An integrated design method for flue gas pollutant control equipment

Abstract

The present invention discloses an integrated design method for flue gas pollutant control equipment, comprising: S1, constructing a mathematical model of multiphase flow, heat transfer, mass transfer and calcium desulfurization in a wet desulfurization tower according to the process flow of a wet desulfurization tower, S2, extracting the structure of the wet desulfurization tower and constructing a physical model of the wet desulfurization tower; S3, analyzing the multiphase flow process in the wet desulfurization tower and obtaining the gas-liquid two-phase flow, heat transfer and mass transfer and desulfurization reaction characteristics in the wet desulfurization tower. S4, verifying the desulfurization mechanism of the wet desulfurization tower using actual values ​​from experiments and field measurements. S5, analyzing and comprehensively evaluating the multi-factor influence on the operating parameters of the wet desulfurization tower based on an artificial intelligence model. The present invention adopts a variety of research methods to obtain the influencing factors of multiple factors on the desulfurization and pollution removal of large-scale pollutant removal equipment, so as to more comprehensively and deeply grasp the operation of the equipment, so as to facilitate the optimization design of the wet desulfurization tower.

Description

Technical Field

[0001] The present invention relates to the technical field of flue gas pollutant control, and in particular to an integrated design method for flue gas pollutant control equipment. Background Art

[0002] Because post-combustion desulfurization (FGD) does not affect coal processing or boiler combustion, and its desulfurization effectiveness is excellent, and current pre-combustion and in-combustion desulfurization technologies are insufficient to meet desulfurization targets, FGD is currently the primary method for controlling sulfur dioxide emissions. Widely recognized in the market as the most direct, effective, and economical method, FGD is a method for desulfurizing power plant exhaust gases, encompassing dry, semi-dry, and wet methods. Wet FGD is the most widely used, offering stable operation, mature technology, and significant desulfurization results.

[0003] By gaining an in-depth understanding of the internal desulfurization mechanisms of desulfurization equipment, especially the most widely used flue gas wet desulfurization equipment frequently used by coal-fired power plant pollutant emission control companies, we can provide a foundation or support for the smooth operation and upgrading of desulfurization equipment. Because understanding and in-depth research on the internal operating mechanism of wet desulfurization equipment is the main basis for achieving successful design, amplification and optimization, understanding its operating mechanism will be of great help to the exploration of related technical potential, technological innovation, and engineering practice application. Secondly, with the advent of the current era of industrial Internet and blockchain, since any industrial product can be data, the construction of a core database in the direction of energy conservation and environmental protection, especially the construction and improvement of a core database platform based on wet desulfurization equipment parameters and desulfurization system operation, often plays an important role in the updating and iteration of flue gas desulfurization technology.

[0004] At present, the influencing factors of flue gas pollutant control devices are mostly studied as single-factor variables, and the experimental method is mainly used. However, due to the multi-structure coupling within large-scale flue gas multi-pollutant removal equipment, the various operating parameters are interrelated, making its internal flow, heat and mass transfer, and desulfurization mechanism complex and changeable, making the prediction of the importance indicators of operating parameters easily distorted. Some researchers have also used mathematical and physical models to study and optimize the desulfurization performance of wet desulfurization systems. However, due to the high content of flue gas impurities and rapid load changes, researchers usually use a variety of research methods, including operation mechanism analysis, desulfurization prediction based on real-time operation parameters, computational fluid dynamics (CFD) simulation, and statistical models.

[0005] The aforementioned experimental or field-tested studies on wet desulfurization towers can provide an analysis of factors influencing desulfurization equipment. However, these experiments and field tests also present challenges such as high requirements for an ideal testing environment, long equipment installation and commissioning cycles, and high costs. Furthermore, industrial-grade desulfurization equipment is typically complex in structure, contains numerous internal devices, and experiences rapidly changing operating parameters. Therefore, CFD simulations must consider the impact of various factors on flow within the tower and desulfurization. Furthermore, research and application related to intelligent wet desulfurization systems in power plants is still relatively preliminary and fragmented, leaving much to be explored. Summary of the Invention

[0006] The purpose of the present invention is to overcome the deficiencies of the above-mentioned prior art and to provide an integrated design method for flue gas pollutant control equipment.

