A baghouse dust collection process for zinc oxide preparation

Abstract

This invention relates to the field of baghouse dust collection technology and discloses a baghouse dust collection process for zinc oxide preparation. Addressing the problems of difficult dynamic coupling of multiple parameters, control lag, and insufficient system stability in traditional processes, this solution achieves optimization through the following steps: real-time acquisition of baghouse status, gas status, environmental parameters, and system data, namely pressure difference and filtration velocity; construction of baghouse status evaluation models, gas status evaluation models, and environmental status evaluation models; and construction of a system pressure difference-filtration velocity matching model. An exponential function is used to dynamically correct the deviation between pressure difference and velocity, and a hyperbolic tangent function is used to smoothly adjust the target air intake to avoid airflow impact. This achieves real-time response of multi-parameter coupling, reduces pressure difference fluctuations, extends bag life, and significantly improves the system robustness in zinc oxide production.

Description

Technical Field

[0001] This invention belongs to the field of baghouse dust collection technology, and particularly relates to a baghouse dust collection process for zinc oxide preparation. Background Technology

[0002] In the zinc oxide production process, the purification of high-temperature dust-laden exhaust gas (mainly containing fine zinc oxide dust) is a crucial step in ensuring environmental compliance and production continuity. Baghouse dust collection technology is widely used in this field due to its high efficiency in capturing micron-sized dust. However, traditional baghouse dust collection processes have significant drawbacks:

[0003] Multi-parameter dynamic coupling problem: Zinc oxide dust has a fine particle size, high exhaust gas temperature, and large humidity fluctuations in the production environment. The performance of the filter bag (such as resistance and fiber shrinkage rate), gas state (concentration and particle size distribution), and environmental factors (temperature and humidity) interact with each other, making it difficult for traditional static control models to adapt to dynamic operating conditions in real time.

[0004] Insufficient system stability: The resistance of the filter bags increases over time, leading to an imbalance between the system pressure difference and the filtration velocity. Existing technologies rely on a fixed threshold to adjust the air intake, which can easily cause filter bag clogging or penetration, reducing dust collection efficiency and shortening the filter bag's lifespan.

[0005] Model isolation limitation: Existing methods mostly monitor the bag condition or gas parameters independently, without establishing a multi-factor collaborative evaluation system. For example, they do not quantify the accelerating effect of ambient humidity on bag fiber shrinkage, or the nonlinear impact of sudden changes in dust concentration on the pressure difference-wind speed relationship, leading to control lag.

[0006] Therefore, there is an urgent need to develop a baghouse dust collection process that can integrate multi-source data such as bag status, gas characteristics, environmental parameters and system pressure difference, and achieve dynamic collaborative optimization, so as to improve dust collection efficiency and system robustness in zinc oxide production. Summary of the Invention

[0007] The purpose of this invention is to provide a baghouse dust collection process for zinc oxide preparation, aiming to solve the above-mentioned problems.

[0008] This invention is implemented as follows: a baghouse dust collection process for zinc oxide preparation includes the following steps:

[0009] S1: Acquire bag status data, gas status data, environmental status data, system differential pressure data, and filtration velocity data;

[0010] S2: Construct a bag condition assessment model based on bag condition data and output bag condition evaluation coefficients;

[0011] S3: Construct a gas state assessment model based on gas state data and output gas state evaluation coefficients;

[0012] S4: Construct an environmental status assessment model based on environmental status data and output environmental status evaluation coefficients;

[0013] S5: Under the current environmental state evaluation coefficient, construct a dynamic adaptation model of gas and cloth based on the bag state evaluation coefficient and the gas state evaluation coefficient, and output the dynamic adaptation coefficient of gas and cloth.

[0014] S6: Under the current air distribution dynamic adaptation coefficient, construct a system pressure difference-filtration velocity matching model based on system pressure difference and filtration velocity, and output the system pressure difference-filtration velocity matching coefficient.

[0015] S7: Based on the system pressure difference-filtration velocity matching coefficient and the current air intake, construct an air intake adjustment model, output the target air intake, and adjust the current air intake to the target air intake.

[0016] In a further technical solution, the bag status data includes the bag resistance coefficient, bag fiber shrinkage rate, and bag seam strength attenuation rate; the gas status data includes exhaust gas concentration and dust particle size; and the environmental status data includes temperature and humidity.

[0017] A further technical solution is that the intake volume adjustment model is as follows:

[0018]

[0019] in, For the target air intake volume, This is the current intake volume. To adjust the amplitude, To adjust sensitivity, This is the system pressure difference - filtration velocity matching coefficient. This is the optimal matching threshold.

[0020] A further technical solution is that the system pressure difference-filtration velocity matching model is as follows:

[0021]

[0022] in, This is the system pressure difference - filtration velocity matching coefficient. Sensitivity coefficient This refers to the dynamic adaptation coefficient of the air-to-cloth system. For system pressure difference, To design the differential pressure reference value, To filter airflow velocity, This serves as the design wind speed benchmark.

[0023] A further technical solution is that the air-cloth dynamic adaptation model is as follows:

[0024]

[0025] in, This refers to the dynamic adaptation coefficient of the air-to-cloth system. This is the environmental status evaluation coefficient. This is the evaluation coefficient for the condition of the fabric bag. Here, α is the gas state evaluation coefficient, and α is the bag filter state weighting factor, which is greater than or equal to 0 and less than or equal to 1. This is the gas state weighting factor.

[0026] A further technical solution is that the bag condition assessment model is as follows:

[0027]

[0028] in, This is the evaluation coefficient for the condition of the fabric bag. This is the bag resistance weighting coefficient. This is the weighting coefficient for the shrinkage rate of the fabric fiber. This is the weighting coefficient for the attenuation rate of the fabric bag's seam strength. The resistance coefficient of the filter bag. The threshold value for the bag filter resistance coefficient. The shrinkage rate of the fabric bag fibers. The threshold for the shrinkage rate of the fabric bag fiber. The rate of decrease in seam strength of the fabric bag. The threshold for the attenuation rate of the fabric bag's seam strength.

[0029] A further technical solution is that the gas state assessment model is as follows:

[0030]

[0031] in, This is the gas state evaluation coefficient. This is the weighting coefficient for exhaust gas concentration. This is the particle size weighting coefficient. For exhaust gas concentration, This is the baseline value for exhaust gas concentration. Dust particle size, This is the baseline value for dust particle size.

[0032] A further technical solution is that the environmental status assessment model is as follows:

[0033]

[0034] in, This is the environmental status evaluation coefficient. For temperature, For the optimal temperature, This refers to the allowable temperature fluctuation range. For humidity, Humidity threshold This is the humidity penalty factor.

[0035] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0036] 1. Breaking through the limitations of static models in traditional processes, this method uses cascaded calculations of bag condition assessment models, gas condition assessment models, and environmental condition assessment models to integrate parameters such as bag resistance, fiber shrinkage, seam attenuation, exhaust gas concentration, dust particle size, temperature, and humidity in real time, thus solving the control lag problem caused by the interaction of multiple factors.

[0037] 2. Based on the dynamic adjustment of the air-fabric dynamic adaptation coefficient, the pressure difference-wind speed matching model is dynamically corrected. When the air-fabric dynamic adaptation coefficient decreases (such as filter bag deterioration or gas deterioration), the model becomes more sensitive to pressure difference fluctuations. Combined with the hyperbolic tangent adjustment function, the intake volume is output smoothly to avoid system oscillations caused by step control.

[0038] 3. The dynamic adaptation coefficient triggers air intake adjustment in advance when the environment deteriorates, for example, to prevent the filter bag from being blocked or penetrated due to high humidity and high temperature. Attached Figure Description

[0039] Figure 1 This invention provides a flowchart of a baghouse dust collection process for the preparation of zinc oxide. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0041] In existing technologies, the purification of high-temperature dusty exhaust gas during zinc oxide production relies on baghouse dust collection technology. However, traditional processes suffer from multi-parameter dynamic coupling, insufficient system stability, and model isolation. For example, bag performance is influenced by gas state and environmental factors, leading to control lag; adjusting the intake air volume with a fixed threshold can easily cause filter bag clogging or penetration; and single-parameter monitoring cannot quantify the accelerating effect of humidity on fiber shrinkage or the nonlinear effect of sudden changes in dust concentration on pressure difference-wind speed.

[0042] To address the aforementioned issues, the inventors discovered that the static models of traditional processes are ill-suited to dynamic operating conditions, necessitating the establishment of a multi-factor collaborative evaluation system. First, a state assessment model is constructed by collecting parameters such as bag resistance and fiber shrinkage rate to quantify the degree of performance degradation. Second, exhaust gas concentration and dust particle size are incorporated into the gas state evaluation to reflect the purification difficulty. Simultaneously, an environmental assessment coupled with temperature and humidity is introduced to dynamically correct the compatibility between the bag and the gas. Based on this, a matching model is constructed by fusing multi-source data through a dynamic air-to-cloth compatibility coefficient and considering the nonlinear relationship between system pressure difference and filtration velocity to guide intake volume adjustment and achieve smooth transition control.

[0043] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0044] like Figure 1 As shown, an embodiment of the present invention provides a baghouse dust collection process for zinc oxide preparation, comprising the following steps:

[0045] S1: Acquire bag status data, gas status data, environmental status data, system differential pressure data, and filtration velocity data;

[0046] S2: Construct a bag condition assessment model based on bag condition data and output bag condition evaluation coefficients;

[0047] S3: Construct a gas state assessment model based on gas state data and output gas state evaluation coefficients;

[0048] S4: Construct an environmental status assessment model based on environmental status data and output environmental status evaluation coefficients;

[0049] S5: Under the current environmental state evaluation coefficient, construct a dynamic adaptation model of gas and cloth based on the bag state evaluation coefficient and the gas state evaluation coefficient, and output the dynamic adaptation coefficient of gas and cloth.

