CN119647351B

CN119647351B - Method for constructing analysis model for chlorine jet separation

Info

Publication number: CN119647351B
Application number: CN202510176213.8A
Authority: CN
Inventors: 向可阳; 邱双亮
Original assignee: Shenzhen Jinghengyu Environmental Technology Co ltd
Current assignee: Shenzhen Jinghengyu Environmental Technology Co ltd
Priority date: 2025-02-18
Filing date: 2025-02-18
Publication date: 2025-05-16
Anticipated expiration: 2045-02-18
Also published as: CN119647351A

Abstract

The invention relates to the technical field of fluid mechanics, in particular to a construction method of an analysis model for chlorine jet separation. The method comprises the steps of generating chlorine jet gas by using a jet device, collecting the chlorine jet gas to obtain the chlorine jet gas, injecting the chlorine jet gas into a preset separation area, performing chlorine gas flow separation, performing data monitoring to obtain chlorine gas flow separation data, constructing a separation model based on the chlorine gas flow separation data to obtain the separation model, performing concentration detection on the chlorine gas flow separation data to obtain concentration data, performing stability assessment on the concentration data to obtain stability data, inputting the stability data and the concentration data into the separation model, performing chlorine jet separation effect analysis, and generating the chlorine jet separation effect data. The invention improves the jet flow separation effect based on the hydrodynamic technology and ensures that better separation performance is achieved under various conditions.

Description

Method for constructing analysis model for chlorine jet separation

Technical Field

The invention relates to the technical field of fluid mechanics, in particular to a construction method of an analysis model for chlorine jet separation.

Background

In the traditional method, the separation effect of the chlorine jet flow is often evaluated only through preliminary flow and concentration measurement, and the lack of deep analysis on the complicated air flow distribution and turbulence characteristics leads to lower accuracy of the separation effect evaluation and incapability of timely finding fine anomalies. The conventional model cannot fully consider the fluctuation of jet separation effect under different working conditions. For example, variations in jet velocity, temperature and gas flow density can affect separation efficiency, which is not optimal in these respects with conventional methods, resulting in separation efficiency that is not optimal under all conditions. In conventional approaches, the separation model is typically built based on a simplified fluid dynamics model, ignoring more complex fluid properties such as turbulence layers, velocity gradients, etc. This simplification can affect the true effect of the model, leading to underestimation or misjudgment of the separation efficiency.

Disclosure of Invention

Based on this, the present invention needs to provide a method for constructing an analytical model for chlorine jet separation, so as to solve at least one of the above-mentioned technical problems.

To achieve the above object, a method for constructing an analytical model for chlorine jet separation includes the steps of:

The method comprises the steps of S1, utilizing a jet device to generate chlorine jet gas, collecting the chlorine jet gas, and obtaining the chlorine jet gas;

S2, constructing a separation model based on the chlorine gas flow separation data to obtain a separation model, detecting the concentration of the chlorine gas flow separation data to obtain concentration data, and performing stability evaluation on the concentration data to obtain stability data;

S3, inputting the stability data and the concentration data into a separation model, analyzing the chlorine jet separation effect, generating chlorine jet separation effect data, and marking unreasonable jet separation data if the chlorine jet separation effect data does not meet a preset separation standard;

and S4, performing turbulence diffusion analysis on the unreasonable jet flow separation data to obtain turbulence diffusion data, performing diffusion region control on the separation model according to the unreasonable jet flow separation data to obtain diffusion region data, and performing model separation efficiency optimization on the separation model according to the turbulence diffusion data and the diffusion region data to generate an analysis model.

The invention generates the chlorine jet gas through the jet device and accurately injects the chlorine jet gas into a preset separation area to separate the gas flow. By monitoring the data of the flow and concentration of the chlorine jet gas, the gas flow state in the separation process is tracked in real time. The process ensures the stability and consistency of the air flow and provides high-quality data support for subsequent data analysis and separation model construction. The key of the step is the control precision of the jet device and the accuracy of the data monitoring system, so that the reliability of the generated data is ensured. When a separation model is constructed based on the chlorine gas flow separation data, the flowing states of the chlorine gas under different working conditions are considered, and various conditions in the separation process are fully simulated. By evaluating the stability of the concentration data, the fluctuation range of the concentration of the chlorine in the separation process can be judged, and potential unstable factors can be found in time. The evaluation result can help optimize the separation process, ensure the stability of the separation process under different environments, improve the controllability of the chlorine concentration and reduce the influence of external changes on the separation effect. The key to this step is how to scientifically evaluate and quantify the stability data, and how to control the stability range by reasonable thresholds, thereby guiding the adjustment of the subsequent separation process. And inputting overstable data and concentration data into a separation model, and then analyzing the separation effect of chlorine jet. If the chlorine jet separation effect data does not accord with the preset standard, the chlorine jet separation effect data is marked as unreasonable jet separation data. The process avoids the defect that the traditional method only evaluates the separation effect through rough airflow data, and timely discovers the conditions of low separation efficiency or abnormality. The marked unreasonable data provides a basis for subsequent analysis, especially in the event of changes in airflow velocity, temperature or airflow density, where subtle anomalies can be found and adjusted. By means of an explicit separation effect evaluation criterion, it is ensured that the separation process meets the established performance criteria. Turbulence diffusion analysis is carried out on unreasonable jet separation data, and firstly, the diffusion mode and the behavior of airflow in turbulence diffusion areas are further known through identifying the areas, so that data support is provided for follow-up separation model optimization. Turbulent diffusion zone data can effectively reveal which zones have greater turbulence or flow non-uniformities that lead to reduced separation. Through the deep analysis of the turbulent diffusion region, the basis can be provided for adjusting the speed, the temperature or the direction of the jet flow, and the efficient performance of the separation process is ensured. The turbulent flow diffusion area in the separation model is adjusted accurately, so that the speed, the direction and the temperature of jet flow are optimized in a targeted manner, the jet flow can adapt to complex airflow distribution conditions, and the separation effect can reach a better level under different operation environments. The finally generated analysis model has higher accuracy and adaptability, and the problem of underestimation or misjudgment of the separation effect caused by simplifying the fluid model in the traditional method is avoided. The whole process ensures that each parameter in the airflow separation process can be fully considered and accurately regulated and controlled through multi-level and multi-dimensional data analysis and optimization. The turbulence diffusion data and the real-time monitoring of the air flow concentration can effectively capture abnormal fluctuation in the separation process, and the stability and the accuracy of the separation efficiency are improved. By the method, the separation process can be suitable for various working conditions, model parameters can be dynamically adjusted, and a good separation effect can be realized under any condition. The refinement management and optimization method remarkably improves the application range and effect of the separation model and makes up the defects in the traditional method.

Drawings

Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of a non-limiting implementation, made with reference to the accompanying drawings in which:

FIG. 1 is a schematic flow chart of the steps of the method for constructing an analytical model for chlorine jet separation of the present invention;

FIG. 2 is a detailed step flow chart of step S1 of the present invention;

FIG. 3 is a detailed flowchart illustrating the step S13 of the present invention;

The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

The following is a clear and complete description of the technical method of the present patent in conjunction with the accompanying drawings, and it is evident that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, are intended to fall within the scope of the present invention.

Furthermore, the drawings are merely schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and thus a repetitive description thereof will be omitted. Some of the block diagrams shown in the figures are functional entities and do not necessarily correspond to physically or logically separate entities. The functional entities may be implemented in software or in one or more hardware modules or integrated circuits or in different networks and/or processor methods and/or microcontroller methods.

It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.

To achieve the above object, referring to fig. 1 to 3, the present invention provides a method for constructing an analysis model for chlorine jet separation, the method comprising the steps of:

In this embodiment, the chlorine is delivered to the gas flow nozzle through a gas conduit using a precisely controlled fluidic device. The nozzle ejects chlorine gas through the set pressure, flow and speed of the air flow to form jet flow gas. In the process, the injection pressure of the chlorine gas is required to be set between 1.2MPa and 1.5MPa, and the flow is set to be 50-70L per minute so as to ensure the stability and consistency of jet flow gas. Jet gas is injected into a preset separation area through a designated injection channel, and the injected chlorine gas flow is precisely controlled in flow speed and distribution. The temperature of the gas stream is set at ambient temperature (about 25 ℃) to ensure that the gaseous state of the chlorine jet gas remains stable. The method is characterized in that the real-time gas flow monitoring is carried out through a multi-point gas sensor arranged in the separation area, and the sensor adopts an optical concentration sensing technology and a thermal conductivity analysis technology to carry out real-time measurement on the concentration of the chlorine gas flow, so that gas flow separation data are obtained. The data collected by the monitoring system in real time comprises important parameters such as concentration distribution, airflow speed, temperature change and the like. And the data recording and transmission adopts a data acquisition module, and the monitored airflow information is fed back to the computing system in real time for processing.

