Gas-liquid distributor control method and system based on chemical reaction tower

Abstract

The invention discloses a gas-liquid distributor control method and system based on a chemical reaction tower, specifically relates to the field of equipment control technology, and is used to solve the existing problem that potential hidden dangers in the operation of the gas-liquid distributor cannot be warned in advance in time; a dynamic coupling response model is used to analyze the relationship between liquid film thickness and gas permeability, a machine learning algorithm is used to predict the fatigue life of the gas-liquid distributor, and the safe use frequency of the gas-liquid distributor is dynamically adjusted according to the fatigue life and usable strength; entropy analysis is performed on the fluctuation data of the gas-liquid contact interface, disturbance is introduced and limit analysis is performed on the charge density change to evaluate the charge density fluctuation degree inside the gas-liquid distributor; a comprehensive analysis is performed on the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface and the charge density fluctuation degree inside the gas-liquid distributor, the operation risk of the gas-liquid distributor is evaluated, possible safety risks are avoided, and the safety of the chemical process is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment control, and more specifically, to a gas-liquid distributor control method and system based on a chemical reaction tower. Background Art

[0002] In the chemical industry, the gas-liquid distributor of the reaction tower plays a vital role. It is responsible for ensuring the effective contact area between gas and liquid, which directly affects the efficiency of chemical reactions and the quality of products. The current gas-liquid distributor control strategy is mostly based on static parameter adjustment, such as flow rate, temperature and pressure settings. These measures often cannot reflect the dynamic changes in the process in real time, especially when faced with complex chemical reactions and changing operating conditions.

[0003] Traditional control methods for gas-liquid distributors mainly rely on empirical rules or simple feedback adjustments to control operating parameters such as flow rate, pressure and temperature. However, these methods often ignore the key hidden dangers that affect the long-term stable operation of gas-liquid distributors, especially in high-load and changing operating environments. There is a lack of fine monitoring and prediction of these dynamic factors. If the potential hidden dangers of gas-liquid distributor operation cannot be warned in time, it will lead to reduced efficiency and unstable product quality. In severe cases, it may cause safety accidents such as overpressure or leakage, which not only affects the continuity of production, but also may cause significant economic losses and environmental pollution.

[0004] In order to solve the above problems, a technical solution is now provided. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide a gas-liquid distributor control method and system based on a chemical reaction tower to solve the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A gas-liquid distributor control method based on a chemical reaction tower comprises the following steps:

[0008] The dynamic relationship between liquid film thickness and gas penetration rate was analyzed based on the dynamic coupling response model, and the serviceability of the gas-liquid distributor was evaluated.

[0009] Collect vibration data of the gas-liquid distributor, use time series analysis and Fourier transform to extract key frequency components in the vibration data, and predict the fatigue life of the gas-liquid distributor through machine learning algorithms;

[0010] The safe use frequency of the gas-liquid distributor is determined by the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor, and the actual use frequency of the gas-liquid distributor is controlled based on the safe use frequency of the gas-liquid distributor;

[0011] Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic volatility of the gas-liquid contact interface is evaluated;

[0012] A disturbance is introduced into the operating environment of the gas-liquid distributor, and the charge density fluctuation degree inside the gas-liquid distributor is evaluated by performing limit analysis on the charge density change after the disturbance.

[0013] By comprehensively analyzing the serviceability of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor.

[0014] In a preferred embodiment, the dynamic relationship between the liquid film thickness and the gas permeability is analyzed based on the dynamic coupling response model to evaluate the usable strength of the gas-liquid distributor, specifically:

[0015] Get the liquid film thickness; Get the gas permeability;

[0016] Establish a dynamic coupled response model: Establish a set of differential equations:

[0017] ;in, Indicates at time The thickness of the liquid film, Indicates at time The gas permeability, is the liquid film attenuation coefficient, is the influence coefficient of gas on liquid film, is the gas enhancement coefficient, is the resistance coefficient of liquid film to gas;

[0018] Using numerical methods to solve the differential equations, we get and Time series forecasting;

[0019] Calculate the usage intensity index, the expression is: ;in, yes Time series forecasting, yes Time series forecasting, is the usage intensity index, is a tuning parameter.

[0020] In a preferred embodiment, the vibration data of the gas-liquid distributor is collected, the key frequency components in the vibration data are extracted using time series analysis and Fourier transform, and the fatigue life of the gas-liquid distributor is predicted using a machine learning algorithm, specifically:

[0021] Use sensors to capture vibration signals in real time;

[0022] Preprocess the original vibration data; perform time series analysis and Fourier transform on the preprocessed vibration data to extract the key frequency components of the vibration data;

[0023] Using the key frequency components of the extracted vibration data as input, a fatigue life prediction model is established by applying a machine learning algorithm; the fatigue life of the gas-liquid distributor is output based on the fatigue life prediction model.

[0024] In a preferred embodiment, the safe use frequency of the gas-liquid distributor is determined by the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor, and the actual use frequency of the gas-liquid distributor is controlled based on the safe use frequency of the gas-liquid distributor, specifically:

[0025] Determine the safe use frequency of the gas-liquid distributor, the expression is:

[0026] ;in, The safe use frequency of the gas-liquid distributor is is the fatigue life of the gas-liquid distributor, are the adjustment coefficients of fatigue life and service strength index of gas-liquid distributor, All are greater than 0;

[0027] If the actual use frequency of the gas-liquid distributor is higher than the safe use frequency, an alarm signal is immediately generated, and the actual use frequency of the gas-liquid distributor is controlled to be reduced to below the safe use frequency.