[0007] The purpose of the present invention is achieved through the following technical solutions:

[0008] An integrated design method for flue gas pollutant control equipment, comprising:

[0009] S1. Based on the process flow of the wet desulfurization tower, a mathematical model of multiphase flow, heat transfer, mass transfer and calcium desulfurization in the wet desulfurization tower is constructed. The wet desulfurization tower mainly includes three types: spray tower, bubble tower and spray scattering tower. The spray scattering tower is mainly an engineering application body that belongs to the process of combining the spray tower and the bubble tower, mainly including the spray chamber of the middle warehouse and the bubble chamber of the lower warehouse;

[0010] S2: Extract the structure of the wet desulfurization tower and construct a physical model of the wet desulfurization tower. Grid the physical model of the wet desulfurization tower and perform a grid-independence test. Then, establish boundary conditions and a solution method based on the physical model and its multiphase flow, heat and mass transfer, and desulfurization reaction characteristics.

[0011] S3, analyzes the multiphase flow process in the wet desulfurization tower, and obtains the gas-liquid two-phase flow, heat and mass transfer, and desulfurization reaction characteristics in the wet desulfurization tower, and demonstrates the desulfurization performance of each sub-area in the wet desulfurization tower.

[0012] S4, using experimental and field measured real values ​​to verify the desulfurization mechanism of the wet desulfurization tower, and conduct a single factor variable influencing factor study on the desulfurization of the wet desulfurization tower, so as to preliminarily obtain the influencing factor analysis of the wet desulfurization tower (for example, the influence of operating parameters such as pH value in slurry and inlet flue gas temperature on the desulfurization rate);

[0013] S5, based on the artificial intelligence model, analyzes and comprehensively evaluates the multi-factor impact of the wet desulfurization tower operating parameters to facilitate the optimal design of the wet desulfurization tower.

[0014] Preferably, in step S2, the multiphase flow, heat transfer and mass transfer in the spray chamber (tower) of the wet desulfurization tower are simulated using the Euler-Lagrange model, and the multiphase flow, heat transfer and mass transfer in the bubbling chamber (tower) of the wet desulfurization tower are simulated using the Euler-Euler model.

[0015] Preferably, step S3 includes: the multiphase flow and heat and mass transfer process of the spray tower is simulated using steady-state simulation, and the reaction is regarded as a steady-state process; in the multiphase flow and desulfurization parallel algorithm program of the spray tower, the solution of the flue gas phase and droplet phase flow fields is carried out step by step; and the multiphase flow and heat and mass transfer process of the bubble tower is simulated using unsteady-state simulation, and the reaction is regarded as an unsteady-state process, and the solution of the flue gas phase and droplet phase flow fields is carried out simultaneously; and the spray chamber and the bubble chamber in the spray scattering tower are calculated separately, and are coupled calculated using a coupling algorithm.

[0016] Preferably, the multiphase flow and heat and mass transfer processes in the spray tower are simulated in a steady state, and the reaction is regarded as a steady-state process; in the multiphase flow and desulfurization parallel algorithm program of the spray tower, the solution of the flue gas phase and droplet phase flow fields is performed alternately, specifically: the flue gas flow field and the droplet phase flow field are initialized, and the simple algorithm is used to solve the velocity and pressure field of the gas phase; in the bubbling tower, the PC-simple algorithm is mainly used to solve the velocity and pressure field of the gas phase; in the spray scattering tower, the simple and PC-simple algorithms are used to solve the velocity and pressure fields of the spray chamber and the bubbling chamber respectively.

[0017] Preferably, step S4 includes: respectively verifying that the desulfurization rate is affected by the parameters of spraying volume, slurry level, pH, flue gas temperature change, and flue gas pressure drop.

