Plasma module array optimization method and system based on aerosol exposure index

Abstract

The application belongs to the technical field of air treatment, and discloses a plasma module array optimization method and system based on an aerosol exposure index. The application faces a plasma module, takes the number of layers, the layer spacing and the number of modules per layer of the module array in a cuboid air duct as the structural configuration variables, takes the wind speed, the temperature and humidity and the module array opening vector as the operation control variables, and performs joint optimization. The application defines the aerosol particle exposure index as the time cumulative value of the equivalent action intensity of the particles along the motion trajectory, calculates the exposure index distribution, the low exposure escape ratio, the deposition ratio, the pressure drop and the energy consumption index under different candidate structures and operation combinations through flow field simulation and particle trajectory tracking, screens and recommends the structural configuration in combination with the space demand model constructed by the target space volume, the initial concentration, the target concentration and the expected compliance time, and establishes an operation strategy library through offline simulation, and queries the optimal control parameters according to the space state fed back by the sensor during online operation.

Description

Technical Field

[0001] This invention belongs to the field of air treatment technology, and particularly relates to a method and system for optimizing plasma module arrays based on aerosol exposure index. Background Technology

[0002] Low-temperature plasma air treatment technology is a type of technology that utilizes gas discharge processes to treat pollutants, suspended particulate matter, and aerosol particles in the air. Its basic principle is that under the influence of an applied electric field, the air undergoes localized ionization, forming a low-temperature plasma environment containing electrons, ions, excited-state particles, an electric field interaction region, and reactive oxygen and nitrogen species. When aerosol particles in the air pass through this region, they may be affected by various physicochemical processes, including airflow transport, electric field forces, particle charging, charged particle collisions, reactive species interactions, and wall deposition.

[0003] In air purification, air disinfection, fresh air systems, central air conditioning ducts, and mobile air handling units, plasma modules are typically installed within the ductwork. For commercially available modules, their internal electrode structure, power supply type, discharge gap, material processing technology, and basic operating mode are usually fixed. What users can more easily adjust in actual projects are the external layout and operating conditions of the modules, such as the number of modules, module arrangement, duct size, air velocity, operating time, temperature and humidity conditions, and module start-up and shutdown combinations.

[0004] Existing plasma air treatment technologies have disclosed plasma modules, duct-type air purification devices, multi-stage plasma treatment devices, and sensor- or artificial intelligence-based operation control methods. However, from the perspective of engineering applications of existing commercial modules, the current technology still has the following technical shortcomings:

[0005] First, existing technologies primarily focus on the module body or device structure, lacking methods for structural configuration of existing module arrays. The internal electrode structure, power supply structure, discharge gap, and material processing technology of existing plasma air handling modules are typically determined by the manufacturer. For users, what is actually adjustable is not the internal structure of the module, but rather the arrangement of the modules in the duct, the number of modules, the number of module layers, the layer spacing, and the duct size. Because existing technologies mostly focus on the module body structure or a single device structure, lacking quantitative methods for arraying existing modules within a cuboid duct, engineering applications often rely on experience to determine the number of modules and their installation locations. This approach easily leads to problems such as insufficient module configuration, excessive module configuration, unreasonable duct volume, or unreasonable module spacing, thereby affecting the effective treatment of aerosol particles and the operating efficiency of the device.

[0006] Second, existing technologies lack exposure assessment indicators based on the trajectory of aerosol particles, making it difficult to identify under-treated pathways. Current air handling units often use airflow, number of modules, outlet concentration, filtration efficiency, or air quality sensor data to evaluate treatment effectiveness. These indicators are mostly overall or outlet averages, failing to reflect the electric field, charged particles, active species, effective action zone, and residence time experienced by individual or group aerosol particles along their actual trajectories within the air duct. Due to the lack of exposure assessment indicators along particle trajectories, even if the overall airflow or outlet average indicators meet requirements, some aerosol particles may still escape through the air duct along low-intensity areas—i.e., low-exposure escape paths. These paths lead to under-treatment of some particles, making it difficult to precisely optimize the device's structural configuration and operational control.

[0007] Third, existing technologies typically handle structural design and operational control separately, lacking a unified optimization framework. In existing technologies, structural design usually addresses the questions of "how many modules to place and where to install them," while operational control typically addresses the questions of "how many speeds the fan operates at, how long it runs, and how to adjust its power." These two aspects are often designed separately, lacking a common evaluation metric for unified comparison. Since structural configuration affects the wind field, particle trajectory, particle residence time, and duct pressure drop, while operational control affects wind speed, effective area distribution, air volume processed per unit time, and module operating status, separate optimization may result in a structural design performing well under certain fixed operating conditions but failing to adapt to different aerosol concentrations and target achievement times in actual dynamic operation.

[0008] Fourth, existing technologies lack precise control over the activation and combination of module arrays. Current plasma air handling equipment operation control typically focuses on wind speed, operating time, discharge power, or overall start-up and shutdown status. For devices with multi-layer plasma module arrays arranged along the duct direction, existing technologies rarely treat "which specific module array layers to activate" as an independent control variable. Since different module layer activation combinations alter the spatial distribution of the effective action area within the duct, as well as energy consumption and the risk of byproduct generation, using only overall activation or shutdown may result in excessive energy consumption and increased byproduct risk under low-concentration conditions, while under high-concentration conditions, it is impossible to select a more suitable action area distribution based on particle exposure requirements.

[0009] Fifth, existing technologies lack a method for deriving device structure and operation strategies from spatial purification requirements. The air treatment requirements of the target space are related to the space volume, initial aerosol concentration, target safe concentration, expected attainment time, background ventilation rate, and continuous aerosol generation rate. Existing technologies often design devices based on their own airflow or purification efficiency, but rarely translate the concentration attainment requirements of the target space into modular array structure configuration and operation control strategies. Due to the lack of a correlation between the spatial requirement model and the internal particle exposure model of the device, the device may encounter two types of problems: one is insufficient device configuration, resulting in the inability to reach the expected concentration within the target time; the other is excessive device configuration or operation intensity, leading to increased fan energy consumption, noise, duct pressure drop, and by-product risks.