[0050] S6: Under the current air distribution dynamic adaptation coefficient, construct a system pressure difference-filtration velocity matching model based on system pressure difference and filtration velocity, and output the system pressure difference-filtration velocity matching coefficient.

[0051] S7: Based on the system pressure difference-filtration velocity matching coefficient and the current air intake, construct an air intake adjustment model, output the target air intake, and adjust the current air intake to the target air intake.

[0052] The data includes: bag condition data (resistance coefficient, fiber shrinkage rate, and seam strength attenuation rate), which are weighted to quantify performance degradation and provide fundamental parameters for dynamic adaptation; gas condition data (exhaust gas concentration and dust particle size), which are calculated using an exponential function to determine the gas purification difficulty; and environmental condition data (temperature and humidity), which are calculated using a piecewise function to determine the environmental condition evaluation coefficient and dynamically adjust the adaptation relationship. The system pressure difference-filtration velocity matching model is built on an exponential function to quantify the deviation between the operating state and the design baseline. The intake air volume adjustment model uses a hyperbolic tangent function to achieve nonlinear adjustment, avoiding system oscillations.

[0053] Specifically, firstly, sensors collect parameters such as bag resistance and fiber shrinkage rate in real time, inputting them into the bag condition assessment model to calculate the bag condition evaluation coefficient. For example, the coefficient decreases when the fiber shrinkage rate exceeds a threshold. Simultaneously, exhaust gas concentration and dust particle size are collected and input into the gas condition assessment model to calculate the gas condition evaluation coefficient. For example, the coefficient increases when the dust particle size increases. Ambient temperature and humidity data are input into the environmental condition assessment model; the coefficient is penalized by decreasing when humidity exceeds a threshold. Then, the bag and gas evaluation coefficients are weighted according to an environmental factor to generate a dynamic air-cloth adaptation coefficient. For example, the gas condition weight is reduced in high-temperature and high-humidity environments. Based on the adaptation coefficient, the matching model between system pressure difference and filtration velocity is corrected, outputting a matching coefficient. For example, the coefficient decreases when the pressure difference deviates from the design baseline. Finally, the air intake is adjusted using a hyperbolic tangent function based on the matching coefficient. For example, the air intake is gradually reduced when the matching coefficient is below the optimal threshold.

[0054] Compared to existing technologies, current processes use a single parameter threshold to control the air intake, which cannot cope with the coupled effects of filter bag performance degradation and operating condition fluctuations. This solution constructs a dynamic adaptation model through multi-source data fusion, for example, incorporating ambient humidity into the air-to-fabric adaptation relationship correction, thus solving the problem of traditional methods ignoring the impact of humidity on fiber shrinkage. Existing technologies independently handle the linear relationship between pressure difference and wind speed; this solution establishes a nonlinear matching model using an exponential function, for example, exponentially reducing the matching coefficient when the pressure difference is abnormal, improving system stability. Existing adjustment methods use step control; this solution achieves a smooth transition using a hyperbolic tangent function, for example, slowly adjusting when the matching coefficient approaches the threshold, avoiding dust penetration caused by sudden airflow changes.

[0055] Through the above technical solutions, this application achieves multi-dimensional coordinated control of filter bag status, gas characteristics, and environmental factors, solving the control lag problem caused by isolated parameters in traditional processes. The dynamic adaptation model effectively balances the relationship between filter bag performance degradation and operating condition fluctuations, preventing filter bag clogging or penetration. The nonlinear matching model improves the adjustment accuracy of system differential pressure and filtration velocity, ensuring a balance between dust collection efficiency and equipment lifespan. The smooth adjustment mechanism reduces airflow impact and improves system operational stability.

[0056] The bag condition assessment model is as follows:

[0057]

[0058] in, This is the evaluation coefficient for the condition of the fabric bag. , The larger the size, the better the condition of the bag. The resistance weighting coefficient for the fabric bag is greater than or equal to 0 and less than or equal to 1. The weighting coefficient for the shrinkage rate of the fabric fiber is greater than or equal to 0 and less than or equal to 1. All are weighting coefficients for the attenuation rate of the fabric bag's seam strength, and are greater than or equal to 0 and less than or equal to 1. The weighting coefficient refers to the contribution of each parameter to the condition of the filter bag. It can be set through historical data regression analysis or expert experience to achieve differentiated assessments under different working conditions. The threshold is the critical value that triggers a parameter degradation warning. It can be determined based on the filter bag material characteristics and design life, and is used to limit the excessive influence of abnormal parameters on the assessment results. The filter bag resistance coefficient refers to the gas flow resistance per unit area of ​​the filter bag under a specific pressure difference. It can be measured using a differential pressure sensor and a flow meter, and is used to characterize the degree of filter bag blockage. The threshold value for the bag filter resistance coefficient. Fabric bag fiber shrinkage rate refers to the rate of dimensional change of fiber materials due to high temperature or chemical action. It can be obtained through optical measurement or tensile testing, or indirectly through monitoring methods such as vibration frequency analysis. It is used to reflect the structural integrity of the fabric bag. The threshold for the shrinkage rate of the fabric fiber. The seam strength decay rate of a fabric bag refers to the rate at which the mechanical properties of the seam decrease over time. It can be specifically tested periodically using a tensile testing machine to assess the reliability of the bag's connection points. The threshold for the attenuation rate of the fabric bag's seam strength.

