This negative exponential relationship model quantifies the resistance of materials and coatings to corrosion, reflecting the protective role of materials science factors in the overall corrosion process.

[0031] Finally, the stress effect coefficient R5 is calculated. This coefficient is calculated using a power function model, with the specific formula: R5=[(σ mech 2 +σ thermal 2 ) 0.5 / σ0] q Similar to the material effect coefficient, the stress effect coefficient R5 is also a dimensionless correction factor. A value greater than 1 indicates that stress accelerates corrosion, while a value less than 1 indicates that stress inhibits corrosion. However, in actual working conditions, it is usually greater than 1. In the formula, σ mech The mechanical stress obtained in the second step, σ thermalThe stresses are thermal stresses, all measured in megapascals (MPa). The formula first calculates the square root of the sum of the squares of the mechanical and thermal stresses, employing the von Mises equivalent stress form to comprehensively consider the combined effect of both stresses. σ0 is the reference stress, ranging from 70 to 90 MPa, determined through stress corrosion testing, and used to normalize the equivalent stress. q is the stress exponent, ranging from 1.1 to 1.3, also determined through stress corrosion testing. It reflects the amplification effect of stress on the corrosion rate; the larger the exponent q, the more significant the accelerating effect of stress on corrosion. This power function model accurately reflects the amplification effect of stress on corrosion.

[0032] The fourth step involves combining the five corrosion rate components calculated in the third step using a product-coupled model to obtain the total predicted corrosion rate R. total The specific form of this product coupling model is: R total =(R1+R2+R3)×R4×R5. This coupling method is another core innovation of this invention. It differs from simple linear superposition or pure product forms, but is based on a profound understanding of the actual physical process. Specifically, the temperature field corrosion component R1, the flue gas atmosphere corrosion component R2, and the ash and coking corrosion component R3 mainly characterize the "driving forces" from the boiler operating environment and the working fluid side. They act together on the water-cooled wall surface, and are therefore combined by addition to reflect the superposition effect of multiple corrosion driving factors. The material effect coefficient R4 and the stress effect coefficient R5 mainly characterize the "resistance or regulation ability" of the water-cooled wall itself against corrosion. They play a corrective role in amplifying or reducing the overall corrosion process. Multiplying the sum of the three driving factor components by the two correction coefficients better reflects the actual physical process of "superposition of environmental driving forces and overall regulation by material and stress factors." Through this unique product coupling method, the final predicted total corrosion rate accurately reflects the complex nonlinear interactions between the five major categories of factors.

[0033] Fifth step, based on the predicted total corrosion rate R obtained in step four. total The remaining service life of water-cooled walls is assessed, and optimized operation recommendations are generated. The remaining service life assessment can be calculated by combining the initial wall thickness and the maximum allowable corrosion margin. For example, dividing the total allowable corrosion by the annual corrosion rate yields an estimate of the remaining service life. Simultaneously, based on the prediction results and the contributions of various parameters in the model, the main corrosion factors under current operating conditions can be analyzed, leading to targeted optimized operation recommendations. These recommendations include adjusting combustion to change the flue gas atmosphere, controlling wall temperature, considering material upgrades or adding protective coatings, etc., thereby guiding power plant operators to make scientific decisions, extend equipment life, and ensure safe boiler operation.

[0034] Building upon the above method, this invention may further include extended analytical steps to provide more in-depth guidance. For example, after step five, a sensitivity analysis step may be included. This step employs a single-factor variation method, i.e., while keeping other input parameters constant, changing one parameter one by one and observing its effect on the total corrosion rate R. total The degree of influence is determined by analyzing the sensitivity of each input parameter. Through this analysis, the key control parameters that have the greatest impact on the corrosion rate, such as the wall temperature T, can be identified. w H2S concentration, chromium content of materials (C) Cr By using these key control parameters as the primary indicators for operational monitoring, corrosion control and prevention can be carried out more effectively.

[0035] Furthermore, after step five, an optimization analysis step can be included. This step involves establishing multiple sets of different optimized operating condition parameter combinations, such as simultaneously adjusting combustion parameters to reduce H2S concentration and appropriately increasing O2 concentration, while also considering increasing the chromium content of materials and increasing coating thickness. For each set of optimized operating condition parameter combinations, the calculation process described in steps two through four is repeated to obtain the predicted corrosion rate under each optimized operating condition, and then the reduction in corrosion rate compared to the baseline operating condition can be calculated. In this way, the individual effects and synergistic effects of different anti-corrosion measures such as combustion adjustment, wall temperature control, material upgrades, and coating protection can be quantitatively evaluated, and ultimately one or more optimal operating condition parameter combinations can be output, providing accurate quantitative basis for the technical transformation and operational optimization of power plants.