[0028] In a preferred embodiment, the microscopic volatility of the gas-liquid contact interface is evaluated based on entropy analysis of the fluctuation data of the gas-liquid contact interface, specifically:

[0029] Collect the fluctuation data of the gas-liquid contact interface; obtain The gas-liquid interface fluctuation data ;

[0030] Use K-means clustering algorithm to dynamically divide data into intervals;

[0031] The weight is calculated based on the distance from each data point to its cluster center; the weight calculation formula is: ;in, is the weight of the data point, It is a data point to its cluster center The distance is a small constant to prevent division by zero errors, It is The center point of the cluster, is the first data points;

[0032] The information entropy of the entire data set is analyzed using weighted probability, and the interface microscopic fluctuation entropy index is calculated, and its expression is: ;in, is the interface microscopic fluctuation entropy index, It is The weighted probability of an interval, is the number of clusters defined in the clustering algorithm, Used as an index in a loop or iteration.

[0033] In a preferred embodiment, a disturbance is introduced into the operating environment of the gas-liquid distributor, and the charge density fluctuation degree inside the gas-liquid distributor is evaluated by performing limit analysis on the charge density change after the disturbance, specifically:

[0034] Collect charge density data from the gas-liquid distributor of a chemical reactor;

[0035] Introducing disturbances into the operating environment of the gas-liquid distributor;

[0036] Calculate the rate of change of charge density, the expression is: ;

[0037] By analyzing the limit of the charge density change rate after disturbance, the charge disturbance influence index is calculated, and its expression is: ; is the charge disturbance influence index; Indicates at time The change in charge density is Indicated in Small time period after time The new state of the internal charge density, At the initial time The measured charge density of the state, is the time interval.

[0038] In a preferred embodiment, the operational risk of the gas-liquid distributor is evaluated by comprehensively analyzing the service strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation degree inside the gas-liquid distributor, and the gas-liquid distributor is controlled according to the operational risk of the gas-liquid distributor, specifically:

[0039] The usage intensity index, interface micro-fluctuation entropy index and charge disturbance influence index are normalized, and after the normalized usage intensity index, interface micro-fluctuation entropy index and charge disturbance influence index are assigned preset proportional coefficients respectively, the gas-liquid operation hidden danger warning coefficient is calculated;

[0040] Set the gas-liquid operation hidden danger warning threshold, and compare the gas-liquid operation hidden danger warning coefficient with the gas-liquid operation hidden danger warning threshold:

[0041] When the gas-liquid operation hidden danger warning coefficient is less than or equal to the gas-liquid operation hidden danger warning threshold, a gas-liquid operation hidden danger normal signal is generated; when the gas-liquid operation hidden danger warning coefficient is greater than the gas-liquid operation hidden danger warning threshold, a gas-liquid operation hidden danger warning signal is generated.

[0042] On the other hand, the present invention provides a gas-liquid distributor control system based on a chemical reaction tower, comprising an available strength assessment module, a fatigue life prediction module, a usage frequency control module, an interface fluctuation assessment module, a charge fluctuation assessment module and a risk warning control module;

[0043] Available strength assessment module: Analyzes the dynamic relationship between liquid film thickness and gas permeability based on the dynamic coupling response model, and assesses the available strength of the gas-liquid distributor;

[0044] Fatigue life prediction module: collects vibration data of the gas-liquid distributor, uses time series analysis and Fourier transform to extract key frequency components in the vibration data, and predicts the fatigue life of the gas-liquid distributor through machine learning algorithms;

[0045] Frequency of use control module: determines the safe frequency of use of the gas-liquid distributor through the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor, and controls the actual frequency of use of the gas-liquid distributor based on the safe frequency of use of the gas-liquid distributor;

[0046] Interface fluctuation assessment module: Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic fluctuation degree of the gas-liquid contact interface is assessed;

[0047] Charge fluctuation assessment module: introduces disturbances into the operating environment of the gas-liquid distributor, and evaluates the degree of charge density fluctuation inside the gas-liquid distributor by performing limit analysis on the charge density change after the disturbance;

[0048] Risk warning control module: Through comprehensive analysis of the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation degree inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor.

[0049] The technical effects and advantages of the gas-liquid distributor control method and system based on the chemical reaction tower of the present invention are as follows:

[0050] 1. A dynamic coupled response model is used to analyze the relationship between liquid film thickness and gas permeability, providing an accurate evaluation method for the usable strength of the gas-liquid distributor. This model can dynamically predict and adjust the operating parameters of the gas-liquid distributor by real-time monitoring of the changes in the liquid film and the gas permeability, optimize the reaction efficiency and ensure the safe operation of the equipment. At the same time, the vibration data is analyzed by combining time series analysis and Fourier transform, the key frequency components are extracted, and the fatigue life of the gas-liquid distributor is predicted using a machine learning algorithm, which not only improves the accuracy of the prediction, but also dynamically adjusts the safe use frequency of the gas-liquid distributor according to the fatigue life and usable strength. This method allows operators to significantly improve the safety and reliability of equipment while ensuring production efficiency, reduce maintenance costs and potential downtime caused by premature wear, and thus ensure the continuity and stability of chemical production.