[0059] Specifically, the model normalizes bag resistance, fiber shrinkage rate, and seam strength attenuation rate to a unified dimension, and employs a threshold truncation function to avoid evaluation distortion caused by a single parameter exceeding its limit. A dynamic weighting mechanism allows the model to adjust parameter importance based on actual working conditions; for example, in high-temperature environments, the weight of fiber shrinkage rate can be increased to reflect the impact of thermal stress. The evaluation coefficient generation logic converts the degree of degradation of multiple parameters into a state score through inverse calculation. When any parameter exceeds a threshold, its impact value is limited to 1, preventing abrupt score changes under extreme conditions. A real-time data update mechanism allows the evaluation results to dynamically reflect changes in bag performance; for example, when the seam strength attenuation rate increases with usage time, the model automatically lowers the score and triggers a maintenance warning.

[0060] Compared to existing technologies, traditional methods only monitor a single parameter, filter bag resistance, failing to quantify the coupled impact of fiber shrinkage and seam strength decay on filtration performance. For example, existing technologies cannot distinguish whether increased resistance is caused by dust blockage or fiber shrinkage, leading to biased maintenance decisions. This solution, through multi-parameter weighted fusion, can identify the dominant factors in filter bag degradation. For instance, when the weight of seam strength decay rate is high, the model prioritizes highlighting risks at connection points. Furthermore, existing technologies use fixed thresholds to determine filter bag lifespan, neglecting parameter interactions, while this solution, through a dynamic scoring mechanism, can provide early warnings when parameters have not reached thresholds but the overall score is too low.

[0061] Through the above technical solution, this application solves the problem of misjudgment caused by neglecting the coupling of multiple factors in traditional evaluation methods, and achieves accurate quantification of the filter bag's condition. For example, in scenarios where zinc oxide dust adhesion leads to increased resistance but the fibers do not shrink, the model can avoid over-evaluating the degree of filter bag degradation by reducing the resistance weight. Simultaneously, dynamic monitoring of the seam strength attenuation rate can detect the risk of connection failure caused by mechanical vibration in advance. This model provides reliable state input for subsequent air-fabric adaptation and air intake adjustment, avoiding system pressure differential runaway or filtration velocity fluctuations caused by evaluation distortion.

[0062] The gas state assessment model is as follows:

[0063]

[0064] in, This is the gas state evaluation coefficient. , The larger the value, the more ideal the exhaust gas condition. The exhaust gas concentration weighting coefficient refers to the weight value assigned to the exhaust gas concentration parameter. This can be implemented using a preset ratio or a dynamic adjustment algorithm. It is used to adjust the contribution ratio of concentration and particle size in the evaluation according to operating conditions, and must be greater than or equal to 0 and less than or equal to 1. The particle size weighting coefficient refers to the weight value assigned to the particle size parameter, and is greater than or equal to 0 and less than or equal to 1. , The waste gas concentration refers to the mass percentage of zinc oxide dust per unit volume of waste gas. This can be achieved using a laser-scattered particulate matter concentration sensor to determine changes in gas load. The baseline value for exhaust gas concentration refers to a pre-set ideal reference value for exhaust gas concentration, which can be obtained through process standards or experimental data. It is used to quantify the degree of deviation between the current concentration and the ideal state. Dust particle size refers to the diameter distribution characteristics of zinc oxide particles in exhaust gas, which can be specifically achieved using a dynamic light scattering analyzer and is used to assess the difficulty of filtration. The dust particle size reference value refers to a pre-set ideal dust particle size reference value, which can be determined by historical data statistics or process design requirements. It is used to normalize and compare the actual particle size with the ideal value. (Exponential function) It refers to a negative exponential function with the natural constant e as the base. Specifically, it is used to transform the linearly superimposed concentration and particle size deviation into a nonlinearly decreasing evaluation coefficient, reflecting the negative impact of parameter deviation on the gas state.

[0065] Specifically, this technical solution addresses the shortcomings of traditional methods that rely on isolated monitoring of gas parameters by dynamically coupling and evaluating exhaust gas concentration and dust particle size. Real-time measurements of exhaust gas concentration and dust particle size are normalized to baseline values, resulting in relative deviations. Weighting coefficients are dynamically allocated based on actual operating conditions; for example, when dust particle size has a greater impact on filtration performance, the weighting coefficient is increased. The normalized concentration and particle size deviations are linearly superimposed and input into an exponential function to generate a dynamically changing evaluation coefficient. The evaluation coefficient decreases as concentration increases or particle size decreases, reflecting a deteriorating gas condition. This coefficient serves as an input parameter for the subsequent dynamic adaptation model of the air-to-fabric filter, providing a quantitative basis for adjusting the air intake. For example, when the dust particle size is smaller than the baseline value, the increased particle size deviation leads to a decrease in the evaluation coefficient, triggering an air intake adjustment mechanism to reduce the filtration velocity and thus prevent filter bag clogging.

