An intelligent conditioning system and method for strengthening the removal of multiple pollutants from sintering flue gas

Abstract

The present invention relates to an intelligent conditioning and strengthening system and method for removing multiple pollutants from sintering flue gas. The conditioning agent raw material is dispersed into conditioning agent particles with a set particle size by an air flow pulverization coupling electrostatic dispersion device, and the conditioning agent recycling device recycles and re-pulverizes the conditioning agent particles exceeding the set particle size. The conditioning agent particles meeting the set particle size requirements are sprayed into the upstream flue of the electrostatic precipitator through a Venturi tube and a swirl nozzle to achieve sintering flue gas conditioning. Further, in combination with the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator in the intelligent control subsystem, the composition, spraying amount and spraying frequency of the conditioning agent are adjusted in real time, so that the flue gas characteristics such as the fly ash resistivity are always in the best removal range of the electrostatic precipitator, ensuring that the particulate matter concentration at the outlet of the electrostatic precipitator is always below the set limit value, avoiding the decline of the reaction activity of the downstream SCR denitration system, and improving the removal efficiency of multiple pollutants such as particulate matter, nitrogen oxides, heavy metal mercury, sulfur dioxide and dioxins.

Description

Technical Field

[0001] The present invention belongs to the technical field of air pollutant treatment, and specifically relates to an intelligent conditioning and strengthening multi-pollutant removal system and method for sintering flue gas. Background Technique

[0002] Sintering is the process with the largest pollutant emissions in the entire iron and steel production process. The electrostatic precipitator for sintering machine head flue gas is an important core process equipment in the sintering process, which can capture and remove particulate matter containing a large amount of toxic and harmful components such as heavy metals and dioxins, and ensure the safe and stable operation of the subsequent denitrification and desulfurization processes. Therefore, enhancing the removal efficiency of the sintering machine head electrostatic precipitator is of great significance for the operation of the entire sintering flue gas purification system.

[0003] There is a large amount of high specific resistance dust in sintering flue gas, with high viscosity, and problems such as difficult dust charging and difficult charge release of charged dust, which often lead to a decrease in the efficiency of electrostatic precipitators.

[0004] Chinese Patent CN209531137U discloses a flue gas dust removal device, including a flue gas pipeline for collecting flue gas. A coagulator and an electrostatic precipitator are successively arranged on the flue gas pipeline. The flue gas pipeline is connected with a conveying pipeline for conveying a flue gas conditioner. One end of the conveying pipeline is connected to a storage tank for storing the flue gas conditioner, and the other end extends into the flue gas pipeline and is provided with a nozzle for spraying the flue gas conditioner into the flue gas pipeline; adopting the flue gas dust removal device of the present invention can reduce the specific resistance of high specific resistance dust and increase the particle size of fine dust, so as to greatly improve the dust removal efficiency of the electrostatic precipitator. At present, the flue gas conditioning technology can adjust the fly ash specific resistance by spraying a conditioner into the flue gas and improve the removal efficiency of the electrostatic precipitator. However, in the case of variable load or changes in the characteristics of flue gas dust (such as fly ash specific resistance), it is often difficult to accurately control the spraying amount of the conditioner, which is likely to cause waste of the conditioner, resulting in an increase in conditioning costs, and at the same time, it will also lead to a decrease in the conditioning effect, and it is difficult to stably achieve efficient capture of particulate matter.

[0005] Therefore, aiming at the deficiencies of the prior art, it is urgent to develop a system and method for intelligent conditioning and strengthening multi-pollutant removal of sintering flue gas, which can stably achieve efficient removal of particulate matter under variable load at low cost, improve the reaction activity of the downstream SCR denitrification system, promote the oxidation of zero-valent mercury and the degradation of dioxins in sintering flue gas, and at the same time avoid the decrease in the activity of wet desulfurization slurry. Furthermore, while achieving efficient removal of particulate matter, the removal efficiency of pollutants such as nitrogen oxides, heavy metal mercury, sulfur dioxide, and dioxins can be improved. Summary of the Invention

[0006] In order to overcome the deficiencies of the existing technologies, the present invention provides a system and method for intelligent conditioning of sintering flue gas to enhance the removal of multiple pollutants. By combining the conditioning subsystem with the intelligent control subsystem, a fly ash property model and a particulate matter concentration control model at the outlet of the electrostatic precipitator are established to optimize the operation of the flue gas conditioning system in real time, stably achieve high-efficiency particulate matter removal under variable loads at low cost, avoid the decline in the reaction activity of the downstream SCR denitration system, promote the oxidation of zero-valent mercury and the degradation of dioxins in the sintering flue gas, and at the same time avoid the decline in the activity of the wet desulfurization slurry, and improve the removal efficiency of multiple pollutants such as particulate matter, nitrogen oxides, heavy metal mercury, sulfur dioxide, and dioxins.

