Pneumoconiosis control method and system suitable for metallurgical scene

Abstract

The invention relates to the technical field of dust treatment in the metallurgical industry, in particular to a pneumoconiosis control method and system suitable for a metallurgical scene. The method comprises the following steps: introducing a nonlinear attenuation index to dynamically correct the dust source intensity; analyzing a toxicity weighted concentration coefficient through a component weighting strategy of a pneumoconiosis threshold value, and extracting dust concentration spatial and temporal distribution characteristics according to the dust source intensity, the toxicity weighted concentration coefficient and a dust diffusion coefficient; based on the high-temperature field dust settling rate and the high-temperature spraying effective dust removal efficiency, a synergistic coefficient is introduced to quantify the synergistic gain effect in the settling and dust removal process, and the comprehensive purification rate is obtained; and generating a closed-loop regulation and control strategy in combination with a personnel exposure risk coefficient, the dust concentration spatial and temporal distribution characteristics and the comprehensive purification rate. The problems of inaccurate source intensity, toxicity misjudgment, purification synergy deficiency and static regulation in metallurgical pneumoconiosis protection are solved, and the method is suitable for metallurgical workshops where core dust production equipment such as crushers and smelting furnaces is located.

Description

Technical Field

[0001] This invention relates to the field of dust control technology in the metallurgical industry, specifically to a method and system for controlling pneumoconiosis in metallurgical settings. Background Technology

[0002] The metallurgical industry generates large amounts of dust containing free SiO2 and metal oxides during crushing and smelting processes. Long-term exposure can easily lead to pneumoconiosis in workers. Current dust control technologies have the following key shortcomings: Large source strength calculation deviation: Existing technology uses a fixed source strength coefficient, ignoring the nonlinear wear law of metallurgical equipment under high temperature and high load of 600-1200℃, as well as the nonlinear influence of the hardness and moisture content of materials such as iron ore and coke on dust generation, resulting in a source strength assessment error of more than 40%.

[0003] Diffusion models have poor applicability: Conventional Gaussian diffusion and Lagrange particle tracking models are based on open-space designs and do not consider the local turbulence and spatial obstructions formed by equipment such as blast furnaces and conveyor belts in metallurgical workshops. They cannot accurately describe the unsteady diffusion process after dust flow and collision. In real-world scenarios, the high-temperature jet from the blast furnace roof and the induced draft from the dust collector form a complex vortex field, and the flow velocity of dust particles around the equipment gaps can reach 2-3 times the mainstream wind speed. Traditional models simplify the space as a homogeneous environment, resulting in a deviation of more than 50% between the simulated and measured dust concentration near the wall.

[0004] The settling rate is underestimated: Calculations based solely on Stokes' law for gravitational settling neglect the driving effect of thermophoretic force generated by the high-temperature metallurgical field (temperature gradient exceeding 50℃ / m) on dust particle migration, resulting in a lower calculated settling rate than the actual value. During converter steelmaking, a vertical temperature difference of 100℃ exists in the space near the furnace opening. Dust particles with a diameter of 1μm can migrate horizontally up to 1.2m under the influence of thermophoretic force, far exceeding the prediction of Stokes' law.

[0005] Misjudgment of dust removal efficiency: The calculation of spray dust removal efficiency does not take into account the evaporation loss of droplets under high temperature environment, uses a linear evaporation model or directly uses spray flow rate for calculation, and overestimates the actual dust removal effect.

[0006] Static risk assessment: Risk is assessed based on concentration at fixed monitoring points, without taking into account the coupling effect of personnel activity trajectories and regional risks, and thus cannot reflect the dynamic exposure risks in multiple regions.

[0007] The toxicity assessment is one-sided: judging the hazard only by the total dust concentration ignores the differences in the pathogenic thresholds of components such as free SiO2 and metal oxides, and cannot quantify the actual pathogenic potential.

[0008] The aforementioned defects result in low accuracy of dust concentration control in metallurgical workshops and insufficient targeted protection against pneumoconiosis. There is an urgent need for a comprehensive pneumoconiosis control algorithm that fits the high temperature, high load, and multiple equipment barriers in metallurgical scenarios. Summary of the Invention

[0009] To address the shortcomings of existing methods and the needs of practical applications, and in order to solve the aforementioned problems, this invention provides a method for controlling pneumoconiosis in metallurgical settings, comprising the following steps: A nonlinear decay index is introduced to dynamically correct the dust source strength; a toxicity-weighted concentration coefficient is analyzed using a component-weighted strategy based on the pneumoconiosis pathogenicity threshold; and the spatiotemporal distribution characteristics of dust concentration are extracted based on the dust source strength, the toxicity-weighted concentration coefficient, and the dust diffusion coefficient; a synergy coefficient is introduced to quantify the synergistic gain effect of the settling and dust removal processes based on the dust settling rate in a high-temperature field and the effective dust removal efficiency of high-temperature spray, thereby obtaining the comprehensive purification rate; and a closed-loop control strategy is generated by combining the personnel exposure risk coefficient, the spatiotemporal distribution characteristics of dust concentration, and the comprehensive purification rate.