[0018] Preferably, step S5 includes: using artificial intelligence models such as XGboost to construct a ranking of multi-factor influencing indicators of the wet desulfurization tower, so as to obtain a ranking of the influence indexes of the flue gas desulfurization rate, flue gas outlet temperature, flue gas outlet pressure, fan energy consumption, and slurry consumption in the wet desulfurization tower, and analyzing the operating parameters of the wet desulfurization tower according to the ranking analysis results, so as to put forward optimization suggestions for the operating parameters of the wet desulfurization tower.

[0019] Preferably, in step S2, the spray chamber and the bubbling chamber in the wet desulfurization tower are modeled separately.

[0020] The present invention has the following advantages over the prior art:

[0021] A. Compared with traditional single research methods, this technology uses a combination of multiple research methods to obtain the impact of multiple factors on the desulfurization and pollution removal of large-scale pollutant removal equipment, thereby grasping the operation status of the equipment more comprehensively and deeply, and can comprehensively evaluate the operating performance of the desulfurization equipment, thereby achieving the goal of optimized design.

[0022] B. Through hierarchical technical research methods, we can achieve mutual support, mutual matching, mutual coupling, and mutual verification in technology. For example, we first measure through experiments that the influencing factors of desulfurization and pollution removal of large-scale pollutant equipment include certain elements, and then further study the influence of these factor variables on the desulfurization performance of large-scale pollutant equipment through numerical simulation analysis and artificial intelligence analysis, so as to achieve the effect of mutual verification and mutual matching.

[0023] C. By combining single-factor and multi-factor weighted analysis to determine the impact of large-scale pollutant removal equipment on desulfurization and pollution removal, the needs of various commercial power plants or pollution removal equipment manufacturers can be better met. As the times change and national environmental protection indicators become increasingly stringent, some power plants need to obtain a multi-factor impact hierarchy to guide production practices, equipment operation, and structural parameter optimization. Therefore, the integration of multiple technologies to comprehensively and comprehensively obtain an optimization and upgrade strategy for flue gas pollutant removal equipment has good application scenarios and prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0025] Figure 1 Schematic diagram of the integrated design method of the flue gas pollutant control device of the present invention.

[0026] Figure 2 It is a schematic diagram of the desulfurization process flow of a typical wet desulfurization tower - a spray scattering tower (including a spray chamber and a bubbling chamber structure in the interior) of the present invention.

[0027] Figure 3 (a) is the overall three-dimensional structural diagram of the physical model of the spray (chamber) tower of the present invention.

[0028] Figure 3 (b) is the overall three-dimensional grid diagram of the physical model of the spray (chamber) tower of the present invention.

[0029] Figure 4 (a) is an XOZ cross-sectional structural diagram of the physical model of the bubbling (chamber) column of the present invention.

[0030] Figure 4 (b) is an XOZ cross-sectional grid diagram of the physical model of the bubbling (chamber) tower of the present invention.

[0031] Figure 4 (c) is a cross-sectional structural diagram of the scattering plate of the bubbling (chamber) tower of the present invention.

[0032] Figure 4(d) is a cross-sectional grid diagram of the scattering plate of the bubbling (chamber) tower of the present invention.

[0033] Figure 5 This is a comparison chart of the simulated and measured results of the desulfurization rate under the immersion depth of the variable scattering tube.

[0034] Figure 6 (a) is a comparison chart of the simulated and measured results of the relationship between the desulfurization rate and the immersion depth of the scattering tube.

[0035] Figure 6 (b) is a comparison chart of the simulation and measured results of the effect of the immersion depth of the scattering tube on the SO2 absorption rate ratio of the spray chamber and the bubbling chamber.

[0036] Figure 7 (a) is the prediction effect diagram of the desulfurization rate regression model of the wet desulfurization tower based on the XGB model.

[0037] Figure 7 (b) is the prediction effect diagram of the desulfurization rate regression model of the wet desulfurization tower based on the LGB model.

[0038] Figure 8 (a) is a comparison chart of the importance of five important indicators related to the wet desulfurization tower and desulfurization rate based on the XGB model.

[0039] Figure 8 (b) is a comparison chart of the importance of two importance indicators related to the total current consumption of the fan based on the XGB model.

