An Optimization Design Method for Industrial Ventilation Systems Aiming at Lung Function Protection
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-05-20
- Publication Date
- 2026-06-30
AI Technical Summary
Existing industrial ventilation system designs fail to respond to differentiated health impacts during the optimization process, and health risk information is not dynamically embedded into optimization decisions, resulting in insufficient precision and cost-effectiveness in protection.
By establishing a quantitative response relationship model between pollutant exposure concentration and changes in lung function indicators, the pollutant concentration control threshold is determined as the optimization target of the ventilation system. A multi-level comprehensive evaluation system is constructed, and weights are dynamically assigned to reflect the sensitivity to health impacts, thereby optimizing ventilation parameters to achieve precise protection.
This approach achieves better energy balance by optimizing ventilation parameters while meeting health protection requirements, thus improving the scientific nature and accuracy of the design and avoiding excessive protection and energy waste.
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Abstract
Description
Technical Field
[0001] This invention belongs to the field of industrial ventilation optimization design, and in particular relates to an industrial ventilation system optimization design method oriented towards the goal of lung function protection. Background Technology
[0002] In the field of industrial ventilation and protection, the design and optimization of ventilation systems are core engineering technologies for controlling air pollutants in workplaces and protecting the health of workers. Traditional industrial ventilation system design, such as in typical scenarios involving high-temperature fumes and toxic gases in steel smelting and non-ferrous metal processing, typically relies on two main methods.
[0003] The first category is general designs based on industry design standards and empirical formulas. This type of approach prioritizes controlling the average concentration of pollutants in the work area below national occupational exposure limits. For example, designers will select the type of exhaust hood and empirical airflow based on the characteristics of the pollution source and refer to manuals for calculations. However, this method has significant limitations. It is essentially a one-size-fits-all, static design that only focuses on achieving macroscopic compliance with pollutant concentrations, failing to fully consider the fundamental differences in the toxicological mechanisms and health hazards of different pollutants on the human respiratory system. For example, PM2.5... 2.5 It is easy to deposit in the alveoli, and its potential mechanism of damage to lung function is different from the stimulating effect of SO2 on the upper respiratory tract. However, traditional design methods cannot reflect this difference in sensitivity to health effects when setting control targets, which may lead to a deviation in the focus of protection.
[0004] The second category is the refined design method based on flow field simulation, which has been widely applied with the development of computational fluid dynamics (CFD) technology. This method establishes a numerical model of the physical scenario to simulate the airflow organization and pollutant diffusion patterns under different ventilation parameters, thereby optimizing ventilation efficiency and reducing concentrations in the breathing zone. Although this method has made progress in predicting the spatial distribution of pollutants and evaluating engineering performance indicators such as capture efficiency, its optimization objectives are usually still limited to single fluid dynamics or concentration field indicators. In existing technologies, although some studies have attempted to introduce health risk assessment models to evaluate the simulation results post-hocly, such two-step methods only use health risk as a post-hoc evaluation label for ventilation schemes. The health risk assessment model and the CFD flow field simulation and optimization decision-making process are isolated from each other. This means that the adjustment of ventilation parameters is not directly driven by health protection objectives, and the health risk model is not dynamically embedded in the feedback and optimization loop of CFD simulation. As a result, the final ventilation design scheme is difficult to achieve a precise balance and synergistic optimization between pollution control effectiveness, actual personnel exposure levels, and differentiated health protection needs.
[0005] In summary, the shortcomings of existing technologies lie in two aspects: their design logic either remains at the level of achieving macroscopic concentration standards, failing to respond to differentiated health impacts; or, although advanced simulation tools are employed, their optimization process is disconnected from deeper health protection goals, with health risk information serving only as static evaluation outputs and failing to be transformed into core inputs and constraints driving ventilation parameter optimization decisions. This results in deficiencies in the accuracy, economy, and health goal orientation of traditional ventilation system design. Summary of the Invention
[0006] In view of this, the present invention aims to propose an industrial ventilation system optimization design method oriented towards the goal of lung function protection, so as to solve the problem that the optimization process of the existing technology is disconnected from the deep-level health protection goal, and that health risk information is only used as a static evaluation output.