[0010] Sixth, existing technologies lack comprehensive constraints on duct pressure drop, energy consumption, and byproduct risks. Increasing the number of module layers, reducing the interlayer spacing, or increasing the number of modules in operation generally helps to improve the exposure of aerosol particles in the effective action area. However, at the same time, it may also increase duct resistance, increase the inlet and outlet pressure difference, leading to a decrease in air volume, an increase in fan energy consumption, and an increase in noise. In addition, long-term or high-intensity operation of multi-layer modules may also increase the risk of exceeding standards for byproducts such as ozone and nitrogen oxides. Summary of the Invention

[0011] To address the aforementioned technical shortcomings, the present invention aims to provide a method and system for optimizing plasma module arrays based on the aerosol exposure index. The specific technical solution is as follows:

[0012] A method for optimizing plasma module arrays based on aerosol exposure index includes the following steps:

[0013] S1, Input target space requirement parameters, including space volume, current aerosol concentration, target concentration, expected time to achieve target, and allowable installation size;

[0014] S2, Input existing plasma module parameters, including module dimensions, installation interface, allowable wind speed range, fixed operating status, and equivalent effective action area parameters;

[0015] S3, set the range of values ​​for the structural configuration variables. The structural configuration variables include: candidate values ​​for the number of plasma module array layers Lx set along the airflow direction, candidate values ​​for the spacing d between two adjacent plasma module array layers, candidate values ​​for the number of plasma modules Ly in the air duct width direction of each array layer, and candidate values ​​for the number of plasma modules Lz in the air duct height direction of each array layer.

[0016] S4. Based on the candidate values ​​of Lx, d, Ly, and Lz, candidate cuboid air duct structures are automatically generated, and the length, width, height, and total volume of the corresponding air duct are calculated.

[0017] S5, eliminate candidate structures that do not meet the constraints of installation size, module splicing requirements, maintenance space, minimum inlet and outlet buffer section, or voltage drop prediction constraints;

[0018] S6, perform flow field simulation on the retained candidate structures to obtain wind speed distribution, pressure distribution, residence time distribution and pressure drop;

[0019] S7. Based on the flow field simulation results, release representative aerosol particles, perform particle trajectory calculations, and obtain information on particle penetration state, deposition state, and residence time.

[0020] S8, based on the effective action area formed by the activated module array, calculate the exposure index of each aerosol particle along the particle trajectory;

[0021] S9, Calculate statistical evaluation indicators for each candidate structure, including average exposure index, lowest quantile exposure index, low exposure escape ratio, deposition ratio, pressure drop and energy consumption proxy index.

[0022] S10, Select the recommended structural configuration based on the space requirement model and the statistical evaluation indicators, and determine Lx, d, Ly, Lz and the duct size;

[0023] S11, for the determined structural configuration, set different wind speeds u, temperature and humidity states T / RH and module array opening vector B;

[0024] S12, calculate the airflow field, particle trajectory and aerosol particle exposure index for each operating combination, and obtain the corresponding exposure index distribution, low exposure escape ratio, deposition ratio, pressure drop, fan energy consumption and by-product risk proxy index;

[0025] S13, The running combinations obtained in S12 and their corresponding calculation results are used to form a running strategy library;

[0026] S14, during operation, the current space aerosol concentration, temperature, humidity and equipment status are collected by sensors;

[0027] S15, the spatial demand model calculates the current required processing capacity based on the current concentration, target concentration, and remaining time to achieve the target.

[0028] S16, the controller queries the operating strategy library and selects a combination of operating parameters that meets the processing requirements and has low energy consumption, pressure drop and by-product risk;

[0029] S17, the controller outputs control commands to the fan, temperature and humidity control unit and plasma module array to adjust the wind speed, temperature and humidity compensation parameters and module array opening vector;

[0030] S18: After running for a period of time, the spatial status is re-collected, and S15 to S17 are repeated to achieve dynamic adjustment.

[0031] A plasma module array optimization system based on aerosol exposure index, the system comprising:

[0032] The space requirement input module is used to input the target space volume, current aerosol concentration, target safe concentration, expected time to achieve the target, background ventilation rate, and installation size constraints.

[0033] The existing module parameter input module is used to input the plasma module's external dimensions, allowable wind speed range, module splicing method, effective action area parameters, and fixed operating status.

[0034] A module array structure generation module is used to generate geometric models of cuboid air ducts and plasma module arrays based on Lx, d, Ly, and Lz.

[0035] The flow field simulation module is used to calculate the airflow velocity field, pressure field, pressure drop, and residence time distribution under different structural configurations and wind speed conditions.

[0036] The particle trajectory calculation module is used to calculate the motion trajectory, penetration state, deposition state, and residence time of non-pathogen-specific aerosol particles in the air duct;

[0037] The particle trajectory statistics module is used to statistically analyze the trajectory results of multiple aerosol particles, including residence time distribution, penetration ratio, deposition ratio, trajectory differences of particles of different sizes, and low-exposure escape paths.

[0038] The exposure index calculation module is used to calculate the aerosol particle exposure index along a single particle trajectory;

[0039] The exposure index statistics module is used to calculate the average exposure index, lowest quantile exposure index, low exposure escape ratio, and exposure index distribution of particles of different sizes.

[0040] The structural configuration optimization module is used to determine the recommended structural configuration based on space requirements, exposure index statistics, trajectory statistics, pressure drop, and deposition risk.

[0041] The operation strategy library module is used to store the exposure index, trajectory statistics, energy consumption, pressure drop and risk indicators corresponding to different wind speeds, temperature and humidity and module activation combinations;

[0042] The operation control module is used to determine wind speed, temperature and humidity compensation parameters, and module array activation vector based on the current spatial status and the operation strategy library.

[0043] The results output module is used to output recommended duct size, module array structure, operation control strategy, low exposure escape path, deposition hotspots and exposure index statistics.