[0040] Figure 8 (c) is a comparison chart of the importance of three important indicators related to the wet desulfurization tower and flue gas outlet temperature based on the XGB model.

[0041] Among them, 101-high-temperature flue gas; 102-gypsum storage silo; 103-slurry preparation device; 104-oxidation fan inlet; 105-circulation pump; 106-spray scattering tower; 107-spraying area; 108-water inlet; 109-aerosol capture; 110-upper warehouse; 111-middle warehouse; 112-scattering tube; 113-absorption tank; 114-lower warehouse; 115-auxiliary fan; 116-gypsum; 117-chimney; 118-gypsum hydrocyclone; 201-flue gas inlet; 202-nozzle; 203-scattering tube; 204-flue gas outlet; 205-cavity; 206-supporting platform; 207-flue gas downstream; 301-flue duct outlet; 302-flue duct inlet; 303-flue gas countercurrent upward; 304 scattering plate. DETAILED DESCRIPTION

[0042] The present invention will be further described below with reference to the accompanying drawings and examples.

[0043] The flue gas pollutant control equipment of this embodiment is a wet desulfurization tower. This embodiment adopts a variety of research methods to obtain the influencing factors of single factors and multiple factors on the desulfurization and pollution removal of large-scale pollutant removal equipment in a hierarchical manner, and use this to guide the operation or structural optimization of large-scale desulfurization equipment and provide improvement strategies. Technical means include modeling of large-scale pollutant equipment, combining computational fluid dynamics (CFD) simulation and experimental analysis, single-factor variable influencing factor analysis technology and multi-factor influencing factor analysis technology based on artificial intelligence. By gradually implementing the above technical research means, we can achieve a comprehensive study of the influencing factors of single factors and multiple factors on large-scale pollutant removal equipment.

[0044] In this embodiment, see Figure 1 First, the wet desulfurization process flow was described, and mechanistic and mathematical models of the multiphase flow and desulfurization within the desulfurization tower were constructed. Specifically, the structural characteristics of the wet desulfurization tower were analyzed, including the flow, heat and mass transfer within typical wet desulfurization towers, including spray and bubble towers, as well as the calcium desulfurization process. Within the physical model, the structure was simplified and refined to determine the model structure. A mesh was drawn and a mesh-independence check was performed. Boundary conditions and solution methods were then established for the physical model, multiphase flow, and desulfurization process. The desulfurization mechanism of the wet desulfurization tower was then verified using real-world experimental and field measurements. The multiphase flow within the wet desulfurization tower was analyzed, and the three-dimensional dynamic mechanisms of the gas-liquid two-phase flow, heat and mass transfer, and desulfurization reaction were derived. Finally, the desulfurization performance of the spray and bubble chambers within the wet desulfurization tower was compared. Subsequently, an analysis of the factors affecting desulfurization in wet desulfurization towers was obtained based on the multiphase flow, heat and mass transfer, and desulfurization mechanism of wet desulfurization towers. Finally, based on six machine learning models such as XGBoost, the distribution of the influence index of the result parameters such as the desulfurization rate of the wet desulfurization tower, flue gas temperature, and oxidation fan current was explored. Based on the results of the influence weight analysis, the operating parameters of the wet desulfurization tower were analyzed, and optimization suggestions for the operating parameters of the wet desulfurization tower were proposed. Specifically, an integrated design method for flue gas pollutant control equipment includes the following steps:

[0045] 1.1 Wet desulfurization tower process flow

[0046] Since the multiphase flow field and mass transfer reaction in the wet desulfurization tower are the basis for determining its physical and chemical processes, and the special structure of the wet desulfurization tower also determines the characteristics of the multiphase flow and mass transfer reaction in the tower. In order to study the flue gas desulfurization mechanism of the wet desulfurization tower, the process flow of the wet desulfurization tower is first introduced, and the tower is divided into the spray chamber and the bubbling chamber according to its process, and described separately.

