Denitration ammonia spraying flue gas mixing system and ammonia spraying control method thereof

Abstract

The invention relates to the technical field of flue gas pollutant control of a thermal power plant, and discloses a denitration ammonia spraying flue gas mixing system and an ammonia spraying control method thereof.The denitration ammonia spraying flue gas mixing system comprises a sensing module used for measuring three-dimensional space distribution data of a flue gas flow velocity field, a temperature field and a component concentration field in an SCR reactor inlet flue in real time; the prediction module is connected with the output end of the sensing module; the collaborative decision module is connected with the output end of the prediction module; and the jet flow module is connected with the output end of the collaborative decision module. In the invention, the function of the control system is expanded from the traditional chemical process adjustment to the active molding and management of the physical environment of the reactor, and the system can accurately predict the deposition position of the fly ash through the solid-phase particulate matter transport model, so that the dual-channel jet flow module can be instructed to accurately predict the deposition position of the fly ash before the formation of the deposited ash. And targeted airflow injection is carried out on a high-risk area, so that a local flow field is changed, and fly ash particles are difficult to attach and accumulate.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of flue gas pollutant control technology of thermal power plants, and in particular to a denitration ammonia injection flue gas mixing system and an ammonia injection control method thereof. BACKGROUND

[0002] In the flue gas pollutant control system of thermal power plants, the selective catalytic reduction (SCR) technology is the most widely used core technology for controlling nitrogen oxide emissions at present. The basic working principle is that in the reactor, by using the catalytic action of the catalyst, a reducing agent mainly composed of ammonia is injected into the flue gas, so that it reacts with the nitrogen oxides in the flue gas to generate nitrogen and water harmless to the atmosphere. In typical prior art practice, the injection of ammonia gas is realized by setting a fixed ammonia injection grid upstream of the catalyst bed, and the total ammonia injection amount is adjusted through a feedback control loop based on the monitoring value of the NOx concentration at the outlet of the SCR reactor, so as to control the final NOx concentration within the environmental protection standard limit.

[0003] However, the above-mentioned prior art has several inherent defects in actual application, especially when dealing with frequent load change conditions of current generating units. First of all, the core hardware ammonia injection grid and control logic are both based on an idealized assumption that the flue gas in the flue is uniform, but in actual operation, the spatial distribution of flue gas flow rate, temperature and NOx concentration is extremely uneven and dynamic, and the fixed ammonia injection mode cannot adapt to such changes, resulting in a mismatch of the molar ratio of ammonia to NOx in the local area of the catalyst surface, which not only reduces the denitration efficiency and ammonia utilization rate, but also causes ammonia escape problems. Secondly, this uneven reaction environment causes irreversible damage to the catalyst itself, part of the area is accelerated sintering and deactivation due to long-term high temperature or high ammonia concentration, while another part of the area may be poisoned and blocked due to insufficient temperature or by-product generation, eventually leading to non-uniform aging of the overall catalyst, significantly shortening its effective life. Finally, the uneven flue gas flow field also induces the deposition of fly ash particles in certain low-speed areas, gradually forming physical ash deposition, which increases the system resistance and improves the operating energy consumption, and in severe cases, it even threatens the safe and stable operation of the unit. SUMMARY

[0004] The technical problem to be solved by the present application is that the fixed ammonia injection and control mode in the prior art cannot adapt to the dynamic uneven distribution of flue gas parameters in space in actual operation, resulting in the shortcomings of low denitration efficiency, ammonia escape, non-uniform aging of the catalyst and ash deposition. Therefore, we propose a denitration ammonia injection flue gas mixing system and an ammonia injection control method thereof.