[0044] The present invention has the following beneficial effects:

[0045] Since this invention targets application scenarios where the internal structure of existing plasma modules is not adjustable, the optimization focus shifts from the internal electrode structure to the external air duct structure, the number of module array layers, the interlayer spacing, the number of modules assembled, and the operational control parameters. By using Lx, d, Ly, and Lz as structural configuration variables, and combining them with operational variables such as wind speed, temperature and humidity, and the module activation vector B, this invention can form a calculable, comparable, and selectable array configuration scheme without altering the existing plasma module itself. Compared to schemes that rely on experience to determine the number of modules and installation methods, this invention can reverse-engineer the device structure and operational strategy based on the target space volume, aerosol concentration control targets, and expected achievement time. Therefore, it has the advantages of clearer design basis, more flexible applicable scenarios, and lower engineering implementation costs.

[0046] This invention uses the aerosol particle exposure index as the core evaluation indicator, linking airflow field, particle trajectory, residence time, module activation status, and equivalent effect intensity. This allows the optimization process to move beyond relying solely on macroscopic indicators such as airflow, power, or average purification efficiency, and instead identify issues like low-exposure channels, short-circuit flow, particle penetration, and localized deposition. By establishing an operational strategy library through offline simulation and querying and matching control parameters based on spatial conditions during operation, this invention can ensure treatment effectiveness while also considering engineering constraints such as pressure drop, energy consumption, deposition ratio, and byproduct risks. This improves the structural configuration rationality, operational control targeting, and engineering feasibility of plasma module array air handling units. Attached Figure Description

[0047] Figure 1 This is a flowchart of the plasma module array optimization method based on aerosol exposure index of the present invention.

[0048] Figure 2 This is a schematic diagram of the plasma module array structure inside the cuboid air duct described in this invention.

[0049] Figure 3 This is a schematic diagram of the plasma module array activation vector described in this invention. Detailed Implementation

[0050] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. Furthermore, the technical features involved in the various embodiments of the invention described below can be combined with each other as long as they do not conflict with each other. To achieve the above objectives, the invention adopts the following technical solutions.

[0051] This invention provides a method and system for optimizing plasma module arrays based on the aerosol exposure index. This invention targets existing plasma air handling modules with a fixed internal structure, without altering the module's internal electrode structure, power supply structure, or manufacturing process. Instead, it jointly optimizes the structural configuration and operational control parameters of the module array within a cuboid air duct.

[0052] This invention comprises two levels: the first level is structural configuration optimization, used to determine the duct size, the number of module array layers, the layer spacing, and the number of modules per layer; the second level is operation control optimization, used to dynamically determine the wind speed, temperature and humidity compensation parameters, and which plasma module array layers to activate based on the space aerosol state, temperature and humidity state, and target achievement time.

[0053] 1. Overall structural scheme:

[0054] The air handling device of the present invention includes a cuboid air duct, a plasma module array, a fan, a controller, a sensor unit, and an operation strategy storage unit.

[0055] The rectangular air duct has an air inlet and an air outlet. The length direction of the air duct is defined as the x-direction, which is also the airflow direction; the width direction of the air duct is defined as the y-direction; and the height direction of the air duct is defined as the z-direction.

[0056] A multi-layer plasma module array is arranged along the x-direction within the air duct. For example... Figure 2 As shown, each plasma module array is formed by splicing several existing plasma modules within the yz section. Adjacent modules are tightly spliced ​​in the y and z directions, or are spliced ​​adjacently through seals, mounting frames, brackets, or gaps smaller than a preset assembly gap threshold.

[0057] The structural configuration variables include:

[0058] Lx: The number of plasma module array layers set along the x-direction;

[0059] d: Spacing between two adjacent plasma module arrays;

[0060] Ly: The number of plasma modules in the y-direction of each layer of the array;

[0061] Lz: The number of plasma modules in the z-direction of each array layer.

[0062] If a single plasma module has a thickness of Mx in the x-direction, a width of My in the y-direction, and a height of Mz in the z-direction, then the main dimensions of the air duct can be determined or constrained by the following relationship:

[0063] Duct width Y ≈ Ly × My + installation allowance;

[0064] Duct height Z ≈ Lz × Mz + installation allowance;

[0065] Duct length X ≈ Inlet buffer section length + Lx × Mx + (Lx-1) × d + Outlet buffer section length.

[0066] The above dimensional relationships do not need to be mathematically identical. In actual engineering, they can be adjusted according to the sealing structure, support structure, maintenance space, and installation allowance.

[0067] 2. Operation control scheme:

[0068] like Figure 3 As shown, after the structural configuration is determined, the present invention further controls the operating parameters. The operating control variables include:

[0069] u: Inlet velocity or volumetric flow rate;

[0070] T: Air temperature or temperature compensation parameter;

[0071] RH: Relative humidity or humidity compensation parameter;

[0072] B: Module array enable vector.

[0073] The module array enable vector is defined as:

[0074] B = [bL1, bL2, ..., bLx];

[0075] Where bLj=1 indicates that the Lj-th plasma module array is turned on, and bLj=0 indicates that the Lj-th plasma module array is turned off.

[0076] For example, when Lx = 4:

[0077] B = [1,0,0,0]: Only the first layer is enabled;

[0078] B = [1,1,0,0]: Enables layers 1 and 2;

[0079] B = [1,0,1,0]: Enables layers 1 and 3;

[0080] B = [1,1,1,1]: All enabled.

[0081] If the plasma module supports multiple operating levels, then bLj can also be extended to a multi-valued variable. For example, bLj=0 indicates off, bLj=1 indicates low-level operation, and bLj=2 indicates high-level operation. If the module only supports on and off, then bLj is a binary variable.

[0082] Wind speed is controlled by a fan. Temperature and humidity can be actively adjusted by a temperature and humidity control device; if the actual equipment does not have temperature and humidity control capabilities, then temperature and humidity are used as environmental condition inputs to correct exposure index calculations and select operating strategies.

[0083] 3. Calculation and statistics of aerosol particle trajectories:

[0084] This invention tracks the trajectory of non-pathogen-specific aerosol particles entering an air duct. Aerosol particles can be released from the air duct inlet according to preset particle size, particle density, inlet release position, and initial velocity. Particles of different sizes can be calculated individually or grouped by size.