In this embodiment, detailed time-space analysis is performed on the chlorine concentration data collected from the gas sensor, noise in the data is filtered by using a data preprocessing technology, and the average concentration and the local concentration change trend of the chlorine are calculated by a weighted average method. Then, based on the series of data, a mathematical model of chlorine jet separation is constructed by adopting a regression analysis method, and factors such as air flow density, turbulence degree, air flow interference in a separation area and the like are required to be considered in the model. Concentration detection is accomplished by a specialized chlorine sensor, the measuring range of which is set to 0 to 10 ppm, the sensitivity is 0.1 ppm, and the sampling frequency is 1Hz. For stability assessment, the time stability of the concentration data was analyzed, and the root mean square fluctuation of the concentration data was calculated using a fluctuation analysis method. If the fluctuation of the stability data is less than 0.1 ppm, the concentration is considered stable, and if the fluctuation is greater than the value, the instability of the system is indicated, and the airflow separation parameter needs to be further optimized.

in this embodiment, during the input process, the system maps the stability data and the concentration data into corresponding parameters of the model, for example, the stability data may affect the calculation of the turbulence of the air flow in the model, and the concentration data may affect the determination of the separation efficiency in the model. On the basis, prediction and evaluation are carried out in the running process of the model, and chlorine jet separation effect data are generated. If the generated separation effect data does not meet the preset standard (for example, the separation efficiency is lower than 90%), the data is marked as unreasonable jet separation data through a calculation method. The preset standard is set such that the separation efficiency should be not lower than 95% in the region where the chlorine concentration reaches 1 ppm, and the separation efficiency should be maintained at 90% or more when the chlorine concentration is lower than 1 ppm. If the separation effect does not meet this criterion, the system will automatically initiate a further optimization process.

In this embodiment, a high-speed imaging technique is used to capture a dynamic image of the airflow in the separation area, and the turbulent diffusion behavior of the airflow is analyzed in combination with an image processing technique. At this point, turbulence diffusion data is recorded and the gas velocity field is accurately analyzed using Particle Image Velocimetry (PIV) techniques. The turbulence spread data includes turbulence intensity, spread velocity, and vortex structure of the air flow, and the flow quality of the air flow is evaluated. Based on these turbulence data, control of the diffusion area is performed, and by precisely adjusting the jet angle of the air flow and the nozzle configuration in the separation area, uniform distribution of turbulence is achieved, thereby controlling the diffusion area of the air flow. The diffusion region data includes information such as a gas flow density distribution map, a gas flow impact point and the like in the region. These data are input into the separation model, which the system uses to optimize the model, with the primary objective of improving separation efficiency and ensuring that the chlorine concentration in each zone meets preset safety standards. The optimized separation model generates a final analysis model, and subsequent operation is performed on the basis of the final analysis model, so that the stability of the separation effect is ensured and the industrial application requirements are met.

Preferably, step S1 specifically includes:

S11, generating chlorine gas jet flow gas by using a jet device, and collecting the chlorine gas jet flow gas, wherein the speed range of the chlorine gas jet flow is set to be 10m/S-20m/S, and the aperture range of a nozzle is set to be 8mm-15mm, so that the chlorine gas jet flow gas is obtained;

In this embodiment, it is necessary to generate chlorine gas by using a jet device. The jet device is provided with a precise gas flow control system, and can convey chlorine into the gas flow nozzle through a pipeline. The nozzle bore was set to 8mm to 15mm and the appropriate nozzle size was selected according to the different experimental requirements. To ensure the stability of the jet gas, the velocity of the chlorine gas stream is controlled between 10m/s and 20 m/s. The pressure regulating system in the jet device ensures that the speed of the sprayed chlorine gas flow is within a set range, and the air flow speed monitoring system at the inlet of the nozzle usually adopts an ultrasonic measurement method to ensure that the speed reaches the set value. The gas flow is monitored in real time through the built-in flowmeter, so that the sprayed chlorine gas meets the preset flow standard (50-70L per minute). After being sprayed out, the jet gas is conveyed to a collecting area through a connecting pipeline, the collecting device quantitatively collects the chlorine gas flow, and collected gas data are transmitted to a data recording device, so that the subsequent analysis is facilitated.

Step S12, injecting chlorine jet gas into a preset separation area, carrying out preliminary collection on the jet gas in the separation area, and carrying out data monitoring to obtain jet separation preliminary data;

In this embodiment, gas flow guides are used to ensure that gas is evenly distributed in the separation zone. A plurality of gas sensors are arranged in the separation area, and the concentration, the speed and the distribution of the gas flow are monitored in real time. Preliminary data of jet gas is obtained through the sensors, and mainly comprises the flow rate, concentration distribution, temperature and the like of the gas. The gas flow rate is measured in the area through a plurality of sensors, and the measurement area is always set to be a grid of every 5cm to 10cm, so that the comprehensiveness of data is ensured. The concentration sensor adopts gas chromatography to accurately measure, the measuring range of the concentration is set between 0.1ppm and 10ppm, and the sensitivity of the sensor is 0.01ppm, so that the small change of the chlorine concentration can be monitored. In addition, the temperature sensor monitors the gas temperature in the area in real time, and adjusts the temperature control system of the separation area according to the gas flow speed and the temperature change, so as to prevent uneven distribution of chlorine caused by overhigh temperature. All monitoring data are transmitted to an analysis system in real time through a data acquisition device so as to analyze and store the preliminary data.

S13, performing abnormal separation analysis based on the jet separation preliminary data to obtain abnormal jet separation data;

In this embodiment, data cleaning is performed on the preliminary data to remove abnormal data caused by equipment failure or external factors. Abnormal data are typically manifested as excessive gas concentration fluctuations (exceeding a set threshold of 0.5 ppm), or large gas flow velocity deviations (exceeding a + -10% error range). The abnormal separation analysis mainly adopts a statistical analysis method, such as standard deviation analysis and trend analysis, to detect the fluctuation of the data. Outliers in the dataset need to be marked and isolated. During the analysis, the normal ranges of concentration, air flow speed and temperature are determined by setting the threshold values. If the data is severely fluctuated or deviates from the normal range, the abnormal data can be judged. The anomaly data is analyzed by comparison with historical data to ensure that invalid or distorted data can be accurately identified and isolated. The identified anomaly data is further analyzed to investigate its underlying causes, such as equipment problems, airflow disturbances, etc.

And S14, screening out the jet flow separation preliminary data according to the abnormal jet flow separation data, so as to obtain chlorine gas flow separation data.

In this embodiment, a filtering algorithm based on a threshold is adopted to reject abnormal data. In the screening process, a tolerance threshold of the concentration was set to 0.5ppm, and any concentration fluctuation data higher than this threshold was regarded as abnormal data. The air flow speed deviation threshold value is set to + -10%, and data outside this range will be determined to be abnormal. And once all abnormal data are confirmed, the abnormal data are removed from the preliminary separation data, and the effective data meeting the standard are reserved. And (3) for the screened effective data, further processing and correcting are carried out to ensure that the finally obtained data meets the standard requirements. During the screening process, special attention is paid to the integrity of the data, and the loss of useful data caused by excessive cleaning is avoided. Thus, the screening process will integrate the statistical properties of the data with the expected results of the physical model, ensuring that the final separation data is reliable and efficient. On this basis, the obtained chlorine flow separation data will be used as the basis for subsequent analysis and optimization.

Preferably, step S13 specifically includes:

S131, extracting air flow density characteristics based on jet flow separation preliminary data to obtain air flow density data;

In this embodiment, the airflow in the separation area is monitored in real time using a plurality of airflow sensor arrays. Each sensor measures chlorine concentration at a different location by gas sensing techniques (e.g., thermal conduction, infrared absorption). Based on the concentration data, in combination with known gas flow rate information, the gas flow density is calculated by the following formula:

;

Wherein P is air pressure, R is air constant, T is air temperature, and C is chlorine concentration. The output data of each sensor will be used to obtain the corresponding air flow density value according to the above formula. In the process, the air flow density data is collected in units of every second, and the sampling frequency is set to be 100 times per second so as to ensure timeliness and accuracy of the data. The obtained air flow density data is used as basic data for subsequent analysis and is stored in a data recording system, and the data are marked according to the area and the time so as to facilitate the subsequent analysis and comparison.

Step S132, carrying out non-uniform time period identification on the air flow density data to obtain non-uniform air flow density time period data;

In this embodiment, a reference threshold is set based on the collected air flow density data, typically with a value within + -5% of the normal fluctuation of the air flow. For example, the gas flow density should normally be between 1.2kg/m3 and 1.5kg/m3, and any fluctuations exceeding or falling below this interval are considered abnormal fluctuations. Sliding window analysis was performed on the data for the entire time period, dividing the data into multiple time periods, each containing 1000 data points. If the magnitude of the change in the air flow density exceeds 10% of the set threshold value over a certain period of time, the period is marked as "uneven period". The method ensures that the large fluctuation of the air flow density can be captured in real time, so that the non-uniformity of the air flow distribution is identified, and the non-uniform time period data is accurately identified for subsequent analysis and processing.