[0010] This invention dynamically corrects dust source strength using a nonlinear decay exponent, and strengthens the foundation for concentration calculation by combining high-temperature, high-load wear of equipment with material characteristics. Based on a weighted toxicity concentration using the pneumoconiosis pathogenic threshold, it accurately identifies the risk of highly toxic components such as free SiO2. Coupled with source strength, toxicity concentration, and diffusion coefficient, it extracts the spatiotemporal distribution characteristics of dust concentration, enabling visualized concentration positioning. Simultaneously, it introduces a synergy coefficient to quantify the synergistic gain between high-temperature settling and spray dust removal, improving the accuracy of purification rate assessment. Finally, by combining personnel exposure risk coefficients, spatiotemporal concentration distribution, and comprehensive purification rate, a closed-loop control strategy is generated to dynamically optimize dust control measures. This invention solves the problems of inaccurate source strength, misjudgment of toxicity, lack of purification synergy, and static control in metallurgical pneumoconiosis prevention, significantly improving the accuracy and dynamism of protection, safeguarding the health of workers, and providing key technical support for pneumoconiosis prevention and control in the metallurgical industry.

[0011] Optionally, the step of introducing a nonlinear decay exponent to dynamically correct the dust source strength includes the following steps: This invention first collects an initial source strength benchmark value to lay the foundation for accurate calculations; then, by establishing a wear depth-time model to fit the wear coefficient, it accurately captures the actual wear patterns of equipment under high temperature and high load conditions in metallurgy; and introduces a nonlinear decay index to adapt to the non-uniform wear characteristics of equipment, effectively reducing source strength calculation errors. This provides reliable data support for subsequent dust diffusion and dust removal efficiency optimization, making the source strength quantification for pneumoconiosis protection more accurate and contributing to the effective management of pneumoconiosis risks in metallurgical workshops.

[0012] Optionally, the analysis of the toxicity-weighted concentration coefficient using a component-weighted strategy based on the pathogenicity threshold of pneumoconiosis includes the following steps: Based on the pathogenicity threshold of dust components, the component weights are analyzed; the component weights are synthesized to obtain the toxicity-weighted concentration coefficient.

[0013] This invention first calculates the weight of each component based on the specific pneumoconiosis pathogenic threshold of different dust components (such as free SiO2 and metal oxides), and then synthesizes the toxicity weighted concentration coefficient. This solves the problem of the hazard of highly toxic components (such as low-threshold free SiO2) being diluted by ordinary dust and the risk being misjudged. It can accurately quantify the actual pathogenic contribution of each component and provide a targeted basis for pneumoconiosis protection in metallurgical workshops.

[0014] Optionally, obtaining the dust diffusion coefficient includes the following steps: The turbulence amplification factor is calculated using the Reynolds number of obstacles; the dust diffusion coefficient is obtained based on the basic diffusion coefficient, the spatial constraint correction factor, and the turbulence amplification factor. This invention addresses the multi-equipment barrier characteristics of metallurgical workshops, comprehensively considering equipment turbulence and spatial limitations, effectively reducing the calculation error of the diffusion coefficient. It provides accurate basic data for dust concentration field modeling and personnel exposure risk assessment, assisting in the precise formulation of pneumoconiosis prevention strategies in metallurgical scenarios and improving the targeting and effectiveness of dust control.

[0015] Optionally, extracting the spatiotemporal distribution characteristics of dust concentration based on the dust source strength, the toxicity-weighted concentration coefficient, and the dust diffusion coefficient includes the following steps: Based on Fick's second law, a three-dimensional diffusion equation is constructed using the dust source strength and dust diffusion coefficient. The spatiotemporal distribution is then solved using the toxicity-weighted concentration coefficient and the three-dimensional diffusion equation to obtain the dust concentration field. This invention comprehensively considers the differences in dust toxicity and spatial dimensions, ensuring that the concentration field reflects both diffusion patterns and toxic hazards. The precise spatiotemporal distribution provides a basis for locating high-risk areas in metallurgical workshops, assisting in targeted dust control, improving the accuracy of pneumoconiosis protection, and safeguarding the health of workers.

[0016] Optionally, calculating the dust settling rate in the high-temperature field includes the following steps: Based on the physical parameters of dust and the temperature field distribution in the workshop, the gravity settling rate and the second settling rate driven by thermophoretic force are analyzed; based on the gravity settling rate and the second settling rate, the high-temperature dust settling rate is synthesized.

[0017] This invention comprehensively considers the effects of gravity and thermophoretic force in high-temperature fields, reducing the deviation between the calculated settling rate and the actual value in the metallurgical workshop. It provides accurate parameters for optimizing the efficiency of dust removal systems and modeling the spatiotemporal distribution of dust concentration, avoiding dust accumulation caused by inaccurate settling assessments. It also helps to accurately delineate high-dust-risk areas, reduce the risk of pneumoconiosis exposure for workers, and meet the dust control needs of high-temperature metallurgical scenarios.

[0018] Optionally, calculating the effective dust removal efficiency of the high-temperature spray includes the following steps: Based on the collision dynamics principle of droplets and dust particles, the theoretical dust removal efficiency is calculated; the theoretical dust removal efficiency is then corrected by the effective mist volume ratio to obtain the effective dust removal efficiency of high-temperature spray.

[0019] This invention corrects the theoretical dust removal efficiency by adjusting the effective mist volume ratio, providing a precise basis for the design of dust removal systems and the optimization of spray parameters in metallurgical workshops, avoiding dust accumulation due to efficiency misjudgment; it helps to accurately control dust concentration, reduce the risk of pneumoconiosis exposure for workers, and meets the dust control needs of high-temperature scenarios.