[0085] Particle motion can be analyzed using the Lagrange particle tracking method. The motion of particles in the duct is influenced by airflow drag, electric field force, gravity, Brownian diffusion force, and wall interaction. For the i-th aerosol particle, its position within the duct over time is recorded as follows: ;in, This represents the position of the i-th aerosol particle at time t.

[0086] The particle trajectory calculation module records the following information for each particle:

[0087] The trajectory of the motion; the dwell time in the air duct; whether it penetrates through the air outlet; whether it is deposited on the surface of the module; whether it is deposited on the wall of the air duct; the number of times it passes through the effective action area of ​​each plasma module; and the dwell time in each effective action area.

[0088] This invention not only calculates the trajectory of a single particle but also performs statistical analysis on the trajectory results of multiple particles. The statistical results include: particle residence time distribution; particle penetration ratio; particle deposition ratio; module surface deposition ratio; duct wall deposition ratio; trajectory differences between particles of different sizes; trajectory differences between particles at different inlet locations; and the location of low-exposure escape paths.

[0089] The above trajectory statistics are used to determine whether the duct structure and operating parameters lead to insufficient particle residence time, excessive local deposition, or some particles bypassing the effective action area.

[0090] 4. Aerosol Particle Exposure Index and Calculation Method:

[0091] The aerosol particle exposure index in this invention refers to the cumulative characterization value obtained by integrating over time the equivalent intensity of the combined effects of electric field, charged particles, and active species on aerosol particles along their motion trajectory within the effective area formed by the plasma module array.

[0092] For the i-th aerosol particle, its exposure index can be expressed as:

[0093] ;

[0094] Exposure index of the i-th aerosol particle;

[0095] : The residence time of the i-th aerosol particle in the air duct;

[0096] : The position of the i-th aerosol particle at time t;

[0097] : Equivalent action intensity function;

[0098] : Module array enable vector;

[0099] Inlet air velocity or volumetric flow rate;

[0100] Air temperature;

[0101] Relative humidity;

[0102] Time element;

[0103] The formula means that the exposure index of the i-th aerosol particle is obtained by integrating the equivalent intensity of the action at each location along its trajectory from entering the air duct to leaving the air duct or being deposited.

[0104] In practical calculations, a discrete summation form can be used:

[0105] ;

[0106] : The position of the i-th aerosol particle at the k-th trajectory point;

[0107] : The number of trajectory points of the i-th aerosol particle;

[0108] : The time interval corresponding to the kth trajectory point.

[0109] In a more complete form, the equivalent action strength K can be determined by the following factors:

[0110] ;

[0111] in, Electric field strength;

[0112] Equivalent ion or charged particle density;

[0113] The dimensionless spatial weights in the equivalent action zone model are used to characterize the relative degree of action in the corresponding region, and do not directly represent the concentration or density of active species.

[0114] : Particles carry a charge;

[0115] The residence time of particles in the local area is included by the time increment of time integration or discrete summation in the exposure index formula, and is no longer used as an independent input parameter of the equivalent action intensity function K.

[0116] To calculate the aerosol particle exposure index, this invention establishes an equivalent action intensity model to determine the equivalent action intensity K at different spatial locations near the plasma module array. When complete plasma physicochemical parameters are available, the equivalent action intensity model can be established based on a plasma fluid model and a chemical reaction model; when experimental or manufacturer test data is available, it can be established through experimental calibration; in the initial stage where a complete plasma chemical model or experimental calibration data is lacking, the equivalent action intensity model can be implemented using an equivalent action zone model. The equivalent action zone model defines a preset spatial region near the activated plasma module array as the effective action zone and assigns a preset equivalent action intensity or spatial weight to this region. The longer aerosol particles enter and remain in the effective action zone, the higher their exposure index; when particles do not enter the effective action zone, or pass through the corresponding area of ​​the closed module, their exposure index does not increase or only increases according to the preset background action intensity. The "equivalent effective action zone model" here is not a complete model among the four models mentioned above, but rather a simplified sub-model in the aerosol particle exposure index model used to determine the spatial distribution of the equivalent action intensity K.

[0117] As a specific implementation of the above equivalent action area model, the effective action area corresponding to the activated module is determined based on the input effective action area parameters, module installation location, and activation vector B. In this implementation, the equivalent action strength is represented by a dimensionless spatial weight, where:

[0118] ;

[0119] The entry and exit times of particles into the effective treatment area are determined based on their trajectories. The exposure index of a particle is obtained by summing its residence times within that area. Here, both K and R are dimensionless quantities. The unit is seconds, representing the cumulative residence time of particles within the effective action area; threshold. The same units are used to represent the preset minimum effective zone dwell time requirement. When comparing the same batch of candidate solutions, consistent zone definition rules, weight scales, and thresholds are used.

[0120] This invention couples the structural geometry model, airflow field, aerosol particle trajectory, equivalent action intensity, aerosol particle exposure index, and space requirement model, and then implements structural configuration optimization and operational control optimization based on this.

[0121] 5. Statistics on aerosol particle exposure index:

[0122] For the particle population, this invention further statistically analyzes the exposure index distribution. Assuming a total of N aerosol particles are calculated, the exposure index for each particle can be obtained:

[0123] , , ..., ;

[0124] Based on the above results, the following can be calculated: average exposure index; maximum exposure index; minimum exposure index; median exposure index; lowest quantile exposure index; low exposure escape ratio; exposure index distribution for particles of different sizes; and exposure index distribution for particles in different inlet regions.

[0125] The average exposure index can be expressed as:

[0126] ;

[0127] The low-exposure escape ratio can be expressed as:

[0128] ;

[0129] in:

[0130] Low exposure escape rate;

[0131] : Preset exposure index threshold;

[0132] The number of particles with an exposure index below the threshold;

[0133] N: Total number of particles.

[0134] The low-exposure escape ratio characterizes how many particles, although passing through the device, have insufficient cumulative exposure. This indicator can be used to identify low-exposure channels or structural defects.