[0007] A system and method for intelligent conditioning of sintering flue gas to enhance the removal of multiple pollutants, the system comprising a conditioning subsystem, an intelligent control subsystem, and an electrostatic precipitator. The conditioning subsystem includes a conditioning agent raw material storage tank, an air flow crushing and electrostatic dispersion device, a screw feeder, a conditioning agent circulation device, a Venturi tube, and a swirl nozzle connected in sequence. The air flow crushing and electrostatic dispersion device includes a housing, a DC nozzle, and a discharge electrode. The conditioning agent circulation device includes a conditioning agent circulation tower and a conditioning agent particle size screening device. The intelligent control subsystem is composed of a fly ash property model and a particulate matter concentration control model at the outlet of the electrostatic precipitator. The air flow crushing and electrostatic dispersion device is connected to the conditioning agent raw material storage tank. The inlet of the conditioning agent circulation tower is connected to the outlet of the screw feeder. The inlet of the conditioning agent particle size screening device is connected to the outlet of the conditioning agent circulation tower. The Venturi tube is connected to the outlet of the conditioning agent particle size screening device, and the conditioning agent particles flow through the throat of the Venturi tube and are mixed with compressed air. The swirl nozzle is connected to the Venturi tube and is arranged in the upstream flue of the electrostatic precipitator. The intelligent control subsystem, based on the constructed fly ash property prediction model and the particulate matter concentration prediction and control model at the outlet of the electrostatic precipitator, changes the composition of the conditioning agent in real time and adjusts the injection amount and injection frequency of the conditioning agent.

[0008] Preferably, a control valve is provided on the pipeline connecting the air flow crushing and electrostatic dispersion device and the conditioning agent raw material storage tank.

[0009] Preferably, the housing of the air flow crushing and electrostatic dispersion device is in a cuboid shape, and the DC nozzles are symmetrically arranged on the four upper walls of the housing. The discharge electrode is a barbed discharge electrode, including a round rod and surface barbs. The diameter of the round rod is 2-8 mm, and the length of the barbs is 1-5 mm. A number of groups of barbs are arranged on the round rod, and the distance between each group of barbs is 30-70 mm.

[0010] Preferably, a control valve is provided on the connecting pipeline between the Venturi tube and the swirl nozzle, and the swirl nozzle is arranged in the upstream flue of the electrostatic precipitator.

[0011] The screw feeder is arranged downstream of the airflow crushing coupled with electrostatic dispersion device to control the feeding amount of the conditioning agent particles.

[0012] Preferably, the conditioning agent particles at the outlet of the screw feeder are transported into the conditioning agent recycling device through hot air, and at the same time, the re-agglomeration of the conditioning agent particles after dispersion can be effectively prevented by controlling the flow rate of the hot air.

[0013] Preferably, the critical separation particle size of the conditioning agent particle size sieve is 15 μm. The conditioning agent particles larger than 15 μm return to the airflow crushing coupled with electrostatic dispersion device for cyclic re-crushing to avoid poor conditioning effect caused by over-large particle size of the conditioning agent particles.

[0014] The conditioning agent particles are sprayed into the flue through a swirling nozzle at high speed to strengthen the collision and contact between the conditioning agent particles and the flue gas, increase the coverage area, improve the effective utilization rate of the conditioning agent, and at the same time, the charged and dispersed conditioning agent particles are sprayed into the flue to combine and agglomerate with the neutral particles in the flue gas into large particles for easy capture by the downstream electrostatic precipitator.

[0015] The conditioning agent adopts fly ash, active absorbent, etc. to realize the resource utilization of waste. The mass ratio of the conditioning agent injection amount to the flue gas particulate matter is 1:0.5 - 1:5.

[0016] The present invention also provides a method for intelligent conditioning and strengthening the removal of multiple pollutants from sintering flue gas. The conditioning agent raw material is dispersed into conditioning agent particles with a set particle size by an airflow crushing coupled with electrostatic dispersion device, and the conditioning agent recycling device recycles and re-crushes the conditioning agent particles exceeding the set particle size; the conditioning agent particles meeting the set particle size requirements are sprayed into the flue upstream of the electrostatic precipitator through a Venturi tube and a swirling nozzle to realize the conditioning of the sintering flue gas; at the same time, the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator constructed by the intelligent control subsystem are used to adjust the composition, injection amount and injection frequency of the conditioning agent in real time to improve the conditioning effect of the conditioning agent on the flue gas characteristics.