[0051] 2. By performing entropy analysis on the fluctuation data of the gas-liquid contact interface, this scheme can carefully evaluate the microscopic volatility of the gas-liquid contact interface, providing key information for controlling and optimizing the chemical reaction process. This analysis helps reveal unstable factors at the microscopic level, which may lead to reduced efficiency or product quality problems. In addition, by introducing disturbances and performing limit analysis on the charge density change, the degree of charge density fluctuation inside the gas-liquid distributor can be accurately evaluated, thereby predicting and controlling the potential risks caused by charge fluctuations; by comprehensively analyzing the usable intensity of the gas-liquid distributor, the degree of microscopic volatility of the gas-liquid contact interface, and the degree of charge density fluctuation inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated, so that the control strategy of the gas-liquid distributor is not limited to traditional empirical adjustments, but is based on real-time data and advanced analysis, achieving precise adaptation to complex chemical reaction environments. In this way, operators can adjust operating conditions in a timely manner to avoid possible safety risks such as overpressure and material leakage, significantly improving the safety and environmental friendliness of chemical processes. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a schematic diagram of a gas-liquid distributor control method based on a chemical reaction tower of the present invention;

[0053] Figure 2 It is a structural schematic diagram of a gas-liquid distributor control system based on a chemical reaction tower according to the present invention. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] Example 1

[0056] Figure 1 The present invention provides a gas-liquid distributor control method based on a chemical reaction tower, which comprises the following steps:

[0057] The dynamic relationship between liquid film thickness and gas permeability is analyzed based on the dynamic coupling response model, and the usable strength of the gas-liquid distributor is evaluated.

[0058] The vibration data of the gas-liquid distributor is collected, and the key frequency components in the vibration data are extracted using time series analysis and Fourier transform. The fatigue life of the gas-liquid distributor is predicted using a machine learning algorithm.

[0059] The safe frequency of use of the gas-liquid distributor is determined by the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor, and the actual frequency of use of the gas-liquid distributor is controlled based on the safe frequency of use of the gas-liquid distributor.

[0060] Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic volatility of the gas-liquid contact interface is evaluated.

[0061] A disturbance is introduced into the operating environment of the gas-liquid distributor, and the charge density fluctuation degree inside the gas-liquid distributor is evaluated by performing limit analysis on the charge density change after the disturbance.

[0062] By comprehensively analyzing the serviceability of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor.

[0063] The dynamic relationship between liquid film thickness and gas permeability is analyzed based on the dynamic coupling response model, and the serviceability of the gas-liquid distributor is evaluated, specifically:

[0064] Get the thickness of the liquid film. The thickness of the liquid film is expressed in time The thickness of the liquid coverage in the gas-liquid distributor at any moment is a key operating variable in the gas-liquid distributor, which affects the contact area between gas and liquid and the mass transfer efficiency.

[0065] Get the gas permeability, which is expressed in time The ability of gas to pass through the liquid film at any time reflects the effectiveness of the gas-liquid distributor.

[0066] Liquid film thickness is usually measured in real time by laser displacement sensors or capacitive sensors. These sensors can accurately monitor the thickness of the liquid layer and output real-time data for use in the model. Gas penetration can be measured by flow meters or pressure sensors. These devices can monitor the rate or pressure difference of gas penetration in the liquid film and provide necessary input data for the model.

[0067] Establishing a dynamic coupling response model: The dynamic coupling response model can be expressed as establishing a set of differential equations to achieve: ;in, Indicates at time The thickness of the liquid film, Indicates at time The gas permeability, is the liquid film attenuation coefficient, is the influence coefficient of gas on liquid film, is the gas enhancement coefficient, is the resistance coefficient of liquid film to gas.

[0068] Among them, the liquid film attenuation coefficient is a parameter used to describe the natural attenuation rate of the liquid film. It is related to the physical and chemical properties of the liquid film and is usually determined experimentally, involving observing the natural reduction of the liquid film thickness under specific operating conditions and then obtaining it through data fitting.

[0069] The gas influence coefficient on the liquid film indicates the intensity of the effect of gas flow on the liquid film thickness. It is obtained by controlling the experiment, changing the gas flow rate or type, observing the change in liquid film thickness, and fitting from the experimental data using statistical or machine learning methods.

[0070] The gas enhancement factor describes the rate at which gas permeability increases over time. It is measured experimentally by recording the increase in gas permeability under controlled experimental conditions, and performing data analysis and parameter fitting.

[0071] The gas barrier coefficient of the liquid film represents the barrier effect of the liquid film thickness on the gas permeability. In the experiment, the thickness of the liquid film is adjusted, the change in gas permeability is observed, and the coefficient is determined by regression analysis and other methods.

[0072] Solving the above differential equations using numerical methods (such as the Runge-Kutta method) yields and Time series prediction can be used to dynamically track the changes in liquid film thickness and gas permeability.

[0073] Among them, the Runge-Kutta method is relatively mature in the prior art, and the specific steps of using the Runge-Kutta method to solve the differential equation system will not be repeated here.

[0074] The service strength index is calculated to evaluate the service strength of the gas-liquid distributor. The service strength index is calculated based on the stability of the liquid film thickness and the gas penetration rate. Its expression is: ;in, yes Time series forecasting, yes Time series forecasting, is the usage intensity index, is a tuning parameter (used to adjust the sensitivity of the response curve).