[0135] Exposure index statistics and particle trajectory statistics are used together as the basis for structural configuration optimization and operation control strategy selection. If the average exposure index is high under a certain structural configuration, but the low-exposure escape ratio is also high, it indicates that there are local insufficient treatment channels in the structure, and it is necessary to adjust the number of module layers, layer spacing, wind speed, or module operation combination. If the exposure index is high under a certain operating combination, but the pressure drop, energy consumption, or deposition ratio is too high, then this scheme is not considered the preferred operating scheme.

[0136] 6. Spatial Demand Model:

[0137] The present invention also includes a space requirement model, which is used to convert the purification requirements of the target space into device structure and operation control requirements.

[0138] The inputs to a space requirement model typically include: target space volume V; current aerosol concentration C0; target safe concentration Cs; expected time to reach the target; allowable installation dimensions; and, depending on the requirements, may also include: background ventilation rate; continuous aerosol generation rate; and allowable noise, energy consumption, or pressure drop range.

[0139] Spatial concentration changes can be described using the following model:

[0140] ;

[0141] C: Space aerosol concentration;

[0142] G: Persistent aerosol generation rate;

[0143] V: Target space volume;

[0144] Q: Air volume handled by the device;

[0145] η: The single-pass processing efficiency of the device for the evaluated aerosol, a dimensionless quantity. Under stable operating conditions with equal inlet and outlet volumetric flow rates, ,in and The inlet and outlet aerosol concentrations of the device, measured using the same evaluation caliber, are respectively. >0; under net removal conditions, 0≤η≤1.

[0146] Background ventilation or natural attenuation coefficient.

[0147] When there is no continuous source of generation, it can be simplified to:

[0148] ;

[0149] In this invention, η can be determined based on the inlet and outlet aerosol concentrations of devices using the same evaluation caliber. When a correlation has been established between the statistical results of the exposure index and the treatment efficiency under corresponding operating conditions, this correlation can also be used to estimate η. The statistical results of the exposure index are used to characterize the degree of particle exposure and are not directly equivalent to the value of η.

[0150] 7. Structural configuration optimization methods:

[0151] The structural configuration optimization process of this invention is as follows, such as... Figure 1 As shown.

[0152] S1, Input the target space requirement parameters, including space volume, current aerosol concentration, target concentration, expected time to achieve the target, and allowable installation dimensions.

[0153] S2, input the parameters of the existing plasma module, including the module's external dimensions, installation interface, allowable wind speed range, fixed operating status, and equivalent effective action area parameters.

[0154] S3 sets the value range of the structure configuration variables, including: candidate values ​​for Lx; candidate values ​​for d; candidate values ​​for Ly; and candidate values ​​for Lz.

[0155] S4 automatically generates candidate cuboid air duct structures based on Lx, d, Ly, and Lz, and calculates the corresponding air duct length, width, height, and total volume.

[0156] S5 eliminates candidate structures that do not meet the constraints of installation dimensions, module splicing requirements, maintenance space, minimum inlet / outlet buffer section, or estimated pressure drop.

[0157] S6 performs flow field simulation on the retained candidate structures to obtain wind speed distribution, pressure distribution, residence time distribution, and pressure drop.

[0158] S7 releases representative aerosol particles based on the flow field results, performs particle trajectory calculations, and obtains information on particle penetration, deposition, and residence time.

[0159] S8 calculates the aerosol particle exposure index along the particle trajectory based on the effective action area formed by the activated module array.

[0160] S9 calculates the average exposure index, lowest quantile exposure index, low exposure escape ratio, deposition ratio, pressure drop, and energy consumption proxy index for each candidate structure.

[0161] S10, Select the recommended structural configuration based on the space requirement model and evaluation results, that is, determine Lx, d, Ly, Lz and the duct size.

[0162] 8. Operation control optimization methods:

[0163] After the structural configuration is determined, this invention further establishes an operational control strategy library. This strategy library is used to store the exposure index and engineering constraint results under different combinations of operational parameters.

[0164] The operation control optimization process is as follows.

[0165] S11, for the determined structural configuration, set different wind speeds u, temperature and humidity states T / RH, and module array activation vector B.

[0166] S12 calculates the airflow field, particle trajectory, and aerosol particle exposure index for each operating combination, and obtains the corresponding exposure index distribution, low exposure escape ratio, deposition ratio, pressure drop, fan energy consumption, and by-product risk proxy index.

[0167] S13, The above results are used to form the runtime strategy library.

[0168] S14 During operation, the sensor collects the current space aerosol concentration, temperature, humidity and equipment status.

[0169] S15, the spatial demand model calculates the current required processing capacity based on the current concentration, target concentration, and remaining time to achieve the target.

[0170] S16, the controller queries the operating strategy library and selects a combination of operating parameters that meets the processing requirements and has low energy consumption, pressure drop and by-product risks.

[0171] S17, the controller outputs control commands to the fan, temperature and humidity control unit and plasma module array to adjust the wind speed, temperature and humidity compensation parameters and module array opening vector.

[0172] S18: After running for a period of time, the spatial status is re-collected, and S15 to S17 are repeated to achieve dynamic adjustment.

[0173] 9. System components:

[0174] This invention also provides a plasma module array optimization system based on aerosol exposure index, the system comprising:

[0175] The space requirement input module is used to input the target space volume, current aerosol concentration, target safe concentration, expected time to achieve the target, background ventilation rate, and installation size constraints.

[0176] The existing module parameter input module is used to input the plasma module's external dimensions, allowable wind speed range, module splicing method, effective action area parameters, and fixed operating status.

[0177] The module array structure generation module is used to generate the geometric model of the cuboid air duct and plasma module array based on Lx, d, Ly, and Lz.

[0178] The flow field simulation module is used to calculate the distribution of airflow velocity field, pressure field, pressure drop, and residence time under different structural configurations and wind speed conditions.

[0179] The particle trajectory calculation module is used to calculate the movement trajectory, penetration state, deposition state, and residence time of non-pathogen-specific aerosol particles in the air duct.

[0180] The particle trajectory statistics module is used to statistically analyze the trajectory results of multiple aerosol particles, including residence time distribution, penetration ratio, deposition ratio, trajectory differences of particles of different sizes, and low-exposure escape paths.