[0066] Compared to existing technologies, traditional methods typically monitor exhaust gas concentration and dust particle size independently, without establishing a synergistic evaluation mechanism for both. For example, existing technologies may only set concentration threshold alarms or particle size classification monitoring, failing to quantify the nonlinear coupling effect of concentration abrupt changes and particle size variations on system pressure differential. This proposed solution, however, transforms concentration and particle size parameters into unified evaluation coefficients through normalization and weight allocation, dynamically reflecting the combined impact of both and thus reducing control lag issues caused by isolated parameter monitoring.

[0067] Through the above technical solution, this application can quantify the dynamic coupling effect of exhaust gas concentration and dust particle size in real time, and accurately identify the trend of gas state deterioration. By normalization and weight allocation mechanisms, the synergistic influence of multiple parameters is transformed into quantifiable evaluation coefficients, providing precise input for subsequent dynamic control. This solves the control lag problem caused by isolated parameter monitoring in traditional methods, improves the system's response speed to sudden changes in gas state, and avoids the risk of filter bag clogging or penetration.

[0068] The environmental status assessment model is as follows:

[0069]

[0070] in, This is the environmental status evaluation coefficient. , The larger the value, the more ideal the environmental condition. Temperature refers to the real-time thermodynamic parameter of the environment in which the fabric bag is located. It can be achieved using a thermocouple sensor and is used to quantify the impact of high temperatures on fiber materials. For the optimal temperature, The allowable temperature fluctuation range refers to the permissible interval between temperature deviations from the optimal temperature. This can be achieved using a preset fixed value or by dynamically adjusting based on the bag material properties. It is used to convert absolute temperature deviations into a standardized proportion, avoiding interference from dimensional differences in the evaluation results. Humidity refers to the moisture content in ambient air, which can be achieved using a capacitive humidity sensor to monitor the effects of water vapor on dust adhesion and fiber shrinkage. The humidity threshold is the minimum critical value that triggers the humidity penalty mechanism. It can be determined experimentally by measuring the inflection point of the fabric fiber shrinkage rate as a function of humidity, and is used to delineate the linear and nonlinear regions of humidity influence. The humidity penalty coefficient refers to the reduction strength of the evaluation coefficient when the humidity exceeds the threshold. Specifically, it can be calibrated based on the accelerating effect of humidity on fiber shrinkage rate, and is used to dynamically reflect the degree of degradation of bag performance under high humidity conditions.

[0071] Specifically, when humidity does not exceed the threshold, the environmental condition evaluation coefficient is calculated solely based on the degree of temperature deviation from the optimal value; the closer the temperature is to the optimal value, the higher the coefficient. When humidity exceeds the threshold, the evaluation coefficient consists of two parts: a linear decay term for the degree of temperature deviation multiplied by a dynamic reduction term for excessive humidity. The humidity reduction term uses a reciprocal function; the greater the humidity exceeds the threshold, the greater the reduction, and it has a combined effect with the degree of temperature deviation. The introduction of a permissible temperature fluctuation range makes the temperature evaluation results dimensionless, the humidity threshold division enables a nonlinear response to high humidity conditions, and the humidity penalty coefficient quantifies the accelerating effect of humidity on fiber shrinkage.

[0072] Compared to existing technologies, traditional methods typically employ independent monitoring of temperature and humidity or linear superposition models, failing to distinguish the impact of humidity exceeding limits and lacking a dynamic reduction mechanism for humidity-temperature evaluation. This proposed solution, through a piecewise function structure, triggers a penalty term only when humidity exceeds limits, simplifying computational complexity and accurately characterizing the combined negative effects of high humidity and temperature deviation. This solves the problem of traditional models' inability to quantify the accelerated fiber shrinkage caused by humidity.

[0073] Through the above technical solution, this application achieves a coordinated response to dynamic changes in temperature and humidity. By dividing the humidity threshold and dynamically reducing the penalty coefficient, it effectively suppresses the decrease in dust collection efficiency caused by accelerated fiber shrinkage in the filter bag under high humidity conditions. At the same time, by standardizing the allowable temperature fluctuation range, it avoids the control lag problem caused by temperature deviation.