[0007] To achieve the above objectives, the present invention adopts the following technical solution: an optimized design method for industrial ventilation systems aimed at lung function protection, the method comprising: Step S1: Obtain pollutant exposure concentration data for different work processes and positions in the target industrial scenario, as well as quantitative data on lung function indicators of the corresponding workers. Step S2: Based on the data collected in step S1, establish a quantitative response relationship model between pollutant exposure concentration and changes in lung function indicators; Step S3: Based on the quantitative response relationship model established in step S2, determine the pollutant concentration control threshold corresponding to the range of change of the target lung function index, and transform the pollutant concentration control threshold into the optimization target of the ventilation system. Step S4: Establish a computational fluid dynamics model of the ventilation system in the target industrial scenario. Using the optimization objective determined in step S3 as the optimization constraint, set various ventilation parameter conditions for numerical simulation and obtain multiple performance index values reflecting pollutant control and personnel exposure under each condition. Step S5: Construct a comprehensive evaluation system that includes the multiple performance indicators described in Step S4, and obtain the sensitivity of different pollutants to changes in lung function indicators based on the quantitative response relationship model established in Step S2, and assign weights to each indicator in the comprehensive evaluation system. Step S6: Based on the performance index values of each operating condition obtained in step S4 and the index weights determined in step S5, calculate the comprehensive evaluation value of each ventilation parameter operating condition, and select the optimal ventilation operating condition based on the comprehensive evaluation value to complete the optimization of ventilation system parameters.
[0008] Furthermore, a preferred method is proposed, in step S1, the pollutant exposure concentration data is obtained through on-site measurement, including particulate matter concentration and SO2 concentration; the measured lung function index data includes at least one of FEV1, FVC, and FEV1 / FVC.
[0009] Furthermore, a preferred approach is proposed: in step S2, when establishing the quantitative response relationship model, the age, gender, length of service, and body mass index (BMI) data of the workers are simultaneously collected as control variables, and a multiple regression analysis method is used for modeling.
[0010] Furthermore, a preferred approach is proposed, in which step S2 further includes: using restricted cubic spline functions to analyze the nonlinear relationship between pollutant concentration and lung function index values, and determining lung function index thresholds corresponding to different pollutant concentrations based on the nonlinear relationship.
[0011] Furthermore, a preferred approach is proposed, wherein in step S3, the optimization objectives of the ventilation system include: the pollutant concentration in the breathing area of the operator is lower than the pollutant concentration control threshold, or the proportion of locations in the work area where the pollutant concentration is lower than the pollutant concentration control threshold reaches a preset value.
[0012] Furthermore, a preferred embodiment is proposed, wherein in step S4, the plurality of performance indicators include at least two of the following: capture efficiency reflecting the pollution source control capability, inhalation ratio reflecting the actual exposure level of workers, and non-carcinogenic health risk HQ value reflecting the protective effect.
[0013] Furthermore, a preferred method is proposed, wherein step S5 includes: Step S51: Construct a multi-level comprehensive evaluation index system that includes a target layer, a criterion layer, and an indicator layer, wherein the indicator layer contains multiple sub-indicators corresponding to different pollutants; Step S52: Obtain the sensitivity differences of the effects of different pollutants on changes in lung function indicators based on the quantitative response relationship model; Step S53: Based on the difference in sensitivity to influence, adjust the importance of the sub-indicators of the corresponding pollutants in the weight calculation in the indicator layer. Among them, the sub-indicators of pollutants that are more sensitive to changes in lung function indicators are given a higher weight tendency. Step S54: Calculate the combined weights of each indicator in the comprehensive evaluation index system using the combined weighting method.
[0014] Furthermore, an optimal method is proposed: in step S6, a linear weighted method is used to calculate the comprehensive evaluation value of each ventilation parameter condition. The calculation formula is as follows:
[0015] Where Z is the comprehensive evaluation value. The combined weight of the i-th indicator is... Let be the standardized value of the i-th indicator, and n be the number of indicators.
[0016] Based on the same inventive concept, the present invention also proposes a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes an industrial ventilation system optimization design method for lung function protection objectives as described in any of the preceding claims.
[0017] Based on the same inventive concept, the present invention also proposes a computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of an industrial ventilation system optimization design method for lung function protection as described in any one of the above.