[0181] The exposure index calculation module is used to calculate the aerosol particle exposure index along a single particle trajectory.

[0182] The exposure index statistics module is used to calculate the average exposure index, lowest quantile exposure index, low exposure escape ratio, and exposure index distribution of particles of different sizes.

[0183] The structural configuration optimization module is used to determine the recommended structural configuration based on space requirements, exposure index statistics, trajectory statistics, pressure drop, and deposition risk.

[0184] The operation strategy library module is used to store the exposure index, trajectory statistics, energy consumption, pressure drop and risk indicators corresponding to different wind speeds, temperature and humidity and module activation combinations.

[0185] The operation control module is used to determine wind speed, temperature and humidity compensation parameters, and module array activation vectors based on the current spatial status and the operation strategy library.

[0186] The results output module is used to output recommended duct size, module array structure, operation control strategy, low exposure escape path, deposition hotspots and exposure index statistics.

[0187] The present invention can also employ the following alternative solutions or equivalent embodiments to achieve the joint optimization of plasma module array structure configuration and operation control based on aerosol particle exposure index. These alternative solutions can be used individually or in combination.

[0188] 1. Alternative solutions for air duct structure:

[0189] In the preferred embodiment, the air duct is a cuboid air duct, and the airflow direction is defined as the x-direction. However, without changing the core idea of ​​the invention, the air duct can also adopt other regular or near-regular cross-sectional shapes, such as circular air ducts, elliptical air ducts, polygonal air ducts, partially variable cross-section air ducts, tapering air ducts, expanding air ducts, or combined air ducts with transition sections.

[0190] In the above alternative, the main airflow direction can still be defined as the x-direction, the cross-sectional direction perpendicular to the main airflow direction can be defined as the transverse arrangement direction, and the number of module array layers, layer spacing and splicing quantity can be determined according to the module size and installation boundary.

[0191] 2. Alternative solutions for modular array layout:

[0192] In the preferred embodiment, an Lx-layer module array is arranged along the x-direction, with an adjacent layer spacing of d. Each layer is formed by tightly splicing Ly×Lz modules within the yz cross section. Alternatively, the module array can also adopt non-equidistant arrangement, partially staggered arrangement, zoned arrangement, grouped arrangement, alternating arrangement, or different arrangement methods where the number of modules in different layers is not exactly the same. Or, empty spaces can be set in some areas to accommodate installation space, maintenance space, or airflow uniformity requirements.

[0193] In the above alternatives, the structural variables can be expanded from Lx, d, Ly, and Lz to a sequence of interlayer spacing, a sequence of the number of modules in each layer, a module position matrix, or a module occupancy matrix, as long as they are still used to determine the arrayed configuration of plasma modules in the duct.

[0194] 3. Alternative solutions for interlayer spacing parameters:

[0195] In the preferred embodiment, a uniform layer spacing d is used between adjacent module array layers. Alternatively, different layer spacings can be used between different adjacent layers, for example, defined as:

[0196] dL1, dL2, ..., dLx-1;

[0197] That is, the spacing between the first and second layers is dL1, the spacing between the second and third layers is dL2, and so on. This method is suitable for situations where it is necessary to adjust local residence time, airflow redistribution, or particle exposure uniformity.

[0198] 4. Alternatives to enabling vector B:

[0199] In the preferred scheme, a binary enable vector B=[bL1,bL2,...,bLx] is used to represent the enable or disable state of each layer of the module array, where bLj=1 indicates enable and bLj=0 indicates disable.

[0200] Alternatively, the activation vector B can be extended to a multi-valued vector or a continuous variable vector. For example, when the module supports multiple power levels, different power frequencies, different duty cycles, or different operating modes, bLj can take multiple discrete value levels; when the module supports continuous adjustment, bLj can represent the relative operating intensity of the Lj-th layer of the module array. The activation vector can also be extended to a two-dimensional or three-dimensional matrix to represent the independent activation state of different modules within the same layer.

[0201] 5. Alternative solutions for operating control parameters:

[0202] In the preferred embodiment, the operating control parameters include wind speed or air volume, temperature, humidity, and the module array activation vector B. Alternatively, the operating control parameters may also include fan speed, recirculated air ratio, fresh air ratio, bypass ventilation ratio, module runtime, intermittent operation cycle, duty cycle, operating speed, operating mode switching threshold, and the activation status of the pre-treatment or post-treatment units.

[0203] In practical engineering, only some of the operating parameters can be selected as control variables, such as controlling only the wind speed and module opening vector, or controlling only the number of module opening layers and the running time.

[0204] 6. Alternatives to the definition of the aerosol particle exposure index:

[0205] In the preferred embodiment, the aerosol particle exposure index can be expressed as the time integral of the particle's effect on the equivalent intensity along its trajectory. Alternatively, the exposure index can also be expressed as spatial integral, path integral, weighted residence time, normalized exposure dose, segmented cumulative exposure value, number of times the effective action zone is crossed, residence time within the effective action zone, equivalent reaction zone transit time, or a multi-index weighted function.

[0206] The equivalent interaction strength can be characterized by electric field strength, ion density, active particle concentration, electron density, local power density, discharge region weight, empirical interaction strength field, or a combination thereof. As long as this index can reflect the equivalent interaction strength and duration of aerosol particles passing through the module array, it is an equivalent substitute for the exposure index concept of this invention.

[0207] 7. Alternatives to particle trajectory calculation methods:

[0208] In the preferred embodiment, the Lagrange particle tracking method can be used to calculate the particle trajectory. Alternatively, the Eulerian concentration transport model, the Eulerian-Lagrange coupled model, the random walk model, the swarm equilibrium model, the Monte Carlo particle tracking model, the simplified streamline tracking model, or a statistical model based on the residence time distribution can also be used.

[0209] For scenarios with limited computing resources, reduced-order models, surrogate models, or empirical correction models can be used to approximate the statistical results of particle residence time, penetration ratio, and exposure index.