[0017] The conditioning agent of the present invention can adjust the fly ash specific resistance, reduce the fly ash viscosity of the sintering flue gas, strengthen the removal effect of the electrostatic precipitator on heavy metals, dioxins, particulate matter, etc., avoid the decline of the reaction activity of the downstream SCR denitration system, promote the oxidation of zero-valent mercury and the degradation of dioxins in the sintering flue gas, and at the same time avoid the decline of the activity of the wet desulfurization slurry, and improve the removal efficiency of multiple pollutants such as particulate matter, nitrogen oxides, heavy metal mercury, sulfur dioxide, dioxins, etc.

[0018] Preferably, the construction process of the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator is as follows:

[0019] (1) Based on the operating process characteristics of the electrostatic precipitator, an operating database of the electrostatic precipitator is established, covering fly ash characteristics, the operating voltage and current of the electrostatic precipitator, the sintering flue gas velocity and temperature, and the particulate matter concentration parameters at the inlet and outlet of the electrostatic precipitator;

[0020] (2) Based on the established operating database of the electrostatic precipitator above, a fly ash characteristics model is constructed using machine learning methods as follows:

[0021] Y = f(ρ, μ, T, α, β, N)

[0022] In the formula, Y is the fly ash characteristics, ρ is the fly ash specific resistance, μ is the flue gas viscosity, N is the atomic number percentage of fly ash elements such as K, Na, Li, Fe, Ca, Mg, and Al, T is the temperature, and α, β are the flue gas characteristic constants;

[0023] (3) Based on the established operating database of the electrostatic precipitator and the fly ash characteristics model above, an outlet concentration control model of the electrostatic precipitator is further established as follows:

[0024] W out = f(W in , t, v, Y, U, I)

[0025] In the formula, W out is the particulate matter mass concentration at the outlet of the electrostatic precipitator, W in is the particulate matter mass concentration at the inlet of the electrostatic precipitator, t is the sintering flue gas temperature, v is the sintering flue gas velocity, Y is the fly ash characteristics, U is the operating voltage of the electrostatic precipitator, and I is the operating current of the electrostatic precipitator.

[0026] Preferably, in step (3), the model is optimized based on the particle swarm optimization algorithm. For the electrostatic precipitation system, the optimization objective function is set as the relationship between the predicted outlet particulate matter concentration and the operating parameters of the conditioning subsystem, and the best operating parameters of the conditioning subsystem (such as the injection frequency and injection volume of the conditioner) are solved.

[0027] Preferably, the particle swarm optimization algorithm includes the following steps:

[0028] Step 1: Randomly initialize the positions (x) and velocities (v) of all N particles;

[0029] Step 2: Calculate the fitness of each particle;

[0030] Step 3: For each particle, compare its fitness value with the best position pbest it has passed through. If the current value is better, update the best position pbest;

[0031] Step 4: For each particle, compare its fitness value with the best position gbest it has passed through. If the current value is better, update gbest;

[0032] Step 5: Adjust the particle velocity and position;

[0033] Step 6: If the end condition (reaching the iteration number or accuracy requirement) is met, output the parameters and end; otherwise, go to Step 2;

[0034] The velocity update formula is as follows:

[0035] V i (t + 1) = wV i (t) + c1r1(pbest i (t) - x i (t)) + c2r2(gbest(t) - x i (t))

[0036] Where: w is the inertia weight; c1 and c2 are both learning factors; r1 and r2 are random numbers between 0 and 1; V i (t), V i (t + 1) are the particle migration velocities of particle i at times t and t + 1 respectively; x i (t), pbest i (t) are the historical position and historical optimal position experienced by particle i at time t; gbest(t) is the global optimal value at time t;

[0037] In addition to updating the particle velocity, the position of each particle is updated in each iteration, and the objective function corresponding to the new position is calculated. The position update formula is as follows:

[0038] x i (t + 1) = x i (t) + v i (t)

[0039] The iteration stops after the stop condition is met or the iteration number upper limit is reached. At this time, gbest(t) is the global optimal value, and the corresponding model parameters are the optimal parameters. Therefore, the best operating parameters of the conditioning subsystem are obtained.

[0040] Preferably, the conditioning agent raw material is made into conditioning agent particles with a particle size of 5 - 15 μm by a pneumatic grinding coupled electrostatic dispersion device; the mass ratio of the conditioning agent injection amount to the flue gas particulate matter is 1:0.5 - 1:5.

[0041] The intelligent control subsystem is based on the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator, and combines the particle swarm optimization algorithm to obtain the best operating parameters of the conditioning subsystem under the current working conditions, and changes the composition of the conditioning agent and the feeding amount of the conditioning agent in real time, so that the fly ash characteristics under different fly ash compositions and temperatures are always within the best operating range of the electrostatic precipitator, ensuring that the particulate matter concentration at the outlet of the electrostatic precipitator is always below the set limit value, and realizing stable and efficient removal of particulate matter under variable load at low cost.

[0042] In the present invention, the quenching agent raw material is dispersed into quenching agent particles with a set particle size by an air flow crushing coupled electrostatic dispersion device, and the quenching agent circulation device recycles and re-crushes the quenching agent particles exceeding the set particle size; the quenching agent particles meeting the set particle size requirements are sprayed into the upstream flue of the electrostatic precipitator through a Venturi tube and a swirl nozzle to achieve sintering flue gas quenching. Further combined with the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator in the intelligent control subsystem, the composition, injection amount and injection frequency of the quenching agent are adjusted in real time, so that the flue gas characteristics such as the fly ash resistivity are always in the best removal range of the electrostatic precipitator, ensuring that the particulate matter concentration at the outlet of the electrostatic precipitator is always below the set limit value, stably achieving efficient particulate matter removal under variable load, avoiding the decrease in the reaction activity of the downstream SCR denitration system, promoting the oxidation of zero-valent mercury and the degradation of dioxins in the sintering flue gas, and at the same time avoiding the decrease in the activity of the wet desulfurization slurry, thereby improving the removal efficiency of various pollutants such as particulate matter, nitrogen oxides, heavy metal mercury, sulfur dioxide, and dioxins.

[0043] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0044] 1. The present invention uses an air flow crushing coupled electrostatic dispersion device to disperse the quenching agent raw material into quenching agent particles with a set particle size, and the quenching agent circulation device recycles and re-crushes the quenching agent particles exceeding the set particle size; the quenching agent particles meeting the set particle size requirements are sprayed into the upstream flue of the electrostatic precipitator through a Venturi tube and a swirl nozzle to achieve sintering flue gas quenching. At the same time, the selected quenching agent uses low-cost raw materials such as fly ash, realizing the low-cost and high-efficiency preparation of quenching agent particles;

[0045] 2. Through the particulate matter concentration control model at the outlet of the electrostatic precipitator, the present invention can predict the particulate matter concentration at the outlet of the electrostatic precipitator in advance, and accurately control the feeding amount and injection frequency of the quenching agent by regulating the feeding speed of the screw feeder and the control valve in front of the swirl nozzle, further realizing the intelligent quenching of sintering flue gas stably at low cost;

[0046] 3. The present invention combines the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator in the intelligent control subsystem, and adjusts the composition, injection amount and injection frequency of the quenching agent in real time for fly ash with different characteristics, realizing the real-time regulation of fly ash resistivity, particle viscosity, etc., creating the best removal operating conditions for the electrostatic precipitator, stably achieving efficient particulate matter removal under variable load, further reducing the fluctuation range of the particulate matter concentration at the outlet of the electrostatic precipitator, creating favorable conditions for the stable operation of the subsequent desulfurization and denitration processes, and improving the removal efficiency of various pollutants such as particulate matter, nitrogen oxides, heavy metal mercury, sulfur dioxide, and dioxins. Brief Description of the Drawings

[0047] Figure 1 is a schematic structural diagram of the sintering flue gas quenching system of the present invention;

[0048] Figure 2 It is a schematic structural diagram of the airflow crushing coupled electrostatic dispersion device of the present invention;

[0049] Figure 3 It is a schematic structural diagram of the discharge electrode of the present invention;

[0050] Figure 4 is Figure 3 the top view of. Specific embodiments

[0051] The present invention will be further described below in conjunction with specific embodiments, but the protection scope of the present invention is not limited thereto. Those of ordinary skill in the art can and should know that any simple changes or substitutions based on the essential spirit of the present invention should fall within the protection scope required by the present invention.

[0052] Example 1

[0053] Referring to Figures 1 to 4 , a system for intelligent conditioning and strengthening the removal of multiple pollutants from sintering flue gas, the system includes a conditioning subsystem, an intelligent control subsystem 1 connected to the conditioning subsystem, and an electrostatic precipitator. The conditioning subsystem includes an airflow crushing coupled electrostatic dispersion device 2, a screw feeder 3, a conditioning agent circulation device, a Venturi tube 4, and a swirl nozzle 5 connected in sequence; the airflow crushing coupled electrostatic dispersion device 2 includes a housing 6, a DC nozzle 7, and a discharge electrode 8; the conditioning agent circulation device includes a conditioning agent circulation tower 9 and a conditioning agent particle size screening device 10; the intelligent control subsystem 1 includes a fly ash characteristic model and a particulate matter concentration control model at the outlet of the electrostatic precipitator; the airflow crushing coupled electrostatic dispersion device 2 is connected to a conditioning agent raw material storage tank 15; the inlet of the conditioning agent circulation tower 9 is connected to the outlet of the screw feeder 3; the inlet of the conditioning agent particle size screening device 10 is connected to the outlet of the conditioning agent circulation tower 9; the Venturi tube 4 is connected to the outlet of the conditioning agent particle size screening device 10, and the conditioning agent particles flow through the throat of the Venturi tube 4 and are mixed with compressed air; the swirl nozzle 5 is connected to the Venturi tube 4 and is arranged in the upstream flue of the electrostatic precipitator 12; the intelligent control subsystem 1 can control the conditioning agent subsystem in real time.

[0054] A regulating valve 16 is provided on the pipeline connecting the airflow crushing coupled electrostatic dispersion device 2 and the conditioning agent raw material storage tank 15 to facilitate the feeding of the conditioning agent raw material.

[0055] The housing 6 of the airflow crushing coupled electrostatic dispersion device 2 is in a cuboid shape, and the DC nozzles 7 are symmetrically arranged on the upper four walls of the housing as Figure 2 shown.

[0056] The discharge electrode 8 is a spike-type discharge electrode, including a round rod 13 and surface spikes 14. The diameter of the round rod is 4 mm, and the length of the spikes is 3 mm. A total of five groups of spikes are arranged on the round rod, with a spacing of 50 mm between each group, and six spikes are evenly distributed along the diameter of the round rod in each group.

[0057] The screw feeder 3 is arranged downstream of the air-flow crushing and electrostatic dispersion device 2 to control the feeding amount of the conditioning agent particles. The conditioning agent particles at the outlet of the screw feeder 3 are transported into the conditioning agent recycling device by hot air. At the same time, the re-agglomeration of the conditioning agent particles after dispersion can be effectively prevented by controlling the flow rate of the hot air.

[0058] The critical separation particle size of the conditioning agent particle sieve 10 is 15 μm. The conditioning agent particles larger than 15 μm return to the air-flow crushing and electrostatic dispersion device 2 for cyclic re-crushing to avoid poor conditioning effect caused by over-large particle size of the conditioning agent particles.

[0059] A control valve 11 is provided on the connecting pipeline between the venturi tube 4 and the swirl nozzle 5, and the swirl nozzle 5 is arranged in the upstream flue of the electrostatic precipitator.

[0060] The conditioning agent particles are sprayed into the flue at a high speed by the swirl nozzle 4 to strengthen the collision and contact between the conditioning agent particles and the flue gas, increase the coverage area, and improve the effective utilization rate of the conditioning agent. At the same time, the charged and dispersed conditioning agent particles are sprayed into the flue and combined with the neutral particles in the flue gas to agglomerate into large particles for easy capture by the downstream electrostatic precipitator 12. The conditioning agent raw material is made into conditioning agent particles with a particle size of 5-15 μm by the air-flow crushing and electrostatic dispersion device; the mass ratio of the conditioning agent injection amount to the flue gas particulate matter is 1:0.5-1:5.

[0061] When the system of the present invention is used for sintering flue gas conditioning, the conditioning agent raw material is dispersed into conditioning agent particles with a set particle size by the air-flow crushing and electrostatic dispersion device 2, and the conditioning agent recycling device recycles and re-crushes the conditioning agent particles exceeding the set particle size; the conditioning agent particles meeting the set particle size requirements are sprayed into the upstream flue of the electrostatic precipitator through the venturi tube 4 and the swirl nozzle 5 to achieve sintering flue gas conditioning. Further combined with the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator in the intelligent control subsystem 1, the composition, injection amount and injection frequency of the conditioning agent are adjusted in real time, so that the flue gas characteristics such as the fly ash specific resistance are always in the best removal range of the electrostatic precipitator, ensuring that the particulate matter concentration at the outlet of the electrostatic precipitator is always below the set limit value, stably realizing the efficient removal of particulate matter under variable load, avoiding the decrease in the reaction activity of the downstream SCR denitration system, promoting the oxidation of zero-valent mercury and the degradation of dioxins in the sintering flue gas, and at the same time avoiding the decrease in the activity of the wet desulfurization slurry, thereby improving the removal efficiency of various pollutants such as particulate matter, nitrogen oxides, heavy metal mercury, sulfur dioxide and dioxins.

[0062] The construction process of the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator is as follows:

[0063] (1) Based on the operating process characteristics of the electrostatic precipitator, an operating database of the electrostatic precipitator is established, covering parameters such as fly ash characteristics, operating voltage and current of the electrostatic precipitator, sintering flue gas velocity and temperature, and particulate matter concentration at the inlet and outlet of the electrostatic precipitator;

[0064] (2) Based on the established operating database of the electrostatic precipitator, a fly ash characteristic model is constructed using machine learning methods as follows:

[0065] Y = f(ρ, μ, T, α, β, N)

[0066] Where Y is the fly ash characteristic, ρ is the fly ash specific resistance, μ is the flue gas viscosity, N is the atomic number percentage of fly ash elements such as K, Na, Li, Fe, Ca, Mg, Al, Si, etc., T is the temperature, and α, β are flue gas characteristic constants;

[0067] (3) Based on the established operating database of the electrostatic precipitator and the fly ash characteristic model, a concentration control model at the outlet of the electrostatic precipitator is further established as follows:

[0068] W out = f(W in , t, v, Y, U, I)

[0069] Where W out is the particulate matter mass concentration at the outlet of the electrostatic precipitator, W in is the particulate matter mass concentration at the inlet of the electrostatic precipitator, t is the sintering flue gas temperature, v is the sintering flue gas velocity, Y is the fly ash characteristic, U is the operating voltage of the electrostatic precipitator, and I is the operating current of the electrostatic precipitator.

[0070] In step (3), the model is optimized based on the particle swarm optimization algorithm. For the electrostatic dust removal system, the optimization objective function is set as the relationship between the predicted outlet concentration and the operating parameters of the conditioning subsystem, and the best operating parameters of the conditioning subsystem (such as the injection frequency and injection volume of the conditioner) are solved.

[0071] The particle swarm optimization algorithm includes the following steps:

[0072] Step 1: Randomly initialize the positions (x) and velocities (v) of all N particles;

[0073] Step 2: Calculate the fitness of each particle;

[0074] Step 3: For each particle, compare its fitness value with the best position pbest it has passed. If the current value is better, update the best position pbest;

[0075] Step 4: For each particle, compare its fitness value with the best position gbest it has passed through. If the current value is better, update gbest;

[0076] Step 5: Adjust the particle velocity and position;

[0077] Step 6: If the end condition is met (the number of iterations or the accuracy requirement is reached), output the parameters and end; otherwise, go to Step 2;

[0078] The velocity update formula is as follows:

[0079] V i (t + 1) = wV i (t) + c1r1(pbest i (t) - x i (t)) + c2r2(gbest(t) - x i (t))

[0080] Where: w is the inertia weight; c1 and c2 are both learning factors; r1 and r2 are random numbers between 0 and 1; V i (t), V i (t + 1) are the particle migration velocities of particle i at times t and t + 1 respectively; x i (t), pbest i (t) are the historical position and historical optimal position experienced by particle i at time t; gbest(t) is the global optimal value at time t;

[0081] In addition to updating the particle velocity, each iteration also updates the position of each particle and calculates the objective function corresponding to the new position. The position update formula is as follows:

[0082] x i (t + 1) = x i (t) + v i (t)

[0083] The iteration stops after the stop condition is met or the iteration upper limit is reached. At this time, gbest(t) is the global optimal value, and the corresponding model parameters are the optimal parameters. Therefore, the best operating parameters of the conditioning subsystem are obtained.

[0084] The intelligent control subsystem is based on the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator, and combines the particle swarm optimization algorithm to obtain the best operating parameters of the conditioning subsystem under the current working conditions, and changes the composition of the conditioning agent and the feeding amount of the conditioning agent in real time, so that the fly ash characteristics under different fly ash compositions and temperatures are always within the best operating range of the electrostatic precipitator, ensuring that the particulate matter concentration at the outlet of the electrostatic precipitator is always below the set limit value, and realizing the stable and efficient removal of particulate matter under variable load at low cost.

[0085] Example 2

[0086] Using the sintering flue gas conditioning system described in Example 1, the inlet sintering flue gas temperature is 120°C, the operating voltage of the electrostatic precipitator is 24.8 kV, the flue gas flow rate is 0.466 m / s, and the inlet flue gas particulate matter concentration is 660.4 mg / m 3 , and the mass ratio of the conditioning agent to the flue gas particulate matter is preset to 1:1. The raw material of the conditioning agent is prepared into the required conditioning agent particles by the air impact coupling electrostatic dispersion method in the air flow crushing coupling electrostatic dispersion device 2, and then is sprayed into the upstream of the electrostatic precipitator through the screw feeder 3, the conditioning agent circulation device, the Venturi tube 4 and the swirl nozzle 5 in sequence to realize the conditioning effect on the sintering flue gas. The intelligent control subsystem 1 changes the composition of the conditioning agent in real time, and controls the injection amount of the conditioning agent through the screw feeder 3 and the control valve 11 in front of the nozzle 5 to adjust the injection amount and injection frequency of the conditioning agent, so that the characteristics such as the fly ash resistivity in the flue gas are always in the optimal fly ash characteristic range of the electrostatic precipitator, and the enhanced capture of particulate matter is realized. Third-party tests show that: using the results of the present invention, the particulate matter concentration at the outlet of the electrostatic precipitator is stably lower than 10 mg / m 3 ; at the same time, the operation data for three consecutive months show that the reaction activity of the downstream SCR denitration system is not affected, and the activity of the wet desulfurization slurry has always been in the stable operation range. While realizing the stable removal of particulate matter under variable load, the removal efficiency of various pollutants such as nitrogen oxides, heavy metal mercury, sulfur dioxide, and dioxins is improved.