[0005] In order to achieve the above-mentioned purpose, the following technical scheme is adopted in the present application: a denitration ammonia injection flue gas mixing system, characterized in that it comprises: a perception module configured to measure the three-dimensional spatial distribution data of the velocity field, temperature field and component concentration field of the flue gas in the SCR reactor inlet flue in real time; a prediction module connected to the output end of the perception module, wherein a flue gas flow model, a catalytic reaction kinetics model and a solid particle transport model are embedded in the prediction module, and the prediction module is configured to predict the three-dimensional flue gas field distribution at a future time, generate an activity state distribution map of each region of the catalyst bed layer, and generate an expected deposition risk distribution map of fly ash particles in the flue gas at the catalyst inlet cross section based on the three-dimensional spatial distribution data and the unit operation parameters; a collaborative decision-making module connected to the output end of the prediction module, configured to generate partitioned control instructions including ammonia liquid injection parameters and gas jet parameters as optimization targets of system denitration efficiency, catalyst activity balance and catalyst bed layer physical patency according to the predicted three-dimensional flue gas field, catalyst activity state distribution map and fly ash deposition risk distribution map; a jet module connected to the output end of the collaborative decision-making module, wherein the jet module is provided with a plurality of independently controllable jet units on the flue cross section, each jet unit is provided with a liquid channel for injecting ammonia liquid and a gas channel for injecting compressed gas to respectively execute the ammonia liquid injection parameters and the gas jet parameters.

[0006] Preferably, the perception module comprises an acoustic tomography sensor array for measuring the temperature field and the velocity field, and a laser spectral absorption sensor grid for measuring the component concentration field.

[0007] Preferably, the prediction module calculates and generates the fly ash deposition risk distribution map by a Lagrangian particle tracking algorithm combined with the predicted three-dimensional flue gas velocity field.

[0008] Preferably, the liquid channel of each jet unit of the jet module is provided with an adjustable liquid control valve at the outlet, and the gas channel is provided with a gas control valve that can be independently switched and adjusted in flow from the liquid control valve.

[0009] Preferably, the collaborative decision-making module is configured to execute a preventive soot cleaning operation mode, in which the collaborative decision-making module identifies high-risk areas according to the fly ash deposition risk distribution map and issues instructions to the jet units in the corresponding areas to make the gas channels of the jet units inject pulsed or continuous gas jets to change the local flue gas flow lines and prevent the deposition of fly ash particles in the areas.

[0010] Preferably, the collaborative decision-making module calculates the ammonia liquid injection mass flow of each jet unit by the following formula: ​wherein and is a predicted value, is a comprehensive decision weight factor calculated by the collaborative decision module.

[0011] Preferably, the comprehensive decision weight factor is determined jointly by a reaction efficiency weight , a catalyst activity maintenance weight , and a physical passability weight .

[0012] Preferably, the collaborative decision module, when determining the catalyst activity maintenance weight , gives a weight coefficient less than 1 to a region with high activity in the catalyst activity state distribution map, and gives a weight coefficient greater than 1 to a region with low activity, so as to balance the long-term chemical load of the catalyst bed.

[0013] In addition, the present application also relates to an embodiment, specifically a kind of ammonia injection control method of denitration ammonia injection flue gas mixing system, comprising the following steps: S1: real-time measurement of three-dimensional space distribution parameters of flue gas in the inlet of SCR reactor; S2: based on the three-dimensional space distribution parameters, predict the flue gas field distribution at future time, and evaluate the activity state distribution map and fly ash deposition risk distribution map generated by catalyst bed; S3: taking denitration efficiency, catalyst activity balance degree and bed physical passability as comprehensive optimization target, independently calculate control instruction containing ammonia liquid injection parameter and gas jet parameter for multiple regions divided in flue cross section; S4: control the jet unit corresponding to each region, and independently execute ammonia liquid injection operation and / or gas jet operation according to the control instruction.

[0014] Preferably, the step S4 comprises: when the fly ash deposition risk prediction value of any region exceeds a preset threshold, preferentially executing the gas jet operation for the region to actively intervene in local aerodynamic environment and prevent the occurrence of ash deposition blockage The technical effects and advantages of the present application are: In the present application, the function of the control system is expanded from the traditional chemical process regulation to the active shaping and management of the reactor physical environment, because the system can accurately predict the deposition position of fly ash through its solid-phase particle transport model, so it can instruct the jet module to perform targeted air flow injection on high-risk areas before the formation of fly ash, this active aerodynamic intervention changes the local flow field, making it difficult for fly ash particles to adhere and accumulate, its direct effect is to avoid the physical plugging of the catalyst, thereby eliminating the resulting flue pressure difference rise, induced draft fan power consumption increase and unplanned shutdown purging, significantly improving the availability and economy of the unit.