[0020] Optionally, the step of introducing a synergy coefficient to quantify the synergistic gain effect of the settling and dust removal processes to obtain the overall purification rate includes the following steps: By utilizing the quantitative relationship between settling amount and spatial volume, the settling rate of dust in a high-temperature field is corrected to obtain the settling purification efficiency per unit volume; by converting the dust collection amount of mist droplets into the volumetric purification rate through water density, the purification efficiency of the dust removal equipment is obtained; by combining the synergy coefficient, the settling purification efficiency, and the purification efficiency of the dust removal equipment, the comprehensive purification rate is obtained.

[0021] This invention obtains the unit volume sedimentation purification efficiency by modifying the sedimentation rate in a high-temperature field, combines the conversion of water density into droplet dust collection capacity to obtain the purification efficiency of the dust removal equipment, and then quantifies the gain effect of both with a synergy coefficient. This solves the one-sided problem of traditional separate calculation of sedimentation and dust removal efficiency, avoids underestimating or overestimating the overall purification effect, provides accurate data support for the optimization of dust removal system parameters in high-temperature metallurgical scenarios, helps to improve the overall dust control efficiency, more accurately control the dust concentration in the workshop, effectively reduce the risk of pneumoconiosis exposure for workers, and makes up for the deficiency of traditional purification rate calculation that does not consider the synergistic effect of sedimentation and dust removal, thus meeting the actual needs of high-temperature metallurgical pneumoconiosis protection.

[0022] Optionally, the step of generating a closed-loop control strategy by combining the personnel exposure risk coefficient, the spatiotemporal distribution characteristics of dust concentration, and the comprehensive purification rate includes the following steps: Risk warnings are determined by analyzing the personnel exposure risk coefficient and the spatiotemporal distribution characteristics of dust concentration. Based on the risk warning results, a collaborative control strategy is generated according to the comprehensive purification rate and theoretical requirements. This invention achieves accurate early warning through the linkage of risk and concentration, formulates targeted strategies based on collaborative purification data, avoids blind dust control, improves response efficiency, and more accurately reduces the risk of pneumoconiosis exposure in personnel, meeting the protection needs of high-temperature and complex metallurgical scenarios.

[0023] Secondly, to efficiently execute the method for controlling pneumoconiosis in metallurgical settings provided by this invention, this invention also provides a system for controlling pneumoconiosis in metallurgical settings, including a processor, an input device, an output device, and a memory. The processor, input device, output device, and memory are interconnected. The memory stores a computer program containing program instructions. The processor is configured to invoke the program instructions to execute the method for controlling pneumoconiosis in metallurgical settings as described in the first aspect of this invention. The system for controlling pneumoconiosis in metallurgical settings provided by this invention has a compact structure and stable performance, and can stably execute the method for controlling pneumoconiosis in metallurgical settings provided by this invention, further enhancing the overall applicability and practical application capability of this invention. Attached Figure Description

[0024] Figure 1 A flowchart of a method for controlling pneumoconiosis in a metallurgical setting, provided by an embodiment of the present invention; Figure 2 This is a system framework diagram for controlling pneumoconiosis in a metallurgical setting, provided as an embodiment of the present invention. Detailed Implementation

[0025] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0026] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0027] Please see Figure 1 To address the aforementioned problems, this invention provides a method for controlling pneumoconiosis in metallurgical settings, such as... Figure 1 As shown, in one embodiment, the method includes the following steps: S1. Introduce a nonlinear attenuation index to dynamically correct the dust source strength.

[0028] The method of introducing a nonlinear attenuation index to dynamically correct the dust source strength includes the following steps: S11. Collect the initial source strength reference value.

[0029] The rated parameters are obtained from the equipment design documents, and the initial source strength reference value is calculated by combining CFD simulation with theoretical formulas, based on the material density. ,satisfy: in, Indicates the density of the material (kg / m³) 3 ), This indicates the material conveying speed (m / s). The denominator represents the median particle size of the material (μm). This is a unit conversion factor (converting kg to mg).

[0030] S12. Establish a mathematical model of the wear depth of metallurgical equipment as a function of time, and fit the model to obtain the wear coefficient and nonlinear decay index.

[0031] Wear coefficients were fitted based on wear experiments of high-chromium cast iron / tungsten carbide alloy at stepped temperatures (every 100°C intervals). To characterize the wear depth per unit time, and a nonlinear decay exponent characterizing the wear acceleration trend. .

[0032] Furthermore, by combining the wear coefficient and the nonlinear decay exponent, the equipment wear correction factor is calculated. ,satisfy: in, Indicates the cumulative operating time of the device. This indicates that wear and tear leads to increased dust production.

[0033] S12. Based on the initial source strength reference value, synthesize the source strength correction value using the nonlinear attenuation index and the wear coefficient.

[0034] Based on a simulation of the crushing process of metallurgical materials with a Mohs hardness of 3-7 and a moisture content of 2%-15%, data were acquired using a laser particle size analyzer and a dust sampler to regress material coefficients. (Characteristic of the control coefficient of particle size on dust generation) (Characterizing the control coefficient of moisture content on dust generation), and then calculating the material property correction factor. ,satisfy: in, This indicates the moisture content of the material.

[0035] It is understandable that the smaller S is, the larger W is. The smaller the particle size, the lower the dust generation, which aligns with the principle that fine particles are easily dispersed and high moisture content easily adheres.