S133, performing diffusivity statistics on the non-uniform airflow density period data to obtain non-uniform diffusivity data;

in this embodiment, the identified data of the uneven period is extracted, and the diffusivity analysis is performed. The diffusivity can be obtained by calculating the standard deviation of the non-uniform gas flow density, namely:

;

where D is the diffuseness, N is the number of data points in the non-uniform period, For the gas flow density value for each data point,Is the average value of the air flow density during this period. By the formula, the diffusivity value in each non-uniform period is obtained and is used for measuring the fluctuation degree of the airflow density. The diffuseness data is further processed by a time series analysis method, and periods with abnormal diffuseness are screened out, wherein the diffuseness values of the periods exceed a set threshold (for example, 3kg/m 3). These periods of high diffusivity mark extreme instability in gas flow distribution and often affect the effectiveness of chlorine separation. All the diffusivity data are stored and transmitted to the data analysis platform in time sequence, so that the subsequent processing and optimization are facilitated.

And step S134, performing abnormal separation judgment on the jet separation preliminary data according to the non-uniform diffusivity data to obtain abnormal jet separation data.

In this embodiment, the non-uniform diffusivity data obtained in step S133 needs to be combined and compared with the air flow density data in the preliminary separation data. The abnormal separation judgment criteria is that when the value of the uneven diffusivity in a certain period of time is greater than a set threshold (for example, exceeds 3kg/m 3), it indicates that there is a large fluctuation in the air flow distribution in that period of time, resulting in poor jet separation effect. On this basis, the marking process is performed by comparing these abnormal diffusion periods with the gas flow density data in the preliminary separation data. If the period in which the uneven diffusivity is high is accompanied by an abnormal change in the air flow density (e.g., exceeds a set air flow density fluctuation threshold), abnormal jet separation data can be determined. These outlier data need to be removed from the preliminary data to avoid interference with subsequent analysis and model optimization. Finally, the rejected valid data will be further processed as the basis data for optimizing the separation model.

Preferably, step S134 specifically includes:

When any one of the conditions that the fluctuation of jet speed exceeds +/-10%, the non-uniformity of the air flow density exceeds a set threshold value +/-15% and the thickness of the air flow in a separation area deviates from a preset range by more than 5cm is judged to be abnormal in jet separation, and jet separation abnormal data are obtained;

In this embodiment, the correlation velocity is monitored in real time. Jet velocity data is acquired at different positions of the separation area through a set flow velocity sensor (such as an ultrasonic flow velocity sensor), and fluctuation amplitude of the jet velocity data is calculated. If the variation of the jet velocity exceeds + -10%, it is determined that the fluctuation of the jet velocity is abnormal. In addition, the air flow density in the separation area is acquired in real time by the air flow density sensor, and if the uniformity fluctuation of the air flow density exceeds + -15% (judged according to the reference value and the deviation limit value of the air flow density), the air flow non-uniformity problem is considered to exist. The thickness of the gas stream is then measured at various locations in the separation region using a gas stream thickness measuring instrument, such as a laser rangefinder. If the measured thickness of the air flow deviates from the predetermined range by more than 5cm, it is marked as abnormal thickness of the air flow. And comparing and analyzing all the data in time sequence, judging that jet separation is abnormal if the three conditions are met simultaneously, and storing the corresponding abnormal data in a data recording system.

When the conditions that the outlet pressure of the nozzle is reduced by more than 15%, the jet speed is lower than the lower limit of a preset range, the jet range is deviated by more than +/-15 degrees, and the deviation between the air flow diffusion angle and the design angle is more than 10 degrees are simultaneously judged to be abnormal in separation caused by the blocking of the jet nozzle, and nozzle blocking separation abnormal data are obtained;

In this embodiment, a pressure sensor is used to measure the gas pressure at the nozzle outlet and compare it to a preset normal pressure range. If the measured nozzle outlet pressure value is reduced by more than 15% from normal, the nozzle is considered to have a problem of clogging. At the same time, the flow rate of the jet is monitored by the flow meter, and if the flow rate is below a preset lower limit value (e.g., below 10 m/s), it is further determined that there is nozzle blockage. In addition, a laser radar or a high-precision sensor is used for monitoring the jet range of the jet, if the jet deviates from the design angle by more than +/-15 degrees, the jet range is indicated to deviate, and the judgment of nozzle blockage is further supported. And finally, measuring the diffusion angle of the air flow by adopting an angle sensor, and if the deviation between the measured diffusion angle of the air flow and the design angle exceeds 10 degrees, confirming that the nozzle is blocked. If the four conditions are satisfied at the same time, it is determined that the separation is abnormal due to nozzle clogging, and corresponding abnormal data is recorded.

When the conditions that the temperature fluctuation of the air flow exceeds +/-10 ℃, the temperature of a separation area deviates from the normal range of 20-30 ℃ and the temperature of an air flow spraying area is continuously lower than 10 ℃ or higher than 50 ℃ are simultaneously met, the air flow temperature abnormality of the jet flow separation system is judged, and air flow temperature abnormality data are obtained;

In this embodiment, the temperature of the gas stream in the separation region is measured in real time by a temperature sensor (e.g., thermocouple). If the temperature fluctuation of the air flow exceeds + -10 ℃, namely exceeds a set temperature fluctuation threshold value, the air flow is judged to be abnormal in temperature fluctuation. Next, by arranging a plurality of temperature measurement points in the air flow injection region, the temperature of the injection region is monitored in real time. If the measured injection zone temperature deviates from the normal range (20 ℃ to 30 ℃) beyond the upper or lower limit of the range, a temperature anomaly is marked. In addition, if the temperature of the air flow injection region continues to be lower than 10 ℃ or higher than 50 ℃, it is also considered that the air flow temperature is abnormal. All temperature data are recorded and analyzed in real time through a monitoring system, if any condition is met, the temperature of the air flow is judged to be abnormal, and corresponding abnormal data are stored.

And integrating the jet flow separation abnormal data, the nozzle blockage separation abnormal data and the air flow temperature abnormal data, thereby obtaining abnormal jet flow separation data.

In this embodiment, after the individual determination of the jet separation abnormality, the nozzle clogging separation abnormality, and the airflow temperature abnormality is completed, various types of abnormal data are integrated by the data processing platform. Each type of abnormal data is associated according to different marking modes (such as time stamp and abnormal type) and is combined into a complete abnormal record. The abnormal data is comprehensively evaluated through the rule engine, so that multiple abnormal conditions occurring in the same time period can be accurately associated, and abnormal jet separation data are generated. All integrated abnormal data are stored in a central database and displayed to operators through a visual interface for subsequent analysis and decision making.

Preferably, step S2 specifically includes:

s21, carrying out data preprocessing based on the chlorine flow separation data to obtain preprocessed chlorine flow separation data;

In this embodiment, the air flow sensor is used to collect the air flow data of chlorine gas, including parameters such as air flow speed, air flow density, temperature, etc. A Digital Signal Processor (DSP) with a filtering function is adopted to carry out noise filtering treatment on the original airflow data, and a low-pass filter is used to remove high-frequency noise signals. In addition, the data is smoothed by adopting a mean value smoothing method, so that the stability of the data is ensured, and sudden fluctuation and abnormal values are removed. In the data preprocessing process, the set smoothing parameter is 3 seconds window length, namely, the data in 3 continuous seconds is smoothed. Next, the data is normalized, mapping the chlorine flow data to a range of 0-1 for subsequent processing. The pretreated chlorine flow separation data will be stored in a data storage system, ensuring that it is uniform in format and easy to analyze later.

S22, extracting chlorine gas flow distribution characteristics according to the pretreated chlorine gas flow separation data, so as to obtain chlorine gas flow distribution data;

In this embodiment, the two-dimensional gas flow distribution sensor is used to collect gas flow data for chlorine at a plurality of measurement points. The airflow sensor adopts a hot film anemometer, has the characteristics of high precision and quick response, and can monitor the distribution characteristics of airflow in real time. The average velocity, maximum velocity, direction of the air flow, and density distribution of the air flow are calculated by a data processing algorithm. The set extraction standard is that data sampling is carried out every 5 seconds, and the air flow speed and the density at each time point are subjected to statistical analysis to obtain the distribution data of the chlorine air flow. In the process, the numerical integration method is adopted to synthesize the data of each measuring point, so as to obtain the distribution characteristics of the chlorine gas flow in the separation area. And finally, outputting chlorine gas flow distribution data, including parameters such as gas flow speed, density, temperature and the like, and storing the data in a database for subsequent analysis.

S23, performing turbulence layer analysis on the chlorine gas flow distribution data to obtain turbulence data, and constructing a separation model by utilizing a Reynolds average equation to obtain the separation model;

in this embodiment, the flow field of the separation region is analyzed by a turbulence model from the airflow velocity distribution and density data. And (3) carrying out turbulence characteristic analysis by adopting a turbulence calculation formula and a Reynolds average equation, and calculating parameters such as the intensity, the energy and the vortex structure of the turbulence. When the Reynolds number Re is set to be larger than 4000, turbulent flow is set, a turbulent flow model is used for analyzing the speed fluctuation of the airflow, and a Reynolds average equation is solved through a numerical method, so that turbulent flow data are obtained. Subsequently, a numerical model of the gas flow separation zone is built from the turbulence data and boundary conditions are set for the model, the boundary conditions including gas flow density, pressure distribution and temperature of the separation zone. And solving by a finite difference method to obtain a separation model. The flow rate threshold value set in the separation model is 20 m/s, and if the flow rate threshold value exceeds the flow rate threshold value, the air flow is considered to enter a turbulent flow area, and the separation effect is influenced. And finally, outputting a model result and storing the model result into a separation model database.