[0036] The method of the present invention will be described in more detail below through specific embodiments, but these embodiments should not be construed as any limitation on the present invention.

[0037] Example 1 This embodiment uses a specific operating condition as an example to fully demonstrate the implementation process of the high-temperature corrosion rate prediction method for water-cooled walls of pulverized coal boilers provided by this invention.

[0038] First, the operating status parameters of the water-cooled wall at a specific moment were collected through the power plant's distributed control system and laboratory analysis. The specific parameter values ​​collected are as follows: wall temperature T w The value is 873K, which corresponds to 600 degrees Celsius; the dust accumulation thickness δ ash The distance is 0.001 meters; the concentration of H2S in the flue gas is 500 ppm, the concentration of O2 is 2.0%, the concentration of HCl is 50 ppm, and the concentration of CO is 1.0%; the content of alkali metal elements in the ash is C. KNa The chlorine content is 5.0%, C. Cl The sulfur content is 0.5%, C. S It is 3.0 percent; the chromium content of the material is C. CrThe value is 2.5%, and since there is currently no protective coating, the coating thickness t is 0 mm; mechanical stress σ mech The thermal stress is σ = 50 MPa. thermal It is 30 MPa.

[0039] Next, the empirical parameters in the model are configured. These parameters were determined in advance through extensive laboratory experiments and field data regression, and their specific values ​​are as follows: For the calculation of the corrosion component in the temperature field, the pre-exponential factor A1 is set to 1 × 10⁻⁶. 6 Activation energy E a1 Take 1×10 5 Joules per mole, with the ash accumulation influence coefficient α set to 100. For the calculation of the corrosion components in the flue gas atmosphere, the flue gas corrosion coefficient K2 is set to 0.1, the H2S concentration index m to 0.8, the O2 concentration index n to -0.5, the HCl concentration index p to 0.3, and the CO influence coefficient C... CO The value is set to 0.05. For the calculation of the ash and coking corrosion component, the alkali metal influence coefficient β1 is set to 0.02, the chlorine element influence coefficient β2 is set to 0.05, the sulfur element influence coefficient β3 is set to 0.01, and the temperature synergy coefficient γ is set to 0.005. For the calculation of the material effect coefficient, the material reference coefficient K4 is set to 1.0, the chromium element protection coefficient λ is set to 0.3, and the coating protection coefficient k is set to 2.0. For the calculation of the stress effect coefficient, the reference stress σ0 is set to 80 MPa, and the stress exponent q is set to 1.2.

[0040] Then, based on the collected parameters and configured model parameters, the five corrosion rate components are calculated respectively. First, the temperature field corrosion component R1 is calculated, and the value is substituted into the formula R1=A1×exp[-E a1 / (K×T w )]×exp(α×δ ash It should be noted here that the Boltzmann constant K is taken as 8.314, and the calculated value of R1 is 0.045678. Next, the corrosion component of the flue gas atmosphere R2 is calculated and substituted into the formula R2=K2×[H2S]ᵐ×[O2] n ×[HCl]ᵖ+C CO ×[CO], the calculated value of R2 is 0.089234. Next, the ash and coke corrosion component R3 is calculated and substituted into the formula R3=(β1×C KNa +β2×C Cl +β3×C S )×δ ash ×exp(γ×T w The calculated value of R3 is 0.023456. Then, the material effect coefficient R4 is calculated and substituted into the formula R4=K4×exp(-λ×C). CrSince the coating thickness t is 0, the exp(-k×t) term is 1, and the calculated value of R4 is 0.472367. Finally, the stress effect coefficient R5 is calculated and substituted into the formula R5=[(σ mech 2 +σ thermal 2 ) 0.5 / σ0] q The calculated value of R5 is 1.123456.

[0041] Next, the five calculated components are substituted into the product coupling model for comprehensive calculation to obtain the predicted total corrosion rate R. total According to formula R total =(R1+R2+R3)×R4×R5, substitute the above values ​​to calculate R. total It is 0.078901 millimeters per year.