[0047] Based on the characteristics of a wet desulfurization tower, mathematical models for gas-liquid two-phase flow, heat transfer, and mass transfer within the tower, as well as a calcium-based desulfurization model, were constructed for each structure. Subsequently, an industrial-grade wet desulfurization tower was selected as the primary research object, and its key structures were extracted and obtained for physical model construction. After grid-independence verification, boundary conditions for each tower were determined based on the actual operating parameters of each tower. Finally, the multiphase flow and desulfurization process in the wet desulfurization tower were discretized to determine a solution method. The flue gas at the boiler tailgate undergoes multi-stage, multi-directional, and multi-angle physical and chemical reactions with the slurry, resulting in multi-stage desulfurization. For example, in the wet spray tower, the flue gas flows downstream to the slurry for a contact reaction, while in the bubbling chamber, the flue gas flows countercurrently through a bubble generator, generating bubbles that react with the slurry. The purified flue gas then flows through the wet desulfurization tower's demister for demisting and other treatments before exiting. Wet desulfurization towers primarily utilize various desulfurization methods, including calcium, ammonia, and magnesium.

[0048] 1.2 Construction of mathematical model

[0049] Since desulfurization in typical wet desulfurization towers such as spray towers mainly relies on slurry spraying to react with flue gas to achieve desulfurization, or in bubbling chambers or bubbling towers, desulfurization is mainly achieved by bubbles contacting with slurry in the slurry pool to achieve desulfurization, these centralized desulfurization methods are completely different. The Euler-Lagrange model is generally used to simulate the multiphase flow, heat transfer, and mass transfer in the spray chamber, while the Euler-Euler model is mainly used to simulate the multiphase flow, heat transfer, and mass transfer in the bubbling chamber.

[0050] Based on the gas-liquid multiphase flow characteristics of two typical wet desulfurization towers, the spray tower and the bubble tower, corresponding multiphase coupling algorithm models were constructed. The models focus on the standard κ-ε turbulence model, droplet motion model, liquid evaporation model, and double-membrane theory desulfurization model used in the spray chamber. Meanwhile, the RNG per-phase κ-ε turbulence model, bubble force model, multi-bubble population equilibrium model, and double-membrane theory mass transfer model are mainly used in the bubble tower.

[0051] 1.3 Construction of physical model

[0052] 1.3.1 Wet desulfurization tower

[0053] Due to the extremely complex internal structure of the wet flue gas desulfurization tower, the non-main desulfurization structures such as the flue gas duct system, limestone slurry preparation system, gypsum dehydration, and process wastewater discharge system contained in the main flue gas desulfurization chamber are not considered for the time being. This helps to draw out the main structures of the spray tower such as the flue gas inlet, spray chamber cavity, and baffle, or the main structures of the bubble tower such as the scattering tube, slurry pool, scattering plate, bubble chamber cavity, and flue gas outlet.

[167] .

[0054] To allocate computing resources as rationally as possible and ensure efficient simulation, the structures of the spray chamber and bubbling chamber were simplified. Within the simplified model, half of the symmetrical structure was taken for physical model rendering, or the structure or portion that did not affect the main gas-liquid flow pattern was extracted for physical model rendering.

[0055] 1.4 Solution Algorithm

[0056] In a typical wet desulfurization spray chamber tower, the physical quantities in the flue gas and slurry vary little over time, and the turbulent pulsation is relatively low. Therefore, the spray tower multiphase flow and heat and mass transfer processes are mainly simulated using steady-state simulation, and the reaction is regarded as a steady-state process. In the spray tower multiphase flow and desulfurization parallel algorithm program, the flue gas phase and droplet phase flow fields are solved alternately, including the following process: First, the flue gas flow field and droplet phase flow field are initialized, and the simple algorithm is used to solve the gas phase velocity and pressure field.

[173] In a typical bubble tower, since the physical quantities in the flue gas and slurry vary greatly with time and the turbulent pulsation is relatively low, the unsteady simulation is mainly used for the multiphase flow and heat and mass transfer process in the bubble tower, and the reaction is regarded as an unsteady process.

[172] .

[0057] After model validation, the single-factor variable influencing factors of wet desulfurization tower desulfurization were studied. For example, the desulfurization rate was verified to be affected by multiple or multivariate parameters such as spray volume, slurry level, pH, flue gas temperature changes, and flue gas pressure drop.