Step S24, detecting the concentration of the chlorine flow distribution data to obtain concentration data;

In this embodiment, the concentration of chlorine gas is monitored in real time by a chemical gas concentration sensor (e.g., an electrochemical sensor) installed in the separation area. The detection range of the sensor configuration is 0-1000 ppm, the resolution is 1 ppm, and the detection precision is +/-3%. The sensor regularly samples and generates concentration data, and in the data acquisition process, the sampling interval is set to be 5 seconds, so that the accuracy of continuous data is ensured. The concentration data is processed through an algorithm and is compared and analyzed with a set concentration threshold (for example, when the chlorine concentration is greater than 500 ppm, an alarm is given), and the chlorine concentration data at each time point is obtained. The concentration data is filtered to remove errors and noise caused by environmental fluctuation, so that the accuracy of the data is ensured. Finally, the concentration data is stored in a centralized monitoring system and each data point is time stamped for subsequent analysis and report generation.

And S25, performing stability evaluation on the concentration data so as to obtain stability data.

In this embodiment, in the stability evaluation of the concentration data, the concentration fluctuation condition in each period is first calculated. The concentration data was subjected to fluctuation assessment using statistical analysis methods such as standard deviation and Root Mean Square Error (RMSE), and the threshold value was set to be unstable when the standard deviation exceeded 5 ppm. On the basis, the concentration data is processed by adopting a sliding window technology, and the concentration fluctuation in each window is calculated. If the concentration change exceeds the set stability range for a certain period of time, the concentration is considered to be unstable for the period of time. In the evaluation, the window size was set to 10 minutes, and the average value calculation was performed on the concentration data in each window. According to the set threshold criteria, if the average fluctuation range of the concentration data exceeds the set upper and lower limits (e.g., + -5 ppm), then the concentration is considered to be unstable. And finally, the stability data is stored in a database after being processed, so that the subsequent analysis and monitoring are facilitated.

Preferably, step S23 specifically includes:

Step S231, performing speed calculation on the chlorine gas flow distribution data, wherein the speed effective data interval of each node is set to be 10 seconds, so as to obtain the chlorine gas flow distribution speed data;

In this embodiment, it is necessary to determine the valid data interval of each node. And setting the speed effective data interval of each node to be 10 seconds, wherein the interval length is set based on experimental requirements, so that the acquired speed data is ensured to have enough stability and accuracy. Chlorine gas flow distribution data are collected by sensors and recorded at each node. The airflow distribution data for each node includes airflow intensity, direction, and other relevant parameters. And collecting the airflow parameters of each node in a data interval of 10 seconds, and calculating the speed data by a difference method. In particular, the airflow position data of each node is acquired first over a 10 second period, ensuring that the position data is time synchronized and calibrated. Then, a differential formula of speed= (current node position-previous node position)/time interval is used, wherein the time interval is fixed to 10 seconds. For each node, this calculation method will result in corresponding speed data. The process employs a high precision time synchronization tool and a differential algorithm to ensure accurate computation of the flow data between nodes. The time synchronization error of all nodes is ensured to be less than 1 millisecond so as to avoid overlarge calculation error. The time synchronization adopts a high-precision time synchronization instrument, so that the time marking of each node is ensured to be accurate. By controlling the sampling frequency of the air flow sensor to be 1Hz, data is ensured to be collected once per second, and the data integrity of each node is ensured. In the calculation process, for each node, a time stamp must be recorded and the data ordered according to the time stamp in order to correctly apply the differential formula calculation speed. The validity and accuracy of all the speed data are verified through a subsequent data quality verification mechanism, such as comparing different sensor data with actual chlorine flow rate model results. After the speed calculation is completed, the generated chlorine gas flow distribution speed data are used for subsequent process optimization and analysis.

S232, carrying out speed gradient calculation based on the chlorine gas flow distribution speed data, wherein the sampling interval is set to be 0.1 meter, and the calculation range is set to be 5-10 meters so as to obtain speed gradient data;

In this embodiment, the speed data in the region of 5 to 10 meters is extracted from the air flow speed data collected by the chlorine air flow sensor. To ensure accuracy of the data, the sampling interval is set to 0.1 meter, i.e., the airflow velocity value is collected every 0.1 meter. In this range, velocity data of the air flow is acquired stepwise, and a gradient calculation formula is applied:

;

Where Δv represents a change in speed, and Δx is a change in distance, i.e., a sampling interval (0.1 meters). And calculating the speed change rate between each two sampling points through the formula to obtain speed gradient data. In the process, the set speed change threshold value is 1 m/s, and if the speed change exceeds the value, the speed change is considered to be obvious. Finally, the resulting velocity gradient data is stored in a data storage system for use in subsequent analysis.

Step S233, carrying out speed fluctuation assessment on the chlorine flow distribution speed data so as to obtain speed fluctuation data;

In this embodiment, time sequence analysis is performed on the collected airflow velocity data, and the fluctuation conditions of the airflow velocity data in different time periods are analyzed. The sampling frequency is set to 1Hz, i.e. the speed data is acquired once per second. In the speed fluctuation evaluation, the fluctuation evaluation is performed by using a standard deviation and Root Mean Square Error (RMSE) method. Specifically, the average speed value in each time period is calculated, and deviation comparison is performed with each data point to obtain the fluctuation value in each time period. The set fluctuation threshold is 5% (i.e., a fluctuation in speed exceeding 5% is regarded as a large fluctuation). For each time period, if the speed fluctuation is greater than the threshold, fluctuation data for the time period is recorded. In the evaluation process, one fluctuation calculation is performed on the speed data in 5 continuous seconds, and overall speed fluctuation data is obtained through accumulation analysis, and finally the data are recorded in a database.

Step S234, calculating vorticity according to the speed gradient data and the speed fluctuation data, wherein the fluctuation time of the set vorticity data is more than 3 seconds and is regarded as effective vortex, and the vorticity data is obtained;

In this embodiment, the vorticity is calculated according to the velocity gradient data obtained in step S232 and the velocity fluctuation data obtained in step S233, in combination with the basic principle of turbulent flow. The vorticity represents the intensity and direction of rotation in the air flow, and the calculation formula is:

;

Wherein, Is a symbol representing the partial derivative. Partial derivatives are a way to mathematically describe the rate of change of a multi-variable function, particularly where multiple variables are involved,Representing the velocity of the air flow in the y direction) Sensitivity to x-coordinate changes,Representing the velocity of the air flow in the x direction) Sensitivity to changes in the y-coordinate,AndThe velocity components of the airflow in the x-axis and y-axis directions, respectively. The vortex quantity calculation process needs to perform discretization calculation based on speed differences among different sampling points, and the vortex quantity is determined by combining local speed changes. In the vortex data calculation, the set effective vortex time is 3 seconds, that is, if the vortex fluctuation is greater than the set time (3 seconds), the vortex is considered to be effective vortex, and the influence thereof is further analyzed. In the calculation process, a sliding window technology is adopted, the window size is 3 seconds, and the fluctuation condition of vorticity in the window is analyzed. The obtained vortex data is filtered to eliminate noise and abnormal data, and the data is stored in a data storage system.

And S235, carrying out turbulent layer identification on the chlorine gas flow distribution data according to the vortex quantity data to obtain turbulent flow data, and constructing a separation model by utilizing a Reynolds average equation to obtain the separation model.

In this embodiment, the vortex structure in the airflow is identified by vortex data. And setting a vortex intensity threshold value to be 0.5 by combining vortex quantity data, and considering a turbulence area if the vortex quantity is larger than the threshold value. By analyzing the space-time distribution of the vorticity data, a turbulent flow area and a laminar flow area are divided. Next, a separation model of the airflow is established based on the RANS average equation. The set boundary conditions comprise airflow speed, density and temperature distribution, and a numerical solution method (finite difference method) is adopted to solve the Reynolds average equation. By the model, the speed distribution, the pressure distribution and the turbulence intensity of the airflow in the turbulence layer are calculated, and a complete separation model is established. The separation model is used for simulating the flow characteristics of the chlorine gas flow in different areas and further optimizing the gas flow separation effect. Finally, the resulting turbulence data and separation model are stored in a model database for subsequent application and analysis.

Preferably, step S24 specifically includes:

Step S241, performing concentration preliminary detection on the chlorine gas flow distribution data by using a concentration sensor, wherein a sampling period is set to be once per minute, so as to obtain concentration preliminary detection data;

In this embodiment, a suitable concentration sensor is selected, which is required to have high accuracy and high stability, and to be able to detect the concentration of chlorine in the gas stream. The sampling period of the concentration sensor is set to record the chlorine concentration once every minute, i.e., every 60 seconds. The data for each measurement should contain the chlorine concentration (in ppm) and related characteristics of the gas stream (e.g., temperature, humidity, etc.). In a specific implementation, the sensor will be mounted at a specific location in the different air flow areas and detect the air flow at a certain height. The detection data are stored in a local database or a cloud server, and are further processed later. At each sampling, a data timestamp is recorded for subsequent analysis, ensuring the time continuity of the detection process.