[0036] Furthermore, based on the initial source strength reference value, a source strength correction value is synthesized using the nonlinear attenuation exponent and the wear coefficient. ,satisfy: .

[0037] This invention introduces a nonlinear correction factor, wherein ( A power-law wear model is constructed to accurately quantify the accelerating effect of equipment wear over time under high-temperature conditions through exponential calculations; simultaneously, it is combined with... The exponential function deeply characterizes the coupled influence of material particle size S and moisture content W on dust characteristics. By organically combining nonlinear and exponential functions, it successfully achieves dynamic capture of the high-temperature accelerated equipment wear and high dust risk of fine particulate materials in metallurgical scenarios, demonstrating higher environmental adaptability and prediction accuracy.

[0038] S2. Analyze the toxicity weighted concentration coefficient using a component weighting strategy based on the pathogenic threshold of pneumoconiosis, and extract the spatiotemporal distribution characteristics of dust concentration based on the dust source strength, the toxicity weighted concentration coefficient, and the dust diffusion coefficient.

[0039] In one embodiment, the analysis of the toxicity-weighted concentration coefficient using a component-weighted strategy based on the pneumoconiosis pathogenicity threshold includes the following steps: S211. Analyze the component weights based on the pathogenicity thresholds of dust components.

[0040] Based on the "Occupational Exposure Limits for Hazardous Factors in the Workplace", the weights of each component are analyzed to ensure that: Free SiO2 weights: ; Metal oxide weights: ; Other dust weights: ; in, Indicates the concentration of free SiO2. This indicates the threshold for free SiO2 (a core factor contributing to silicosis). Indicates the concentration of metal oxides. Indicates the threshold value of metal oxide (Fe2O3). Indicates the total dust concentration. This indicates the threshold for other dusts (such as coal dust).

[0041] This invention enables precise classification of the pathogenic risk of each component, and has higher sensitivity and specificity in predicting the pathogenic risk of pneumoconiosis.

[0042] S212. Synthesize the component weights to obtain the toxicity-weighted concentration coefficients.

[0043] Specifically, the component weights are synthesized to obtain toxicity-weighted concentration coefficients. ,satisfy: .

[0044] Further, obtaining the dust diffusion coefficient includes the following steps: S221. Calculate the turbulence amplification factor using the Reynolds number of the obstacle.

[0045] Specifically, the turbulence amplification factor is calculated using the obstacle Reynolds number, satisfying: in, Indicates the turbulence amplification factor. Indicates the test calibration coefficient. Represents the Reynolds number of the obstacle. Indicates air density, Indicates the wind speed in the workshop. Indicates the characteristic dimensions of the obstacle. This indicates aerodynamic viscosity.

[0046] S222. Obtain the dust diffusion coefficient based on the basic diffusion coefficient, the spatial constraint correction factor, and the turbulence amplification coefficient.

[0047] In this embodiment, based on molecular diffusion theory and the air characteristics of a metallurgical workshop, the basic diffusion coefficient is calculated and satisfies the following: in, Represents the basic diffusion coefficient. This represents the Schmitt number.

[0048] Furthermore, considering the irregular shape of obstacles within the metallurgical workshop, their distribution density, and the workshop's geometric proportions, a spatial coefficient is constructed. ,satisfy: in, This represents the obstacle shape coefficient, which characterizes the intensity of the obstacle's disturbance to the airflow. The more irregular the shape, the larger the value, and the stronger the spatial constraint. The value ranges from 1.0 (sphere) to 3.5 (irregular components, such as blast furnace supports). This represents the volume density of obstacles, the ratio of the total volume of obstacles to the volume of the workshop. The larger the value, the smaller the space for diffusion. The value ranges from 0.1 (sparse layout, such as a raw material warehouse) to 0.6 (dense layout, such as a converter workshop). This represents the height-to-width ratio of the workshop, the ratio of the workshop's height to its span. The further it deviates from 1, the more uneven the airflow distribution and the stronger the spatial constraint. The value range is 0.3 (long and narrow type, such as a continuous casting workshop) to 2.0 (tall type, such as a blast furnace workshop). The calibration coefficients are determined by fitting CFD simulations and in-situ diffusion experiments (using the tracer gas method) from at least three different metallurgical workshops (such as converter workshops, continuous casting workshops, and raw material workshops) to ensure accuracy. The value range is [0.5, 3.0].

[0049] Furthermore, a spatial constraint correction factor is constructed by combining the volume ratio of obstacles. ,satisfy: in, This represents the equivalent feature size of the obstacle, that is, the side length of a cube with the same volume as the obstacle. This indicates the volume of the workshop space. When there are multiple types of obstacles within the workshop... The weighted average of the equivalent characteristic dimensions of various obstacles is taken (the weight is the proportion of the volume of each type of obstacle to the total volume of obstacles) to ensure a comprehensive reflection of the combined constraint effect of multiple obstacles.

[0050] Furthermore, the dust diffusion coefficient is obtained based on the basic diffusion coefficient, the spatial constraint correction factor, and the turbulence amplification factor. ,satisfy: .