[0087] Example 3

[0088] Using the sintering flue gas conditioning system described in Example 1, the inlet sintering flue gas temperature is 120°C, the operating voltage of the electrostatic precipitator is 24.8 kV, the flue gas flow rate is 0.466 m / s, and the inlet flue gas particulate matter concentration is 1000.5 mg / m 3 , and the mass ratio of the conditioning agent to the flue gas particulate matter is preset to 1:2. The particulate matter concentration at the outlet measured by the particle sampling measurement system is 11.8 mg / m 3 ; The operation data of the denitration and desulfurization systems collected for three consecutive months show that: the reaction activity of the downstream SCR denitration system is not affected, and the activity of the wet desulfurization slurry has always been in the stable operation range. While realizing the stable removal of particulate matter under variable load, the removal efficiency of various pollutants such as nitrogen oxides, heavy metal mercury, sulfur dioxide, and dioxins is improved.

[0089] The present invention conditions the sintering flue gas, strengthens the capture of particulate matter by the electrostatic precipitator, stably realizes the efficient removal of particulate matter under variable load at low cost, avoids the decrease in the reaction activity of the downstream SCR denitration system, promotes the oxidation of zero-valent mercury and the degradation of dioxins in the sintering flue gas, and at the same time avoids the decrease in the activity of the wet desulfurization slurry. Furthermore, while realizing the efficient removal of particulate matter, the removal efficiency of pollutants such as nitrogen oxides, heavy metal mercury, sulfur dioxide, and dioxins is improved.

Claims

1. An intelligent conditioning system for sintering flue gas to enhance the removal of multiple pollutants, characterized in that: The system includes a conditioning subsystem, an intelligent control subsystem, and an electrostatic precipitator. The conditioning subsystem includes a conditioning agent raw material storage tank, an air flow crushing and electrostatic dispersion device, a screw feeder, a conditioning agent circulation device, a Venturi tube, and a swirl nozzle connected in sequence. The air flow crushing and electrostatic dispersion device includes a housing, a DC nozzle, and a discharge electrode. The conditioning agent circulation device includes a conditioning agent circulation tower and a conditioning agent particle size sieve. The intelligent control subsystem consists of a fly ash characteristic model and a particulate matter concentration control model at the outlet of the electrostatic precipitator. The air flow crushing and electrostatic dispersion device is connected to the conditioning agent raw material storage tank. The inlet of the conditioning agent circulation tower is connected to the outlet of the screw feeder. The inlet of the conditioning agent particle size sieve is connected to the outlet of the conditioning agent circulation tower. The Venturi tube is connected to the outlet of the conditioning agent particle size sieve, and the conditioning agent particles flow through the throat of the Venturi tube and mix with compressed air. The swirl nozzle is connected to the Venturi tube and is arranged in the upstream flue of the electrostatic precipitator. The intelligent control subsystem, based on the constructed fly ash characteristic prediction model and particulate matter concentration prediction and control model at the outlet of the electrostatic precipitator, changes the composition of the conditioning agent in real time, adjusts the injection amount and injection frequency of the conditioning agent. The housing of the air flow crushing and electrostatic dispersion device is in a cuboid shape, and the DC nozzles are symmetrically arranged on the four upper walls of the housing. The discharge electrode is a spike-type discharge electrode, including a round rod and surface spikes. The diameter of the round rod is 2 - 8 mm, and the length of the spikes is 1 - 5 mm. A number of groups of spikes are arranged on the round rod, and the distance between each group of spikes is 30 - 70 mm.

2. The intelligent conditioning and strengthening system for removing multiple pollutants from sintering flue gas according to claim 1, wherein: A control valve is provided on the pipeline connecting the air flow crushing and electrostatic dispersion device and the conditioning agent raw material storage tank.

3. The intelligent conditioning and strengthening system for multiple pollutant removal from sintering flue gas according to claim 1, wherein: A control valve is provided on the connecting pipeline between the Venturi tube and the swirl nozzle.

4. The intelligent conditioning and strengthening system for multiple pollutant removal from sintering flue gas according to claim 1, characterized in that: The conditioning agent particles at the outlet of the screw feeder are transported into the conditioning agent circulation device by hot air; the critical separation particle size of the conditioning agent particle size sieve is 15 μm.