Step S242, performing out-of-standard division on the concentration preliminary detection data based on preset reference concentration data so as to obtain out-of-standard concentration preliminary detection data;

In this example, the reference concentration value is set to the maximum value of the safe concentration range, and is normally in the normal range of 0.5ppm or less. For each concentration detection value, if it is greater than the reference concentration value (0.5 ppm), it is considered to be out of specification. The concentration data for each test will be compared to a baseline concentration value and over 0.5ppm will be marked as an over-standard concentration. By setting the concentration threshold to 0.5ppm, filtering and marking are performed to ensure that the over-standard concentration data can be accurately identified and classified. All the out-of-standard data will be recorded and identified, facilitating subsequent area identification and analysis. At this time, the out-of-standard concentration preliminary detection data has passed the out-of-standard determination as the basis for the next processing.

Step S243, carrying out area identification on the chlorine gas flow distribution data according to the primary detection data of the exceeding concentration, wherein the safety concentration limit value of the chlorine gas is set to be less than 0.5ppm, and obtaining the area data of the exceeding concentration;

In this example, the chlorine safety concentration limit was set to 0.5ppm. During the detection, all areas with concentration data values greater than 0.5ppm will be considered as superscalar concentration areas. In order to identify these regions, it is necessary to compare each over-standard concentration data point with the data of the surrounding regions using the concentration sensor data and the spatial distribution information of the airflow model, find out the adjacent over-standard concentration regions, and mark them. In a specific operation, data in a small time window (for example, 5 minutes) is selected for region division, all points exceeding a threshold value in the window are regarded as an out-of-standard region, the coordinate range of each region is recorded, and effective region data is provided for subsequent analysis. The spatial distribution of each out-of-tolerance region is recorded in detail for further analysis.

Step S244, carrying out sensing coordinate marking based on the concentration preliminary detection data, wherein the size of a neighborhood is set to be 10-15 meters, and the minimum neighborhood number is set to be 3, so as to obtain sensing coordinate data;

In the embodiment, the size of the neighborhood is set to be 10-15 meters, and the sensor data in the neighborhood range is determined to carry out coordinate marking. The neighborhood size represents the range of influence of the sensor within a certain spatial range. The coordinates of each sensor are marked according to its mounting location (e.g., X, Y, Z coordinates). Setting the minimum neighborhood number to 3, i.e. at least 3 sensor data are needed to co-participate in the coordinate marking within the influence of each sensor. During operation, the coordinates of each sensor are firstly determined, and then the coordinates of the adjacent sensors and the concentration data are marked in a neighborhood of 10-15 meters, so that the sensing coordinate data can reflect the overall distribution of the airflow. All data will be stored and combined with the actual airflow model for subsequent analysis.

Step S245, performing target coordinate marking based on the concentration preliminary detection data to obtain target coordinate data;

In this embodiment, the actual coordinates of the target area or target device are to be determined. During the object coordinate marking process, coordinate data of the object, such as X, Y, Z axis positions of the object, are determined. When the target is marked, the coordinate value precision is set to be 0.01 meter, and the coordinate information of the target is recorded through a high-precision positioning system. And marking the concentration data of each target according to the position relation between the detected concentration data and the targets, and recording the coordinates of the targets. The purpose of this operation is to ensure that the concentration data is consistent with the spatial coordinates, providing accurate position data for subsequent concentration analysis and sensor weight calculation.

Step S246, euclidean distance calculation is carried out according to the sensing coordinate data and the target coordinate data to obtain Euclidean distance data;

In this embodiment, the target coordinates and the sensor coordinates are acquired. For each pair of sensor coordinates and target coordinates, its Euclidean distance is calculated using the following formula:

;

Where (x 1, y1, z 1) is the sensor coordinates and (x 2, y2, z 2) is the target coordinates. By this formula, the distance between each sensor and the target is calculated. The distance data are used for further analyzing influence of the sensor in the target area, and a basis is provided for weight calculation and concentration analysis of the sensor. All the calculation results are stored in a database and combined with other data to facilitate subsequent concentration analysis.

Step S247, sensor weight calculation is carried out based on Euclidean distance data to obtain sensor weight data;

in this embodiment, the weight of the sensor is calculated from the Euclidean distance between the sensor and the target. The weight calculation formula is set as follows:

;

The closer the sensor is to the target, the greater its weight, whereas the farther the distance is, the less weight. By this formula, a weight value of each sensor is calculated and a weight value is assigned to each sensor. This ensures that the effect of the distance of the sensor on its concentration analysis is reasonably well reflected. The weight data of all sensors will be recorded and stored for subsequent concentration analysis.

And S248, carrying out concentration analysis on the data of the exceeding concentration area according to the weight data of the sensor to obtain concentration data.

In this embodiment, the data of the over-standard concentration region is weighted-averaged by the weight of each sensor. And in specific operation, multiplying the concentration data in each exceeding area by the weight of the corresponding sensor, and summing all the data to obtain a weighted concentration value. The weighted concentration value reflects the measured influence of the sensor in the region. And finally, summarizing the concentration weighted values of all the exceeding regions to obtain a concentration analysis result of the whole region for subsequent decision and processing.

Preferably, step S25 specifically includes:

s251, performing fast Fourier transform on the concentration data, wherein the sampling frequency is 10 Hz-1000 Hz, and obtaining concentration frequency domain data;

In this embodiment, concentration data of the chlorine gas stream needs to be collected, and typically, these data are collected at fixed time intervals (e.g., once per minute) by a concentration sensor. In order to ensure that the converted frequency domain data can reflect the periodic fluctuation of the chlorine gas flow concentration, the sampling frequency needs to be set between 10Hz and 1000Hz, and the specific frequency is selected by taking the characteristic of chlorine gas concentration change and the requirement of data acquisition into consideration. The choice of sampling frequency determines the accuracy and information content of the signal after conversion. In this step, the acquired time domain concentration data is converted into frequency domain data using a fast fourier transform algorithm (FFT), resulting in amplitude and phase information of different frequency components. The FFT algorithm can effectively convert time series data into frequency domain data revealing potentially periodic variations in the gas flow concentration signal. To achieve this conversion, a built-in FFT function of a programming tool such as Matlab, python may be used. The frequency domain data obtained by carrying out Fourier transform on the concentration signal can show the intensity distribution of concentration fluctuation at different frequencies, thereby providing a basis for further analyzing the periodic components in the signal.

Step S252, extracting amplitude spectrum based on the concentration frequency domain data, so as to obtain amplitude spectrum data;

In this embodiment, the frequency domain data after the fast fourier transform needs to be processed. The goal is to extract amplitude values for each frequency bin, which represent the intensity of the concentration signal at the corresponding frequency. The extraction of the magnitude spectrum can be obtained by calculating the modulo length of each frequency component, which is the size of the frequency domain data in complex form, representing the intensity of the concentration fluctuations at that frequency. During the extraction, special care is taken to remove the direct current component, i.e. the part with zero frequency, since the direct current component usually represents the average value of the signal and does not belong to the periodically fluctuating part. By analysing the amplitude spectrum data it is possible to further identify which frequency components of the signal have strong fluctuations and which belong to background noise or extraneous components. The extraction of the amplitude spectrum data is beneficial to the screening of the subsequent significant amplitude, which frequency components play a leading role in the concentration change is clear, and the data support is provided for the subsequent periodic fluctuation identification.

Step S253, carrying out significant amplitude statistics on the amplitude spectrum data to obtain significant amplitude data;

In this embodiment, amplitude data is obtained from the history data, and the mean value and standard deviation of these data are calculated. For example, assuming that the amplitude value in the history data ranges from 0 to 1, the average value obtained by analysis is 0.3, and the standard deviation is 0.05. Then, based on these statistics, the amplitude threshold is set to the mean plus three times the standard deviation, i.e. 0.3+3×0.05=0.45. By this means, those frequency components of smaller amplitude, which are typically representative of background noise or extraneous fluctuations, can be effectively removed. When the threshold is set, all frequency components with an amplitude greater than 0.45 will be considered significant, representing the main periodic fluctuating component in the concentration signal. These significant frequency components will serve as the basis for subsequent periodic wave analysis to help identify the dominant wave patterns in the chlorine flow. The purpose of the significant amplitude statistics is to filter noise, so that follow-up analysis can be focused on truly meaningful fluctuation data, and the accuracy of periodic fluctuation identification is improved.