[0018] Compared with the prior art, the beneficial effects of the present invention are: Unlike traditional methods that use only a general, static value—the pollutant concentration limit—as the design target, this invention establishes a quantitative response relationship model between pollutant exposure concentration and changes in lung function indicators. This model extracts objective patterns of the impact of different pollutants on human health from historical population data. Furthermore, this pattern is combined with pre-set health protection measures to obtain dynamic engineering protection concentration thresholds for guiding specific ventilation designs. These thresholds are not diagnostic criteria, but rather transform abstract health protection goals into concrete, verifiable engineering constraints regarding spatial concentration distribution in CFD numerical simulations, achieving a leap from observing health effects to setting engineering parameters.
[0019] Unlike evaluation systems that use fixed weights or are based solely on the degree of data dispersion, this invention incorporates the information obtained from the quantitative response relationship model above—which reveals the differences in the sensitivity of different pollutants to key lung function indicators—into the weight generation process of a multi-indicator comprehensive evaluation system. As a result, the comprehensive evaluation results of ventilation conditions can automatically reflect the emphasis on the differentiated health impacts of different pollutants.
[0020] Existing evaluation systems often focus on the efficiency of pollutant control itself or general risk values, failing to reflect the differences in the impact of various pollutants on lung function, leading to discrepancies between evaluation results and ultimate protection goals. This invention constructs a new evaluation system by dynamically injecting health impact sensitivity information into the evaluation weights. When evaluating ventilation conditions, this system automatically strengthens the assessment of the control effect of pollutants with more sensitive health effects. This ensures that the optimal ventilation parameter combination selected ultimately, while maintaining overall protection effectiveness, can more specifically suppress the pollutants that pose the greatest threat to lung function and health, achieving precise protection.
[0021] Traditional designs, aiming for conservative safety goals, often employ a crude strategy of increasing airflow and velocity, leading to high energy consumption and potential airflow turbulence. This invention, through the aforementioned goal coupling, first establishes a reasonable engineering protection threshold based on a scientific model, filtering out operating conditions that do not meet basic health constraints and avoiding insufficient protection. Then, among candidate operating conditions that meet these hard constraints, a refined comparison is performed using an evaluation coupling system. This method can identify schemes that achieve a better balance in energy-related aspects such as airflow and airflow organization while meeting core health protection requirements. This overcomes the shortcomings of traditional methods, which rely on experience and are prone to over-protection and energy waste, achieving synergistic optimization of protection effectiveness and operational economy.
[0022] Existing technologies typically involve first performing CFD simulations and then substituting the data into a health risk model for evaluation. This is a post-event assessment model, where the health model is not involved in the generation of optimization decisions. This invention, through goal coupling and evaluation coupling, deeply embeds the health data model into the entire process of design constraint generation, multi-condition simulation, comprehensive evaluation, and decision optimization, forming a closed-loop optimization framework driven by health protection goals. The health protection goal is no longer merely a final score, but rather transforms into constraints and weighting criteria that permeate the design process, dynamically guiding the optimization of ventilation parameters and significantly improving the scientific rigor and accuracy of the optimized design results. Attached Figure Description
[0023] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a flowchart of an industrial ventilation system optimization design method for lung function protection as described in this invention. Figure 2 The PM described in this invention 2.5 A schematic diagram of the nonlinear relationship curve between lung function and lung function; Figure 3 This is a schematic diagram of the nonlinear relationship curve between SO2 and lung function as described in this invention; Figure 4 This invention provides a physical model for pollutant control in the ventilation system of a magnesium smelting process in a non-ferrous metal plant. Figure 5 This is a schematic diagram for verifying the grid independence of the pollutant control model of the ventilation system for the magnesium smelting process described in this invention, wherein (a) shows the variation of velocity above the pollution source with height, and (b) shows the variation of temperature above the pollution source with height. Figure 6This is a schematic diagram for verifying the effectiveness of the pollutant control model of the ventilation system for the magnesium smelting process described in this invention. In this diagram, (a) is a comparison of the deviation between simulated and measured data of pollutant airflow velocity, (b) is a comparison of the deviation between simulated and measured data of pollutant airflow temperature, and (c) is a comparison of the deviation between simulated and measured data of SO2 concentration in pollutant airflow. Figure 7 This is a schematic diagram comparing the results of three single performance indicators of the ventilation system for magnesium smelting process described in this invention under different exhaust volumes: capture efficiency, worker inhalation ratio, and non-carcinogenic health risk. Among them, (a) is a comparison of the capture efficiency indicator under different exhaust volumes, (b) is a comparison of the worker inhalation ratio indicator under different exhaust volumes, and (c) is a comparison of the non-carcinogenic health risk indicator under different exhaust volumes. Figure 8 This is a schematic diagram showing the comprehensive weighting of different evaluation indicators for the ventilation system of the magnesium smelting process described in this invention. Detailed Implementation
[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of the present invention can be combined with each other, and the described embodiments are only some embodiments of the present invention, not all embodiments.