[0075] The smaller the usage intensity index is, the lower the usable intensity of the gas-liquid distributor is. In this case, the greater the degree to which the liquid film thickness is less than the gas permeability, the more likely it is that the liquid film is too thin to effectively block the passage of gas. This may indicate that the gas has a strong ability to penetrate the liquid film, the liquid film is thin, or the gas flow rate is high. This situation may indicate that the gas-liquid distributor is at risk of overload or damage, resulting in lower operational stability and possible efficiency problems.

[0076] In chemical reaction towers, the performance of gas-liquid distributors is crucial because it directly affects the efficiency of chemical reactions and product quality. Liquid film thickness and gas permeability are key parameters that determine the effectiveness of gas-liquid contact. Real-time analysis and prediction of liquid film thickness and gas permeability through dynamic coupled response models can provide a deeper understanding of the working status of gas-liquid distributors. This approach enables operators to monitor the key performance indicators of the equipment in real time and make adjustments accordingly to maintain or improve production efficiency and product quality. By combining mathematical models with experimental data, the operating conditions of the reaction tower can be precisely adjusted to adapt to changing industrial needs and environmental conditions, thereby maximizing resource utilization efficiency and reducing energy consumption and raw material waste.

[0077] The analysis method based on the dynamic coupled response model can predict and optimize the performance of the gas-liquid distributor and reduce production interruptions caused by equipment failure or inefficiency. By dynamically tracking the liquid film thickness and gas penetration rate, operators can promptly identify potential problems, such as too thin liquid film or too high gas penetration, which may lead to reduced processing efficiency or substandard product quality. In addition, the data support provided by this method makes maintenance planning more scientific, from preventive maintenance to condition-based maintenance, which can not only extend equipment life, but also significantly reduce operating costs.

[0078] The vibration data of the gas-liquid distributor is collected, and the key frequency components in the vibration data are extracted using time series analysis and Fourier transform. The fatigue life of the gas-liquid distributor is predicted using a machine learning algorithm. Specifically:

[0079] Highly sensitive accelerometers or vibration sensors are fixed on key structural components of the gas-liquid distributor. These sensors can capture vibration signals caused by mechanical operation, fluid dynamic effects and other external factors in real time.

[0080] The electrical signal directly output by the sensor is converted into a digital signal, usually sampled and digitized by an analog-to-digital converter (ADC).

[0081] The collected raw vibration data needs to be preprocessed, including denoising, normalization and filtering. Denoising can use low-pass filters or wavelet denoising technology to remove non-correlated high-frequency noise.

[0082] Fourier transform is a technique that converts a time or space signal into a frequency or wave number signal. For vibration data, Fourier transform can reveal the various frequency components contained in the data, which is very helpful in understanding the source and nature of the vibration.

[0083] Fast Fourier Transform (FFT) is a computationally very efficient Fourier transform algorithm that can quickly extract frequency components from time series data.

[0084] Time series analysis not only focuses on the data at a single point in time, but also considers the overall trend and periodic changes of the data over time. This is very important for predicting the future behavior of equipment and possible failure trends. Time series analysis includes methods such as autoregressive models, moving average models, and seasonal decomposition, which are common techniques for analyzing and predicting time series data.

[0085] The preprocessed vibration data is subjected to time series analysis and Fourier transform to analyze the vibration signal in the frequency domain and extract the key frequency components of the vibration data.

[0086] Using the key frequency components of the extracted vibration data as input, a machine learning algorithm (such as support vector machine (SVM)) is applied to establish a fatigue life prediction model.

[0087] For example, the fatigue life prediction model can be expressed as: ; Among them, the features include key frequency components extracted from the vibration signal, and the parameters are the hyperparameters of the SVM model, including the kernel function type, regularization parameters, etc.

[0088] The SVM model was trained using historical vibration data and known fatigue failure records, and the model parameters were optimized through cross-validation to ensure that the model had high accuracy and generalization ability.

[0089] The fatigue life of the gas-liquid distributor is predicted in real time using the trained machine learning model. The fatigue life of the gas-liquid distributor is output based on the fatigue life prediction model, and the fatigue life of the gas-liquid distributor is marked as .

[0090] When discussing gas-liquid distributors, the ideal situation is to hope that the fatigue life is as long as possible. Long fatigue life means that the equipment can operate stably for a longer period of time, thereby reducing maintenance costs, extending maintenance cycles and reducing the frequency of equipment replacement, all of which help to improve production efficiency and reduce overall operating costs.

[0091] This approach allows real-time monitoring of the health of equipment, early detection of potential failures, and timely maintenance and repairs to avoid production interruptions and safety accidents caused by sudden equipment failures. This approach improves equipment reliability and availability, reduces maintenance costs, and improves the efficiency of the entire production system. Through detailed data analysis and machine learning models, we can deeply understand the causes and nature of equipment failures, optimize equipment design and operating conditions, and achieve preventive maintenance, thereby reducing unplanned downtime and improving production efficiency.

[0092] The safe use frequency of the gas-liquid distributor is determined by the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor. The actual use frequency of the gas-liquid distributor is controlled based on the safe use frequency of the gas-liquid distributor, specifically:

[0093] When the fatigue life of the gas-liquid distributor is long, it indicates that the material and structure of the gas-liquid distributor are still in good condition and can withstand normal or slightly higher than normal operating pressure and frequency; when it is short, it is necessary to reduce the operating frequency or operating intensity to avoid accelerated equipment loss and potential failure risks.

[0094] The lower the service intensity of the gas-liquid distributor, the greater the risk of overload or damage to the gas-liquid distributor, which may increase the rate of equipment fatigue and loss, thereby requiring a reduction in the frequency of use to extend the life of the equipment.