Step S254, acquiring amplitude threshold data;

In this embodiment, in the amplitude threshold value obtaining step, statistical analysis is required according to the amplitude spectrum data to determine an appropriate amplitude threshold value. To set this threshold, the amplitude spectrum data first needs to be fully analyzed, in particular its statistical properties, such as mean and standard deviation. In actual operation, the mean and standard deviation of the amplitude of the data can be calculated by analyzing historical data or data acquired in real time. For example, in analyzing the history data, it is assumed that the obtained amplitude data ranges from 0 to 1, and after calculation, the obtained amplitude average value is 0.35 and the standard deviation is 0.04. On this basis, in order to ensure that normal fluctuations can be effectively distinguished from abnormal fluctuations, the threshold can be set to the mean plus three times the standard deviation, i.e. 0.35+3×0.04=0.47. This setup method helps to reject most of the background noise and retains the main components in the concentration fluctuations. The amplitude threshold must be set to ensure that normal and abnormal fluctuations can be effectively distinguished, thereby avoiding erroneous judgment of noise as a meaningful signal. In practice, the adjustment of the threshold value must be fine to ensure that the main fluctuation component of the air flow can be accurately captured without introducing unnecessary errors or missing important fluctuation information due to too high or too low threshold value. Thus, the setting of the amplitude threshold plays a critical role in the concentration signal analysis.

Step S255, carrying out periodic fluctuation identification on the significant amplitude data according to the amplitude threshold data to obtain periodic fluctuation concentration frequency domain data;

in this embodiment, the significant amplitude data needs to be screened by using the amplitude threshold determined in the previous step, and all frequency components with amplitudes exceeding the set threshold are selected. At this time, assuming that the amplitude threshold is 0.47, the frequency component in the significant amplitude data is regarded as a frequency component having significant fluctuations if its amplitude is greater than 0.47. These significant frequency components are then processed using an Inverse Fast Fourier Transform (IFFT), converting them from the frequency domain back to the time domain in order to analyze their performance in the time domain. In particular, assuming that these frequency components are composed of concentration fluctuation data with sampling points between 10 Hz and 1000 Hz, after IFFT processing, the time domain data will exhibit a periodic fluctuation pattern. The IFFT, by recovering the time domain waveform of these frequency components, can reveal specific characteristics of the periodic fluctuations in the concentration signal, helping to identify potential periodic variations in the airflow. The key of the process is to accurately extract and recover the main periodic fluctuation component in the frequency domain, so as to avoid misjudging the noise component as a periodic signal. In actual operation, a programming tool such as Matlab or Python may be used to efficiently convert the frequency domain data into time domain data by calling a built-in IFFT function, thereby obtaining a time domain representation of the periodic fluctuations. The identification result provides key data support for subsequent concentration stability evaluation and safety analysis, so that dynamic changes of the air flow can be known more accurately, and references are provided for safety precaution or optimal control.

Step S256, performing extremely poor calculation on the concentration data to obtain extremely poor concentration data;

in this embodiment, concentration data needs to be collected over a certain period of time, and these data are typically sampled by the sensor every minute, or a higher sampling frequency is used according to the actual situation. For example, assume that concentration data is collected every minute over an hour, thus yielding 60 data points. Then, the collected concentration data are traversed to find out the maximum concentration value and the minimum concentration value in the time period. Assuming that the maximum value of the concentration data is 0.98ppm and the minimum value is 0.12ppm in this hour, the concentration range is the difference between the two values, i.e., 0.98-0.12=0.86 ppm. The magnitude of the concentration range reflects the magnitude of the concentration fluctuation, and the larger the range is, the more severe the change of the concentration of the air flow is, otherwise, the smaller the range is, the more stable the concentration change is. Therefore, the extremely poor data becomes an important index for measuring the fluctuation intensity of the airflow concentration, and can provide a basic measurement standard for subsequent stability evaluation. By the method, fluctuation characteristics of the gas flow concentration can be effectively captured, and necessary data support is provided for subsequent analysis such as periodic fluctuation identification and stability evaluation.

And S257, performing stability evaluation according to the concentration range data and the periodic fluctuation concentration frequency domain data, so as to obtain stability data.

In this embodiment, comprehensive analysis is performed to evaluate the stability of the air flow concentration by combining the concentration range data and the periodic fluctuation concentration frequency domain data. Specifically, the intensity of the concentration fluctuation is evaluated first based on the concentration range data. Assuming that the concentration data collected is extremely poor at 0.86ppm within a certain period, if the value is large, the fluctuation of the concentration of the air flow is severe, and an unstable factor exists. A very poor concentration generally means that the concentration fluctuates in a short time with a large amplitude, which affects the safety of the system. Next, the periodic variation of the density signal is analyzed from the periodically fluctuating density frequency domain data. For example, if obvious frequency components appear in the frequency domain data and the fluctuation is regular, the periodic fluctuation can show repeatability and regularity in the time domain data, which means that the airflow concentration fluctuation has a certain rule and tends to be stable. If the fluctuation in the frequency domain data is irregular or weak in periodicity, the concentration variation is lack of regularity, the fluctuation is random, and the instability of the airflow concentration is high. By combining the concentration range data and the periodic fluctuation concentration frequency domain data and using a preset stability standard, the stability of the air flow concentration can be comprehensively estimated, and a stability estimation result can be obtained. For example, assume that the stability criteria set are that if the margin is greater than 0.8ppm and the periodic fluctuation is weak, it is evaluated as unstable, and if the margin is less than 0.5ppm and the periodic fluctuation is significant, it is evaluated as stable. Based on the standards, the finally obtained stability evaluation result provides data support for subsequent air flow adjustment and safety monitoring, is beneficial to timely identifying the risk of concentration fluctuation abnormality, and adopts corresponding safety measures in advance to avoid potential safety hazards.

Preferably, the step S3 specifically includes:

Step S31, converting the stability data and the concentration data into CSV files to obtain a stability CSV file and a concentration CSV file;

In this embodiment, the stability data and the concentration data need to be converted into a CSV file format, and first two electronic forms containing related data are prepared. The stability data is composed of the stability evaluation result obtained in step S257, and the concentration data is composed of the concentration value obtained in step S256. First, the two sets of data are organized into two distinct files, respectively, where each data contains a time point stability or concentration value, ensuring that the time stamps are aligned. For each data file, the time stamp is accurate to seconds, and the data accuracy requirement is two bits after a decimal point, for example, the stability data file can comprise a time stamp, a stability evaluation value and a unit, and the concentration data file can comprise a time stamp, a concentration value and a unit. The data is ultimately saved in CSV format, and the file names may be named "stability_data.csv" and "concentration_data.csv", respectively. It is ensured that the number of columns and data formats in the file meet the uploading requirement, and all non-numeric data (such as units) must be clearly marked in the header.

S32, inputting a stability CSV file and a concentration CSV file into a separation model, and uploading the separation model to simulation software;

In this embodiment, the stability CSV file and the concentration CSV file obtained in step S31 need to be input into the separation model, and the separation model is uploaded to the simulation software. The separation model is constructed based on stability and concentration data obtained from previous analysis, and comprises initial parameters of the jet separation system and an optimization algorithm. Firstly, confirming that simulation software supports the importing of CSV format data, and importing the two CSV files into a separation model input interface through an interface function of the simulation software. Next, it is confirmed that all data columns have been correctly mapped into the input parameters of the model, and it is checked whether additional data processing such as unit conversion or format adjustment is required. When the separation model is uploaded to simulation software, the stability and concentration data can be correctly called by the model in a simulation environment, so that jet separation simulation can be carried out later.

S33, performing chlorine jet separation simulation in simulation software, wherein jet type is set as transverse jet, jet speed is set to be 5-20 m/S, and jet temperature is set to be 25-50 ℃;

in this embodiment, according to the setting interface of the simulation software, the "transverse jet" is selected as the jet type. Next, the velocity of the jet was set in the range of 5m/s to 20m/s. The jet flow speed can be selected according to the actual working condition, and the flow intensity of the chlorine jet flow is controlled by adjusting the flow speed parameter in simulation software. The speed setting should be selected in accordance with the speed range of the intended air flow and ensure a numerical accuracy of 1 m/s. Then, the temperature range of the jet flow is set to be 25-50 ℃, and proper temperature setting is selected through a temperature control module, so that the model can simulate the chlorine jet flow behaviors at different temperatures. The temperature range is suitable for most of chlorine application scenes, and the influence of different temperatures on the chlorine jet separation effect can be analyzed by gradually adjusting the temperature during simulation.

Step S34, performing chlorine jet separation simulation in simulation software, wherein a separation medium is set to be a porous medium, the porosity is set to be 40-70%, and the separation efficiency parameter is set to be 0.85-0.95;

In this embodiment, a "porous medium" is chosen as the separation medium to ensure that the flow of air through the medium with a certain porosity in the simulated environment. Next, the porosity was set in the range of 40% to 70%. The porosity affects the penetration and distribution of the gas flow in the media, and the range is selected to simulate different types of porous media, such as filter materials or packing in a reactor, etc. By adjusting the porosity of the medium, the influence of different porosities on the chlorine separation effect can be observed. Finally, the separation efficiency parameter is set to 0.85 to 0.95. This parameter determines the separation capacity of the medium for chlorine and a suitable separation efficiency value can be set by experimental or historical data. The value is input in simulation software to ensure that the simulation can correctly reflect the separation efficiency in the separation process.