[0051] Accurately describing the diffusion patterns of dust within enclosed metallurgical workshops is crucial for addressing pollutant migration issues under complex conditions such as multi-source emissions and turbulent airflow. Metallurgical workshops contain various obstacles of different shapes, including steelmaking furnaces, dust removal equipment, and overhead crane tracks, whose three-dimensional spatial layout creates complex turbulent flow fields. By constructing a refined numerical model suitable for enclosed spaces, combined with on-site laser particle dynamics analyzer and computational fluid dynamics (CFD) simulations, the concentration distribution characteristics of dust in special areas such as the leeward vortex zone of obstacles and airflow acceleration channels can be systematically analyzed, effectively filling the gap in the applicability of traditional diffusion models in complex industrial scenarios.

[0052] In this embodiment, extracting the spatiotemporal distribution characteristics of dust concentration based on the dust source strength, the toxicity-weighted concentration coefficient, and the dust diffusion coefficient includes the following steps: S231. Based on Fick's second law, a three-dimensional diffusion equation is constructed using the dust source strength and dust diffusion coefficient.

[0053] Specifically, based on Fick's second law, a three-dimensional diffusion equation is constructed using the dust source strength and dust diffusion coefficient, satisfying: in, Indicates the rate of change of concentration over time. This represents the second spatial derivative of concentration (characterizing the diffusion gradient). Indicates the intensity of the dust source. Represents the point source Dirac function. Indicates the dust-generating point.

[0054] S232. Solve the spatiotemporal distribution based on the toxicity-weighted concentration coefficient and the three-dimensional diffusion equation to obtain the dust concentration field.

[0055] Specifically, the diffusion equation is solved using the finite element method. To simplify the calculation process, the condition of "uniform diffusion and no convection" is set. Under this condition, the spatiotemporal distribution is solved based on the toxicity-weighted concentration coefficient and the three-dimensional diffusion equation to obtain the dust concentration field. ,satisfy: As the cumulative operating time (t) of the equipment increases, and the distance from the dust-generating point also increases, The value gradually decreases, which is entirely consistent with the basic laws of dust diffusion. Therefore, it is possible to accurately grasp the dust concentration at different times and locations, providing crucial data support for formulating reasonable pneumoconiosis control strategies.

[0056] S3. Based on the dust settling rate in the high-temperature field and the effective dust removal efficiency of high-temperature spray, a synergy coefficient is introduced to quantify the synergistic gain effect of the settling and dust removal processes, thereby obtaining the comprehensive purification rate.

[0057] In one embodiment, calculating the dust settling rate in the high-temperature field includes the following steps: S311. Based on the physical parameters of dust and the temperature field distribution in the workshop, analyze the gravity settling rate and the second settling rate driven by thermophoretic force.

[0058] Based on Stokes' law, and adapted to the particle size of metallurgical dust, the gravity settling rate was analyzed. ,satisfy: in, Indicates dust particle size, Indicates dust density, It represents the acceleration due to gravity.

[0059] Furthermore, the temperature gradient is extracted. The thermophoretic coefficient was calibrated through high-temperature experiments. Furthermore, the second sedimentation rate driven by thermophoretic force was analyzed. ,satisfy: The negative sign indicates that dust migrates to the low-temperature zone, with the top of the metallurgical workshop being high-temperature and the bottom being low-temperature.

[0060] S312. Based on the gravity settling rate and the second settling rate, synthesize the high-temperature field dust settling rate.

[0061] Specifically, based on the gravity settling rate and the second settling rate, the high-temperature field dust settling rate is synthesized, satisfying: In real-world metallurgical scenarios, the complex thermal environments generated by processes such as high-temperature reactors and continuous casting and rolling can induce significant thermophoresis effects. This step introduces temperature gradients and thermophoresis characteristic parameters, nonlinearly coupling the thermophoretic force generated by the uneven collision of gas molecules with the gravitational field force under the temperature gradient. This not only accurately describes the complex motion trajectory of high-temperature dust under the combined effects of thermal buoyancy and thermophoretic force, but also effectively captures the asymmetric settling behavior of dust in temperature-stratified environments, providing a more realistic theoretical model for the prevention and control of metallurgical pneumoconiosis.

[0062] In another embodiment, calculating the effective dust removal efficiency of the high-temperature spray includes the following steps: S321. Calculate the theoretical dust removal efficiency based on the collision dynamics principle of droplets and dust particles.

[0063] Based on droplet-dust collision dynamics, the collision coefficient is fitted. Calculate the theoretical dust removal efficiency ,satisfy: in, This represents the total dust-collecting area of ​​the fog droplets. Indicates spray flow rate, Indicates droplet size, Indicates air circulation. This indicates the cross-sectional area of ​​the workshop.

[0064] S322. By correcting the theoretical dust removal efficiency through the effective mist volume ratio, the effective dust removal efficiency of high-temperature spray is obtained.

[0065] Based on high-temperature evaporation experiments, the evaporation coefficient was fitted. Then calculate the evaporation rate. satisfy: in, Indicates ambient temperature. This indicates relative humidity.

[0066] Then, the effective fog volume ratio is calculated. ,satisfy: .