[0074] The air-distribution dynamic adaptation model is as follows:

[0075]

[0076] in, The air-to-fabric dynamic adaptation coefficient is a parameter used to quantify the degree of adaptation between the fabric bag and the gas state under the influence of environmental factors. Specifically, it can be achieved by multiplying a weighted combination of the environmental state evaluation coefficient and the fabric bag and gas state evaluation coefficients. This coefficient reflects the global corrective effect of environmental parameters on the air-to-fabric adaptability. , The larger the value, the better the fit. The environmental condition evaluation coefficient refers to the environmental condition score calculated based on temperature and humidity data. Specifically, it can be implemented using a piecewise function combined with temperature deviation and humidity penalty terms. This coefficient can incorporate the impact of environmental fluctuations on fabric fiber shrinkage and gas flow into the suitability assessment. This is the evaluation coefficient for the condition of the fabric bag. Here, α is the gas state evaluation coefficient, and α is the bag state weighting factor. This factor is used to adjust the relative importance of the bag state and the gas state in the compatibility assessment. Specifically, it can be dynamically adjusted according to the aging degree of the bag or sudden changes in gas parameters. This factor allows for prioritizing changes in the bag's state when its performance deteriorates, or enhancing the response weight of gas parameters when the gas state is abnormal. It is greater than or equal to 0 and less than or equal to 1. This is the gas state weighting factor.

[0077] Specifically, the environmental condition evaluation coefficient, as a global correction factor, is multiplied by the weighted result of the bag condition evaluation coefficient and the gas condition evaluation coefficient, ensuring that changes in temperature and humidity directly affect the final assessment value of the air-to-cloth compatibility. When the ambient humidity exceeds a threshold, the environmental condition evaluation coefficient reduces the overall compatibility coefficient through a humidity penalty term, thus proactively addressing the risk of increased bag fiber shrinkage due to rising humidity. Simultaneously, by adjusting the bag condition weighting factor—for example, increasing the weighting factor when the bag resistance coefficient exceeds a threshold—the compatibility assessment focuses more on the impact of bag performance degradation on system stability; conversely, decreasing the weighting factor when exhaust gas concentration or dust particle size is abnormal, the compatibility assessment focuses more on the interference of gas parameter changes on filtration efficiency. Thus, the synergistic effect of environmental factors and air-to-cloth parameters is quantified, mitigating the dynamic coupling control lag problem.

[0078] Compared to existing technologies, traditional methods monitor and evaluate the bag condition, gas condition, and environmental condition parameters independently, failing to establish a correction mechanism for environmental factors on the air-to-bag adaptability and lacking the ability to dynamically adjust weighting factors. For example, existing technologies only superimpose parameter scores at fixed ratios, failing to quickly reduce the adaptability coefficient to trigger airflow adjustment during sudden humidity increases, and also failing to automatically increase the priority of bag condition assessment as the bag ages. This solution introduces an environmental condition evaluation coefficient as a global correction factor, combined with adjustable weighting factors, achieving dynamic adaptability assessment through multi-factor coupling, thus solving the problem of lack of coordinated control caused by isolated parameter evaluation.

[0079] Through the above technical solution, this application can dynamically correct the air-to-baghouse compatibility assessment results based on real-time environmental parameters, and automatically adjust the assessment weights when the bag performance deteriorates or the gas state is abnormal, thereby avoiding control deviations caused by sudden changes in a single parameter or environmental fluctuations. For example, in high-temperature and high-humidity environments, a decrease in the environmental state evaluation coefficient can trigger the air intake adjustment in advance to prevent bag blockage; when the exhaust gas concentration rises sharply, an increase in the gas state weight can accelerate the response speed of the compatibility assessment and prevent dust penetration. This dynamic adaptation mechanism based on multi-factor synergy effectively improves the control accuracy and robustness of the baghouse dust collection system.

[0080] The system pressure difference-filtration velocity matching model is as follows:

[0081]

[0082] in, This is the system pressure difference - filtration velocity matching coefficient. , The larger the value, the more ideal the match. The sensitivity coefficient is used to adjust the model's response to changes in pressure difference and wind speed. It can be determined through experimental calibration or dynamic optimization algorithms to balance the sensitivity and stability of the matching coefficient. The air-to-fabric dynamic adaptation coefficient, derived from the output of the air-to-fabric dynamic adaptation model, specifically reflects the combined influence of the bag's state, gas characteristics, and environmental factors. It is used to dynamically adjust the weights in the matching coefficient calculation. The system pressure differential refers to the pressure difference between the inlet and outlet of the bag filter. This can be monitored in real time using pressure sensors to quantify whether the system's operating resistance is close to the design baseline value. The design differential pressure baseline value refers to the preset differential pressure of the system under ideal operating conditions. It can be set using historical operating data or process parameters and serves as a reference benchmark for judging the degree of deviation of the actual differential pressure. Filtration velocity refers to the amount of exhaust gas that can be processed per unit area of ​​filter bag. It can be calculated using a flow meter and the effective filtration area of ​​the filter bag, and is used to characterize the system's processing load. The design wind speed benchmark value refers to the allowable wind speed of the filter bag under standard operating conditions. It is usually determined based on the filter bag material and dust characteristics and is used to measure the compatibility between the real-time wind speed and the design value.