Step S35, a chlorine jet separation effect analysis module in simulation software is operated to obtain chlorine jet separation effect data, and the chlorine separation effect data is recorded every 10 seconds;

In this embodiment, a chlorine jet separation effect analysis module is operated in simulation software, and chlorine separation effect data is recorded periodically. The recording interval was set to 10 seconds in order to obtain the change in the chlorine jet separation in time. In the setup interface of the simulation software, an "effect analysis module" is selected and the data recording frequency is set to once every 10 seconds. The data recorded each time should include key indicators of the effect of jet separation, such as chlorine concentration, separation efficiency, temperature, etc. By analyzing the data, the separation performance of the chlorine jet under different setting conditions can be evaluated, and simulation parameters can be adjusted in real time to optimize the separation effect.

And S36, if the chlorine jet separation effect data does not meet the preset separation standard, marking as unreasonable jet separation data.

In this embodiment, a reasonable separation standard is set to determine whether the chlorine jet separation effect data meets the requirements. Common separation criteria include separation efficiency, degree of chlorine concentration reduction, pressure change during separation, and the like. Specifically, the separation efficiency is set to be greater than 90% as a criterion, that is, the concentration of chlorine gas during separation should reach a preset reduction ratio, and it is generally aimed at reducing the concentration of chlorine gas by at least 90%. The standard can be realized by a separation efficiency calculation module in simulation software, and the module can output the current chlorine concentration and the change trend thereof according to the simulation data in real time. If the simulation result shows that the separation efficiency at a certain time is lower than 90%, the data at the certain time is regarded as not meeting the separation standard. The reduction in chlorine concentration should reach a preset threshold. For example, it is standard to set a concentration drop above a certain percentage (e.g., a drop above 50%). When the concentration at a certain time in the simulation is reduced by less than 50%, the data is judged as unreasonable data. In addition, if parameters such as the temperature, the speed and the like of the chlorine jet flow cannot reach the expected range under the specific conditions, the parameters can also be used as the basis for judging unreasonable data. The data is marked by the output function of the simulation software. When the separation effect data at a certain moment is detected to fail to meet the preset separation standard, the simulation software automatically marks the data as abnormal or unreasonable and stores the data in a special abnormal data output file. The marked unreasonable data comprises specific information such as time stamp, separation efficiency, chlorine concentration and the like, and is convenient for subsequent analysis and processing. By means of the logging function of the simulation software, the occurrence time periods, parameter settings and other relevant variables of the unreasonable data can be tracked. These markers provide basis for subsequent optimization for unreasonable data. Based on these data, the user can further analyze the reasons, such as whether the jet speed, jet temperature, media porosity, etc. parameters need to be adjusted. Through gradual adjustment and re-simulation, the jet flow separation effect is optimized, and higher separation efficiency and better chlorine concentration reduction effect are ensured to be achieved.

Preferably, step S4 specifically includes:

S41, turbulence characteristic identification is carried out on unreasonable jet separation data, so that turbulence characteristic data are obtained;

In this embodiment, analysis is required based on flow field parameters (e.g., velocity field, pressure field, etc.) obtained from the unreasonable jet separation data. By means of a turbulence model module in simulation software, a suitable turbulence model (e.g. k-epsilon model or k-omega model) is selected for calculation. Then, based on the information such as the velocity field and the vorticity in the simulation data, the turbulence characteristics of the jet region are identified by using a turbulence model. The turbulence characteristics are mainly reflected in turbulence degree of the flow, and the turbulence characteristics comprise indexes such as vorticity, turbulence intensity, turbulence energy and the like. These characteristics can be obtained by analyzing the pulsating component in the velocity field. For example, by calculating the standard deviation of the velocity components, a turbulence intensity index is obtained. All turbulence characteristics data will be generated and saved in the simulation software for subsequent analysis.

S42, performing diffusion analysis on unreasonable jet separation data based on the turbulence characteristic number to obtain turbulence diffusion data;

in this embodiment, the turbulence diffusion model is used to calculate the diffusion characteristics of the airflow in the jet separation region. Turbulent flow diffusion analysis generally relies on turbulent kinetic energy and viscosity coefficient of fluid, and kinetic energy data of a flow field is obtained through a turbulent flow model in simulation. Then, by calculating the turbulent kinetic energy gradient, a turbulent diffusion coefficient is obtained, which represents the diffusion rate of the air flow. Thereafter, the diffusion amount at each time and each position is calculated from the diffusion coefficient, thereby obtaining turbulence diffusion data. The turbulence diffusion data includes diffusion rates, concentration profiles, etc. at different locations, at different times, for determining whether turbulence has resulted in incomplete separation.

S43, carrying out intensity statistics according to the turbulence diffusion data to obtain high-intensity turbulence diffusion data;

In this embodiment, the turbulent diffusion data is statistically analyzed, and a region with a diffusion intensity higher than a certain threshold is screened out. For example, by calculating the mean and standard deviation of the turbulent diffusion rate, the threshold is set to the mean plus three times the standard deviation, and the region where the diffusion rate is greater than this threshold is screened out. These areas generally correspond to locations where turbulent diffusion is strong. By this method, a region having a large diffusion intensity can be identified, and thus marked as a high-intensity turbulent diffusion region. The high-intensity turbulence diffusion data provide basis for subsequent region optimization and model improvement.

S44, carrying out separation point position identification on the separation model according to the unreasonable jet separation data to obtain separation point position data;

In this embodiment, key points of the airflow separation, i.e., separation points, are identified by spatially analyzing flow field data (such as velocity, pressure, etc.) during the airflow separation. The separation point typically occurs at a location where the fluid velocity is suddenly changed and the pressure is greatly changed. First, by calculating the velocity gradient in the flow velocity field, a region where the flow velocity change is large is identified, and its corresponding separation point is determined. And secondly, combining pressure field data, and further confirming the accurate position of the separation point at the position with larger pressure gradient. Finally, separation point location data are obtained, which will be used for subsequent diffusion region analysis.

Step S45, determining a diffusion area according to the high-intensity turbulence diffusion data and the separation point position data, so as to obtain diffusion area data;

In this embodiment, the turbulent diffusion region is identified by performing a superposition analysis of the high-intensity turbulent diffusion region and the separation point. This process requires determining the boundaries of the diffusion region by matching and comparing the spatial coordinates in simulation software. Firstly, marking a key area for separating air flow by using separation point position data, and marking a position with stronger diffusion according to turbulence diffusion data. By means of the superposition analysis, the actual diffusion area is determined, relevant area parameters, such as the area, the position and the like of the diffusion area are recorded, and diffusion area data are generated.

Step S46, carrying out flow incidence angle adjustment on the diffusion area data to obtain flow incidence angle data;

In this embodiment, the flow incidence angle refers to the angle at which the gas flow enters the separation medium at the diffusion region. In this step, the incidence angle is optimized to improve the separation efficiency by adjusting the flow direction in the diffusion region. First, the angle of incidence of the current flow is calculated based on the geometry of the flow path and the diffusion region. Then, according to the simulation result and the requirement of separation efficiency, the incidence angle is adjusted so that the angle of the air flow is close to the optimal incidence angle. The adjustment process needs to rely on a fluid dynamic model and a geometric optimization algorithm, so that the adjusted incident angle can be ensured to effectively improve the separation effect. And finally obtaining the adjusted flow incidence angle data.

And S47, optimizing the model separation efficiency of the separation model according to the turbulence diffusion data and the flow incidence angle degree, and generating an analysis model.

In this embodiment, based on the turbulent diffusion data, the behavior of the airflow in the diffusion area during the separation process, including parameters such as flow velocity and turbulent intensity, is analyzed, and factors affecting the separation efficiency are identified. Then, the adjusted flow incidence angle data is input into a separation model, and jet parameters (such as speed, temperature, angle and the like) are adjusted through an optimization algorithm, so that the separation efficiency is improved. In the optimization process, multiple simulation results are used for correcting the optimization strategy, and finally an analysis model with higher separation efficiency is generated. The optimization process needs to be based on a plurality of simulation results and parameter combinations, so that the optimized model can achieve a better chlorine separation effect under different working conditions.

Of particular importance, step S41 comprises the steps of:

S411, extracting speed field characteristics of unreasonable jet separation data to obtain unreasonable jet separation speed fields;

In this embodiment, the velocity field features are extracted from the irrational jet separation experimental or simulation data. The jet separation process is simulated using a steady state or transient flow model using a numerical simulation software (such as ANSYS Fluent or OpenFOAM) for calculations. Through fine grid division, speed distribution data of fluid in a jet flow area are accurately acquired. The velocity field data typically includes a velocity vector for each grid point, with the velocity vector data having a distinct signature at each time step. In practice, the size and resolution of the grid should be selected based on the scale of jet separation to ensure accuracy of the velocity field data. By setting specific boundary conditions, such as fixed flow rates or flows, boundary layer conditions must be identified for each boundary during the data extraction process and flow rate values at different locations are indicated. For example, the inlet is set to a constant flow rate and the outlet is set to a pressure outlet. Finally, through the steps, the speed data of each grid node of the jet flow area are obtained, and complete speed field data are constructed.