[0067] Furthermore, by correcting the theoretical dust removal efficiency through the effective mist volume ratio, the effective dust removal efficiency of high-temperature spray is obtained. ,satisfy: In metallurgical scenarios, high temperatures, high dust concentrations, and complex flow field characteristics significantly reduce the prediction accuracy of linear models. To more accurately capture the destructive effects of high temperatures on spray, this step introduces two key parameters: and ,in, This represents a nonlinear evaporation acceleration mechanism. By introducing a 1.5 power relationship with temperature, it can more realistically reflect the physical phenomenon that the evaporation rate increases nonlinearly with increasing temperature under high-temperature metallurgical conditions; and This parameter characterizes the evaporation characteristics of small droplets and reveals the inverse relationship between droplet size and evaporation rate. It is particularly suitable for describing the rapid evaporation of small droplets due to their large specific surface area in metallurgical environments. The introduction of these two parameters constructs an unconventional efficiency correction mechanism, significantly improving the model's dynamic response capability and prediction accuracy to spray evaporation processes in complex metallurgical environments.

[0068] In this embodiment, the step of introducing a synergy coefficient to quantify the synergistic gain effect of the sedimentation and dust removal processes to obtain the overall purification rate includes the following steps: S331. By using the quantitative relationship between settling amount and spatial volume, the settling rate of dust in high-temperature fields is corrected to obtain the settling purification efficiency per unit volume.

[0069] Specifically, based on the quantitative relationship between settlement and spatial volume, an evaluation index for the settlement purification capacity per unit volume is constructed. ,satisfy: in, Indicates the settlement velocity. Indicates the settlement area. It represents the volume of space.

[0070] S332. The dust collection capacity of mist droplets is converted into volumetric purification rate by water density to obtain the purification efficiency of dust removal equipment.

[0071] In industrial dust removal systems, efficient removal of particulate matter is crucial for ensuring air quality. To more accurately quantify the performance of dust removal equipment, the density of water is introduced. Convert the amount of dust collected from mist droplets into volumetric purification rate to establish the purification efficiency of dust removal equipment. The calculation model is as follows: in, This indicates the processing air volume, reflecting the volume of gas that the equipment can process per unit of time. This indicates the effective internal volume of the dust removal equipment, and its size directly affects the contact time and space between the gas and liquid phases.

[0072] Based on the law of conservation of mass, the microscopic process of droplet capture of particulate matter is combined with the macroscopic operating parameters of the equipment, providing a theoretical basis for the optimized design and operation control of dust removal systems. By adjusting the processing air volume and optimizing the equipment structure, the purification rate under different operating conditions can be predicted, thereby maximizing dust removal efficiency.

[0073] S333. By combining the synergy coefficient, the sedimentation purification efficiency, and the dust removal equipment purification efficiency, a comprehensive purification rate is obtained.

[0074] Considering the synergistic gain effect of sedimentation and dust removal processes, i.e., the physical mechanism by which settled particles are more easily captured by mist droplets, a synergistic coefficient is introduced. Quantification is performed to satisfy: Based on this, a comprehensive purification rate is constructed. The calculation formula satisfies: S4. Combining the personnel exposure risk coefficient, the spatiotemporal distribution characteristics of the dust concentration, and the comprehensive purification rate, a closed-loop control strategy is generated.

[0075] In this embodiment, step S4, which combines the personnel exposure risk coefficient, the spatiotemporal distribution characteristics of dust concentration, and the comprehensive purification rate to generate a closed-loop control strategy, includes the following steps: S41. Risk warning is determined by the personnel exposure risk coefficient and the spatiotemporal distribution characteristics of the dust concentration.

[0076] First, based on historical dust concentration and temperature data, cluster analysis or spatial statistical methods were used to divide the production workshop into n independent risk assessment units. Each unit area... By calculating quantitative indicators such as dust explosion index, frequency of exceeding limits, and temperature fluctuation coefficient, and combining them with the analytic hierarchy process (AHP), a risk assessment model is constructed to ultimately determine the corresponding risk level. (Values ​​range from 1 to 5, with higher values ​​indicating greater risk). This level not only quantitatively reflects the inherent hazardous characteristics of the area, such as dust particle size distribution and the proportion of combustible components, but also integrates dynamic risk factors such as ventilation efficiency and equipment sealing, comprehensively depicting the potential risk level of dust explosions and fires.

[0077] Secondly, personnel in the area Internal exposure Quantification using a risk exposure model satisfies: in, This refers to the cumulative time a person spends in the area.

[0078] Considering the diffusion and propagation characteristics of dust in the workshop environment, a spatial coupling factor is introduced. The spatial attenuation law of risk propagation, representing the interaction between regions, is defined based on the Gaussian diffusion principle: in, This represents the spatial distance between regions i and j. This indicates the radius of influence of dust diffusion.

[0079] Finally, by integrating single-region exposure and regional coupling effects, a calculation model for the comprehensive exposure risk coefficient R is established, satisfying the following: This invention constructs a dynamic risk quantification model to collect data such as the duration of personnel stay in each area and the concentration of risk factors in real time, accurately calculates the cumulative exposure risk value, and can effectively identify high-risk activity paths and areas, providing a scientific basis for formulating differentiated and refined protection strategies. At the same time, it comprehensively considers factors such as spatial distance between areas, airflow direction, and material diffusion patterns to quantify the degree of correlation between different risk areas, thereby obtaining the overall exposure risk of personnel in complex environments in multiple areas, and providing a scientific basis for occupational health and safety management.

[0080] Furthermore, risk warnings are determined based on the personnel exposure risk coefficient and the spatiotemporal distribution characteristics of the dust concentration. Real-time monitoring of the comprehensive exposure risk coefficient is also used. Dust concentration field Medium spatial coordinates The risk warning level is determined by comparing the dust concentration and free SiO2 concentration at the location with the set threshold.