[0025] Implementation Method 1: This implementation method addresses the problem that existing technologies lack a connection between the optimization process and in-depth health protection goals, and that health risk information is only used as a static evaluation output. It proposes an optimization design method for industrial ventilation systems oriented towards lung function protection goals. The method includes: Step S1: Obtain pollutant exposure concentration data for different work processes and positions in the target industrial scenario, as well as quantitative data on lung function indicators of the corresponding workers. Step S2: Based on the data collected in step S1, establish a quantitative response relationship model between pollutant exposure concentration and changes in lung function indicators; Step S3: Based on the quantitative response relationship model established in step S2, determine the pollutant concentration control threshold corresponding to the range of change of the target lung function index, and transform the pollutant concentration control threshold into the optimization target of the ventilation system. Step S4: Establish a computational fluid dynamics model of the ventilation system in the target industrial scenario. Using the optimization objective determined in step S3 as the optimization constraint, set various ventilation parameter conditions for numerical simulation and obtain multiple performance index values reflecting pollutant control and personnel exposure under each condition. Step S5: Construct a comprehensive evaluation system that includes the multiple performance indicators described in Step S4, and obtain the sensitivity of different pollutants to changes in lung function indicators based on the quantitative response relationship model established in Step S2, and assign weights to each indicator in the comprehensive evaluation system. Step S6: Based on the performance index values of each operating condition obtained in step S4 and the index weights determined in step S5, calculate the comprehensive evaluation value of each ventilation parameter operating condition, and select the optimal ventilation operating condition based on the comprehensive evaluation value to complete the optimization of ventilation system parameters.
[0026] In step S1 of this embodiment, the pollutant exposure concentration data is obtained through on-site measurement, including particulate matter concentration and SO2 concentration; the measured lung function index data includes at least one of FEV1, FVC, and FEV1 / FVC.
[0027] In step S2 of this embodiment, when establishing the quantitative response relationship model, the age, gender, length of service, and body mass index (BMI) data of the workers are also collected simultaneously as control variables, and a multiple regression analysis method is used for modeling.
[0028] Furthermore, step S2 also includes: using restricted cubic spline functions to analyze the nonlinear relationship between pollutant concentration and lung function index values, and determining lung function index thresholds corresponding to different pollutant concentrations based on the nonlinear relationship.
[0029] In step S3 of this embodiment, the optimization objectives of the ventilation system include: the concentration of pollutants in the breathing area of the workers is lower than the pollutant concentration control threshold, or the proportion of locations in the work area where the concentration of pollutants is lower than the pollutant concentration control threshold reaches a preset value.
[0030] In step S4 of this embodiment, the plurality of performance indicators include at least two of the following: capture efficiency reflecting the pollution source control capability, inhalation ratio reflecting the actual exposure level of workers, and non-carcinogenic health risk HQ value reflecting the protective effect.
[0031] Step S5 of this embodiment includes: Step S51: Construct a multi-level comprehensive evaluation index system that includes a target layer, a criterion layer, and an indicator layer, wherein the indicator layer contains multiple sub-indicators corresponding to different pollutants; Step S52: Obtain the sensitivity differences of the effects of different pollutants on changes in lung function indicators based on the quantitative response relationship model; Step S53: Based on the difference in sensitivity to influence, adjust the importance of the sub-indicators of the corresponding pollutants in the weight calculation in the indicator layer. Among them, the sub-indicators of pollutants that are more sensitive to changes in lung function indicators are given a higher weight tendency. Step S54: Calculate the combined weights of each indicator in the comprehensive evaluation index system using the combined weighting method.
[0032] In step S6 of this implementation method, the comprehensive evaluation value of each ventilation parameter condition is calculated using the linear weighted method. The calculation formula is as follows:
[0033] Where Z is the comprehensive evaluation value. The combined weight of the i-th indicator is... Let be the standardized value of the i-th indicator, and n be the number of indicators.