[0042] Finally, the remaining service life is assessed based on this total corrosion rate prediction. Assuming the initial wall thickness of the water-cooled wall is 12 mm, and the maximum allowable corrosion margin according to safety operating specifications is 3 mm, dividing the allowable corrosion margin of 3 mm by the annual corrosion rate of 0.078901 mm / year, the remaining service life of the water-cooled wall under current operating conditions can be estimated to be approximately 38 years. This prediction result can be provided to operators as an important reference for developing maintenance plans and operating strategies. Simultaneously, the system can also preliminarily determine the main corrosion factors under current operating conditions based on the contribution of each component, providing direction for subsequent optimization.

[0043] Example 2 This embodiment, based on Embodiment 1, further demonstrates how to quantitatively evaluate and optimize different anti-corrosion measures using the method of the present invention.

[0044] Under the baseline conditions of Example 1, the total corrosion rate was 0.078901 mm / year. A series of comprehensive optimization measures are now considered to reduce the corrosion rate. These measures include combustion optimization, material upgrades, and increased coating protection. Specifically, combustion optimization measures aim to alter the flue gas atmosphere, such as adjusting air distribution, to reduce the H2S concentration from 500 ppm to 100 ppm, while simultaneously increasing the O2 concentration from 2.0% to 3.0%. Material upgrade measures involve replacing or improving the pipe material, increasing the chromium content (C) of the material. Cr The percentage was increased from 2.5% to 12.0%. The coating protection measure involved adding a protective coating layer to the water-cooled wall surface, increasing the coating thickness t from 0 mm to 0.2 mm. Other operating parameters, such as wall temperature, dust accumulation, mechanical stress, and thermal stress, are assumed to be consistent with those in Example 1 in this embodiment.

[0045] For the optimized parameter combination described above, steps two through four of the method of this invention are repeated, i.e., the five corrosion rate components are recalculated and product-coupled based on the new input parameters. Substituting the new parameters into each calculation formula yields the optimized total corrosion rate prediction value R. total It is 0.010234 millimeters per year.

[0046] By comparing the total corrosion rate before and after optimization, the effectiveness of these comprehensive anti-corrosion measures can be quantitatively evaluated. The reduction in corrosion rate is calculated using the formula: base condition corrosion rate minus optimized condition corrosion rate, then divided by the base condition corrosion rate, and finally multiplied by 100%. Substituting the values ​​into the calculation, the reduction in corrosion rate is found to be 87.03%. This quantitative result demonstrates that the comprehensive optimization measures adopted can significantly reduce the high-temperature corrosion rate of water-cooled walls, thereby greatly extending the service life of the equipment.

[0047] Through this optimization analysis, power plants can conduct preliminary assessments of different corrosion protection retrofit schemes, compare the costs and effects of different combinations of measures, and thus select the optimal technical route and operating strategy. For example, the reduction in corrosion rate can be evaluated separately for combustion optimization alone, material upgrades alone, adding coating protection alone, and different combinations of measures, providing precise quantitative basis for investment decisions. This function greatly enhances the engineering practical value of this invention.

[0048] Next, the high-temperature corrosion rate prediction system for water-cooled walls of pulverized coal boilers of the present invention will be described in detail. This system is used to implement the methods described in any of the above embodiments, and its overall architecture and module composition are as follows.

[0049] Example 3 like Figure 4 As shown in the figure, this embodiment describes a high-temperature corrosion rate prediction system for the water-cooled wall of a pulverized coal boiler for implementing the aforementioned prediction method.

[0050] The system mainly includes a parameter acquisition module, a model calculation module, a result display module, and a data storage module. These modules are interconnected and work together.

[0051] The parameter acquisition module is the system's data entry point, responsible for collecting various operating status parameters of the water-cooled wall in real time or periodically. These parameters are completely consistent with those described in step two of the aforementioned method, specifically including the wall temperature T. w Ash accumulation thickness δ ash Concentrations of H2S, O2, HCl, and CO in flue gas; and alkali metal content (C) in ash. KNa Chlorine content C in ash Cl Sulfur content C in ash S Chromium content of materials C CrCoating thickness t, mechanical stress σ mech and thermal stress σ thermal This module needs to interface with various data sources in the power plant. For example, it needs to communicate with the distributed control system to obtain real-time operational data such as temperature, pressure, and gas concentration output from gas analyzers; interface with the laboratory information management system to obtain offline analytical data such as ash composition analysis results; and interface with the equipment management system to obtain static equipment parameters such as the chromium content of materials. The parameter acquisition module performs necessary preprocessing on all the collected raw data, such as format standardization, outlier removal, and unit conversion, and then organizes it into a standard data format for use by subsequent modules.