Step S412, vortex region identification is carried out based on an unreasonable jet separation speed field, and vortex region data are obtained;

In this embodiment, discretizing the velocity field data typically uses finite difference or finite volume methods to solve for the rotation of the velocity field. In the numerical calculation, a proper discretization method is selected, so that the vortex region can be accurately identified. In the calculation of the curl, a threshold value for the curl value is set, which is typically determined by experimentation or simulation (e.g., 0.01 s-1). When the spin value exceeds the threshold, the region is considered to be a vortex region. This step requires spatial filtering in the vortex region to remove noise, ensuring the accuracy of the identified vortex region. The vortex region is identified according to the magnitude of the rotation value, and the position and the range of the vortex region can be obviously distinguished by comparing the rotation values of other regions. Finally, the obtained vortex region data comprises information such as the size, the position, the direction and the like of the vortex.

Step S413, performing turbulence structure analysis on the vortex region data so as to obtain large-scale vortex structure data;

in this embodiment, by calculating parameters such as the vorticity, strain rate, and shear stress distribution of the vortex region, the turbulence structure in the flow field can be effectively described. First, strain rate tensors and rotation tensors of the vortex region are calculated, and the morphology of the vortex (e.g., annular vortex, vortex street, etc.) can be identified from these data. And further obtaining large-scale vortex structure data by counting the characteristic size, strength, rotation direction and the like of each vortex in the vortex region. The range and intensity of the large-scale vortex are determined by adopting a turbulence structure identification method commonly used in hydrodynamics, such as a Kolmogorov scale theory. In addition, according to gradient data of a speed field, turbulence fluctuation of different scales can be separated, so that vortex information of different scales can be obtained. Specifically, a specific scale threshold (e.g., 1 mm) is set to filter out small scale vortices, focusing on large scale vortex structures. The large-scale vortex structure data output by the step contains information such as strength, position, size, shape and the like of the vortex.

Step S414, carrying out flow field stability analysis according to the large-scale vortex structure data to obtain flow field stability data;

in this embodiment, by calculating the reynolds number in the flow field, it is determined whether the flow field is in a stable or unstable state. Reynolds numbers are the main indicator of flow stability, high Reynolds numbers generally indicate that the flow has turbulent flow characteristics, and low Reynolds numbers indicate that the flow is relatively stable. In this step, a critical reynolds number is set (e.g., re >2000 indicates that the flow field enters a turbulent state), and stability of the flow field in different regions is determined by comparing the reynolds numbers of each grid cell. In the flow field stability analysis process, the stability of the flow field is estimated by solving the local flow velocity gradient and the pressure gradient by further combining large-scale vortex data. Stability data for a flow field includes stability index, turbulence intensity, strain rate of flow, etc. for each region in the flow field.

Step S415, turbulence energy calculation is carried out according to the vortex area data to obtain turbulence energy data;

In this embodiment, the speed fluctuation information of the vortex region, that is, the deviation from the average flow speed in the speed field is first extracted. By summing the squares of these velocity fluctuations, turbulent energy data is obtained. In practice, a suitable grid division is chosen to ensure accurate capture of speed fluctuations, and the grid resolution needs to be sufficiently fine (e.g., 1mm or less) to ensure capture of small turbulence fluctuations. In addition, with appropriate turbulence energy calculation formulas, the energy solution is typically performed using calculation formulas for turbulence energy, and specific calculations require time-series averaging of the velocity components to obtain steady-state turbulence energy. The turbulence energy data includes turbulence energy density within the vortex region, turbulence energy, and spatial distribution of the vortex region.

And S416, integrating the flow field stability data and the turbulence energy data to generate turbulence characteristic data.

In this embodiment, when integrating, a weighted average method or a multi-scale analysis method is used to combine the stability data and the energy data. Specifically, the appropriate weight is set according to the stability index in the stability data and the turbulence energy value in the turbulence energy data (e.g., stability index weight is 0.6, turbulence energy data weight is 0.4). And obtaining comprehensive turbulence characteristic data by weighting calculation, wherein the turbulence characteristics of different areas in the flow field are reflected. In the integration process, specific calculation formulas and parameter settings (such as weight proportion, scale standard and the like) should be adjusted according to the actual flow field condition. Finally, the obtained turbulence characteristic data comprise information such as the overall turbulence state, stability distribution, turbulence energy distribution and the like of the flow field, and provide basis for subsequent optimization design.

Of particular importance, step S47 comprises the steps of:

Step S471, carrying out turbulent diffusion region identification on the separation model according to the turbulent diffusion data to obtain turbulent diffusion region data;

In this embodiment, turbulence diffusion data is obtained for the fluid, which typically results from Computational Fluid Dynamics (CFD) modeling the turbulence behavior of the fluid using a turbulence model (e.g., k-epsilon model or Reynolds stress model). Each region in the flow field is then analyzed for turbulence diffusion coefficient and a threshold is set (e.g., turbulence diffusion coefficient greater than 0.1 m 2/s) to identify regions with significant turbulence diffusion characteristics. The turbulence diffusion coefficient may be obtained by numerical simulation or experimental data, typically calculated in CFD software by setting appropriate boundary conditions and initial conditions. Further, the grid subdivision of specific areas is chosen to ensure that turbulent diffusion conditions are accurately captured in these areas. Finally, the identified turbulent diffusion zone data will include information about the intensity, direction and extent of diffusion within each zone, which will provide a basis for subsequent operations.

Step S472, jet velocity adjustment is carried out on the turbulence diffusion area data to generate jet velocity data;

In this embodiment, the velocity profile of the jet is calculated from the velocity field data of the turbulent diffusion region. In actual operation, CFD software is used to simulate the movement of fluid under different jet conditions and adjust the velocity of the jet inlet. The jet velocity is adjusted based on flow characteristics in the turbulent flow diffusion region, such as inlet flow velocity, swirl intensity, etc., which determine the distribution range and velocity magnitude of the jet. In tuning, it is desirable to set the range of inlet flow rates (e.g., from 5 m/s to 10 m/s) and to set a threshold value based on the turbulence spread characteristics to ensure that the jet velocity is within a controllable range. The jet velocity is gradually adjusted through numerical simulation, the adjusted jet velocity distribution is matched with the change of the turbulent flow diffusion area, and jet velocity data are finally generated. These data include jet velocity, direction, acceleration, etc. characteristics and are used with the data for turbulent diffusion zones to provide a basis for further separation efficiency adjustment.

And step S473, inputting jet flow speed data and flow incidence angle degrees into the separation model, and adjusting separation efficiency parameters to generate an analysis model.

In this embodiment, the angle of incidence of the flow is obtained, which is determined by the direction and angle of the incoming flow of the fluid, typically by simulating the flow path of the fluid inside the separator. The angle of incidence is calculated using CFD models to ensure that the angle of fluid incidence is accurately reflected, and typically this angle needs to be adjusted between 0 ° and 30 ° to ensure better flow incidence conditions. Then, the jet velocity data is input into the separation model together with the angle of incidence degrees, and the separation efficiency is adjusted. The separation efficiency is fine-tuned using an optimization algorithm (e.g., particle swarm optimization algorithm or genetic algorithm) by adjusting a number of parameters in the separation model, such as flow rate, angle of incidence, turbulence intensity, etc. In this process, the parameters need to be adjusted stepwise through multiple simulations until the desired separation efficiency is obtained. In the analytical model, the parameter changes for each adjustment are recorded, including jet velocity, angle of incidence, flow velocity in the separator, vortex intensity, etc. Finally, the generated analysis model not only contains separation efficiency parameters, but also comprises the flow characteristics of fluid, jet flow influence areas and separation effects under different parameter combinations, and provides a basis for optimizing hydrodynamic parameters in the welding process of the industrial robot.

The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.

The foregoing is only a specific embodiment of the invention to enable those skilled in the art to understand or practice the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The method for constructing the analysis model for chlorine jet separation is characterized by comprising the following steps of:

Step S4, performing turbulence diffusion analysis on the unreasonable jet flow separation data to obtain turbulence diffusion data, performing diffusion area control on the separation model according to the unreasonable jet flow separation data to obtain diffusion area data, and performing model separation efficiency optimization on the separation model according to the turbulence diffusion data and the diffusion area data to generate an analysis model, wherein the step S4 specifically comprises the following steps:

and S47, optimizing the model separation efficiency of the separation model according to the turbulence diffusion data and the flow incidence angle data, and generating an analysis model.

2. The method for constructing an analytical model for chlorine jet separation according to claim 1, wherein step S1 is specifically:

3. The method for constructing an analytical model for chlorine jet separation according to claim 2, wherein step S13 is specifically:

4. The method for constructing an analytical model for chlorine jet separation according to claim 3, wherein step S134 is specifically:

5. The method for constructing an analytical model for chlorine jet separation according to claim 1, wherein step S2 is specifically:

6. The method for constructing an analytical model for chlorine jet separation according to claim 5, wherein step S23 is specifically:

7. The method for constructing an analytical model for chlorine jet separation according to claim 5, wherein step S24 is specifically:

8. The method for constructing an analytical model for chlorine jet separation according to claim 5, wherein step S25 is specifically:

step S254, acquiring amplitude threshold data;

9. The method for constructing an analytical model for chlorine jet separation according to claim 1, wherein step S3 is specifically: