Green cycle gas purification system

Abstract

The invention discloses a green cycle gas purification system. Belongs to the technical field of circulating water systems and filtration, and comprises a waste gas detection device, an intelligent decision optimization system, a water curtain purification system, a spray dust fall system and a water purification device.According to the invention, dust removal of waste gas discharged by a factory is realized, so that harm to the environment and human beings caused by direct discharge of the waste gas is reduced; firstly, the content of particulate matters in waste gas is measured through a waste gas detection device, then obtained information is transmitted to an intelligent decision-making optimization system, and the intelligent decision-making optimization system calculates the optimal water flow size and the optimal spraying thickness through an optimization algorithm, so that it is guaranteed that when the water curtain spraying impurity removal system is used, the water consumption in the process is reduced as much as possible, and the service life of the system is prolonged. And the recycled wastewater can be reused by a factory through the water purifying device, so that the water resource is further saved. The method has the characteristics that the water is recycled, the waste gas is harmlessly treated, the environmental pollution and the energy waste are reduced, and the carbon emission is also reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of circulating water system and filtration, and relates to a green circulating air purification system. Background Art

[0002] With the rapid development of mechanical manufacturing, especially in the field of metal and non-metal grinding and cutting, dust pollution has led to metal and non-metal dust explosions, posing a serious threat to production safety and the physical and mental health of workers, and causing huge pollution to the environment; dust pollution not only causes wear and tear on equipment and shortens equipment life, but also causes safety accidents, seriously affecting people's quality of life; traditional dry dust removal technologies, such as gravity settling devices, cyclone dust removal devices, rotary screen dust filters, bag dust collectors, etc., have problems such as large footprint, low efficiency, high energy consumption, and poor working environment for employees. Summary of the invention

[0003] In view of the above problems, the purpose of the present invention is to propose a more effective, environmentally friendly and low-cost dust removal solution, namely a green cycle purification system, which is used to improve air quality, reduce dust and particulate matter emissions generated in industrial production processes, so as to protect the environment and human health; and reduce the impact on the environment; water curtain dust removal technology uses a water curtain to capture and remove dust, and has the characteristics of small footprint, easy operation and maintenance, large gas processing capacity, no pipe blocking, and standard emission.

[0004] The technical solution of the present invention is: a green cycle air purification system described in the present invention specifically comprises:

[0005] Exhaust gas detection device, used to measure the particulate matter content in the exhaust gas in real time;

[0006] Intelligent decision-making optimization system, which is used to calculate the optimal water flow size and spray thickness based on the information of the exhaust gas detection device to maximize the dust removal efficiency;

[0007] Water curtain purification system, used to capture and separate particulate matter in the air through a water curtain;

[0008] Spray dust suppression system, used for secondary purification of dust-laden gas;

[0009] Water purification device is used to purify the waste water used in the whole system and form a circulation.

[0010] Furthermore, the exhaust gas detection device uses the following particle concentration calculation formula to calculate the particle content entering the pipeline:

[0011]

[0012] Where C is the particle concentration in mg / m 3; m is the amount of particulate matter obtained from the sample, in g; V is the dry gas volume under standard conditions, in L.

[0013] Furthermore, the intelligent decision-making optimization system includes sensors for monitoring the particle concentration, water flow size, spray particle size, spray flow rate and spray speed in the system;

[0014] a processor for receiving sensor data and executing an optimization algorithm;

[0015] The controller is used to adjust the operating parameters of the water curtain purification system and the spray dust reduction system according to the output of the processor.

[0016] Furthermore, the specific steps of optimizing the dust removal efficiency of the intelligent decision-making optimization system are as follows:

[0017] Step 1: Data Collection:

[0018] Sensor monitoring: The system monitors particle concentration, water flow size, spray particle size, spray flow rate and spray speed in real time through deployed sensors;

[0019] Data collection: Sensor data is collected and transmitted to the central processor, including but not limited to: particle inlet concentration C in ; Water flow size Q. ; Spray particle size d; Spray flow rate F; Spray velocity v;

[0020] Step 2: Data Processing:

[0021] Data preprocessing: Clean and standardize the collected data to ensure data quality;

[0022] Feature selection: Select the key parameters that have the greatest impact on dust removal efficiency from the monitoring data, such as water flow size and spray particle size;

[0023] Step 3: Model training and optimization:

[0024] Model selection: Select appropriate fluid dynamics model and diffusion-agglomeration kernel function model to simulate water flow and particle adsorption;

[0025] Parameter optimization: Use the Noctua Optimization Algorithm (NOA) to optimize key parameters to maximize the dust removal efficiency E;

[0026] Step 4: System Adjustment:

[0027] Parameter adjustment: According to the optimization results of the NOA algorithm, the controller of the intelligent decision-making optimization system adjusts the operating parameters of the water curtain purification system and the spray dust reduction system;

[0028] Effect evaluation: After adjustment, the system monitors the outlet concentration of particulate matter again. out , to evaluate the optimization effect;

[0029] Step 5: Feedback and loop optimization:

[0030] Performance feedback: The outlet concentration C out And water purification efficiency E f Feedback to the intelligent decision-making optimization system;

[0031] Cycle optimization: The system conducts self-learning and adjustment based on feedback results to continuously optimize dust removal efficiency;

[0032] Step 6: Water purification system adjustment:

[0033] Water purification efficiency calculation:

[0034]

[0035] Parameter adjustment: According to the water purification efficiency E f , the intelligent decision-making optimization system adjusts the filtering and circulation parameters of the water purification device to maintain the cleanliness of the water curtain and the dust removal effect.

[0036] Furthermore, the intelligent decision-making optimization system uses a star-sparrow optimization algorithm (NOA) to optimize dust removal efficiency, and the NOA algorithm includes the following steps:

[0037] Initialization: Randomly initialize the initial population of nutcrackers in the algorithm search space, each nutcracker represents a possible solution;

[0038] Foraging and storage strategy: simulate the behavior of collecting and storing food in summer and autumn by using the following position update formula:

[0039]

[0040] In the formula, is the position of the nutcracker at the t+1th iteration, is the mean value of the jth dimension in the population, and are the positions of two randomly selected individuals in the population in the jth dimension, U j and L j are the upper and lower bounds of the search space, γ is the random number obtained by Levy flight, and μ is the step size generated based on normal distribution, Levy flight or random number;

[0041] Cache search and recovery strategy: simulates the behavior of the nutcracker in winter and spring to find stored food based on memory, and is updated through the following reference point formula:

[0042]

[0043] In the formula, is the kth reference point of the i-th nutcracker, α is a parameter that ensures that NOA converges regularly, θ is a random angle, U and L are the upper and lower bounds of the search space, respectively, and τ3 is a random number.

[0044] Furthermore, the water curtain purification system uses a fluid dynamics model to simulate water flow and particle adsorption, where the water flow size Q is used as the core parameter and the objective function E represents the dust removal efficiency, which can be expressed as:

[0045]

[0046] In the formula, C in is the concentration of particles in the influent, C out is the concentration of particulate matter in the outlet water, and g(Q, d, F, v) is a function that takes into account the effects of water flow size Q, spray particle size d, spray flow rate F and spray velocity v on dust removal efficiency.

[0047] Furthermore, the spray dust suppression system obtains the macroscopic spray parameters of the nozzle through a spray test, and uses the following diffusion agglomeration kernel function model to describe the diffusion agglomeration caused by the Brownian motion of particles smaller than 1 μm:

[0048]

[0049] Where N is the number of particles diffusing toward the stationary particle per unit time; d p and d f The diameters of fly ash particles and atomized particles respectively; C f is the particle number concentration contained in the smoke per unit time; D is the diffusion coefficient.

[0050] Furthermore, the water purification device includes: a filtration module for removing particulate matter in wastewater; a circulation module for re-inputting purified water into the water curtain purification system; the filtration efficiency formula of the water purification system is:

[0051]

[0052] In the formula, C in is the concentration of particles in the influent; C out is the concentration of particles in the effluent; the efficiency E f Feedback will be given to the intelligent decision-making optimization system to evaluate the water purification effect and adjust the water purification parameters.

[0053] The overall process and control logic of the system includes the following steps:

[0054] (1): After the exhaust gas enters the system, the particulate matter content is first detected by the exhaust gas detection device;

[0055] (2): The detection data is transmitted to the intelligent decision-making optimization system, and the system calculates the optimal operating parameters based on the Noctua Optimization Algorithm (NOA);

[0056] (3): The controller adjusts the operating parameters of the water curtain purification system and the spray dust suppression system according to the calculation results;

[0057] (4): The dust-laden gas is removed through the water curtain purification system and the spray dust reduction system;

[0058] (5): The water purification system purifies the wastewater and the purified water is recycled;

[0059] (6): Clean gas is discharged through the exhaust port.

[0060] The beneficial effects of the present invention are: 1. The present invention utilizes an intelligent optimization decision-making system to accurately control the amount of water used for purifying dust-containing gases, effectively saving water; and according to the diversity and complexity of dust removal materials, the water curtain dust removal system adopts a modular design, which is more intelligent and greatly saves costs; 2. The starfinch optimization algorithm (NOA) is used to optimize the dust removal efficiency. The starfinch optimization algorithm (NOA) has a strong global search capability and can avoid falling into local optimal solutions, which is particularly important for the parameter optimization of the water curtain dust removal system. In addition, the starfinch optimization algorithm (NOA) can adapt to various working conditions and conditions in the water curtain dust removal system. The starfinch optimization algorithm (NOA) shows a fast error convergence effect on the hybrid function F5 and the basic multimodal function F3 in the CEC2020 optimization function test set, which is of great significance for the water curtain dust removal system to quickly reach a high-efficiency dust removal state. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a structural block diagram of the present invention;

[0062] Figure 2 It is the work flow chart of the purification system in the present invention. DETAILED DESCRIPTION

[0063] The specific technical scheme of the present invention is further described in detail below with reference to specific examples.

[0064] As shown in the figure, this embodiment provides a green cycle air purification system, including an exhaust gas detection device for measuring the particulate matter content in the exhaust gas in real time;

[0065] Intelligent decision-making optimization system, which is used to calculate the optimal water flow size and spray thickness based on the information of the exhaust gas detection device to maximize the dust removal efficiency;

[0066] Water curtain purification system, used to capture and separate particulate matter in the air through a water curtain;

[0067] Spray dust suppression system, used for secondary purification of dust-laden gas;

[0068] Water purification device is used to purify the waste water used in the whole system and form a circulation.

[0069] Furthermore, the exhaust gas detection device uses the following particle concentration calculation formula to calculate the particle content entering the pipeline:

[0070]

[0071] Where C is the particle concentration in mg / m 3 ; m is the amount of particulate matter obtained from the sample, in g; V is the dry gas volume under standard conditions, in L. This concentration value will be used as an input parameter of the intelligent decision-making optimization system.

[0072] Furthermore, the intelligent decision-making optimization system includes a sensor for monitoring the particle concentration, water flow size, spray particle size, spray flow rate and spray speed in the system; a processor for receiving sensor data and executing an optimization algorithm; and a controller for adjusting the operating parameters of the water curtain purification system and the spray dust reduction system according to the output of the processor.

[0073] Specific steps for optimizing dust removal efficiency in the intelligent decision-making optimization system:

[0074] Step 1: Data Collection:

[0075] Sensor monitoring: The system monitors particle concentration, water flow size, spray particle size, spray flow rate and spray speed in real time through deployed sensors;

[0076] Data collection: Sensor data is collected and transmitted to the central processor, including but not limited to: particle inlet concentration C in ; Water flow size Q. ; Spray particle size d; Spray flow rate F; Spray velocity v;

[0077] Step 2: Data Processing:

[0078] Data preprocessing: Clean and standardize the collected data to ensure data quality;

[0079] Feature selection: Select the key parameters that have the greatest impact on dust removal efficiency from the monitoring data, such as water flow size and spray particle size;

[0080] Step 3: Model training and optimization:

[0081] Model selection: Select appropriate fluid dynamics model and diffusion-agglomeration kernel function model to simulate water flow and particle adsorption;

[0082] Parameter optimization: Use the Noctua Optimization Algorithm (NOA) to optimize key parameters to maximize the dust removal efficiency E;

[0083] Step 4: System Adjustment:

[0084] Parameter adjustment: According to the optimization results of the NOA algorithm, the controller of the intelligent decision-making optimization system adjusts the operating parameters of the water curtain purification system and the spray dust reduction system;

[0085] Effect evaluation: After adjustment, the system monitors the outlet concentration of particulate matter again. out , to evaluate the optimization effect;

[0086] Step 5: Feedback and loop optimization:

[0087] Performance feedback: The outlet concentration C out And water purification efficiency E f Feedback to the intelligent decision-making optimization system;

[0088] Cycle optimization: The system conducts self-learning and adjustment based on feedback results to continuously optimize dust removal efficiency;

[0089] Step 6: Water purification system adjustment:

[0090] Water purification efficiency calculation:

[0091]

[0092] Parameter adjustment: According to the water purification efficiency E f , the intelligent decision-making optimization system adjusts the filtering and circulation parameters of the water purification device to maintain the cleanliness of the water curtain and the dust removal effect.

[0093] Furthermore, in the intelligent decision-making optimization system, a star-sparrow optimization algorithm (NOA) is used to optimize the dust removal efficiency. The NOA algorithm includes the following steps:

[0094] Initialization: Randomly initialize the initial population of nutcrackers in the algorithm search space, each nutcracker represents a possible solution;

[0095] Foraging and storage strategy: simulate the behavior of collecting and storing food in summer and autumn by using the following position update formula:

[0096]

[0097] In the formula, is the position of the nutcracker at the t+1th iteration, is the mean value of the jth dimension in the population, and are the positions of two randomly selected individuals in the population in the jth dimension, U j and Lj are the upper and lower bounds of the search space, γ is the random number obtained by Levy flight, and μ is the step size generated based on normal distribution, Levy flight or random number;

[0098] Cache search and recovery strategy: simulates the behavior of the nutcracker in winter and spring to find stored food based on memory, and is updated through the following reference point formula:

[0099]

[0100] In the formula, is the kth reference point of the i-th nutcracker, α is a parameter that ensures that NOA converges regularly, θ is a random angle, U and L are the upper and lower bounds of the search space, respectively, and τ3 is a random number.

[0101] Furthermore, the water curtain purification system uses a fluid dynamics model to simulate water flow and particle adsorption, where the water flow size Q is used as the core parameter and the objective function E represents the dust removal efficiency, which can be expressed as:

[0102]

[0103] In the formula, C in is the concentration of particles in the influent, C out is the concentration of particulate matter in the outlet water, g(Q, d, F, v) is a function that considers the effects of water flow size Q, spray particle size d, spray flow rate F and spray velocity v on dust removal efficiency. The model will optimize the operating parameters based on the sensor data and the objective function E.

[0104] Furthermore, the spray dust suppression system obtains the macroscopic spray parameters of the nozzle through a spray test, and uses the following diffusion agglomeration kernel function model to describe the diffusion agglomeration caused by the Brownian motion of particles smaller than 1 μm:

[0105]

[0106] Where N is the number of particles diffusing toward the stationary particle per unit time; d p and d f The diameters of fly ash particles and atomized particles respectively; C f is the particle number concentration contained in the flue gas per unit time; D is the diffusion coefficient. This model will be used to optimize the spray parameters to improve the dust removal efficiency.

[0107] Furthermore, the water purification device includes: a filtering module for removing particulate matter in wastewater; a circulation module for re-inputting purified water into the water curtain purification system; the filtering efficiency formula of the water purification system is:

[0108]

[0109] In the formula, C in is the concentration of particles in the influent; C out is the concentration of particles in the effluent water, the efficiency E f Feedback will be given to the intelligent decision-making optimization system to evaluate the water purification effect and adjust the water purification parameters.