[0034] Implementation Method 2, see below Figures 1 to 8 This embodiment describes a specific example of the industrial ventilation system optimization design method for lung function protection described in Embodiment 1. It should be noted that this embodiment is intended to explain the invention and not to limit it. The industrial ventilation system optimization design method of this invention involves all data processing and calculations completed in a computer or server, and does not involve direct intervention or diagnostic procedures on the human body.
[0035] This embodiment uses the optimized design of a local ventilation system in a non-ferrous metal smelting plant, such as a magnesium smelting process, as an example to illustrate the invention in detail. (Refer to...) Figure 1 The method flowchart shown in this embodiment includes the following steps: S1. Historical Data Acquisition and Processing in Industrial Scenarios The pollutant concentration data and worker lung function index data collected in this step are all from existing, routine occupational health monitoring and surveillance records in this industrial setting, and not from tests specifically designed to expose workers to specific pollutant concentrations for the purpose of implementing this invention. Specifically: Historical monitoring data from the past year was retrieved from the factory's occupational health records for key work positions in the magnesium smelting process (such as smelting and casting). This data included particulate matter (PM2.5). 10 PM 2.5 The short-term exposure concentrations or time-weighted average concentrations of SO2 and SO2, along with the corresponding monitoring times and job information, were obtained. Statistical analysis was used to obtain representative pollutant exposure concentration ranges for each job position.
[0036] Routine pulmonary function test results were obtained from occupational health examination records of workers in the same occupational group during the same period. These results primarily included specific measurements of forced vital capacity (FVC), forced expiratory volume in one second (FEV1), and their ratio (FEV1 / FVC). These data were used as continuous physiological parameters. Simultaneously, basic information such as age, gender, length of service in the occupation, height, and weight were also collected. Weight and height were used to calculate Body Mass Index (BMI), serving as control variables in subsequent statistical analysis.
[0037] All data were anonymized, and based on the work activity records, a time-weighted average method was used to convert the monitored concentrations at different work stages and locations into individualized time-weighted average exposure concentration estimates for the corresponding job personnel. The calculation formula is shown below: Based on the time allocation characteristics of the work activities, the actual exposure concentration of workers is estimated using the time-weighted average method:
[0038] In the formula: E C represents the worker's exposure level. i为 No. i Stage pollutant exposure concentration, mg / m³ 3 ; t i No. i The duration of the phase is h.
[0039] The BMI (Body Mass Index) calculation method is as follows:
[0040] In the formula: m is the worker's weight (kg); h is the worker's height (m), used for subsequent control of confounding factors.
[0041] S2. Establish a quantitative response relationship model between pollutant exposure concentration and changes in lung function indicators; This step is crucial for transforming historical health data into quantitative parameters that can guide engineering design. Based on the desensitized historical population data collected in S1, statistical methods are used to establish an exposure-response model.
[0042] With pollutant PM 2.5With SO2 exposure concentration as the independent variable and lung function indicators (such as FEV1) as the dependent variable, and age, gender, length of service, and BMI as control variables, a quantitative relationship model was established using multiple linear regression analysis. This model describes the average expected change in lung function indicators for every unit change in pollutant exposure concentration, after controlling for other factors. The multiple regression analysis method was used to analyze the association between pollutant concentration and continuous lung function indicators (FEV1, FVC, and FEV1 / FVC), and the model formula is as follows:
[0043] in, This is a constant term, also known as the intercept. , ... This is called the partial regression coefficient, or simply the regression coefficient. This formula indicates that the dependent variable Y in the data can be approximately represented by the independent variable... , … A linear function. e It is to remove m Random error (i.e., residual) after considering the influence of each independent variable on Y. Partial regression coefficients ( j = 1, 2, ..., m) means that when other independent variables remain constant, The average change in Y when it changes by one unit.
[0044] Model parameters based on sample data , ... An estimation is performed to obtain the multiple linear regression equation, as shown in the following equation:
[0045] In the formula, , … These are the estimated values of the model parameters. Let Y be an estimate of Y, representing a set of independent variables. , … The value is the average of Y. Parameter estimation can be obtained using the least sum of squares (LS) method, that is, based on the observed values. n Given example data, to minimize the sum of squared residuals, solve a system of mathematical equations, and then use the following formula to find the constant term of the regression equation. : .
[0046] Restricted cubic splines (RCS) were used to analyze the nonlinear relationship between pollutant concentration and the risk of abnormal lung function. The 5th, 50th, and 95th quantiles were selected as three nodes. By fitting the curves of this model, the nonlinear trend of pollutant concentration and the risk of abnormal lung function were clarified, and the risk threshold was determined.