[0083] Specifically, the model transforms the dynamic relationship between system pressure difference and filtration velocity into matching coefficients using an exponential function. It quantifies the deviation between actual operating conditions and design benchmarks. Physical basis: In a baghouse dust collector, the system pressure difference... It is usually directly proportional to the filtration velocity V, ideally, and hour, , indicating perfect match (M=1 at this time), when Time (e.g.) Too high or (too low) and Mismatch, worsening of match, when Time (e.g.) Too low or (too high) and It doesn't match either, and the matching also worsens. The matching coefficient is used to amplify the degree to which the actual pressure difference deviates from the design value. When the actual pressure difference is close to the design reference and the wind speed matches the reference value, it is used to amplify the degree to which the actual pressure difference deviates from the design value. A value approaching 1 indicates that the system is in an ideal operating state. (Air-distribution dynamic adaptation coefficient) As a product term and sensitivity coefficient The model output is adjusted in a coordinated manner, making it more sensitive to abnormal changes in pressure differential and wind speed when the bag condition or gas characteristics deteriorate, thus triggering the air intake adjustment mechanism in advance. For example, when the bag resistance increases due to fiber shrinkage, the air-cloth dynamic adaptation coefficient... The decrease leads to a lower matching coefficient. The pressure difference increases, then decreases more rapidly, prompting the intake volume adjustment model to reduce the target intake volume in advance, thus preventing the cover from becoming clogged due to a continuous increase in pressure difference; in the system pressure difference-filtration velocity matching coefficient Even at lower levels, pulse cleaning of the filter bags can be triggered first. After dust removal, the system pressure difference - filtration velocity matching coefficient is adjusted. If the airflow remains low or continues to decrease, then the airflow adjustment will be triggered again.

[0084] Compared to existing technologies, traditional methods typically rely on fixed thresholds to determine the compatibility between pressure difference and wind speed; for example, they directly reduce the air intake when the pressure difference exceeds a preset threshold. Such methods do not consider the dynamic influence of factors such as bag filter condition and gas concentration on the pressure difference-wind speed relationship, easily leading to adjustment lag or misjudgment. This solution, however, introduces a dynamic air-bag filter adaptation coefficient, embedding the coupled effects of multiple factors into the matching calculation, enabling the model to dynamically adjust the sensitivity of the matching coefficient according to real-time operating conditions. For example, when a sudden increase in dust concentration leads to a deterioration of the gas state, the dynamic air-bag filter adaptation coefficient... The pressure difference is reduced, and even if the differential pressure does not reach the design reference value, the model will still be affected by the matching coefficient. The decrease in air volume triggers intake volume adjustment, thereby avoiding system imbalance caused by a sudden change in a single parameter.

[0085] Through the above technical solution, this application can quantify the dynamic matching degree between system pressure difference and filtration velocity in real time, and reflect the influence of multiple factors on matching through the dynamic adaptation coefficient of the air-cloth system. When the pressure difference or velocity deviates from the design benchmark, the model rapidly reduces the matching coefficient through an exponential function, providing accurate input for subsequent air intake adjustment. This triggers adjustment action in the early stage of the increase in filter bag resistance, avoiding filter bag blockage or penetration caused by continuous increase in pressure difference. At the same time, by normalizing the design pressure difference and velocity benchmark values, the model can adapt to parameter fluctuations under different operating conditions, reducing control failures caused by changes in process conditions.

[0086] The intake volume adjustment model is as follows:

[0087]

[0088] in, For the target air intake volume, This is the current intake volume. The adjustment range refers to the allowable variation range of the target intake volume relative to the current intake volume. This can be achieved through a preset gain coefficient, used to limit the amplitude of the adjustment process to avoid over-adjustment. Sensitivity adjustment refers to the system's response speed to deviations of the matching coefficient from the optimal threshold. This can be achieved by adjusting the slope parameter of the exponential function, thus balancing sensitivity and stability. The system pressure difference-filter velocity matching coefficient is a dynamic parameter reflecting the degree of matching between the current system pressure difference and filter velocity. It can be calculated using a nonlinear relationship model between pressure difference and velocity, and is used to quantify the deviation between the system's operating state and the ideal operating condition. The optimal matching threshold refers to the preset benchmark value when the system pressure difference and filtration velocity reach equilibrium. It can be determined through experimental calibration or optimization using historical data and serves as a reference benchmark for the adjustment process.

[0089] Specifically, this technical solution uses the current intake volume as a benchmark and generates a smooth adjustment factor through the nonlinear characteristics of the hyperbolic tangent function. When the system pressure difference-filter velocity matching coefficient deviates from the optimal threshold, the adjustment sensitivity controls the response speed, ensuring that the intake volume adjustment quickly tracks changes in operating conditions while avoiding system oscillations caused by step adjustments. The adjustment amplitude is constrained by the gain coefficient to limit the range of change of the target intake volume, ensuring that the adjustment process always remains within a controllable range. For example, the saturation characteristics of the hyperbolic tangent function can automatically limit the adjustment amplitude to the positive and negative adjustment range, preventing over-adjustment under extreme operating conditions. Thus, the system can dynamically adjust the intake volume according to real-time operating conditions, gradually restoring the balance between pressure difference and velocity.