[0110] Furthermore, the overall process and control logic of the system includes the following steps:

[0111] (1): After the exhaust gas enters the system, the particulate matter content is first detected by the exhaust gas detection device;

[0112] (2): The detection data is transmitted to the intelligent decision-making optimization system, and the system calculates the optimal operating parameters based on the Noctua Optimization Algorithm (NOA);

[0113] (3): The controller adjusts the operating parameters of the water curtain purification system and the spray dust suppression system according to the calculation results;

[0114] (4): The dust-laden gas is removed through the water curtain purification system and the spray dust reduction system;

[0115] (5): The water purification system purifies the wastewater and the purified water is recycled;

[0116] (6): Clean gas is discharged through the exhaust port.

[0117] In addition, if Figure 2 After the exhaust gas enters the system, the particulate matter content is first detected by the exhaust gas detection device, and the sensor is used to monitor the particulate matter concentration, water flow size, spray particle size, spray flow rate and spray speed in the system; the processor is used to receive sensor data and execute the optimization algorithm; the controller is used to adjust the operating parameters of the water curtain purification system and the spray dust reduction system according to the output of the processor. The controller adjusts the operating parameters of the water curtain purification system and the spray dust reduction system according to the calculation results. The dust-containing gas is dust-removed by the water curtain purification system and the spray dust reduction system. The water purification system purifies the wastewater, and the purified water is recycled. Finally, the clean gas is discharged through the exhaust port.

Claims

1. A green cycle air purification system, characterized in that: It includes interconnected exhaust gas detection devices, intelligent decision-making optimization systems, water curtain purification systems, spray dust reduction systems and water purification devices; The exhaust gas detection device is used to measure the particulate matter content in the exhaust gas in real time; The intelligent decision-making optimization system is used to calculate the optimal water flow size and spray thickness according to the information of the exhaust gas detection device to maximize the dust removal efficiency; The water curtain purification system is used to capture and separate particulate matter in the air through a water curtain; The spray dust suppression system is used to perform secondary purification on dust-laden gas; The water purification device is used to purify the waste water used in the whole system and form a cycle.

2. A green cycle air purification system according to claim 1, characterized in that: The exhaust gas detection device uses the following particle concentration calculation formula to calculate the particle content entering the pipeline: Where C is the particle concentration in mg / m 3 ; m is the amount of particulate matter obtained from the sample, in g; V is the dry gas volume under standard conditions, in L. This concentration value will be used as an input parameter of the intelligent decision-making optimization system.

3. A green cycle air purification system according to claim 1, characterized in that: The intelligent decision-making optimization system includes a sensor, a processor and a controller; The sensor is used to monitor the particle concentration, water flow size, spray particle size, spray flow rate and spray speed in the system; The processor is used to receive sensor data and execute an optimization algorithm; The controller is used to adjust the operating parameters of the water curtain purification system and the spray dust reduction system according to the output of the processor.

4. A green cycle air purification system according to claim 3, characterized in that: The specific steps of optimizing the dust removal efficiency of the intelligent decision-making optimization system are as follows: Step (1): Data collection; Step (2): data processing; Step (3): Model training and optimization; Step (4): System adjustment; Step (5): Feedback and loop optimization; Step (6): Adjust the water purification system.

5. A green cycle air purification system according to claim 4, characterized in that: In step (1), the data collection specifically includes: Sensor monitoring: The system monitors particle concentration, water flow size, spray particle size, spray flow rate and spray speed in real time through deployed sensors; Data collection: Sensor data is collected and transmitted to the central processor, including but not limited to: particle inlet concentration C in ; Water flow size Q. ; Spray particle size d; Spray flow rate F; Spray velocity v; In step (2), the data processing specifically includes: Data preprocessing: Clean and standardize the collected data to ensure data quality; Feature selection: Select the key parameters that have the greatest impact on dust removal efficiency from the monitoring data; In step (3), the model training and optimization specifically includes: Model selection: Select appropriate fluid dynamics model and diffusion-agglomeration kernel function model to simulate water flow and particle adsorption; Parameter optimization: Use NOA algorithm to optimize key parameters to maximize dust removal efficiency E; In step (4), the system adjustment specifically includes: Parameter adjustment: According to the optimization results of the NOA algorithm, the controller of the intelligent decision-making optimization system adjusts the operating parameters of the water curtain purification system and the spray dust reduction system; Effect evaluation: After adjustment, the system monitors the outlet concentration of particulate matter again. out , to evaluate the optimization effect; In step (5), the feedback and loop optimization specifically includes: Performance feedback: The outlet concentration C out And water purification efficiency E f Feedback to the intelligent decision-making optimization system; Cycle optimization: The system conducts self-learning and adjustment based on feedback results to optimize dust removal efficiency; In step (6), the water purification system adjustment specifically includes: Water purification efficiency calculation: Parameter adjustment: According to the water purification efficiency E f , the intelligent decision-making optimization system adjusts the filtration and circulation parameters of the water purification device.