[0047] To further accurately describe the nonlinear relationship between exposure and response, a restricted cubic spline (RCS) model was used for fitting. This model allows for the analysis of the nonlinear trend curves of lung function indicators as a function of pollutant concentration, such as... Figure 2 and Figure 3 As shown in the figure. Based on this curve, one or more engineering protection concentration thresholds T are determined for ventilation design. For example, it can be set as "the upper limit of contaminant concentration that would allow 95% of workers to experience a decrease in their predicted FEV1 value within a clinically recognized small variation range (e.g., 5%)". This threshold T is an engineering constraint value used for CFD simulation, not a diagnostic conclusion of any individual's health condition.
[0048] S3. Quantification of ventilation protection optimization targets The engineering protection concentration threshold T determined in S2 is concretized into a quantitative target that can be executed and evaluated in CFD simulation. This embodiment sets two objectives: Objective A: The average concentration of contaminants in the breathing zone of workers (usually defined as a spherical space 1.5m above the mouth and nose) should be below the threshold T.
[0049] Objective B: Within the work area (the entire work area of concern), the proportion of grid points with pollutant concentrations below the threshold T should be no less than 80% of the total number of grid points.
[0050] These two objectives will serve as rigid constraints for subsequent CFD simulations and operating condition screening, ensuring that any ventilation scheme considered must first meet these basic health protection requirements.
[0051] S4. CFD Simulation and Performance Index Extraction of Ventilation Conditions Based on Protected Targets This step embodies the core of this invention: deeply coupling health goals into engineering simulation.
[0052] Based on the actual geometric dimensions of the target magnesium smelting plant, a three-dimensional physical model including pollution sources, ventilation devices (such as overhead suction hoods), obstacles, and workers was created using CFD software. Figure 4 As shown. Set boundary conditions (pollution source intensity, initial supply / exhaust air parameters, etc.) that match the actual field measurements. Verify grid independence (e.g.) Figure 5 (a) and (b)) and model validity verification (simulation results compared with some historical measured data, error <15%, such as... Figure 6(a) to 6(c)) to ensure model reliability.
[0053] Under the premise of satisfying objectives A and B in step S3, multiple different combinations of ventilation parameters (operating conditions) are designed. As shown in the table below, this embodiment designs 5 operating conditions (G1-G5), mainly varying the supply air jet velocity and exhaust air velocity.
[0054]
[0055] CFD simulations were performed on each operating condition that met the basic protection constraints, and three engineering performance indicators were extracted through post-processing: capture efficiency (reflecting the pollution source control capability), inhalation ratio (IF, reflecting the actual exposure level of workers), and non-carcinogenic risk index (HQ, reflecting the risk level based on the reference concentration). The calculation results are as follows: Figure 7 As shown in (a)-(c). The HQ calculation here uses only the general reference concentration (RfC), which is a standard risk characterization model in the field of environmental health and does not involve judgment of the health status of a specific individual.
[0056] The capture efficiency is calculated using the following formula:
[0057] in, To improve the collection efficiency of the exhaust hood, The mass flow rate of pollutants directly captured by the exhaust hood is expressed in kg / s. The mass flow rate of pollutants escaping into the environment, in kg / s; The mass flow rate of pollutants emitted from the source is kg / s.
[0058] Workers' workplace pollutant inhalation rate The calculation formula is as follows:
[0059] In the formula: C(x,y,z) is the concentration value at the spatial coordinates (x,y,z). Further, the inhalation ratio is calculated using the following formula:
[0060] The formula for assessing the non-carcinogenic health risks of workplace environmental pollutants to workers is as follows:
[0061] In the formula: This represents the average daily exposure (mg / m³) of workers to SO2 and particulate matter. 3 ). This indicates the actual exposure concentration of workers (mg / m³). 3 ). Indicates the duration of a worker’s continuous exposure (a); The frequency of continuous exposure for workers (d / a) is equivalent to the number of days per year; This represents the average daily exposure time of workers (h / d). AT The average exposure time (d) of workers is given by ×365 d / a.
[0062] Furthermore, the non-carcinogenic risk (HQ) under different operating conditions was calculated to characterize the lung function protection indicators of the ventilation system:
[0063] In the formula, For reference concentration, mg / m³ 3 .