[0095] The safe use frequency of the gas-liquid distributor can be determined by establishing the following formula: ;in, The safe use frequency of the gas-liquid distributor is are the adjustment coefficients of fatigue life and service strength index of gas-liquid distributor, Both are greater than 0.

[0096] The smaller the safe use frequency, the more important it is to control the operating load of the gas-liquid distributor and prevent it from being overloaded.

[0097] In the actual operation of the gas-liquid distributor, the control system must ensure that the frequency of use of the gas-liquid distributor is not higher than the safe frequency of use; if the actual frequency of use of the gas-liquid distributor is higher than the safe frequency of use, an alarm signal is immediately generated, including:

[0098] Visual alarms: such as red warning lights in the control room.

[0099] Audio alarm: Sounds to alert operators.

[0100] System notification: Notify key personnel via email or SMS.

[0101] And control the actual use frequency of the gas-liquid distributor to be lower than the safe use frequency.

[0102] By determining the safe frequency of use of the gas-liquid distributor, the operational safety and efficiency of the gas-liquid distributor can be significantly improved. Specifically, this method can prevent the gas-liquid distributor from operating under excessive load, thereby reducing the failure rate of the gas-liquid distributor caused by overload and extending the service life of the gas-liquid distributor. At the same time, by real-time monitoring and adjusting the operating frequency, the operation team can promptly detect potential problems and perform preventive maintenance, which not only reduces downtime but also optimizes production efficiency. In addition, the setting of the safe frequency of use also helps the maintenance team better plan maintenance work and ensure that the gas-liquid distributor operates in the best condition, thereby improving the stability and reliability of the entire production line. Overall, this strategy brings enterprises the comprehensive benefits of reducing maintenance costs, improving production efficiency and enhancing the safety of gas-liquid distributors.

[0103] Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic fluctuation degree of the gas-liquid contact interface is evaluated, specifically:

[0104] Collect fluctuation data about the gas-liquid contact interface from the gas-liquid distributor of the chemical reaction tower. These data can be captured by high-precision pressure sensors or high-speed cameras.

[0105] Get in time The gas-liquid interface fluctuation data , at time The gas-liquid interface fluctuation data It can be pressure change data or image data of interface morphology changes.

[0106] Perform necessary preprocessing on the collected data, including data cleaning, denoising and preliminary analysis, to prepare for subsequent complex data processing.

[0107] Preprocessing operations include:

[0108] Denoising: Use a digital filter (such as a low-pass filter) to remove high-frequency noise from the recorded data.

[0109] Data normalization: Normalize the data to a uniform scale to eliminate the impact of dimension, especially for data from different sensors.

[0110] The K-means clustering algorithm is used to dynamically divide the data into intervals to more flexibly adapt to the actual distribution of the data.

[0111] In order to improve the sensitivity and responsiveness of information entropy, the weight is calculated according to the distance from each data point to its cluster center, and then the weighted probability is calculated.

[0112] The weight calculation formula is: ;in, is the weight of the data point; It is a data point to its cluster center The distance (Euclidean distance); is a small constant (usually a small positive value) to prevent division by zero errors; It is The center point of a cluster represents the mean position of all data points in the cluster; is the first data points, which represent the Observed values ​​of gas-liquid interface fluctuations.

[0113] The weighted probability is used to analyze the information entropy of the entire data set, and the interface micro-fluctuation entropy index is calculated. The interface micro-fluctuation entropy index reflects the complexity and uncertainty of interface fluctuations.

[0114] The expression of interface microscopic fluctuation entropy index is: ;in, is the interface microscopic fluctuation entropy index; It is The weighted probability of the interval; is the number of clusters defined in the clustering algorithm, that is, the data set is divided into different groups; Used as an index in a loop or iteration to indicate the element number of the current operation.

[0115] The larger the interface micro-fluctuation entropy index is, the higher the micro-volatility of the gas-liquid contact interface is. The higher the micro-volatility of the gas-liquid contact interface is, the greater the safety hazards and risks of the gas-liquid distributor in operation. High volatility may lead to uneven gas-liquid mixing, affecting the efficiency and uniformity of the chemical reaction, which may lead to incomplete reaction or the production of unexpected by-products. In addition, excessive fluctuations may aggravate the mechanical wear of the equipment and increase the probability of equipment failure, especially in high-pressure or high-temperature operating environments. Increased fluctuations may lead to serious safety accidents such as seal failure and pipeline rupture. Therefore, monitoring and controlling the volatility of the gas-liquid contact interface is crucial to ensure the safe and stable operation of the chemical process.

[0116] A disturbance is introduced into the operating environment of the gas-liquid distributor, and the charge density fluctuation degree inside the gas-liquid distributor is evaluated by performing limit analysis on the charge density change after the disturbance. Specifically:

[0117] Collect charge density data from the gas-liquid distributor of a chemical reaction tower. These data can be obtained in real time using charge sensors installed at key locations.

[0118] Introduce disturbances to the operating environment of the gas-liquid distributor to simulate changes in the environment or operating parameters and observe how the system responds to these changes.

[0119] Perturbation methods: Perturbations can be introduced by fine-tuning parameters in the chemical process such as temperature, pressure or flow rate.

[0120] Data recording: Record the change in charge density after the disturbance is introduced.

[0121] The rate of change of charge density is calculated using the initial and perturbed values ​​of the charge density.