[0015] In the present application, the catalyst life can be extended in two ways, first, through the balanced regulation of catalyst activity maintenance weights, it avoids the premature failure of some catalyst regions due to long-term overwork, and realizes the balanced decay of chemical activity; second, through the preventive fly ash removal function, it avoids the scrapping of the catalyst due to physical plugging, because the chemical activity and the integrity of the physical structure are maintained at the same time, so the comprehensive effective life of the catalyst is maximized.

[0016] In the present application, the three goals of denitration efficiency, catalyst life and equipment unobstructedness, which were previously independent or even conflicting, are included in a unified collaborative decision-making framework for global optimization, the decision is based on a comprehensive prediction of the future, the system can make a globally optimal choice, to ensure that the outlet meets the standard, and to achieve operation with the smallest catalyst loss and plugging risk, so as to minimize the total cost of the system throughout its life cycle and maximize the overall operation benefit. BRIEF DESCRIPTION OF DRAWINGS

[0017] The disclosure of the present application will be described with reference to the accompanying drawings. It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present application. In the drawings, the same reference numerals are used to refer to the same parts: Figure 1 is a module relationship diagram of the system of the present application; Figure 2 is a method flowchart of the present application. DETAILED DESCRIPTION

[0018] It is easy to understand that, according to the technical scheme of the present application, those skilled in the art can propose a variety of structures and implementation ways that can be replaced with each other without changing the essential spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical scheme of the present application, and should not be considered as the whole or as a limitation or restriction on the technical scheme of the present application.

[0019] As Figure 1As shown, a denitrification ammonia injection flue gas mixing system consists of four core modules: a sensing module, a prediction module, a collaborative decision-making module, and a jet module. These four modules are progressive in function and tightly coupled in data, forming a complete forward-looking intelligent control system.

[0020] For the perception module: This sensing module serves as the system's real-time data source. Its function is to comprehensively and accurately measure the changing flue gas state within the SCR reactor inlet flue. To achieve this function, the module preferably consists of two sensor arrays: Acoustic tomography sensor array: Multiple pairs of ultrasonic transmitters and receivers are arranged in a matrix along the circumference and axial direction of the flue wall. By measuring the propagation time of sound waves on different paths, algebraic reconstruction technology and tomographic imaging algorithm are used to perform high-speed inversion calculations on a large amount of path data. This array can reconstruct the temperature field and velocity field distribution of any cross section inside the flue in real time.

[0021] Laser spectral absorption sensor grid: Multiple pairs of tunable diode lasers and photodetectors are arranged on the same or adjacent flue gas sections to form a measurement grid covered by multiple intersecting optical paths. By analyzing the absorption attenuation of laser beams of specific wavelengths after passing through the flue gas and combining the absorption data from different optical paths, this grid can measure the concentration of laser light in the flue gas, either in sections or as a whole. Concentration and Real-time distribution of concentration.

[0022] The data from these two sensor arrays are aligned and integrated in time and space by a data fusion processor, and finally output to the downstream prediction module in the form of a three-dimensional data matrix.

[0023] For the prediction module: This prediction module is responsible for sensing the current state and predicting the future state. It receives real-time 3D data streams from the sensing module and global operating parameters from the power plant's distributed control system. Internally, this module runs three interconnected prediction and evaluation models for different physicochemical processes in parallel: Flue gas flow model: This model is a data-enhanced model based on computational fluid dynamics principles and continuously corrected using historical operating data. It can quickly solve the simplified Navier-Stokes equations based on the current boundary conditions, thereby accurately predicting the evolution trend of the three-dimensional flue gas velocity field and temperature field of the entire flue in a short period of time in the future.

[0024] Catalytic reaction kinetics model: This model generates a real-time distribution map of the catalyst's active state, logically dividing the catalyst bed into segments corresponding to jet units. A microelement, for each microelement , the model can keep track of its cumulative running time, experienced temperature history and reactant concentration history, and calculate an online quantified relative activity coefficient based on a pre-defined catalyst performance degradation sub-model based on Arrhenius equation and deactivation empirical formula .