[0210] 8. Alternative solutions for obtaining the equivalent action intensity field:

[0211] In the preferred embodiment, the equivalent action intensity field can be given by a simplified electric field model, an effective discharge region model, or a plasma interaction region model. Alternatively, the equivalent action intensity field can also be obtained through experimental calibration, product parameters, manufacturer test data, empirical formulas, literature data, local electric field simulation, plasma fluid models, chemical reaction models, or machine learning regression models.

[0212] Even when complete plasma multiphysics simulation conditions are not available, the region near the module can be set as an equivalent reaction zone with a preset intensity distribution, and the influence of different intensity distributions on the optimization results can be determined through parameter sensitivity analysis.

[0213] 9. Alternatives to the optimized algorithm:

[0214] In the preferred scheme, evaluation and selection can be performed by traversing candidate structures and combinations of operating parameters. Alternatively, genetic algorithms, particle swarm optimization, simulated annealing, Bayesian optimization, response surface methodology, multi-objective optimization, integer programming, dynamic programming, reinforcement learning, or machine learning-assisted optimization methods can also be used.

[0215] When the number of variables is small, exhaustive search or grid search can be used; when the number of variables is large, heuristic algorithms or surrogate models can be used to accelerate optimization.

[0216] 10. Alternatives to running the policy library:

[0217] In the preferred embodiment, offline simulation results can form a runtime strategy library, which can be queried and matched with control parameters during online runtime. Alternatively, instead of establishing a fixed strategy library, runtime parameters can be output using online simplified calculations, lookup table interpolation, rule-based control, model predictive control, fuzzy control, expert rule bases, machine learning predictive models, or digital twin models.

[0218] For small devices, the strategy library can be fixed into several preset operating modes; for large systems, the strategy library can be dynamically updated based on sensor feedback.

[0219] 11. Alternatives to the spatial demand model:

[0220] In the preferred approach, the spatial demand model can determine the treatment requirements based on parameters such as spatial volume, initial concentration, target concentration, time to reach the target, aerosol generation intensity, and background attenuation. Alternatively, multi-region models, fully mixed models, ventilation models, computational fluid dynamics spatial models, sensor feedback models, or empirical ventilation efficiency models can also be used.

[0221] For different application scenarios, the space requirement model can also be simplified to target clean air volume, target number of cycles, target treatment efficiency, or target risk level.

[0222] 12. Alternative solutions to the evaluation indicator system:

[0223] In the preferred embodiment, the comprehensive evaluation may include aerosol particle exposure index, pressure drop, energy consumption, deposition ratio, and byproduct risk. Alternatively, evaluation indicators may also include treatment efficiency, penetration rate, escape rate, clean air output, unit energy consumption treatment capacity, operating noise, module lifespan, maintenance cycle, temperature and humidity deviation, space occupation, material cost, operating cost, or reliability indicators.

[0224] In different application scenarios, some indicators can be selected for single-objective or multi-objective optimization based on weighting functions or constraints.

[0225] 13. Alternatives for sensors and input parameters:

[0226] In the preferred embodiment, the spatial state can be represented by parameters such as aerosol concentration, temperature, and humidity. Alternatively, particulate matter sensors, particle size analyzers, wind speed sensors, differential pressure sensors, temperature and humidity sensors, ozone sensors, nitrogen oxide sensors, current and voltage detection units, module operation status detection units, or door / window / personnel status sensors can be introduced.

[0227] If real-time sensor input is not available, spatial status parameters can be provided manually, through preset scene parameters, or by an external building control system.

[0228] 14. Alternative solutions for system implementation:

[0229] In the preferred embodiment, the system may include a parameter input module, a candidate structure generation module, an airflow field calculation module, a particle trajectory calculation module, an exposure index calculation module, a comprehensive evaluation module, a structure configuration optimization module, an operation strategy library module, a spatial state input module, and an operation control output module.

[0230] Alternatively, the above modules can be deployed on local computers, embedded controllers, industrial PCs, cloud servers, edge computing devices, or building automation systems; the modules can also be combined, split, or implemented in the form of software, hardware, firmware, cloud services, etc.

[0231] 15. Alternatives to plasma module types:

[0232] The preferred solution is applicable to existing wire-plate or wire-arc perforated electrode plasma modules. Alternatively, it can also be applied to other cryogenic plasma air handling modules, such as plate-plate, needle-plate, wire-cylinder, needle-mesh, dielectric barrier discharge, corona discharge, glow discharge, micro-discharge array, or composite discharge modules.

[0233] As long as the module can form an equivalent working area in the air handling channel and influence the aerosol particle exposure process through array arrangement and operation control, the structural configuration and operation control joint optimization method of the present invention can be adopted.

Claims

1. A method for optimizing plasma module arrays based on aerosol exposure index, characterized in that, Includes the following steps: S1, Input target space requirement parameters, including space volume, current aerosol concentration, target concentration, expected time to achieve target, and allowable installation size; S2, Input existing plasma module parameters, including module dimensions, installation interface, allowable wind speed range, fixed operating status, and equivalent effective action area parameters; S3, set the range of values ​​for the structural configuration variables. The structural configuration variables include: candidate values ​​for the number of plasma module array layers Lx set along the airflow direction, candidate values ​​for the spacing d between two adjacent plasma module array layers, candidate values ​​for the number of plasma modules Ly in the air duct width direction of each array layer, and candidate values ​​for the number of plasma modules Lz in the air duct height direction of each array layer. S4. Based on the candidate values ​​of Lx, d, Ly, and Lz, candidate cuboid air duct structures are automatically generated, and the length, width, height, and total volume of the corresponding air duct are calculated. S5, eliminate candidate structures that do not meet the constraints of installation size, module splicing requirements, maintenance space, minimum inlet and outlet buffer section, or voltage drop prediction constraints; S6, perform flow field simulation on the retained candidate structures to obtain wind speed distribution, pressure distribution, residence time distribution and pressure drop; S7. Based on the flow field simulation results, release representative aerosol particles, perform particle trajectory calculations, and obtain information on particle penetration state, deposition state, and residence time. S8, based on the effective action area formed by the activated module array, calculate the exposure index of each aerosol particle along the particle trajectory; S9, Calculate statistical evaluation indicators for each candidate structure, including average exposure index, lowest quantile exposure index, low exposure escape ratio, deposition ratio, pressure drop and energy consumption proxy index. S10, Select the recommended structural configuration based on the space requirement model and the statistical evaluation indicators, and determine Lx, d, Ly, Lz and the duct size; S11, for the determined structural configuration, set different wind speeds u, temperature and humidity states T / RH and module array opening vector B; S12, calculate the airflow field, particle trajectory and aerosol particle exposure index for each operating combination, and obtain the corresponding exposure index distribution, low exposure escape ratio, deposition ratio, pressure drop, fan energy consumption and by-product risk proxy index; S13, The running combinations obtained in S12 and their corresponding calculation results are used to form a running strategy library; S14, during operation, the current space aerosol concentration, temperature, humidity and equipment status are collected by sensors; S15, the spatial demand model calculates the current required processing capacity based on the current concentration, target concentration, and remaining time to achieve the target. S16, the controller queries the operating strategy library and selects a combination of operating parameters that meets the processing requirements and has low energy consumption, pressure drop and by-product risk; S17, the controller outputs control commands to the fan, temperature and humidity control unit and plasma module array to adjust the wind speed, temperature and humidity compensation parameters and module array opening vector; S18: After running for a period of time, the spatial status is re-collected, and S15 to S17 are repeated to achieve dynamic adjustment.