6. A green cycle air purification system according to claim 5, characterized in that: In step (3), the specific steps of the NOA algorithm are as follows: (3.1) Initialization: Randomly initialize the initial population of nutcrackers in the algorithm search space, each nutcracker represents a solution; (3.2) Foraging and storage strategy: simulate the behavior of the nutcracker collecting and storing food in summer and autumn, and use the following position update formula: In the formula, is the position of the nutcracker at the t+1th iteration, is the mean value of the jth dimension in the population, and are the positions of two randomly selected individuals in the population in the jth dimension, U j and L j are the upper and lower bounds of the search space, γ is the random number obtained by Levy flight, and μ is the step size generated based on normal distribution, Levy flight or random number; (3.3) Cache search and recovery strategy: simulate the behavior of the nutcracker in searching for stored food based on memory in winter and spring, and use the following update formula: In the formula, is the kth reference point of the i-th nutcracker, α is a parameter that ensures that NOA converges regularly, θ is a random angle, U and L are the upper and lower bounds of the search space, respectively, and τ3 is a random number.

7. A green cycle air purification system according to claim 1, characterized in that: The water curtain purification system uses a fluid dynamics model to simulate water flow and particle adsorption, where the water flow size Q is used as the core parameter and the objective function E represents the dust removal efficiency, which can be expressed as: In the formula, C in is the concentration of particles in the influent, C out is the concentration of particulate matter in the outlet water, and g(Q, d, F, v) is a function that takes into account the effects of water flow size Q, spray particle size d, spray flow rate F and spray velocity v on dust removal efficiency.

8. A green cycle air purification system according to claim 1, characterized in that: The spray dust suppression system obtains the macroscopic spray parameters of the nozzle through a spray test, and uses the following diffusion agglomeration kernel function model to describe the diffusion agglomeration caused by the Brownian motion of particles smaller than 1 μm: Where N is the number of particles diffusing toward the stationary particle per unit time; d p and d f The diameters of fly ash particles and atomized particles respectively; C f is the particle number concentration contained in the smoke per unit time; D is the diffusion coefficient.

9. A green cycle air purification system according to claim 1, characterized in that: The water purification device includes a filtering module for removing particulate matter from wastewater and a circulation module for re-inputting purified water into the water curtain purification system; The filtration efficiency formula is: In the formula, C in is the concentration of particles in the influent; C out is the concentration of particulate matter in the effluent water.

10. A process and control method for a green cycle air purification system according to any one of claims 1 to 9, characterized in that: The operation steps are as follows: Step (10.1): After the exhaust gas enters the system, it is first tested for particulate matter content by an exhaust gas detection device to obtain C in ; Step (10.2): Detection data C in Transmitted to the intelligent decision-making optimization system, the system calculates the optimal operating parameters based on the NOA algorithm; Step (10.3): The controller adjusts the operating parameters of the water curtain purification system and the spray dust reduction system according to the calculation results to achieve the best dust removal effect; Step (10.4): The dust-laden gas is subjected to dust removal treatment by a water curtain purification system and a spray dust reduction system to obtain C out ; Step (10.5): The water purification system purifies the wastewater, and the purified water is recycled. At the same time, the water purification efficiency E is calculated. f ; Step (10.6): The clean gas is discharged through the exhaust port, and the water purification efficiency E is fed back at the same time f To the intelligent decision-making optimization system to evaluate the overall system performance and make adjustments.

Images