[0025] Solid particle transport model: This model is used to predict the risk of physical plugging, generate a fly ash deposition risk distribution map, release a large number of virtual fly ash particles in the predicted future flue gas flow field using Lagrangian particle tracking method, calculate the force on each particle in the complex flow field, including drag force, gravity, lift force, etc., and the motion trajectory, and the model can count the number of particles that collide or may deposit due to low speed per unit time in each microelement at the catalyst inlet cross section, and after normalization, the fly ash deposition risk index of the unit is obtained .

[0026] For the collaborative decision-making module: This module is the decision-making center of the system, which receives the flue gas field, activity map and risk map from the prediction module, and executes a multi-objective optimization algorithm, which calculates a set of optimal control instructions for each jet flow unit, i.e. ammonia liquid injection mass flow and gas jet flow . .

[0027] The specific formula for calculating the ammonia liquid injection flow of this module is as follows: , wherein: is the ammonia gas mass flow assigned to the th jet flow unit, with the unit of .

[0028] is the concentration of the th region at the future time obtained by the prediction module , with the unit of .

[0029] is the flue gas normal flow velocity of the th region at the future time obtained by the prediction module , with the unit of .

[0030] is the cross-sectional area of the th region, which is a design constant with the unit of .

[0031] The target global ammonia-nitrogen molar ratio set for the system is a dimensionless operating parameter.

[0032] , Here, are the molar masses of ammonia and nitrogen oxides, respectively; are physical constants, with units of . .

[0033] It is a comprehensive decision weighting factor, dimensionless, which couples multiple optimization objectives of different dimensions through a multiplicative model. Its expression is: ,in: This is the reaction efficiency weight, which is mainly determined by the predicted temperature. The decision was made that the functional relationship is set to be close to 1 within the optimal activity temperature range of the catalyst, and less than 1 when deviating from the optimal range, so as to ensure that the chemical reaction takes place under the most efficient conditions. This is the catalyst activity maintenance weight, which is used to achieve long-term equilibrium of the chemical load on the catalyst bed. Its preferred calculation formula is: ,in, It is calculated by the prediction module The relative activity coefficient of the catalyst in a unit; It is the average activity coefficient of the entire bed, representing the current overall health level of the catalyst; It is an adjustment coefficient with a value between 0 and 1, used to adjust the strength of the balancing strategy. The mechanism of this formula is that for areas with activity higher than the average value, its weight will be less than 1, thereby appropriately reducing its ammonia injection load. Conversely, for areas with activity lower than the average value, its weight will be greater than 1, so as to appropriately increase its load, thereby delaying the overall aging.

[0034] This is the physical accessibility weight, which is directly related to the risk of fly ash deposition. The preferred calculation formula is as follows: ,in It is calculated by the prediction module The fly ash deposition risk index of the unit; It is an adjustable influence coefficient. This formula allows for a proactive reduction in the ammonia injection weight in areas with higher ash accumulation risk, because liquid injection increases particle adhesion, and reducing the amount of ammonia injected helps to slow down ash accumulation.

[0035] In addition, the gas jet flow rate of this module The decision-making logic is set as a threshold-based triggering mechanism: when Exceeding a preset security threshold At that time, the gas channel of the unit is activated. configured to a flow rate value sufficient to generate an effective purging or disturbance, thereby performing a preventive soot blowing operation.

[0036] For the jet flow module: The module is the physical execution mechanism to realize the above-mentioned decision, abandoning the traditional ammonia injection grid, and through a matrix composed of Each jet flow unit has a double-channel design: The liquid channel: the inlet is connected with the ammonia liquid supply main pipe, and the outlet is provided with a liquid control valve capable of rapid and accurate flow regulation, preferably a pulse width modulation solenoid valve, for executing ammonia liquid injection instructions .

[0037] The gas channel: the inlet is connected with the compressed air supply main pipe, and the outlet is provided with a gas control valve capable of switching and flow regulation independently of the liquid control valve. The channel is used to execute gas jet instructions .

[0038] The nozzles of the two channels are integrally designed to ensure effective jet flow patterns in separate injection or mixed injection. This functional separation in hardware gives the system flexibility, which can be used as a chemical reactant dosing device and an aerodynamic intervention tool, thereby providing a physical basis for realizing preventive soot blowing and other advanced maintenance functions.