[0058] Subsequently, a typical artificial intelligence model was used to study the factors affecting the removal of multiple pollutants in wet desulfurization towers. For example, XGboost was used to construct a ranking of multi-factor influencing indicators of typical wet desulfurization towers, thereby obtaining a ranking of the influence indexes of various results in the wet desulfurization towers, such as flue gas desulfurization rate, flue gas outlet temperature, flue gas outlet pressure, fan energy consumption, and slurry consumption in the tower.

[0059] Application level:

[0060] Given the unique distribution of influence indices for key parameters, such as desulfurization rate, in each typical wet desulfurization tower, an importance index analysis of outcome parameters, such as desulfurization rate, flue gas outlet temperature, flue gas outlet pressure, and fan energy consumption, can optimize the adjustment space for operating parameters of different wet desulfurization towers. By optimizing operating parameters in a targeted manner and compressing or reducing the adjustment space for operating parameters, the operator can avoid the need to simultaneously or repeatedly adjust parameters such as pH value, liquid-gas ratio, and circulating slurry volume, and can control the outlet SO2 concentration at a low and stable level in the shortest possible time.

[0061] The prototype design of an industrial-scale wet flue gas desulfurization tower is shown in the figure. Due to the extreme complexity of the actual internal structure of a wet flue gas desulfurization tower, non-core desulfurization structures, such as the flue gas ducting system, limestone slurry preparation system, gypsum dehydration, and process wastewater discharge system, are not considered. This physical model separately depicts the prototype's main spray chamber structure, including the flue gas inlet, spray chamber cavity, and scattering tube, and the main bubbling chamber structure, including the scattering tube, slurry tank, scattering plate, bubbling chamber cavity, and flue gas outlet. Furthermore, considering that the spray chamber and bubbling chamber of the wet flue gas desulfurization tower are interconnected by the scattering tube, the flue gas flows downstream in the middle chamber spray chamber above the scattering tube and countercurrently in the lower chamber bubbling chamber below the scattering tube, and that backmixing is virtually nonexistent in the scattering tube, the spray chamber and bubbling chamber of the wet flue gas desulfurization tower are modeled separately. To maximize the allocation of computing resources and ensure efficient simulation, the structures of the spray chamber and bubbling chamber are simplified.

[0062] Since the immersion depth of the scattering tube is linearly related to the liquid level of the slurry pool, it can be adjusted through the slurry circulation system and is an adjustable and controllable parameter. Figure 6 The inlet flue gas temperature of the wet desulfurization tower desulfurization model in (a) is 413K and the SO2 concentration is 3000mg / m 3 The initial mass fraction of CaCO3 in the slurry is 13.3%, the pH is 5.5, the slurry temperature is 310K, and the initial liquid-gas ratio is 3.03L / Nm 3 . When the immersion depth of the scattering tube of the wet desulfurization tower increases from 0.1m to 0.25m, the measured spray tower desulfurization rate increases from 91% to 98.4%, and the simulated spray tower desulfurization rate increases from 94% to 98%, with an error of less than 7%. Among them, the desulfurization rate of the bubbling chamber increases from 48% to 81.5%. When the immersion depth of the scattering tube increases, the turbulent kinetic energy of the gas-liquid two phases increases because deeper slurry is impacted by bubbles and participates in back mixing. Therefore, the residence time of the flue gas in the slurry and the gas-liquid contact area in the bubbling chamber increase, thereby greatly improving the SO2 absorption rate of the bubbling chamber. Secondly, since the scattering tube is connected to the outlet of the spray chamber, it does not affect the multiphase flow and heat transfer of the spray chamber. In addition, when the immersion depth of the scattering tube exceeds 0.2m, the increase in the desulfurization rate of the wet desulfurization tower gradually flattens with the increase in the immersion depth of the scattering tube. From Figure 6 Figure (b) shows that the ratio of the SO2 absorption rates of the spray chamber and the bubbling chamber is significantly affected by the immersion depth of the scattering tube. As the immersion depth increases from 0.1m to 0.25m, the ratio decreases from 1.85 to 1.01, indicating that increasing the immersion depth of the scattering tube significantly increases the desulfurization rate of the bubbling tank. However, when the immersion depth of the scattering tube is too high, problems such as wear and corrosion of the scattering tube and increased overall power and energy consumption occur. Therefore, the recommended immersion depth of the scattering tube in practice is (0.15m, 0.2m).