[0122] The expression of charge density change rate is: .

[0123] By analyzing the limit of the charge density change rate after disturbance, the charge disturbance influence index is calculated, and its expression is: ; is the charge disturbance influence index; Indicates at time The change in charge density, in the formula of the charge perturbation influence index, Used to indicate a specific moment The new state of the charge density state after being affected by a small change; Indicated in Small time period after time The new state of the charge density inside the body, which is a measure of the charge density after the disturbance. This parameter is used to observe the Add a small time interval The behavior of the post-system helps evaluate the dynamic changes of the system over time; At the initial time The state of the measured charge density; is the time interval, indicating the time from arrive The time difference between.

[0124] The larger the charge disturbance impact index is, the greater the charge density fluctuation inside the gas-liquid distributor is. This fluctuation may lead to uneven gas-liquid contact, thus affecting the efficiency of the chemical reaction and product quality. Large fluctuations may cause more frequent changes in pressure and flow rate, thus affecting the overall stability of the equipment. In some cases, excessive fluctuations may even lead to safety risks, such as volatilization or leakage of materials, especially when dealing with highly active or volatile compounds.

[0125] By monitoring the charge density in the gas-liquid distributor of the chemical reaction tower in real time and introducing controllable disturbances to evaluate the system's response to changes, the charge density fluctuation degree of the gas-liquid contact interface can be accurately evaluated. By combining real-time data monitoring with dynamic feedback analysis, the subtle changes in charge density can be captured immediately when the operating environment changes. By conducting in-depth analysis of these changes and calculating the charge disturbance impact index, the size of the charge fluctuation inside the gas-liquid distributor can be intuitively reflected, and then its impact on the stability of the chemical process can be evaluated. It can help operators to promptly identify potential risks caused by charge fluctuations, such as incomplete reactions or the production of undesirable by-products, to ensure the efficiency of chemical reactions and product quality. Secondly, by adjusting the reaction conditions in real time to adapt to changes in charge density, the reaction process can be optimized, energy and raw material waste can be reduced, and production efficiency can be improved. Most importantly, this solution enhances the safety of the equipment, because excessive fluctuations can lead to increased structural stress, and potential safety hazards such as leakage and material volatilization can be controlled and prevented in a timely manner.

[0126] Through comprehensive analysis of the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor, specifically:

[0127] The usage intensity index, interface micro-fluctuation entropy index and charge disturbance influence index are normalized, and after assigning preset proportional coefficients to the normalized usage intensity index, interface micro-fluctuation entropy index and charge disturbance influence index respectively, the gas-liquid operation hidden danger warning coefficient is calculated.

[0128] The specific implementation method of calculating the gas-liquid operation hidden danger warning coefficient is not specifically limited here. Any method that can calculate the gas-liquid operation hidden danger warning coefficient by assigning the normalized usage intensity index, the interface microscopic fluctuation entropy index, and the charge disturbance influence index to preset proportional coefficients respectively is acceptable. In order to implement the technical solution of the present invention, the present invention provides a specific method for calculating the gas-liquid operation hidden danger warning coefficient, and its expression is:

[0129] ;in, is the gas-liquid operation hidden danger warning coefficient, are the preset proportional coefficients of the usage intensity index, interface microscopic fluctuation entropy index, and charge perturbation influence index, respectively, and Both are greater than 0.

[0130] From the expression of the gas-liquid operation hidden danger warning coefficient, it can be seen that the larger the gas-liquid operation hidden danger warning coefficient is, the higher the operation risk of the gas-liquid distributor is.

[0131] Set the gas-liquid operation hidden danger warning threshold, and compare the gas-liquid operation hidden danger warning coefficient with the gas-liquid operation hidden danger warning threshold:

[0132] When the gas-liquid operation hidden danger warning coefficient is less than or equal to the gas-liquid operation hidden danger warning threshold, a gas-liquid operation hidden danger normal signal is generated, indicating that the gas-liquid distributor is currently operating in a safe and stable state with a low risk level. In this state, the current operating parameters can be maintained and no immediate adjustment is required. However, all key indicators should continue to be monitored in real time to ensure continued stability and safety.

[0133] When the gas-liquid operation hidden danger warning coefficient is greater than the gas-liquid operation hidden danger warning threshold, a gas-liquid operation hidden danger warning signal is generated, which indicates that the gas-liquid distributor may face higher operation risks, including possible excessive fluctuations, charge instability or excessive usage intensity.

[0134] When receiving a warning signal of a potential gas and liquid operation hazard, you should immediately assess which parameter is out of the normal range and take prompt measures to adjust the corresponding operating parameters, such as reducing flow rate, adjusting pressure or temperature, to reduce potential risks. In addition, a detailed system inspection or maintenance may be required to identify and resolve the root cause of the alarm, which may include the following control strategies:

[0135] Data review: First, quickly check and analyze the specific parameters that trigger the warning signal, such as the usage intensity index, interface micro-fluctuation entropy index, and charge disturbance impact index. Determine which one or which indicators exceed the safety threshold.

[0136] Check sensor and data integrity: Confirm the accuracy of the monitored data and whether the sensors are functioning properly to avoid false warning signals leading to unnecessary operations.

[0137] Reduce operating intensity: If the usage intensity index is too high, it may be necessary to reduce the operating pressure or flow rate of the gas-liquid distributor to reduce the burden on the equipment and prevent excessive wear or damage.