[0039] Through the precise cooperation of the above four modules, the invention integrates the originally separate denitration efficiency control, catalyst life management and equipment physical maintenance into an organic and intelligent whole, realizing a fundamental change from passive response to proactive active management.

[0040] As shown in Figure 2 The present application also relates to an ammonia injection control method for a denitration ammonia injection flue gas mixing system, specifically comprising the following steps: Step 1: Perform the sensing step, continuously measure the three-dimensional spatial distribution parameters of flue gas flow rate, temperature and component concentration in real time through the sensing module deployed at the inlet of the SCR reactor, to provide accurate, full-section instantaneous operating data for the system.

[0041] Step 2: Perform the prediction and evaluation step, input the sensed real-time data and unit operation parameters into the prediction module. The module not only predicts the three-dimensional flue gas field distribution state in the future short period of time, but more importantly, also uses the built-in catalyst performance degradation model and particulate matter transport model to online evaluate and generate two key diagnostic maps: one is the catalyst activity state distribution map reflecting the chemical health status of each region of the catalyst, and the other is the fly ash deposition risk distribution map warning the risk of physical plugging.

[0042] Step 3, decision-making step, the collaborative decision-making module receives the above-mentioned prediction results and diagnostic atlas, the module optimizes the three targets of system denitration efficiency, long-term balance of catalyst activity, and physical unobstructedness of the catalyst bed, and independently calculates the optimal control instruction for each region divided on the flue cross section through a core algorithm containing multiple weight factors. The instruction specifically includes two parts: ammonia liquid injection parameters for chemical reaction and gas jet parameters for physical intervention.

[0043] Step 4, execution step, the jet module receives and accurately executes the decision-making instruction. Each jet unit independently executes ammonia liquid injection or gas jet operation through its liquid channel and / or gas channel according to the exclusive instruction it receives. This step includes a priority processing logic: when the fly ash deposition risk prediction value of a region exceeds the preset threshold, the system will preferentially execute the gas jet operation for that region to actively intervene in the local aerodynamic environment to prevent the occurrence of ash deposition blockage, thus placing the protection of the physical structure of the equipment in an important position.

[0044] The detailed working principle is as follows: the working principle of the system is a continuous closed-loop process of perception, prediction, decision-making, and execution. First, the perception module continuously collects acoustic and spectral data of the flue cross section to generate a three-dimensional distribution map reflecting the current flue gas temperature, flow rate, and concentration in real time. Subsequently, the prediction module receives these real-time data, deduces the dynamics of the flue gas in the next few minutes through fluid dynamics models, and updates the activity state distribution map of the catalyst bed based on real-time conditions and historical performance. At the same time, it calculates the fly ash deposition risk index that each region will face and transmits it to the collaborative decision-making module. The optimization algorithm of the module considers the following: how much ammonia is needed for each region to achieve denitration; whether some regions need to bear less chemical load to protect the catalyst; whether some regions need to be intervened by gas flow to prevent blockage; based on the above, the collaborative decision-making module calculates the optimal ammonia liquid injection amount and gas injection amount of the unit through the weight factor formula mentioned above, and finally, the instruction is issued to the jet module. The units of the module accurately execute the received instruction: the liquid channels of most units inject precisely metered ammonia liquid on demand, while the gas channels of a few units in the ash deposition risk area may be activated to inject pulsed gas flow, actively changing the local aerodynamic environment, thereby achieving the high integration of efficient denitration and equipment self-maintenance.

[0045] The technical scope of the present application is not limited to the above description, and those skilled in the art can make various modifications and changes to the above embodiments without departing from the technical idea of the present application, and these modifications and changes should all belong to the protection scope of the present application.