[0090] Compared to existing technologies, traditional methods rely on fixed thresholds for air intake adjustment, which cannot adapt to dynamic operating conditions such as changes in bag filter resistance and fluctuations in dust concentration, easily leading to adjustment lag or overshoot. This solution introduces a dynamic matching coefficient and a nonlinear adjustment function to transform real-time state variables such as system pressure difference and filtration velocity into continuous and smooth adjustment signals, achieving adaptive air intake control. Compared to the limitations of existing technologies based on piecewise linear or on / off adjustment, this model can effectively suppress oscillations during the adjustment process and improve system stability.

[0091] Through the above technical solution, this application solves the problem of pressure difference-wind speed imbalance caused by fixed threshold adjustment in traditional processes, effectively preventing filter bag clogging or penetration. Through the synergistic effect of dynamic matching coefficients and nonlinear adjustment, the system can automatically optimize the air intake according to real-time operating conditions, extending the filter bag's service life while maintaining dust collection efficiency. This adjustment model is particularly suitable for complex operating conditions involving frequent fluctuations in exhaust gas concentration, dust particle size, and ambient temperature and humidity during zinc oxide preparation, significantly improving system robustness.

[0092] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A baghouse dust collection process for zinc oxide preparation, characterized in that, Includes the following steps: S1: Acquire bag status data, gas status data, environmental status data, system differential pressure data, and filtration velocity data; S2: Construct a bag condition assessment model based on bag condition data and output bag condition evaluation coefficients; S3: Construct a gas state assessment model based on gas state data and output gas state evaluation coefficients; S4: Construct an environmental status assessment model based on environmental status data and output environmental status evaluation coefficients; S5: Under the current environmental state evaluation coefficient, construct a dynamic adaptation model of gas and cloth based on the bag state evaluation coefficient and the gas state evaluation coefficient, and output the dynamic adaptation coefficient of gas and cloth. S6: Under the current air distribution dynamic adaptation coefficient, construct a system pressure difference-filtration velocity matching model based on system pressure difference and filtration velocity, and output the system pressure difference-filtration velocity matching coefficient. S7: Based on the system pressure difference-filtration velocity matching coefficient and the current air intake, construct an air intake adjustment model, output the target air intake, and adjust the current air intake to the target air intake. The intake volume adjustment model is as follows: in, For the target air intake volume, This is the current intake volume. To adjust the amplitude, To adjust sensitivity, The system pressure difference - filtration velocity matching coefficient. The optimal matching threshold; The bag condition assessment model is as follows: in, This is the evaluation coefficient for the condition of the fabric bag. This is the bag resistance weighting coefficient. This is the weighting coefficient for the shrinkage rate of the fabric fiber. This is the weighting coefficient for the attenuation rate of the fabric bag's seam strength. The resistance coefficient of the filter bag. The threshold value for the bag filter resistance coefficient. The shrinkage rate of the fabric bag fibers. The threshold for the shrinkage rate of the fabric fiber. The rate of decrease in seam strength of the fabric bag. The threshold for the attenuation rate of the fabric bag's seam strength; The gas state assessment model is as follows: in, This is the gas state evaluation coefficient. This is the weighting coefficient for exhaust gas concentration. This is the particle size weighting coefficient. For exhaust gas concentration, This is the baseline value for exhaust gas concentration. Dust particle size, This is the baseline value for dust particle size.

2. The baghouse dust collection process for zinc oxide preparation according to claim 1, characterized in that, The bag condition data includes the bag resistance coefficient, bag fiber shrinkage rate, and bag seam strength attenuation rate; the gas condition data includes exhaust gas concentration and dust particle size; and the environmental condition data includes temperature and humidity.

3. The baghouse dust collection process for zinc oxide preparation according to claim 1, characterized in that, The system pressure difference-filtration velocity matching model is as follows: in, The system pressure difference - filtration velocity matching coefficient. Sensitivity coefficient This refers to the dynamic adaptation coefficient of the air-to-cloth system. For system pressure difference, To design the differential pressure reference value, To filter airflow velocity, This serves as the design wind speed benchmark.

4. The baghouse dust collection process for zinc oxide preparation according to claim 3, characterized in that, The air-distribution dynamic adaptation model is as follows: in, This refers to the dynamic adaptation coefficient of the air-to-cloth system. This is the environmental status evaluation coefficient. This is the evaluation coefficient for the condition of the fabric bag. Here, α is the gas state evaluation coefficient, and α is the bag filter state weighting factor, which is greater than or equal to 0 and less than or equal to 1. This is the gas state weighting factor.

5. The baghouse dust collection process for zinc oxide preparation according to claim 4, characterized in that, The environmental status assessment model is as follows: in, This is the environmental status evaluation coefficient. For temperature, For the optimal temperature, This refers to the allowable temperature fluctuation range. For humidity, Humidity threshold This is the humidity penalty factor.

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