[0138] Adjust temperature and pressure: According to the specific analysis results of charge density and interface fluctuations, the reaction temperature and pressure are appropriately adjusted to optimize the gas-liquid contact efficiency and reduce unstable fluctuations.

[0139] Increase monitoring frequency: In the early warning state, increase the frequency of key parameter monitoring to capture any further fluctuations or anomalies in real time.

[0140] Use advanced diagnostic tools: Leverage online analytical tools and algorithms, such as machine learning models, to predict and diagnose possible causes of problems and adjust operational strategies in a timely manner.

[0141] Among them, the setting of the warning threshold for gas-liquid operation hidden dangers is based on historical data analysis, risk assessment and safety standards. By analyzing past accident records, equipment failure cases and normal operation data, the critical indicator level that causes system instability or failure can be determined. Combined with the safety standards and engineering experience of chemical processes, a threshold is formulated that can reflect the indicator level before the potential risk occurs. This threshold needs to be adjusted periodically based on new operating data and system upgrades to ensure that it reflects the latest operating conditions and technical standards.

[0142] The setting of the preset proportionality factor involves the evaluation of the importance and influence of each risk indicator. This requires comprehensive consideration of the degree of influence of each indicator on the performance and safety of the gas-liquid distributor. The weight of each indicator is determined through expert consultation, technical analysis and simulation experiments. Indicators with higher weights have greater influence in calculating the early warning coefficient of gas-liquid operation hazards. In addition, the weight setting should also consider the mutual influence and potential synergistic effect between the indicators. The final proportionality factor should ensure the sensitivity and accuracy of the early warning system, and be able to promptly reflect any abnormal conditions in the operation, prompting the necessary preventive or corrective measures.

[0143] By comprehensively analyzing the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the internal charge density fluctuation, and implementing an early warning system for operational hazards, the operational safety and efficiency of the chemical reaction tower have been significantly improved. It can monitor key indicators in real time and quickly respond to potential risks through preset thresholds and proportional coefficients, allowing timely adjustment of operating parameters to prevent or mitigate failures and accidents. This active monitoring and early warning mechanism not only enhances the reliability of the equipment, but also optimizes the production process, ensures product quality, and reduces maintenance costs and potential environmental impacts.

[0144] Example 2

[0145] The difference between Example 2 of the present invention and Example 1 is that this example introduces a gas-liquid distributor control system based on a chemical reaction tower.

[0146] Figure 2A structural schematic diagram of a gas-liquid distributor control system based on a chemical reaction tower of the present invention is given. The gas-liquid distributor control system based on a chemical reaction tower includes an available strength assessment module, a fatigue life prediction module, a usage frequency control module, an interface fluctuation assessment module, a charge fluctuation assessment module and a risk warning control module.

[0147] Available strength assessment module: Analyzes the dynamic relationship between liquid film thickness and gas permeability based on the dynamic coupling response model, and assesses the available strength of the gas-liquid distributor.

[0148] Fatigue life prediction module: collects vibration data of the gas-liquid distributor, uses time series analysis and Fourier transform to extract key frequency components in the vibration data, and predicts the fatigue life of the gas-liquid distributor through machine learning algorithms.

[0149] Frequency of use control module: Determine the safe frequency of use of the gas-liquid distributor through the fatigue life of the gas-liquid distributor and the usable strength of the gas-liquid distributor, and control the actual frequency of use of the gas-liquid distributor based on the safe frequency of use of the gas-liquid distributor.

[0150] Interface fluctuation assessment module: Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic volatility of the gas-liquid contact interface is assessed.

[0151] Charge fluctuation assessment module: Introduce disturbances into the operating environment of the gas-liquid distributor, and evaluate the degree of charge density fluctuation inside the gas-liquid distributor by performing limit analysis on the charge density change after the disturbance.

[0152] Risk warning control module: Through comprehensive analysis of the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation degree inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor.