Claims

1. A flue gas mixing system for de-NOx ammonia injection, characterized by, The application comprises: a perception module configured to measure the three-dimensional spatial distribution data of the flue gas flow velocity field, temperature field and component concentration field in the SCR reactor inlet flue in real time; a prediction module connected to the output end of the perception module, which is embedded with a flue gas flow model, a catalytic reaction kinetics model and a solid particle transport model, and is configured to predict the three-dimensional flue gas field distribution at a future time, generate an activity state distribution map of each region of the catalyst bed layer, and generate an expected deposition risk distribution map of fly ash particles in the flue gas at the catalyst inlet cross section based on the three-dimensional spatial distribution data and the unit operation parameters; a collaborative decision-making module connected to the output end of the prediction module, which is configured to generate a partitioned control instruction containing ammonia liquid injection parameters and gas jet parameters according to the predicted three-dimensional flue gas field, catalyst activity state distribution map and fly ash deposition risk distribution map, with the system denitration efficiency, catalyst activity balance and catalyst bed layer physical patency as optimization targets; a jet module connected to the output end of the collaborative decision-making module, which is provided with a plurality of independently controllable jet units on the flue cross section, each of which is provided with a liquid channel for injecting ammonia liquid and a gas channel for injecting compressed gas to respectively execute the ammonia liquid injection parameters and the gas jet parameters.

2. The denitration ammonia injection flue gas mixing system according to claim 1, characterized in that: The perception module comprises an acoustic tomography sensor array for measuring the temperature field and flow velocity field, and a laser spectral absorption sensor grid for measuring the component concentration field.

3. The ammonia injection grate gas mixing system of claim 1, wherein: The prediction module calculates and generates the fly ash deposition risk distribution map by the Lagrangian particle tracking algorithm combined with the predicted three-dimensional flue gas flow velocity field.

4. The denitration ammonia injection flue gas mixing system according to claim 1, characterized in that: Each jet unit of the jet module is provided with a liquid control valve at the outlet of the liquid channel for adjusting the injection flow rate, and a gas control valve at the outlet of the gas channel for independently switching and adjusting the flow rate of the gas jet.

5. The denitration ammonia injection flue gas mixing system according to claim 1, characterized in that: The collaborative decision-making module is configured to execute a preventive fly ash removal operation mode, in which the collaborative decision-making module identifies high-risk areas according to the fly ash deposition risk distribution map, and issues instructions to the jet units corresponding to the areas to make the gas channels of the jet units inject pulsed or continuous gas jets to change the local flue gas flow lines and prevent the deposition of fly ash particles in the areas.

6. The de-NOx ammonia injection flue gas mixing system of claim 1, wherein: The synergic decision module calculates the ammonia liquid injection mass flow of each jet flow unit by the following formula : m = (P * A) / (R * T) : wherein and is a predicted value, is a comprehensive decision weight factor calculated by the collaborative decision module, is the cross-sectional area of the region, is a target global ammonia nitrogen molar ratio set by the system, , are the molar masses of ammonia and nitrogen oxides, respectively.

7. The de-NOx ammonia injection flue gas mixing system according to claim 6, wherein: The integrated decision weight factor is determined by the reaction efficiency weight , the catalyst activity maintenance weight and the physical accessibility weight together.

8. The denitration ammonia injection flue gas mixing system according to claim 7, characterized in that: The synergic decision module assigns a weight factor less than 1 to regions of the catalyst activity status profile with high activity and a weight factor greater than 1 to regions of the catalyst activity status profile with low activity when determining the catalyst activity maintenance weight to balance the long-term chemical load of the catalyst bed.

9. A method for controlling ammonia injection in a denitration ammonia injection flue gas mixing system, characterized by, The application comprises the following steps: S1: measuring the three-dimensional spatial distribution parameters of the flue gas in the SCR reactor inlet flue in real time; S2: predicting the flue gas field distribution at a future time based on the three-dimensional spatial distribution parameters, and evaluating and generating the activity state distribution map of the catalyst bed layer and the fly ash deposition risk distribution map; S3: taking the denitration efficiency, catalyst activity balance and bed layer physical patency as the comprehensive optimization targets, independently calculating the control instruction containing the ammonia liquid injection parameters and the gas jet parameters for the multiple regions divided on the flue cross section; S4: controlling the jet units corresponding to each region to independently execute the ammonia liquid injection operation and / or the gas jet operation according to the control instruction.

10. The method of claim 9, wherein the ammonia injection control method of the de-NOx ammonia injection flue gas mixing system is characterized by, The step S4 comprises: when the fly ash deposition risk prediction value of any region exceeds a preset threshold value, preferentially performing the gas jet operation for the region to actively intervene in the local aerodynamic environment and prevent the occurrence of the ash deposition blockage.

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