[0063] The XGB model mainly adopts the leaf-wise method. Since it focuses on selecting the leaf nodes with the largest benefits to split and advance, it reduces the content of data or the number of eigenvalues ​​to a certain extent, and reduces the probability of overfitting. After the data cleaning process, the prediction results of the XGB and LGB regression models for desulfurization rate are compared. Figure 7 (a)-(b). Among them, the closer the point in the figure is to the y=x function graph, the higher the degree of fit of the model to the output variable. Although LGB does not have a great overfitting, its prediction performance for desulfurization rate is not strong enough due to its predicted value. The XGB model uses a penalty reduction mechanism to reduce each leaf node tree and participation level (weight), and occasionally picks up some tree properties to fuse the splitting algorithm to build a tree. Since this model mainly uses the bucket splitting method (level-wise), although the weights of each tree are close on the surface, the Taylor second-order expansion used for the loss function makes it less likely to overfit. When the direction of calculating the maximum information gain is used to judge the structure of the tree and the splitting direction of the missing value, it has strong support for sparse data, see Figure 7 (b). At the same time, since the XGB model needs to import all data into memory and participate in splitting, while the LGB model has targeted screening of values ​​and features, the XGB model has a larger computational load than the LGB model. However, since the LGB model uses the split gain method when traversing the nodes during calculation, the calculation cycle is too long and the efficiency is lower than that of the XGB. Figure 7 (b) It can be seen that almost all points on the desulfurization rate prediction values ​​calculated by the XGB model are very close to the straight line, and the prediction responds well to the measured data. The prediction results are in good agreement with the measured data, indicating that the XGB model is more effective and adaptable.

[0064] After the model is established using the XGBoost algorithm, the parameter importance distribution can be obtained by calculating the characteristic frequencies of the characteristic indicators related to the desulfurization rates F1 to F5 in all classification trees. Figure 8(a) represents five important indicators related to the desulfurization rate of the wet desulfurization tower. Among the various characteristic indicators, the slurry level in the slurry pool has the greatest impact on the desulfurization rate, accounting for 46%, while the flue gas inlet and outlet pressure difference has an impact of 16.5%. This result shows that the desulfurization efficiency is most affected by the slurry pool liquid level depth. That is, the higher the liquid level, the less SO2 is discharged, and the better the desulfurization efficiency. In actual operation, in order to meet environmental protection and economic requirements, the flue gas inlet and outlet temperatures are controlled within a very small range of variation to maintain a high desulfurization rate and avoid corrosion. Therefore, the impact of flue gas temperature drop on the desulfurization rate is lower than that of the slurry pool liquid level depth and flue gas pressure drop. Although the pH value of the model has a limited range of variation, its impact on SO2 accounts for nearly 14%, which also reflects the significant impact of pH on desulfurization efficiency.

[0065] Depend on Figure 8 (b) shows two important indicators related to the total current consumption of the fan, among which the oxidation duct pressure has the largest weight, while the slurry pool level has a certain weight. This is because in order to maintain the flow rate of the inlet flue gas (10-20m / s), a larger oxidation duct pressure (>20pa) is required to overcome the resistance generated by the liquid. The higher the slurry pool level, the greater the resistance of the slurry pool area to the flue gas, and therefore the more fan current needs to be consumed. Finally, Figure 8 (c) In terms of the influencing factors of flue gas outlet temperature, flue gas inlet temperature has the greatest impact. Since the slurry temperature and slurry level change relatively little while the inlet flue gas temperature changes significantly, the flue gas outlet temperature is significantly affected by the inlet temperature.