[0153] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0154] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0155] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0156] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0157] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0158] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0160] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0161] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0162] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A gas-liquid distributor control method based on a chemical reaction tower, characterized in that: The steps include: The dynamic relationship between liquid film thickness and gas penetration rate was analyzed based on the dynamic coupling response model, and the serviceability of the gas-liquid distributor was evaluated. Collect vibration data of the gas-liquid distributor, use time series analysis and Fourier transform to extract key frequency components in the vibration data, and predict the fatigue life of the gas-liquid distributor through machine learning algorithms; The safe use frequency of the gas-liquid distributor is determined by the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor, and the actual use frequency of the gas-liquid distributor is controlled based on the safe use frequency of the gas-liquid distributor; Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic volatility of the gas-liquid contact interface is evaluated; A disturbance is introduced into the operating environment of the gas-liquid distributor, and the charge density fluctuation degree inside the gas-liquid distributor is evaluated by performing limit analysis on the charge density change after the disturbance. By comprehensively analyzing the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor. The dynamic relationship between liquid film thickness and gas permeability is analyzed based on the dynamic coupling response model, and the serviceability of the gas-liquid distributor is evaluated, specifically: Get the liquid film thickness; Get the gas permeability; Establish a dynamic coupled response model: Establish a set of differential equations: ;in, Indicates at time The thickness of the liquid film, Indicates at time The gas permeability, is the liquid film attenuation coefficient, is the influence coefficient of gas on liquid film, is the gas enhancement coefficient, is the resistance coefficient of liquid film to gas; Using numerical methods to solve the differential equations, we get and Time series forecasting; Calculate the usage intensity index, the expression is: ;in, yes Time series forecasting, yes Time series forecasting, is the usage intensity index, is the adjustment parameter; Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic fluctuation degree of the gas-liquid contact interface is evaluated, specifically: Collect the fluctuation data of the gas-liquid contact interface; obtain The gas-liquid interface fluctuation data ; Use K-means clustering algorithm to dynamically divide data into intervals; The weight is calculated based on the distance from each data point to its cluster center; the weight calculation formula is: ;in, is the weight of the data point, It is a data point to its cluster center The distance is a small constant to prevent division by zero errors, It is The center point of the cluster, is the first data points; The information entropy of the entire data set is analyzed using weighted probability, and the interface microscopic fluctuation entropy index is calculated, and its expression is: ;in, is the interface microscopic fluctuation entropy index, It is The weighted probability of an interval, is the number of clusters defined in the clustering algorithm, Used as an index in loops or iterations; A disturbance is introduced into the operating environment of the gas-liquid distributor, and the charge density fluctuation degree inside the gas-liquid distributor is evaluated by performing limit analysis on the charge density change after the disturbance. Specifically: Collect charge density data from the gas-liquid distributor of a chemical reactor; Introducing disturbances into the operating environment of the gas-liquid distributor; Calculate the rate of change of charge density, the expression is: ; By analyzing the limit of the charge density change rate after disturbance, the charge disturbance influence index is calculated, and its expression is: ; is the charge disturbance influence index; Indicates at time The change in charge density is Indicated in Small time period after time The new state of the internal charge density, At the initial time The measured charge density of the state, is the time interval; Through comprehensive analysis of the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor, specifically: The usage intensity index, interface micro-fluctuation entropy index and charge disturbance influence index are normalized, and after the normalized usage intensity index, interface micro-fluctuation entropy index and charge disturbance influence index are assigned preset proportional coefficients respectively, the gas-liquid operation hidden danger warning coefficient is calculated; Set the gas-liquid operation hidden danger warning threshold, and compare the gas-liquid operation hidden danger warning coefficient with the gas-liquid operation hidden danger warning threshold: When the gas-liquid operation hidden danger warning coefficient is less than or equal to the gas-liquid operation hidden danger warning threshold, a gas-liquid operation hidden danger normal signal is generated; when the gas-liquid operation hidden danger warning coefficient is greater than the gas-liquid operation hidden danger warning threshold, a gas-liquid operation hidden danger warning signal is generated; Time series analysis and Fourier transform are used to extract key frequency components in vibration data, and machine learning algorithms are used to predict the fatigue life of the gas-liquid distributor. Specifically: Use sensors to capture vibration signals in real time; Preprocess the original vibration data; perform time series analysis and Fourier transform on the preprocessed vibration data to extract the key frequency components of the vibration data; Using the key frequency components of the extracted vibration data as input, a fatigue life prediction model is established by applying a machine learning algorithm; the fatigue life of the gas-liquid distributor is output based on the fatigue life prediction model; The safe use frequency of the gas-liquid distributor is determined by the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor. The actual use frequency of the gas-liquid distributor is controlled based on the safe use frequency of the gas-liquid distributor, specifically: Determine the safe use frequency of the gas-liquid distributor, the expression is: ;in, The safe use frequency of the gas-liquid distributor is is the fatigue life of the gas-liquid distributor, are the adjustment coefficients of fatigue life and service strength index of gas-liquid distributor, All are greater than 0; If the actual use frequency of the gas-liquid distributor is higher than the safe use frequency, an alarm signal is immediately generated, and the actual use frequency of the gas-liquid distributor is controlled to be reduced to below the safe use frequency.

2. A gas-liquid distributor control system based on a chemical reaction tower, used to implement the gas-liquid distributor control method based on a chemical reaction tower according to claim 1, characterized in that: It includes available strength assessment module, fatigue life prediction module, usage frequency control module, interface fluctuation assessment module, charge fluctuation assessment module and risk warning control module; Available strength assessment module: Analyzes the dynamic relationship between liquid film thickness and gas permeability based on the dynamic coupling response model, and assesses the available strength of the gas-liquid distributor; Fatigue life prediction module: collects vibration data of the gas-liquid distributor, uses time series analysis and Fourier transform to extract key frequency components in the vibration data, and predicts the fatigue life of the gas-liquid distributor through machine learning algorithms; Frequency of use control module: determines the safe frequency of use of the gas-liquid distributor through the fatigue life of the gas-liquid distributor and the serviceable strength of the gas-liquid distributor, and controls the actual frequency of use of the gas-liquid distributor based on the safe frequency of use of the gas-liquid distributor; Interface fluctuation assessment module: Based on the entropy analysis of the fluctuation data of the gas-liquid contact interface, the microscopic fluctuation degree of the gas-liquid contact interface is assessed; Charge fluctuation assessment module: introduces disturbances into the operating environment of the gas-liquid distributor, and evaluates the degree of charge density fluctuation inside the gas-liquid distributor by performing limit analysis on the charge density change after the disturbance; Risk warning control module: Through comprehensive analysis of the usable strength of the gas-liquid distributor, the microscopic volatility of the gas-liquid contact interface, and the charge density fluctuation degree inside the gas-liquid distributor, the operating risk of the gas-liquid distributor is evaluated and the gas-liquid distributor is controlled according to the operating risk of the gas-liquid distributor.

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