[0081] S42. Based on the risk warning results, generate a collaborative control strategy according to the comprehensive purification rate and theoretical requirements.

[0082] Based on the current concentration exceeding the standard and the target control time Calculate the theoretical purification rate ,satisfy: Furthermore, comparing the actual purification rate In line with theoretical requirements ,like The spray system is activated for intelligent adjustment, employing an exponential compensation formula. Dynamically increase spray flow rate.

[0083] When the equipment generates strong dust source Exceeding the benchmark value Based on the mass conservation inverse model, according to the formula Step-by-step reduction of crusher operating load This indicates the crusher load, and the feeding frequency and crushing ratio are adjusted synchronously to control the power source while ensuring production efficiency. Within the range.

[0084] Furthermore, by combining real-time risk heat maps and using path planning algorithms to generate optimal evacuation routes, voice prompts and electronic map markings are automatically pushed to personnel wearing positioning terminals, reducing the overall exposure risk coefficient R of personnel to a safe threshold and ensuring that all personnel maintain a safe distance of at least 5 meters from high-concentration areas.

[0085] Furthermore, a dynamic data acquisition system based on Kalman filtering is constructed, which performs weighted smoothing of environmental parameters every 10 seconds. Once a safe state is detected, it automatically switches to a low-power monitoring mode; otherwise, it continuously loops the risk assessment-purification calculation-dynamic control process to form an adaptive control closed loop.

[0086] This invention deeply aligns with the complex characteristics of the metallurgical environment—high temperature, high dust, multiple equipment, and high toxicity—and constructs a quantitative model covering the entire chain of dust generation, diffusion, settling, and personnel risk assessment. Specifically, in the dust generation stage, it accurately simulates the initial dust generation amount by combining equipment operating parameters and material characteristics; in the diffusion stage, it dynamically predicts the dust propagation path in space by comprehensively considering factors such as thermophoresis and airflow disturbance; during the settling process, it incorporates metallurgical-specific parameters such as spray evaporation and equipment wear to accurately predict dust deposition patterns. Based on this, through real-time monitoring data feedback, it uses intelligent optimization algorithms for dynamic control, proactively managing the risk of pneumoconiosis and significantly improving the targeting and effectiveness of pneumoconiosis risk prevention and control.

[0087] Besides its application in metallurgical settings, this invention can be modified to suit specific needs and applied to other fields requiring pneumoconiosis control. For example, in hospital pathology slide preparation, the cutting of paraffin-embedded tissues and the operation of cryostats generate minute tissue dust and paraffin dust, which can easily lead to pneumoconiosis in medical personnel with prolonged inhalation. This invention allows for real-time monitoring of dust concentration and automatic adjustment of ventilation and atomized dust suppression equipment to reduce dust hazards.

[0088] For example, in the medical device processing scenario of a sterilization supply center, when cleaning, disinfecting and reusing medical devices, metal dust is generated during rust removal and polishing of metal devices. This invention can accurately capture the dust diffusion trajectory and activate a local negative pressure dust collection device to avoid dust accumulation and protect the respiratory tract of staff.

[0089] In hospital infrastructure maintenance scenarios, such as ward renovation and pipeline installation, wall grinding and drilling will generate construction dust. According to this invention, it can quickly respond to changes in dust concentration, link up with mobile dust suppression equipment, control the range of dust diffusion, and reduce the impact on medical staff and patients.

[0090] Please see Figure 2 In an embodiment, to efficiently execute the method for controlling pneumoconiosis in a metallurgical setting provided by the present invention, the present invention also provides a system for controlling pneumoconiosis in a metallurgical setting, comprising: an input device, an output device, a processor, and a memory, wherein the input device, output device, processor, and memory are interconnected, and the memory contains program instructions for the steps of the method for controlling pneumoconiosis in a metallurgical setting. The system for controlling pneumoconiosis in a metallurgical setting provided by the present invention has a compact structure and stable performance, and can stably execute the method for controlling pneumoconiosis in a metallurgical setting provided by the present invention, further enhancing the overall applicability and practical application capability of the present invention.

[0091] In this embodiment, the processor may be a central processing unit, but it can also be other general-purpose processors, digital signal processors, application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (OPGs), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor. Input devices can be used to acquire data. Output devices can be used to output the results obtained by storing program instructions contained in a computer program in the memory provided by this invention. The memory may include read-only memory and random access memory (RAM), and provides instructions and data to the processor. A portion of the memory may also include non-volatile random access memory (RAM).

[0092] In one possible implementation, the memory may include a stored program area and a stored data area. The stored program area may store the operating system and applications required for at least one function; the stored data area may store data created during use. Furthermore, the memory may include read-only memory and random access memory, and provides instructions and data to the processor. The memory stores the operating system and operating instructions, executable modules, or data structures, or subsets thereof, or extended sets thereof. The operating instructions may include various operation instructions for implementing various operations. The operating system may include various system programs for implementing various basic tasks and handling hardware-based tasks.

[0093] The embodiment also provides a storage medium storing a computer program, which, when executed by a processor, implements the steps of the above-described method for controlling pneumoconiosis in metallurgical settings.

[0094] The storage medium can include various media that can store program code, such as USB flash drives, portable hard drives, read-only memory, random access memory, magnetic disks, or optical disks.