[0066] Compared with the prior art, the present invention has the following beneficial effects:

[0067] A. Compared with the traditional single research method, this technology uses a variety of research methods coupled to obtain the influencing factors of multiple factors on desulfurization and pollution removal of large-scale pollutant removal equipment.

[0068] B. Through hierarchical technical research methods, we can achieve mutual support, matching, coupling and verification of technologies. However, traditional experimental technology is single and due to experimental errors, it is difficult to obtain stronger technical verification by experimental measurement alone.

[0069] C. Combining single-factor impact analysis and multi-factor weight analysis for desulfurization and pollution removal of large-scale pollutant equipment can better meet the needs of different commercial power plants or pollution removal equipment factories. As national environmental protection policies become stricter, traditional field measurements may be difficult to meet factory needs in the future.

[0070] The above specific implementation manner is a preferred embodiment of the present invention and does not limit the present invention. Any other changes or other equivalent replacement methods that do not deviate from the technical solution of the present invention are included in the protection scope of the present invention.

Claims

1. An integrated design method for flue gas pollutant control equipment, characterized in that: include: S1. Based on the process flow of a wet desulfurization tower, a mathematical model of multiphase flow, heat transfer, mass transfer, and calcium desulfurization in the wet desulfurization tower is constructed. The wet desulfurization tower includes a spray scattering tower; the spray chamber of the wet desulfurization tower includes a flue gas inlet, a spray chamber cavity, and a scattering tube; the bubbling chamber includes a scattering tube, a slurry pool, a scattering plate, a bubbling chamber cavity, and a flue gas outlet; the spray chamber and the bubbling chamber are interconnected through the scattering tube; S2, extract the structure of the wet desulfurization tower and construct a physical model of the wet desulfurization tower; draw a grid for the physical model of the wet desulfurization tower and perform a grid-independence test, then construct its boundary conditions and solution methods based on its physical model and its multiphase flow, heat and mass transfer, and desulfurization reaction characteristics; the multiphase flow, heat and mass transfer in the spray chamber are simulated using the Euler-Lagrangian model, and the multiphase flow, heat and mass transfer in the bubble chamber are simulated using the Euler-Euler model; the physical and chemical changes and operation information in the spray chamber and the bubbling chamber are modeled and process diagnosed respectively; the spray chamber adopts the standard κ-ε turbulence model, droplet motion model, liquid phase evaporation model and double-film theory desulfurization model; the bubbling chamber adopts the RNG Per-phase κ-ε turbulence model, bubble force model, multi-bubble population equilibrium model and double-film theory mass transfer model; S3 analyzes the multiphase flow process within the wet desulfurization tower and obtains the gas-liquid two-phase flow, heat and mass transfer, and desulfurization reaction characteristics within the wet desulfurization tower, demonstrating the desulfurization performance of each sub-region within the wet desulfurization tower. The multiphase flow and heat and mass transfer processes in the spray chamber and bubbling chamber are calculated separately and coupled using a coupling algorithm. In the spray scattering tower, the simple and PC simple algorithms are used to solve the velocity and pressure fields of the flue gas flow field and the droplet phase flow field, respectively. S4, using experimental and field-measured real-world values ​​to verify the desulfurization mechanism of the wet desulfurization tower, and to conduct a single-factor variable influencing factor analysis of the wet desulfurization tower, thereby obtaining a preliminary analysis of the influencing factors of the wet desulfurization tower; verifying the effect of the immersion depth of the scattering tube on the desulfurization rate; when the immersion depth of the scattering tube increases, deeper slurry is impacted by bubbles and participates in backmixing, increasing the turbulent kinetic energy of the gas-liquid two-phase; S5, based on artificial intelligence models, analyzes and comprehensively evaluates the multi-factor impact of wet desulfurization tower operating parameters to facilitate the optimal design of wet desulfurization towers; The XGboost artificial intelligence model is used to construct a ranking of multi-factor influencing indicators of the wet desulfurization tower. Based on the ranking analysis results, the operating parameters of the wet desulfurization tower are analyzed to propose optimization suggestions for the operating parameters of the wet desulfurization tower.

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