5. A method for intelligent conditioning of sintering flue gas by the system according to claim 1 to enhance the removal of multiple pollutants, characterized in that: The conditioning agent raw material is dispersed into conditioning agent particles with a set particle size by the air flow crushing and electrostatic dispersion device, and the conditioning agent circulation device recycles and re-crushes the conditioning agent particles exceeding the set particle size. The conditioning agent particles meeting the set particle size requirements are sprayed into the upstream flue of the electrostatic precipitator through the Venturi tube and the swirl nozzle to achieve sintering flue gas conditioning. At the same time, using the fly ash characteristic model and particulate matter concentration control model at the outlet of the electrostatic precipitator constructed by the intelligent control subsystem, the components, injection amount and injection frequency of the conditioning agent are adjusted in real time to improve the conditioning effect of the conditioning agent on the flue gas characteristics.

6. The method for intelligent conditioning and strengthening the removal of multiple pollutants from sintering flue gas according to claim 5, characterized in that: The construction process of the fly ash characteristic model and the particulate matter concentration control model at the outlet of the electrostatic precipitator is as follows: (1) Based on the operating process characteristics of the electrostatic precipitator, an electrostatic precipitator operation database covering fly ash characteristics, the working voltage and current of the electrostatic precipitator, the sintering flue gas velocity and temperature, and the particulate matter concentration parameters at the inlet and outlet of the electrostatic precipitator is established. (2) Based on the established electrostatic precipitator operation database above, a fly ash characteristic model is constructed using machine learning methods as follows: Y = f(ρ, μ, T, α, β, N) Wherein, Y is the fly ash property, ρ is the fly ash specific resistance, μ is the flue gas viscosity, N is the atomic number percentage of fly ash elements K, Na, Li, Fe, Ca, Mg, Al, Si, T is the temperature, and α, β are flue gas property constants; (3) Based on the established operation database of the electrostatic precipitator and the fly ash property model, further establish the outlet concentration control model of the electrostatic precipitator as follows: W out = f(W in , t, v, Y, U, I) Where, W out is the particulate matter mass concentration at the outlet of the electrostatic precipitator, W in is the particulate matter mass concentration at the inlet of the electrostatic precipitator, t is the sintering flue gas temperature, v is the sintering flue gas flow rate, Y is the fly ash property, U is the operating voltage of the electrostatic precipitator, and I is the operating current of the electrostatic precipitator.

7. The method for intelligent conditioning and strengthening the removal of multiple pollutants from sintering flue gas according to claim 6, wherein: In step (3), the model is optimized based on the particle swarm optimization algorithm. For the electrostatic precipitation system, the optimization objective function is set as the relationship between the predicted outlet particulate matter concentration and the operation parameters of the conditioning subsystem, and the optimal operation parameters of the conditioning subsystem are solved.

8. The method for intelligent conditioning and strengthening the removal of multiple pollutants from sintering flue gas according to claim 7, wherein The particle swarm optimization algorithm in step (3) includes the following steps: Step 1: Randomly initialize the positions and velocities of all N particles; Step 2: Calculate the fitness of each particle; Step 3: For each particle, compare its fitness value with the best position pbest it has passed through. If the current value is better, update the best position pbest; Step 4: For each particle, compare its fitness value with the best position gbest it has passed through. If the current value is better, update gbest; Step 5: Adjust the particle velocity and position; Step 6: When the iteration number or accuracy requirement is met, the end condition is satisfied, output the parameters, and end; otherwise, go to step 2; The velocity update formula is as follows: V i (t + 1) = wV i (t) + c1r1(pbest i (t) - x i (t)) + c2r2(gbest(t) - x i (t)) Wherein, w is the inertia weight; c1 and c2 are both learning factors; r1 and r2 are random numbers between 0 and 1; V i V(t), i V(t + 1) are the particle migration velocities of particle i at times t and t + 1 respectively; x i (t), pbest i (t) are the historical position and historical optimal position experienced by particle i at time t; gbest(t) is the global optimal value at time t; In addition to updating the particle velocity, the position of each particle is updated in each iteration, and the objective function corresponding to the new position is calculated. The position update formula is as follows: x i (t + 1)=x i (t)+v i (t) When the stop condition is met or the iteration number upper limit is reached, the iteration stops. At this time, gbest(t) is the global optimal value, and the corresponding model parameters are the optimal parameters, and the optimal operation parameters of the conditioning subsystem are obtained.

9. The method for intelligent conditioning and strengthening the removal of multiple pollutants from sintering flue gas according to claim 5, characterized in that: The conditioning agent raw material is made into conditioning agent particles with a particle size of 5 - 15 μm through a gas flow crushing coupled electrostatic dispersion device; the mass ratio of the conditioning agent injection amount to the flue gas particulate matter is 1:0.5 - 1:5.

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