[0064] S5. Construct and quantify a weighted comprehensive evaluation system. The evaluation system for this step is not fixed, but is generated by a specific model established by S2.
[0065] Establishing an evaluation index system includes: A three-tiered evaluation system is constructed, comprising an objective layer, a criterion layer, and an indicator layer, as shown in the table below.
[0066] Table 1 Evaluation Index System for the Protective Performance of Ventilation Systems
[0067] Dynamic weighting based on the health impact model includes: Based on the analysis results of the S2 model, if PM is found 2.5 The sensitivity of FEV1 changes, i.e., the regression coefficient or curve slope, is significantly higher than that of SO2, which means that in terms of protection, controlling PM2.5 is more effective. 2.5 Exposure to SO2 is more valuable for protecting lung function than SO2 exposure control. This difference in sensitivity is incorporated into the determination of the weights for the evaluation indicators. Specifically: When using the AHP method to determine subjective weights, and when constructing the judgment matrix, the PM levels under the inhalation ratio and HQ criterion are considered. 2.5 Related indicators, compared to SO2-related indicators, are given a higher relative importance scale. For example, in the judgment matrix, PM... 2.5 The importance scale of -HQ relative to SO2-HQ is set to 5, as shown in the table below.
[0068]
[0069] Calculating the combined weights includes: Combining the subjective weights obtained by the AHP method (which already includes health model information) and the objective weights obtained by the entropy weight method, the final combined weights of each indicator are calculated using a linear combination weighting method. The distribution of the results is as follows: Figure 8 As shown. It can be seen that PM 2.5 The weight of relevant indicators has been significantly increased.
[0070] The subjective weights obtained through AHP and the objective weights obtained through the entropy method are used to calculate the combined weights using the linear combination weighting method. The specific calculation model is shown in the following formula:
[0071]
[0072] in: W For combined weights; w 1 represents subjective weight; w 2 represents the objective weight; α This refers to the subjective weighting coefficient. p 1, p 2,..., p n for w The weights of each indicator in section 1 are reordered from smallest to largest.
[0073] This embodiment also includes: Each factor was normalized to ensure that the data were on the same dimension:
[0074]
[0075] Positive indicators:
[0076] Negative indicators:
[0077] Furthermore, the information entropy of the constituent indicators for each operating condition is calculated:
[0078] In the formula ,when When = 0, define = 0; Furthermore, determine the normalized weights, information entropy redundancy (i.e., utility value), and the weights of each indicator:
[0079] .
[0080] S6. Multi-condition integrated evaluation and parameter optimization Based on the performance index data of each operating condition obtained in S4, and the index weights generated by the health model in S5, the comprehensive score Z for each ventilation operating condition is calculated using a linear weighted method:
[0081] The calculation results are shown in Table 2.
[0082] Table 2. Overall scores of different exhaust velocity schemes for the top-suction ventilation system in magnesium smelting process.
[0083] As shown in the table above, the overall performance ranking of the various ventilation system parameters based on jet-assisted ventilation is: G5 > G2 > G3 > G1 > G4. Among them, G5 has the highest score, indicating that it has the best overall performance in terms of pollutant control and personnel exposure protection. Therefore, G5 is recommended as the optimal combination of supply and exhaust air parameters, i.e., supply air velocity of 7 m / s and exhaust air velocity of 10 m / s.
[0084] Implementation Method 3: This implementation method proposes a computer device, including a memory and a processor. The memory stores a computer program. When the processor runs the computer program stored in the memory, the processor executes an industrial ventilation system optimization design method for lung function protection objectives as described in any one of Implementation Methods 1 to 2.
[0085] Implementation Method 4: This implementation method proposes a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of an industrial ventilation system optimization design method for lung function protection objectives as described in any one of Implementation Methods 1 to 2.
[0086] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0087] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0088] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0089] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this disclosure and not to limit its protection scope. Although this disclosure has been described in detail with reference to the above embodiments, those skilled in the art should understand that after reading this disclosure, they can still make various changes, modifications or equivalent substitutions to the specific implementation of the invention, but these changes, modifications or equivalent substitutions are all within the protection scope of the pending claims.