[0052] The model calculation module is the core processing unit of the system. It is connected to the parameter acquisition module, responsible for receiving real-time operating status parameters from the parameter acquisition module, and executing the prediction method described above to calculate the predicted total corrosion rate value R. total Specifically, this module integrates a five-factor coupled mathematical model and a related empirical parameter library. Upon receiving a new set of input parameters, the model calculation module automatically calls preset model parameters such as the pre-exponential factor A1, activation energy Ea1, flue gas corrosion coefficient K2, various influence indices and coefficients, etc., and calculates the temperature field corrosion component R1, flue gas atmosphere corrosion component R2, ash and coking corrosion component R3, material effect coefficient R4, and stress effect coefficient R5 sequentially, following the procedures in steps three and four. Finally, it calculates the product coupling formula R... total The total corrosion rate is calculated as (R1+R2+R3)×R4×R5. This module's calculation can be a periodic, timed task or an event-driven task triggered by new data, thus achieving real-time or near-real-time prediction of the corrosion rate. Furthermore, the model calculation module can also perform the aforementioned sensitivity analysis and optimization analysis functions. During sensitivity analysis, it iteratively changes the input parameters near the current operating point, re-invokes the prediction model, and calculates the sensitivity coefficients of each parameter. During optimization analysis, it receives user-defined optimization objectives or constraints, searches or traverses within a preset parameter space, calls the prediction model to calculate the corrosion rate under different parameter combinations, and provides optimal operating parameter recommendations.

[0053] The results display module is connected to the model calculation module and is responsible for presenting the various results obtained from the model calculation module to the user in an intuitive and user-friendly visualization. The displayed content includes at least the current predicted total corrosion rate, which can be displayed in the form of a digital dashboard, trend chart, etc. It can also display the specific values ​​and relative contribution pie charts of the five corrosion rate components, helping users quickly identify the main corrosion factors. For historical data query and analysis results, the results display module can generate comparison curves. For the prediction and early warning function, when the total corrosion rate exceeds a preset threshold, the module will issue tiered early warning information through pop-ups, color changes, or audible and visual alarms. For optimization analysis, the module can display the corrosion rate reduction and expected effects of different optimization schemes in the form of lists or bar charts. For remaining service life assessment, the module can combine historical wall thickness data to display the estimated remaining service life in the form of countdowns or progress bars. In addition, the results display module will also generate specific and readable optimization operation suggestion text based on the model calculation results and a preset knowledge base, such as "It is recommended to appropriately increase the combustion air volume to reduce H2S concentration" or "The current wall temperature is too high, it is recommended to perform soot blowing operations," which will be directly output to the operators.

[0054] The data storage module, connected to the parameter acquisition module and model calculation module, serves as the system's data warehouse. It stores all historical operational data, including raw input parameters uploaded by the parameter acquisition module, intermediate and final results of each corrosion rate component and the total corrosion rate calculated by the model calculation module, as well as user operation records and system configuration information. The data storage module provides data support for functions such as historical trend analysis, model parameter calibration, and report generation. It typically employs mature database systems such as relational databases or real-time databases to ensure data reliability, security, and efficient read / write performance.

[0055] Through the coordinated work of the four core modules mentioned above, the entire system can achieve a complete closed loop from data acquisition, model calculation, result display to data storage, thereby enabling real-time and accurate prediction and trend analysis of the high-temperature corrosion rate of the water-cooled wall of pulverized coal boiler, providing strong technical support for the safe production and optimized operation of power plants.

[0056] Finally, the present invention also provides a computer-readable storage medium storing a computer program. When executed by a processor, this computer program can implement the high-temperature corrosion rate prediction method for water-cooled walls of pulverized coal boilers as described in any of the above embodiments. The storage medium can be various forms of physical media, such as hard disks, optical disks, read-only memory, random access memory, flash memory, etc. When the program is run on a power plant's host computer system, a dedicated computing server, or an embedded device, the aforementioned prediction system can be constructed, realizing the corresponding prediction function.

[0057] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative in all respects and are not the only ones. All modifications within the scope of this invention or equivalent to the scope of this invention are included in this invention.