2. The plasma module array optimization method based on aerosol exposure index according to claim 1, characterized in that, The module array activation vector B is defined as B=[bL1, bL2, …, bLx], where bLj=1 indicates that the Lj-th plasma module array is activated, and bLj=0 indicates that the Lj-th plasma module array is deactivated. When the plasma module supports multiple operating levels, bLj takes multiple values: bLj=0 indicates off, bLj=1 indicates low-level operation, and bLj=2 indicates high-level operation.

3. The plasma module array optimization method based on aerosol exposure index according to claim 1, characterized in that, The aerosol particle exposure index is calculated in the following form: For the i-th aerosol particle, its exposure index is expressed as: ； Exposure index of the i-th aerosol particle; : Residence time of the i-th aerosol particle in the air duct; : The position of the i-th aerosol particle at time t; : Equivalent action intensity function; : Module array enable vector; Inlet air velocity or volumetric flow rate; Air temperature; Relative humidity; Time microelement.

4. The plasma module array optimization method based on aerosol exposure index according to claim 3, characterized in that, The average exposure index can be expressed as: ； N: Total number of particles.

5. The plasma module array optimization method based on aerosol exposure index according to claim 1, characterized in that, The low-exposure escape ratio is expressed as: ； in: Low exposure escape rate; : Preset exposure index threshold; The number of particles with an exposure index below the threshold; N: Total number of particles.

6. The plasma module array optimization method based on aerosol exposure index according to claim 1, characterized in that, The space demand model uses the following concentration change model to describe the change in aerosol concentration in the target space: ； C: Space aerosol concentration; G: Persistent aerosol generation rate; V: Target space volume; Q: Air volume handled by the device; η: The single-pass processing efficiency of the device for the evaluated aerosol; Background ventilation or natural attenuation coefficient.

7. The plasma module array optimization method based on aerosol exposure index according to claim 1, characterized in that, The flow field simulation, particle trajectory calculation, and exposure index calculation are all completed offline, and the calculation results are stored in the runtime strategy library. During the online operation phase, the controller queries the operation strategy library based on the current spatial state collected by the sensors and matches the corresponding combination of operation parameters, eliminating the need for repeated calculations of the flow field or particle trajectory online.

8. The plasma module array optimization method based on aerosol exposure index according to claim 3, characterized in that, The equivalent action intensity function K is determined using an equivalent action zone model, which is based on the preset effective action zone corresponding to the activation module and the preset dimensionless spatial weight of that zone.

9. The plasma module array optimization method based on aerosol exposure index according to claim 1, characterized in that, In step S14, the sensors also include a particulate matter sensor, a particle size analyzer, a wind speed sensor, a differential pressure sensor, a temperature and humidity sensor, an ozone sensor, a nitrogen oxide sensor, a current and voltage detection unit, a module operation status detection unit, or a door / window / personnel status sensor.

10. A plasma module array optimization system based on aerosol exposure index, characterized in that, The system includes: The space requirement input module is used to input the target space volume, current aerosol concentration, target safe concentration, expected time to achieve the target, background ventilation rate, and installation size constraints. The existing module parameter input module is used to input the plasma module's external dimensions, allowable wind speed range, module splicing method, effective action area parameters, and fixed operating status. A module array structure generation module is used to generate geometric models of cuboid air ducts and plasma module arrays based on Lx, d, Ly, and Lz. The flow field simulation module is used to calculate the airflow velocity field, pressure field, pressure drop, and residence time distribution under different structural configurations and wind speed conditions. The particle trajectory calculation module is used to calculate the motion trajectory, penetration state, deposition state, and residence time of non-pathogen-specific aerosol particles in the air duct; The particle trajectory statistics module is used to statistically analyze the trajectory results of multiple aerosol particles, including residence time distribution, penetration ratio, deposition ratio, trajectory differences of particles of different sizes, and low-exposure escape paths. The exposure index calculation module is used to calculate the aerosol particle exposure index along a single particle trajectory; The exposure index statistics module is used to calculate the average exposure index, lowest quantile exposure index, low exposure escape ratio, and exposure index distribution of particles of different sizes. The structural configuration optimization module is used to determine the recommended structural configuration based on space requirements, exposure index statistics, trajectory statistics, pressure drop, and deposition risk. The operation strategy library module is used to store the exposure index, trajectory statistics, energy consumption, pressure drop and risk indicators corresponding to different wind speeds, temperature and humidity and module activation combinations; The operation control module is used to determine wind speed, temperature and humidity compensation parameters, and module array activation vector based on the current spatial status and the operation strategy library. The results output module is used to output recommended duct size, module array structure, operation control strategy, low exposure escape path, deposition hotspots and exposure index statistics.

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