Claims
1. A method for optimizing design of industrial ventilation system aiming at lung function protection target, characterized in that, The method includes: Step S1: Obtain pollutant exposure concentration data for different work processes and positions in the target industrial scenario, as well as quantitative data on lung function indicators of the corresponding workers. Step S2: Based on the data collected in step S1, establish a quantitative response relationship model between pollutant exposure concentration and changes in lung function indicators; Step S3: Based on the quantitative response relationship model established in step S2, determine the pollutant concentration control threshold corresponding to the range of change of the target lung function index, and transform the pollutant concentration control threshold into the optimization target of the ventilation system. Step S4: Establish a computational fluid dynamics model of the ventilation system in the target industrial scenario. Using the optimization objective determined in step S3 as the optimization constraint, set various ventilation parameter conditions for numerical simulation and obtain multiple performance index values reflecting pollutant control and personnel exposure under each condition. Step S5: Construct a comprehensive evaluation system that includes the multiple performance indicators described in Step S4, and obtain the sensitivity of different pollutants to changes in lung function indicators based on the quantitative response relationship model established in Step S2, and assign weights to each indicator in the comprehensive evaluation system. Step S6: Based on the performance index values of each operating condition obtained in step S4 and the index weights determined in step S5, calculate the comprehensive evaluation value of each ventilation parameter operating condition, and select the optimal ventilation operating condition based on the comprehensive evaluation value to complete the optimization of ventilation system parameters.
2. The industrial ventilation system optimization design method for the lung function protection target according to claim 1, characterized in that, In step S1, the pollutant exposure concentration data is obtained through on-site measurement, including particulate matter concentration and SO2 concentration; the measured lung function index data includes at least one of FEV1, FVC, and FEV1 / FVC.
3. The industrial ventilation system optimization design method for the lung function protection target according to claim 1, characterized in that, In step S2, when establishing the quantitative response relationship model, the age, gender, length of service, and body mass index (BMI) of the workers are also collected simultaneously as control variables, and multiple regression analysis is used for modeling.
4. The industrial ventilation system optimization design method for the lung function protection target according to claim 3, characterized in that, Its features are, Step S2 further includes: using restricted cubic spline functions to analyze the nonlinear relationship between pollutant concentration and lung function index values, and determining lung function index thresholds corresponding to different pollutant concentrations based on the nonlinear relationship.
5. The industrial ventilation system optimization design method for lung function protection target according to claim 1, characterized in that, Its features are, In step S3, the optimization objectives of the ventilation system include: the pollutant concentration in the breathing area of the workers is lower than the pollutant concentration control threshold, or the proportion of locations in the work area where the pollutant concentration is lower than the pollutant concentration control threshold reaches a preset value.
6. The industrial ventilation system optimization design method for lung function protection target according to claim 1, characterized in that, Its features are, In step S4, the plurality of performance indicators include at least two of the following: capture efficiency reflecting the ability to control pollution sources, inhalation ratio reflecting the actual exposure level of workers, and non-carcinogenic health risk HQ value reflecting the protective effect.
7. The industrial ventilation system optimization design method for lung function protection target according to claim 1, wherein, Step S5 includes: Step S51: Construct a multi-level comprehensive evaluation index system that includes a target layer, a criterion layer, and an indicator layer, wherein the indicator layer contains multiple sub-indicators corresponding to different pollutants; Step S52: Obtain the sensitivity differences of the effects of different pollutants on changes in lung function indicators based on the quantitative response relationship model; Step S53: Based on the difference in sensitivity to influence, adjust the importance of the sub-indicators of the corresponding pollutants in the weight calculation in the indicator layer. Among them, the sub-indicators of pollutants that are more sensitive to changes in lung function indicators are given a higher weight tendency. Step S54: Calculate the combined weights of each indicator in the comprehensive evaluation index system using the combined weighting method.
8. The industrial ventilation system optimization design method for lung function protection target according to claim 1, characterized in that, In step S6, the comprehensive evaluation value of each ventilation parameter condition is calculated using the linear weighted method. The calculation formula is as follows: Where Z is the comprehensive evaluation value. Let i be the combined weight of the i-th indicator. Let be the standardized value of the i-th indicator, and n be the number of indicators.
9. A computer device, characterized in that: It includes a memory and a processor, wherein the memory stores a computer program, and when the processor runs the computer program stored in the memory, the processor executes an industrial ventilation system optimization design method for lung function protection as described in any one of claims 1-8.
10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of an industrial ventilation system optimization design method for lung function protection objectives as described in any one of claims 1-8.