Claims

1. A method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers, characterized in that, Includes the following steps: Step 1: Establish a five-factor coupled mathematical model for predicting the high-temperature corrosion rate of water-cooled walls. The five factors include temperature field distribution, flue gas atmosphere, ash accumulation and coking, materials science, and mechanical factors. Step 2: Collect the operating status parameters of the water-cooled wall, including the wall temperature T. w Ash accumulation thickness δ ash Concentrations of H2S, O2, HCl, and CO in flue gas; and alkali metal content (C) in ash. KNa Chlorine content C in ash Cl Sulfur content C in ash S Chromium content of materials C Cr Coating thickness t, mechanical stress σ mech and thermal stress σ thermal ; Step 3: Based on the operating status parameters collected in Step 2, calculate the five corrosion rate components according to the five-factor coupled mathematical model: use the exponential relationship model to calculate the temperature field corrosion component R1, use the power law relationship model to calculate the flue gas atmosphere corrosion component R2, use the linear and exponential synergistic relationship model to calculate the ash and coking corrosion component R3, use the negative exponential relationship model to calculate the material effect coefficient R4, and use the power function relationship model to calculate the stress effect coefficient R5. Step 4: Combine the five corrosion rate components obtained in Step 3 using a product coupling model to obtain the total predicted corrosion rate value R. total The product coupling model is R total =(R1+R2+R3)×R4×R5; Step 5: Based on the predicted total corrosion rate R obtained in Step 4 total The remaining life of the water-cooled wall is assessed, and optimized operation recommendations are provided.

2. The method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers according to claim 1, characterized in that, The temperature field corrosion component R1 mentioned in step 3 is calculated using an exponential relationship model, specifically according to the following formula: R1=A1×exp[-E a1 / (K×T w )]×exp(α×δ ash ); Where A1 is the pre-exponential factor, and its value ranges from 1 × 10⁻⁶. 5 Up to 1×10 7 ; E a1 The activation energy has a range of 8 × 10⁻⁶. 4 Up to 1.2×10 5 J / mol; K is Boltzmann constant; T w The wall temperature is α, ranging from 773 to 1073 K; α is the dust accumulation influence coefficient, ranging from 50 to 150; δ ash The thickness of the ash accumulation ranges from 0 to 0.01 m; exp(α×δ) in the formula ash The term is used to characterize the amplification effect of the thermal resistance of ash accumulation on the wall temperature. This amplification effect accelerates the corrosion reaction by increasing the effective value of the wall temperature.

3. The method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers according to claim 1, characterized in that, The flue gas atmosphere corrosion component R2 mentioned in step 3 is calculated using a power-law model, specifically according to the following formula: R2=K2×[H2S] m ×[O2] n ×[HCl] p +C CO ×[CO]; Where K2 is the flue gas corrosion coefficient, ranging from 0.05 to 0.15; m is the H2S concentration index, ranging from 0.7 to 0.9; n is the O2 concentration index, ranging from -0.6 to -0.4, used to reflect the inhibitory effect of oxygen on reducing atmospheres; p is the HCl concentration index, ranging from 0.2 to 0.4; C CO [CO] represents the CO influence coefficient, ranging from 0.03 to 0.07; [H2S] represents the H2S concentration in the flue gas, ranging from 50 to 1000 ppm; [O2] represents the O2 concentration in the flue gas, ranging from 1% to 5%; [HCl] represents the HCl concentration in the flue gas, ranging from 10 to 100 ppm; [CO] represents the CO concentration in the flue gas, ranging from 0.5% to 2.0%. Through the above power-law relationship, the synergistic effect of multiple corrosive gases and the inhibitory effect of oxygen are quantified and included in the flue gas atmosphere corrosion component.

4. The method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers according to claim 1, characterized in that, The ash and coking corrosion component R3 mentioned in step 3 is calculated using a linear and exponential synergistic relationship model, specifically according to the following formula: R3=(β1×C KNa +β2×C Cl +β3×C S )×δ ash ×exp(γ×T w ); Where β1 is the alkali metal influence coefficient, ranging from 0.015 to 0.025; β2 is the chlorine influence coefficient, ranging from 0.04 to 0.06; β3 is the sulfur influence coefficient, ranging from 0.008 to 0.012; γ is the temperature synergy coefficient, ranging from 0.004 to 0.006; C KNa The content of alkali metal elements in the ash is taken as 2% to 8%; C Cl The chlorine content in the ash is measured from 0.2% to 0.8%; C S The sulfur content in the ash is 1% to 5%; the linear combination in the formula (β1×C) KNa +β2×C Cl +β3×C S The contribution of the chemical composition of ash to corrosion is characterized by exp(γ×T). w The term characterizes the synergistic enhancement effect of temperature on ash corrosion.