[0095] In summary, this invention dynamically corrects dust source strength using a nonlinear decay exponent, and strengthens the foundation for concentration calculation by combining high-temperature, high-load wear of equipment with material characteristics. Based on the pneumoconiosis pathogenic threshold weighted toxicity concentration, it accurately identifies the risk of highly toxic components such as free SiO2. Coupled with source strength, toxicity concentration, and diffusion coefficient, it extracts the spatiotemporal distribution characteristics of dust concentration, enabling visualized concentration positioning. Simultaneously, it introduces a synergy coefficient to quantify the synergistic gain between high-temperature settling and spray dust removal, improving the accuracy of purification rate assessment. Finally, by combining personnel exposure risk coefficient, spatiotemporal concentration distribution, and comprehensive purification rate, it generates a closed-loop control strategy to dynamically optimize dust control measures. This invention solves the problems of inaccurate source strength, misjudgment of toxicity, lack of purification synergy, and static control in metallurgical pneumoconiosis prevention, significantly improving the accuracy and dynamism of protection, safeguarding the health of workers, and providing key technical support for pneumoconiosis prevention and control in the metallurgical industry.

[0096] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the present invention.

Claims

1. A method for pneumoconiosis control suitable for metallurgical scenarios, characterized in that, The method comprises the following steps: introducing a nonlinear decay index dynamic correction dust source intensity; analyzing a toxicity weighted concentration coefficient through a component weighting strategy of pneumoconiosis pathogenic threshold, extracting dust concentration space-time distribution characteristics according to the dust source intensity, the toxicity weighted concentration coefficient and a dust diffusion coefficient; introducing a synergistic coefficient to quantify a synergistic gain effect of the settlement and dust removal process to obtain a comprehensive purification rate based on the dust settlement rate in a high-temperature field and the effective dust removal efficiency of high-temperature spraying; generating a closed-loop control strategy in combination with a personnel exposure risk coefficient, the dust concentration space-time distribution characteristics and the comprehensive purification rate.

2. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, characterized in that, The introducing a nonlinear decay index dynamic correction dust source intensity comprises the following steps: collecting an initial source intensity benchmark value; establishing a mathematical model of the wear depth of the metallurgical equipment changing with time to fit a wear coefficient and a nonlinear decay index; synthesizing a source intensity correction value by using the nonlinear decay index and the wear coefficient based on the initial source intensity benchmark value.

3. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, characterized in that, The analyzing a toxicity weighted concentration coefficient through a component weighting strategy of pneumoconiosis pathogenic threshold comprises the following steps: analyzing component weights according to the pathogenic threshold of dust components; synthesizing the component weights to obtain a toxicity weighted concentration coefficient.

4. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, characterized in that, The obtaining the dust diffusion coefficient comprises the following steps: calculating a turbulent amplification coefficient by using an obstacle Reynolds number; obtaining the dust diffusion coefficient according to a basic diffusion coefficient, a space constraint correction factor and the turbulent amplification coefficient.

5. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, wherein, The extracting dust concentration space-time distribution characteristics according to the dust source intensity, the toxicity weighted concentration coefficient and a dust diffusion coefficient comprises the following steps: constructing a three-dimensional diffusion equation by using the dust source intensity and the dust diffusion coefficient based on Fick's second law; obtaining a dust concentration field by solving the three-dimensional diffusion equation according to the toxicity weighted concentration coefficient and the space-time distribution.

6. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, wherein, The calculating the dust settlement rate in a high-temperature field comprises the following steps: analyzing a gravitational settlement rate and a second settlement rate under the driving of thermal migration force based on dust physical parameters and a workshop temperature field distribution; synthesizing a high-temperature field dust settlement rate according to the gravitational settlement rate and the second settlement rate.

7. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, wherein, The calculating the effective dust removal efficiency of high-temperature spraying comprises the following steps: calculating a theoretical dust removal efficiency based on the collision dynamics principle of mist droplets and dust particles; obtaining a high-temperature spraying effective dust removal efficiency by correcting the theoretical dust removal efficiency through an effective mist amount proportion.

8. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, wherein, The introducing a synergistic coefficient to quantify a synergistic gain effect of the settlement and dust removal process to obtain a comprehensive purification rate comprises the following steps: correcting the high-temperature field dust settlement rate by using a quantitative relationship between the settlement amount and the space volume to obtain a settlement purification efficiency in a unit volume; obtaining a dust removal equipment purification efficiency by converting the mist droplet dust trapping amount into a volume purification rate through a water density; obtaining a comprehensive purification rate in combination with the synergistic coefficient, the settlement purification efficiency and the dust removal equipment purification efficiency.

9. The method for controlling pneumoconiosis applicable to a metallurgical scene according to claim 1, wherein, The generating a closed-loop control strategy in combination with a personnel exposure risk coefficient, the dust concentration space-time distribution characteristics and the comprehensive purification rate comprises the following steps: determining a risk early warning through a personnel exposure risk coefficient and the dust concentration space-time distribution characteristics; Based on the risk early warning result, a synergistic regulation strategy is generated according to the comprehensive purification rate and the theoretical demand.

10. A system suitable for pneumoconiosis control in metallurgical scenarios, characterized by, The system suitable for pneumoconiosis control in a metallurgical scene comprises an input device, an output device, a processor and a memory, the input device, the output device, the processor and the memory are connected to each other, the memory comprises program instructions, and the program instructions are used to execute the method suitable for pneumoconiosis control in a metallurgical scene according to any one of claims 1-9.

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