5. The method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers according to claim 1, characterized in that, The material effect coefficient R4 mentioned in step 3 is calculated using a negative exponential relationship model, specifically according to the following formula: R4=K4×exp(-λ×C Cr )×exp(-k×t); Where K4 is the material reference coefficient, ranging from 0.8 to 1.2; λ is the chromium protection coefficient, ranging from 0.25 to 0.35; k is the coating protection coefficient, ranging from 1.8 to 2.2; C Cr The chromium content of the material is 2% to 25%; t is the coating thickness, ranging from 0 to 1 mm; exp(-λ×C) in the formula Cr The term exp(-k×t) reflects the inhibitory effect of chromium on corrosion, and the term exp(-k×t) reflects the mitigating effect of coating thickness on corrosion. The negative exponential relationship quantifies the corrosion resistance of materials and coatings.

6. The method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers according to claim 1, characterized in that, The stress effect coefficient R5 mentioned in step 3 is calculated using a power function model, specifically according to the following formula: R5=[(σ mech 2 +s thermal 2 ) 0.5 / σ0] q ; Where σ mech For mechanical stress, the value ranges from 30 to 70 MPa; σ thermal σ0 is the thermal stress, ranging from 20 to 40 MPa; σ0 is the reference stress, ranging from 70 to 90 MPa; q is the stress exponent, ranging from 1.1 to 1.

3. The formula adopts the Mises equivalent stress form to comprehensively consider the combined effect of mechanical stress and thermal stress, and amplifies the accelerating effect of stress on corrosion rate through a power function.

7. The method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers according to claim 1, characterized in that, Following step 5 is a sensitivity analysis step: Step 6: Analyze the sensitivity of each input parameter to the total corrosion rate using the single-factor variation method to determine the key control parameters, including the wall temperature T. w H2S concentration, chromium content of materials (C) Cr The key control parameters will be used as the key indicators for operation monitoring.

8. The method for predicting the high-temperature corrosion rate of water-cooled walls in pulverized coal boilers according to claim 1, characterized in that, Step 5 is followed by an optimization analysis step: Step 7: Establish multiple sets of optimized operating condition parameter combinations. Repeat steps 2 to 4 for each set of optimized operating condition parameter combinations, calculate the reduction in corrosion rate under each optimized operating condition, quantify the synergistic effect of different anti-corrosion measures, including combustion adjustment, wall temperature control, material upgrade and coating protection, and output the optimal operating condition parameter combination.

9. A high-temperature corrosion rate prediction system for water-cooled walls of pulverized coal boilers, used to implement the high-temperature corrosion rate prediction method for water-cooled walls of pulverized coal boilers as described in any one of claims 1 to 8, characterized in that, include: The parameter acquisition module is used to acquire real-time operating status parameters of the water-cooled wall, including the wall temperature T. w Ash accumulation thickness δ ash Concentrations of H2S, O2, HCl, and CO in flue gas; and alkali metal content (C) in ash. KNa Chlorine content C in ash Cl Sulfur content C in ash S Chromium content of materials C Cr Coating thickness t, mechanical stress σ mech and thermal stress σ thermal ; The model calculation module, connected to the parameter acquisition module, is used to receive the operating status parameters and execute the high-temperature corrosion rate prediction method for water-cooled walls of pulverized coal boilers as described in any one of claims 1 to 8, to calculate the total corrosion rate prediction value R. total ; The results display module is connected to the model calculation module and is used to display the predicted total corrosion rate and optimization operation suggestions in a visual manner. A data storage module, connected to the parameter acquisition module and the model calculation module, is used to store historical running data and model parameters; The system acquires operating status parameters in real time through the parameter acquisition module, calculates the total corrosion rate in real time through the model calculation module, and displays the results through the result display module, thereby realizing real-time prediction and trend analysis of the corrosion rate.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements a method for predicting the high-temperature corrosion rate of the water-cooled wall of a pulverized coal boiler as described in any one of claims 1 to 8.

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